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Background and Objectives: Revumenib (SNDX-5613) is a powerful and specific inhibitor of the menin–KMT2A binding interaction. It is a small molecule that is currently being researched to treat KMT2A-rearranged (KMT2Ar) acute leukemias. Revumenib (RVB) has received Orphan Drug Designation from the US FDA for treating patients with AML. It has also been granted Fast Track designation by the FDA for treating pediatric and adult patients with R/R acute leukemias that have a KMT2Ar or NPM1 mutation. Materials and Methods: The target of this research was to create a fast, precise, green, and extremely sensitive UPLC-MS/MS technique for the estimation of the RVB level in human liver microsomes (HLMs), employing an ESI source. The validation procedures were carried out in accordance with the bioanalytical technique validation requirements established by the US Food and Drug Administration that involve linearity, selectivity, precision, accuracy, stability, matrix effect, and extraction recovery. The outcome data of the validation features of the UPLC-MS/MS approach were acceptable according to FDA guidelines. RVB parent ions were formed in the positive ESI source and its two fragment ions were estimated employing multiple reaction monitoring (MRM) mode. The separation of RVB and encorafenib was achieved using a C8 column (2.1 mm, 50 mm, and 3.5 µm) and isocratic mobile phase. Results: The RVB calibration curve linearity ranged from 1 to 3000 ng/mL (y = 0.6515x − 0.5459 and R² = 0.9945). The inter-day precision and accuracy spanned from −0.23% to 11.33%, while the intra-day precision and accuracy spanned from −0.88% to 11.67%, verifying the reproducibility of the UPLC-MS/MS analytical technique. The sensitivity of the developed methodology demonstrated its capability to quantify RVB levels at an LOQ of 0.96 ng/mL. The AGREE score was 0.77, confirming the greenness of the current method. The low in vitro t_1/2 (14.93 min) and high intrinsic clearance (54.31 mL/min/kg) of RVB revealed that RVB shares similarities with medications that have a high extraction ratio. Conclusions: The present LC-MS/MS approach is considered the first analytical approach with the application of metabolic stability assessment for RVB estimation in HLMs. These methods are essential for advancing the development of new pharmaceuticals, particularly in enhancing metabolic stability. Full article

Research reveals a positive impact on educational achievement for Indigenous students when their teachers are also Indigenous. The educational value of shared identity between students and teachers manifests in the form of increased student attendance rates, grades, and graduation rates. Fewer than 5% of public-school teachers in Alaska are Indigenous, while nearly 20% of students are Indigenous. Thus, it is unlikely that most Indigenous students in Alaska will experience a shared cultural identity with their teachers—nor would it be desirable, in this age of global mobility, for society to strive for teachers and students to share cultural identity in all instances. Yet it is important to discern what teaching practices and teacher dispositions support the cultural identity development (CID) of Indigenous children. This project brought together Indigenous educators from across Alaska to critically examine their practice as educators and to seek answers to the research question. Utilizing a collaborative autoethnographic framework, qualitative data were coded and analyzed to uncover answers to the research question. Key findings from this study indicate that teaching and using the local Indigenous language, shared cultural history documented in stories, and experiences related to the Land contribute to students’ CID. Furthermore, findings reveal that micro cultural validations, fleeting interactions between teachers and students, play a significant role in supporting the cultural identity development of Indigenous youth. Findings also suggest that Indigenous teachers are best positioned to discern the teaching practices that contribute to students’ cultural identity development. Full article

In recent years, mobility as a service (MaaS) has been thought as one of the opportunities for shifting towards shared travel solutions with respect to private transport modes, particularly owned cars. Although many MaaS aspects have been explored in the literature, there are still issues, such as platform implementations, travel solution generation, and the user’s role for making an effective system, that require more research. This paper extends and improves a previous study carried out by the authors by providing more details and experiments. The paper proposes a diachronic network model for representing travel services available in a given MaaS platform by using an agent-based approach to simulate the interactions between travel operators and travelers. Particularly, the diachronic network model allows the consideration of both the spatial and temporal features of the available transport services, while the agent-based framework allows the representation of how shared services might be used and which effects, in terms of modal split, could be expected. The final aim is to provide insights for setting the architecture of an agent-based MaaS platform where transport operators would share their data for providing seamless travel opportunities to travelers. The results obtained for a simulated test case are promising. Particularly, there are interesting findings concerning the traffic congestion boundary values that would move users towards shared travel solutions. Full article

COSMOS-France is the French part of the COSMOS project, an international prospective cohort study that investigates whether the use of mobile phones and other wireless technologies is associated with health effects and symptoms (cancers, cardiovascular diseases, neurologic pathologies, tinnitus, headaches, or sleep and mood disturbances). Here, we provide the first descriptive results of COSMOS-France, a cohort nested in the general population-based cohort of adults named Constances. Methods: A total of 39,284 Constances volunteers were invited to participate in the COSMOS-France study during the pilot (2017) and main recruitment phase (2019). Participants were asked to complete detailed questionnaires on their mobile phone use, health conditions, and personal characteristics. We examined the association between mobile phone use, including usage for calls and Voice over Internet Protocol (VoIP), cordless phone use, and Wi-Fi usage with age, sex, education, smoking status, body mass index (BMI), and handedness. Results: The participation rate was 48.4%, resulting in 18,502 questionnaires in the analyzed dataset. Mobile phone use was reported by 96.1% (N = 17,782). Users reported typically calling 5–29 min per week (37.1%, N = 6600), making one to four calls per day (52.9%, N = 9408), using one phone (83.9%, N = 14,921) and not sharing it (80.4% N = 14,295), mostly using the phone on the side of the head of their dominant hand (59.1%, N = 10,300), not using loudspeakers or hands-free kits, and not using VoIP (84.9% N = 15,088). Individuals’ age and sex modified this picture, sometimes markedly. Education and smoking status were associated with ever use and call duration, but neither BMI nor handedness was. Cordless phone use was reported by 66.0% of the population, and Wi-Fi use was reported by 88.4%. Conclusion: In this cross-sectional presentation of contemporary mobile phone usage in France, age and sex were important determinants of use patterns. Full article

Nowadays, Edge Intelligence has seen unprecedented growth in most of our daily life applications. Traditionally, most applications required significant efforts into data collection for data-driven analytics, raising privacy concerns. The proliferation of specialized hardware on sensors, wearable, mobile, and IoT devices has led to the growth of Edge Intelligence, which has become an integral part of the development cycle of most modern applications. However, scalability issues hinder their wide-scale adoption. We aim to focus on these challenges and propose a scalable decentralized edge intelligence framework. Therefore, we analyze and empirically evaluate the challenges of existing methods, and design an architecture that overcomes these challenges. The proposed approach is client-driven and model-centric, allowing models to be shared between entities in a scalable fashion. We conduct experiments over various benchmarks to show that the proposed approach presents an efficient alternative to the existing baseline method, and it can be a viable solution to scale edge intelligence. Full article

Electric-powered vehicles (EVs) allow for an environmentally friendly and economic alternative to fuel-running ones. However, such an alternative is expected to impose further usage hikes and periods of instability on cities’ power systems. From their perspective, cities need to scale their infrastructure grids to allow for adequate power resources to feed such new power-hungry consumers. Indeed, for such a green alternative to proceed, our power grids need to be ready to cope with any unexpected hikes in the power consumption rates without compromising the stability of the services provided to our homes and workplaces. Operators’ steps in this path are still modest, and the coverage of EV charging stations is still insufficient as they are trying to avoid any further costs for upgrading their infrastructures. The lack of price consideration for the charging services offered at charging stations may result in EV drivers paying higher costs compared to traditional fuel vehicles to charge their EVs’ batteries, hindering the economic incentive of owning such sorts of vehicles. Hence, it may take a while for sufficient coverage to exist. Although for drivers the adoption of EVs represents a city-friendly alternative with affordable expenses, it usually comes with range anxiety and battery charging concerns. In this work, we are presenting e-Fuel, a charge-sharing model that allows for preference-based mobile EV charging services. In e-Fuel, we are proposing a stable weight-based vehicle-to-vehicle matching algorithm, through which drivers of EVs will be capable of requesting instant mobile charge-sharing service for their EVs. In addition to being mobile, such charging services are customized, as they are chosen based on the drivers’ preferences of price-per-unit, charging speed, and time of delivery. The developed e-Fuel matching algorithm has been tested in various environments and settings. Compared to the benchmark price-based matching algorithm, the resulting matching decisions of e-Fuel come with balanced matching attributes that mostly allow for 6- to 7-fold shorter service delivery times for a minimal increase in service charges that vary between 9% and 65%. Full article

The last decade has seen a significant growth in the adoption of low-cost air quality monitoring systems (LCAQMSs), mostly driven by the need to overcome the spatial density limitations of traditional regulatory grade networks. However, urban air quality monitoring scenarios have proved extremely challenging for their operative deployment. In fact, these scenarios need pervasive, accurate, personalized monitoring solutions along with powerful data management technologies and targeted communications tools; otherwise, these scenarios can lead to a lack of stakeholder trust, awareness, and, consequently, environmental inequalities. The AirHeritage project, funded by the EU’s Urban Innovative Action (UIA) program, addressed these issues by integrating intelligent LCAQMSs with conventional monitoring systems and engaging the local community in multi-year measurement strategies. Its implementation allowed us to explore the benefits and limitations of citizen science approaches, the logistic and functional impacts of IoT infrastructures and calibration methodologies, and the integration of AI and geostatistical sensor fusion algorithms for mobile and opportunistic air quality measurements and reporting. Similar research or operative projects have been implemented in the recent past, often focusing on a limited set of the involved challenges. Unfortunately, detailed reports as well as recorded and/or cured data are often not publicly available, thus limiting the development of the field. This work openly reports on the lessons learned and experiences from the AirHeritage project, including device accuracy variance, field recording assessments, and high-resolution mapping outcomes, aiming to guide future implementations in similar contexts and support repeatability as well as further research by delivering an open datalake. By sharing these insights along with the gathered datalake, we aim to inform stakeholders, including researchers, citizens, public authorities, and agencies, about effective strategies for deploying and utilizing LCAQMSs to enhance air quality monitoring and public awareness on this challenging urban environment issue. Full article

In the realm of shared autonomous vehicle ride-sharing, precise demand prediction is vital for optimizing resource allocation, improving travel efficiency, and promoting sustainable transport solutions. However, existing studies tend to overlook social attributes and demographic characteristics across various regions, resulting in disparities in prediction fairness between areas with plentiful and limited transportation resources. In order to achieve more accurate and fair prediction, an innovative Social Graph Convolution Long Short-Term Memory framework is proposed, incorporating demographic, spatial, and transportation accessibility information into multiple functional graphs, including functional similarity, population structure, and historical demand graphs. Furthermore, Mean Percentage Error indicators are employed in the loss function to balance prediction accuracy and fairness. The findings indicate that there is an enhancement in both prediction accuracy and fairness by at least 8.9% and 12.9%, respectively, compared to base models. Additionally, the predictions for rush hours in both privileged and underprivileged regions exhibit greater precision and rationality, supporting sustainable transport practices. The proposed framework effectively captures the demands of diverse social groups, thereby contributing to the advancement of social equity and long-term sustainability in urban mobility. Full article

Rotator cuff calcific tendinopathy and arthrofibrosis of the shoulder (adhesive capsulitis) are debilitating musculoskeletal disorders that significantly impact joint function and impair quality of life. Despite its high prevalence and common clinical presentation, the metabolic mechanisms underlying these conditions characterized by pain, and reduced mobility, remain poorly understood. This review aims to elucidate the role of metabolic processes implicated in the pathogenesis of calcific tendinopathy and shoulder arthrofibrosis. We will be focusing on the mechanistic role of how these processes contribute to disease progression and can direct potential therapeutic targets. Calcific tendinopathy is marked by aberrant calcium deposition within tendons, influenced by disrupted calcium and phosphate homeostasis, and altered cellular responses. Key molecular pathways, including bone morphogenetic proteins (BMPs), Wnt signaling, and transforming growth factor-beta (TGF-β), play crucial roles in the pathophysiology of calcification, calcium imbalance, and muscle fibrosis. In contrast, shoulder arthrofibrosis involves excessive collagen deposition and fibrosis within the shoulder joint capsule, driven by metabolic dysregulation and inflammation. The TGF-β signaling pathway and inflammatory cytokines, such as interleukin-6 (IL-6), are central to the fibrotic response. A comparative analysis reveals both shared and distinct metabolic pathways between these conditions, highlighting the interplay between inflammation, cellular metabolism, extracellular matrix remodeling, calcific deposition, and calcium migration to the glenohumeral joints, resulting in adhesive capsulitis, thereby providing insights into their pathophysiology. This review discusses current therapeutic approaches and their limitations, advocating for the development of targeted therapies that address specific metabolic dysregulations. Future therapeutic strategies focus on developing targeted interventions that address the underlying metabolic dysregulation, aiming to improve patient outcomes and advance clinical management. This review offers a comprehensive overview of the metabolic mechanisms involved in calcific tendinopathy and shoulder arthrofibrosis, providing a foundation for future research and therapeutic development. Full article

The transport sector worldwide relies heavily on oil products, and private cars account for the largest share of passenger mobility in several countries. Public transport could represent an interesting alternative under many perspectives, including a decrease in traffic, pollutants, and climate emissions. However, for public transport to succeed, it should be attractive for final users, representing a viable alternative to private mobility. In this work, we analyse the spatial distribution of public transport service provision within two metropolitan cities, considering the three key dimensions of mobility, competitiveness, and accessibility of public transport. The results show that private car performs better than public transport in all scopes considered, and that performance indicators are highly variable among city areas, indicating inequalities in social and environmental sustainability in urban systems. The outcomes of the analysis provide interesting insights for policy makers and researchers that deal with similar topics, and can also be extended to other cities and countries. Full article

This paper proposes a novel decentralized federated reinforcement learning (DFRL) framework that integrates deep reinforcement learning (DRL) with decentralized federated learning (DFL). The DFRL framework boosts efficient virtual instance scaling in Mobile Edge Computing (MEC) environments for 5G core network automation. It enables multiple MECs to collaboratively optimize resource allocation without centralized data sharing. In this framework, DRL agents in each MEC make local scaling decisions and exchange model parameters with other MECs, rather than sharing raw data. To enhance robustness against malicious server attacks, we employ a committee mechanism that monitors the DFL process and ensures reliable aggregation of local gradients. Extensive simulations were conducted to evaluate the proposed framework, demonstrating its ability to maintain cost-effective resource usage while significantly reducing blocking rates across diverse traffic conditions. Furthermore, the framework demonstrated strong resilience against adversarial MEC nodes, ensuring reliable operation and efficient resource management. These results validate the framework’s effectiveness in adaptive and efficient resource management, particularly in dynamic and varied network scenarios. Full article

With The integration of renewable energy sources into smart grids and electric vehicle (EV) charger-sharing networks is essential for achieving the goal of environmental sustainability. However, the uneven distribution of distributed energy trading among EVs, fixed charging stations (FCSs), and mobile charging stations (MCSs) introduces challenges such as inadequate supply at FCSs and prolonged latencies at MCSs. In this paper, we propose a multi-agent deep reinforcement learning (MADRL)-based auction algorithm for energy trading that effectively balances charger supply with energy demand in distributed EV charging markets, while also reducing total charging latency. Specifically, this involves a MADRL-based hierarchical auction that dynamically adapts to real-time conditions, optimizing the balance of supply and demand. During energy trading, each EV, acting as a learning agent, can refine its bidding strategy to participate in various local energy trading markets, thus enhancing both individual utility and global social welfare. Furthermore, we design a cross-chain scheme to securely record and verify transaction results of energy trading in decentralized EV charger-sharing networks to ensure integrity and transparency. Finally, experimental results show that the proposed algorithm significantly outperforms both the second-price and double auctions in increasing global social welfare and reducing total charging latency. Full article

The urban landscape of Kinshasa, Democratic Republic of Congo, faces significant mobility challenges, primarily stemming from rapid urbanization, overpopulation, and outdated infrastructure. These challenges necessitate the exploration of modern smart mobility concepts to improve traffic flow, road safety, and sustainability. This study investigates the potential of solutions such as Mobility-as-a-Service, car sharing, micro-mobility, Vehicle-as-a-Service, and electric vehicles in addressing these challenges. Through a comparative analysis of global implementations, this research identifies key success factors and barriers that inform the feasibility of integrating these solutions into Kinshasa’s unique socio-political and infrastructural context. The study presents a conceptual framework, supported by stakeholder analysis, for adapting these solutions locally. A detailed feasibility analysis considers technological, economic, social, environmental, and regulatory factors, offering a clear roadmap for implementation. Drawing on lessons from cities facing similar urban mobility challenges, the paper concludes with actionable recommendations and insights for policymakers and urban planners in Kinshasa. This research not only highlights the viability of smart mobility solutions in Kinshasa but also contributes to the broader discourse on sustainable urban development in rapidly growing cities. While smart mobility studies have largely focused on cities with developed infrastructure, there is a gap in understanding how these solutions apply to cities like Kinshasa with different infrastructural and socio-political contexts. Previous research has often overlooked the challenges of integrating smart mobility in rapidly urbanizing cities with underdeveloped transportation systems and financial constraints. This study fills that gap by offering a feasibility analysis tailored to Kinshasa, assessing smart mobility solutions for its traffic congestion and road safety issues. The smart mobility solutions studied—Mobility-as-a-Service (MaaS), car sharing, electric vehicles (EVs), and micro-mobility—were chosen for their ability to address Kinshasa’s key mobility challenges. MaaS reduces reliance on private vehicles, easing congestion and improving public transport. Car sharing offers affordable alternatives to vehicle ownership, essential in a city with income inequality. EVs align with sustainability goals by reducing emissions, while micro-mobility (bikes and e-scooters) improves last-mile connectivity, addressing public transit gaps. These solutions are adaptable to Kinshasa’s context and offer scalable, sustainable improvements for urban mobility. Full article

Previous work presented a reference model for shared autonomous vehicles in urban areas supported by a systematic literature review and topic modeling. The proposed reference model was then evaluated with two real-world demonstrations: the service provided by Waymo in Phoenix and another offered by Baidu in Beijing. In this paper, we present another evaluation based on a survey conducted with a group of potential stakeholders belonging to the mobility industry who were asked about their agreement with each of the concepts in the reference model. The resulting artifact is stronger and more reliable because it reflects the feedback of mobility experts. Full article

Mobile technology is currently trending toward supporting multiple communication standards on a single device. This means that some reconfigurable techniques must be the foundation of their design. The two essential requirements of channel filters are minimized complexity and reconfigurability. In this research, a novel extension of Frequency Response Masking (FRM) was investigated by employing Time Division Multiplexing (TDM)-based single Multiply and Accumulate (MAC) architecture using the principle of resource sharing to realize multiple sharp filter responses from a single prototype constant group delay low pass filter. This paper uses a single multiply and add units regardless of the quantity of channels and taps. The suggested reconfigurable filter was synthesized on technology based on 0.18-µm CMOS and put into practice. Further trials were carried out on Virtex-II 2v3000ff1152-4 FPGA device. The outcomes revealed that the suggested channel filter, which was synthesized using FPGA, provides 21.36% of the area curtail and 14.88% of power scaling down on average and put into practice using ASIC provides 5.18% of the area reduction and 9.08% of power scaling down on average. Full article