Robust Framework Research Articles

Advancing Video Data Privacy Preservation in IoT Networks through Video Blockchain

In the digital age, where the Internet of Things (IoT) permeates every facet of our lives, the safeguarding of data privacy, especially video data, emerges as a paramount concern. The ubiquity of IoT devices, capable of capturing and disseminating vast quantities of video data, introduces unprecedented challenges in ensuring the privacy and security of such information. This article explores the crucial intersection of video data privacy and blockchain technology within IoT networks. It aims to uncover and articulate the unique challenges video data encounter in the IoT ecosystem, such as susceptibility to unauthorized access and the difficulty in ensuring data integrity and confidentiality. By conducting a thorough literature review, this study not only illuminates the intricate privacy challenges inherent in IoT environments but also showcases the immutable, decentralized nature of blockchain as a potent solution. We systematically explore how blockchain-based methods can be pragmatically implemented to fortify video data privacy, scrutinizing the efficacy of these approaches in the IoT context. Through critical assessment, the paper delineates the strengths and limitations of video blockchain solutions, underscoring the transformative potential of blockchain technology as a cornerstone for enhancing data privacy in IoT networks. Conclusively, this work advocates for blockchain as an indispensable tool in the advancement of data privacy measures for video content, thereby reinforcing trust and security in the increasingly connected fabric of our digital world. As IoT applications burgeon, the fusion of blockchain technology with IoT infrastructures promises a robust framework for protecting sensitive video data, heralding a future of enhanced trust and security in our interconnected ecosystem.

Unlocking Quasi‐Monophase Behavior in NASICON Cathode to Drive Fast‐Charging Toward Durable Sodium‐Ion Batteries

AbstractThe NASICON cathode, Na3V2(PO4)3, has garnered significant attention due to its robust framework with fast Na+ migration. To expand its application scenarios by diversified electronic reaction, the substitution of vanadium with cost‐effective and abundant redox elements is a special research topic. Nevertheless, in terms of reducing toxicity, increasing Na content and widening voltage range, the V4+/5+ redox couple in Na4FeV(PO4)3 often accompanies asymmetric and irreversible electrochemical reactions that pose a dilemma for capacity and structural stability, especially at high currents. Herein, in this work, Na4FeV1/3Ti2/3(PO4)3 (NFVT) has achieved highly reactive of multiple electron transfer (Ti2+/3+, Fe2+/3+, and V3+/4+/5+) by utilizing the redox reaction with quasi‐monophase behavior, and it can reserve great capacity retention after 3,000 cycles. More competitively, its boosting kinetics makes the fast‐charging characteristic, just requiring only 3.63 min to reach 80% state of charge at 2 C. The rapid ion/electron transport dynamics can achieve the decay of only 0.043% per cycle by unlocking the quasi‐monophase behavior in the framework of NFVT full cells. The present study provides a fresh perspective on designing cathode materials with fast‐charging capabilities for sodium‐ion batteries.

A robust machine learning-based framework for handling time-consuming constraints for bi-objective optimization of nonlinear steel structures

In structural design, multi-objective optimization of nonlinear steel structures simultaneously considers various design (often conflicting) purposes during the stage of construction to the operation of projects. Such optimization generally requires excessive computational efforts for the evaluation of time-consuming constraints. To overcome this drawback, this paper presents an efficient machine learning-based (ML) method to perform bi-objective optimization of nonlinear steel structures describing two conflicting objectives, namely minimization of structural weight and nodal displacements, simultaneously. The proposed method combines a nondominated sorting genetic algorithm-II (NSGA2) with an extreme gradient boosting (XGBoost) surrogate-assisted model. Furthermore, a multi-objective comparison technique (MulCT) is implemented, where a safety parameter t is introduced to reduce errors in the predictive ML model. To demonstrate the efficiency of the proposed method, two robust design examples, including a planar 10-bar truss and 47-bar transmission tower structures, are provided. In comparison with various ML algorithms, numerical results illustrate that the XGBoost model is superior in performance and requires significantly less training time. The MulCT technique enhances computational efficiency, leading to a 20% reduction in computing resources as when it was implemented with NSGA2. The proposed method by integrating both MulCT and XGBoost models with NSGA2 can save more than 50% of computational efforts, while maintaining good accuracy, as can be achieved by NSGA2.

INTEGRATING IOT IN PEDIATRIC HEALTHCARE: A SYSTEMATIC REVIEW OF CURRENT APPLICATIONS AND FUTURE DIRECTIONS FOR PANCREATIC DISEASES AND OBESITY Tolulope O. Olorunsogo1

In recent years, the integration of Internet of Things (IoT) technologies into healthcare systems has shown immense promise in revolutionizing patient care, particularly in pediatric populations. This systematic review aims to explore the current landscape of IoT applications in pediatric healthcare, specifically focusing on pancreatic diseases and obesity, two significant health challenges affecting children worldwide. The review comprehensively analyzes existing literature, including peer-reviewed articles, conference papers, and relevant reports. By employing systematic search strategies across various databases, studies were identified, screened, and synthesized to elucidate the diverse applications of IoT in managing pancreatic diseases and obesity among pediatric patients. The findings reveal a multitude of IoT applications tailored to pediatric healthcare, ranging from wearable devices for continuous glucose monitoring in diabetic children to smart scales and activity trackers for managing pediatric obesity. These technologies offer real-time monitoring, data analytics, and personalized interventions, enhancing disease management, improving patient outcomes, and promoting patient engagement and adherence to treatment regimens. Furthermore, this review identifies critical challenges and limitations associated with current IoT implementations in pediatric healthcare, such as data privacy concerns, interoperability issues, and the need for robust regulatory frameworks. Addressing these challenges is crucial to maximizing the potential benefits of IoT in pediatric healthcare and ensuring its safe and effective integration into clinical practice. The review outlines promising future directions for IoT in managing pancreatic diseases and obesity in pediatric populations. These include integrating advanced sensors and artificial intelligence algorithms for predictive analytics, developing user-friendly and child-centric IoT solutions, and establishing collaborative networks for data sharing and knowledge exchange. This systematic review underscores the transformative impact of IoT technologies in pediatric healthcare, particularly in managing pancreatic diseases and obesity. By highlighting current applications and future directions, this study provides valuable insights for clinicians, researchers, and policymakers to harness the full potential of IoT in improving pediatric health outcomes.
 Keywords: IoT, Healthcare, Pancreatic, Disease, Obesity, Pediatric, Review.

The Legal and Political Implications of AI Bias: An International Comparative Study

Purpose: "The Legal and Political Implications of AI Bias: An International Comparative Study" extensively navigates the intricate terrain of AI governance, with a specific focus on the ethical challenges arising from bias in AI systems. The purpose of this study is to underscore the urgent need for robust regulatory frameworks to address issues of bias, discrimination, and fairness within the realm of AI technologies.
 Materials and Methods: The research methodology involved a comprehensive analysis of international perspectives on AI bias. This entailed examining existing literature, legal frameworks, and political dynamics surrounding AI governance in various countries. Comparative analysis was conducted to elucidate the diverse approaches adopted by different nations to tackle AI bias and unravel the corresponding legal and political consequences.
 Findings: The study highlighted the inherent risks associated with biased algorithms and stressed the paramount importance of proactively detecting and mitigating bias to prevent discrimination and promote fairness in AI systems. Additionally, it advocated for comprehensive measures such as risk management strategies, conformity assessments for high-risk AI applications, and the careful handling of sensitive data to identify and rectify biases that could lead to discriminatory outcomes.
 Implication to Theory, Practice and Policy: The study was informed by theories of ethical governance and legal frameworks in AI development and deployment. It was validated through the comparative analysis of international perspectives, which provided insights into the effectiveness of different regulatory approaches in addressing AI bias. Recommendations to practitioners include implementing risk management strategies, conducting conformity assessments for high-risk AI applications, and ensuring the careful handling of sensitive data to identify and rectify biases. Practitioners are urged to prioritize ethical considerations and advocate for responsible deployment practices to mitigate AI bias effectively. Recommendations to policymakers emphasize the need to prioritize ethical considerations and advocate for responsible deployment practices in AI governance. Policymakers are urged to develop robust regulatory frameworks that promote transparency, accountability, and inclusivity in AI development and deployment to build a more equitable and trustworthy AI ecosystem.
 In essence, the study provides crucial insights into the complex interplay between legal frameworks, political dynamics, and ethical considerations in addressing AI bias on a global scale. It paves the way for the establishment of fair and unbiased AI systems that benefit society as a whole.

Exploring neonicotinoid effects on Drosophila: insights into olfactory memory, neurotransmission, and synaptic connectivity.

Neonicotinoid insecticides, the fastest-growing class in recent decades, interfere with cholinergic neurotransmission by binding to the nicotinic acetylcholine receptor. This disruption affects both targeted and non-targeted insects, impairing cognitive functions such as olfaction and related behaviors, with a particular emphasis on olfactory memory due to its ecological impact. Despite the persistent presence of these chemicals in the environment, significant research gaps remain in understanding the intricate interplay between cognitive function, development, neuronal activity, and neonicotinoid-induced toxicity. This study focuses on the fruit fly Drosophila melanogaster, chosen for its genetic tractability, well-characterized neural circuitry, and remarkable parallels with bees in neurotransmitter systems and brain structures. Our aim is to establish the fruit fly as a valuable model organism for studying the effects of neonicotinoids on behavior and neuronal circuitry, with particular attention to olfactory memory and associated brain circuitries. To achieve this aim, we conducted experiments to investigate the effects of short-term exposure to sublethal doses of the neonicotinoid imidacloprid, mimicking realistic environmental insecticide exposure, on the formation of odor memories. Additionally, we evaluated synaptic contacts and cholinergic neurotransmission within the mushroom body, the primary memory network of insects. Our results showed significant impairments in odor memory formation in flies exposed to imidacloprid, with exposure during the adult stage showing more pronounced effects than exposure during the larval stage. Additionally, functional studies revealed a decrease in synaptic contacts within the intrinsic olfactory projection neurons and the mushroom body. Furthermore, another experiment showed an odor-dependent reduction in cholinergic neurotransmission within this network. In summary, employing Drosophila as a model organism provides a robust framework for investigating neonicotinoid effects and understanding their diverse impacts on insect physiology and behavior. Our study initiates the establishment of the fruit fly as a pivotal model for exploring neonicotinoid influences, shedding light on their effects on olfactory memory, neuronal integrity, and synaptic transmission.

Special Education Teachers’ Perceptions of Parental Involvement in Inclusive Education

(1) Background: It is widely agreed that mutual partnerships between school staff and parents of students are key to creating robust frameworks of inclusive education for students with disabilities. This study explores special education teachers’ perceptions of involving parents of students with disabilities in mainstream schools in Jordan. (2) Methods: This research employs a descriptive quantitative method in the form of a Google-Forms-based online questionnaire with closed-ended questions. (3) Results: The findings indicate that parents were moderately involved in the education of their disabled children; that these parents perceived teachers and school principal to be instrumental in encouraging their involvement in this respect; and that parents face many obstacles to such involvement in their children’s education. (4) Conclusions: The findings point to a need for policymakers to create legislation that enables and mandates the involvement of parents with children with disabilities in inclusive education settings in order to implement constructive models of parental involvement where parents can be treated as equal partners in the educational process.

Beyond ethics: How perceived organizational support amplifies the impact of ethical HRM on employee commitment and performance

This study explores the dynamic relationship between ethical human resources management (HRM) strategies, the level of commitment an employee feels towards their organization, and their job performance, paying particular attention to how employees’ perceptions of the support they receive from their organization can influence these interactions, especially during challenging times. Drawing on a sample of full-time non-executive Indonesian employees, the research employs descriptive statistics for initial data analysis, followed by structural equation modeling (SEM) to test the proposed hypotheses rigorously. The investigation reveals a positive relationship between ethical HRM and employee performance (EP) and organizational commitment (OC). Additionally, OC emerges as a pivotal mediator in the ethical HRM-EP link. Notably, employees’ organizational support perception (EOSP), often assumed to enhance positive organizational outcomes, displays a surprising negative moderating effect when combined with OC, suggesting a more intricate relationship than traditionally posited. These findings enhance our comprehension of how ethical HRM practices function in times of crisis, questioning conventional beliefs regarding the influence of organizational support. The study’s methodological approach, combining descriptive and advanced statistical analyses, provides a robust framework for understanding these complex relationships. This research holds significant implications for HRM practices, particularly in crisis response and management, indicating a need for nuanced support strategies that reflect the complexity of employee-organization dynamics.

LEGAL CHALLENGES OF ARTIFICIAL INTELLIGENCE AND ROBOTICS: A COMPREHENSIVE REVIEW

The paper presents an insightful overview of the intricate legal challenges posed by the proliferation of Artificial Intelligence (AI) and Robotics. This comprehensive review explores the multifaceted dimensions of the evolving legal landscape, addressing issues at the intersection of technology and law. Key focal points include the accountability and liability frameworks for autonomous AI systems, ethical considerations in the deployment of intelligent machines, and the complex dynamics of data privacy in the age of pervasive automation. The review delves into the intricate legal nuances surrounding intellectual property rights, particularly as AI systems contribute to creative outputs and innovation. It navigates the blurred lines between human and machine authorship, raising fundamental questions about ownership and protection in this digital era. Moreover, the paper emphasizes the global nature of these challenges, highlighting the imperative for international cooperation to formulate harmonized legal standards. As AI and robotics revolutionize industries and societal frameworks, the analysis underscores the critical need for adaptive and anticipatory legal frameworks. It explores how existing legal paradigms are grappling with the unprecedented speed of technological advancements and the ethical dilemmas arising from the delegation of decision-making to intelligent algorithms. The paper sets the stage for a thorough examination of the legal intricacies surrounding AI and robotics. It advocates for a proactive and collaborative approach, involving legal experts, technologists, ethicists, and policymakers in crafting robust frameworks that balance innovation with ethical, privacy, and accountability considerations. This review serves as a foundational resource for understanding and addressing the legal challenges inherent in the transformative era of Artificial Intelligence and Robotics.
 Keywords: Artificial intelligence, Robotics, Legal, AI challenges, Ethics, Review.

Data Governance in AI - Enabled Healthcare Systems: A Case of the Project Nightingale

The study investigates data governance challenges within AI-enabled healthcare systems, focusing on Project Nightingale as a case study to elucidate the complexities of balancing technological advancements with patient privacy and trust. Utilizing a survey methodology, data were collected from 843 healthcare service users employing a structured questionnaire designed to measure perceptions of AI in healthcare, trust in healthcare providers, concerns about data privacy, and the impact of regulatory frameworks on the adoption of AI technologies. The reliability of the survey instrument was confirmed with a Cronbach's Alpha of 0.81, indicating high internal consistency. The multiple regression analysis revealed significant findings: a positive relationship between the awareness of technological projects and trust in healthcare providers, countered by a negative impact of privacy concerns on trust. Additionally, familiarity with and perceived effectiveness of regulatory frameworks were positively correlated with trust in data, while perceptions of regulatory constraints and data governance issues were identified as significant barriers to the effective adoption of AI technologies in healthcare. The study highlights the critical need for enhanced transparency, public awareness, and robust data governance frameworks to navigate the ethical and privacy concerns associated with AI in healthcare. The study recommends adopting flexible, principle-based regulatory approaches and fostering multi-stakeholder collaboration to ensure the ethical deployment of AI technologies that prioritize patient welfare and trust.

Na4Fe1.5Mn1.5(PO4)2(P2O7): A low-cost and earth-abundant cathode for robust sodium storage

The mixed compounds of phosphates and pyrophosphates are attractive cathodes for sodium-ion batteries (SIBs) owing to their robust open framework structure and superior diffusion dynamics. However, most reported mixed phosphate cathodes generally suffer from low operating potential. Herein, we develop a bimetallic Na4Fe1.5Mn1.5(PO4)2(P2O7)/C-rGO (NFMPP/C-rGO) cathode, which possesses two working plateaus at 2.92 and 3.95 V. The obtained NFMPP/C-rGO demonstrates a stable high capacity of over 120 mAh g−1 at 0.1 C. In-situ XRD characterization discloses a solid solution reaction for the Fe2+/3+ redox couple and a two-phase reaction for the Mn2+/3+ redox couple. First-principles calculations reveal the migration of Na+ in NFMPP has low barriers. This work provides a new, low-cost, earth-abundant, and stable cathode choice for practical SIBs.

Using Machine Learning Techniques to Forecast Mehram Company's Sales: A Case Study

Sales forecasting, situated at the intersection of art and science, is critical for inspiring managers toward achieving profitable outcomes. Its precision sustains production levels and capital and plays a pivotal role in the company's and its leaders' overall success and career progression. In the context of Mahram Food Industries, the challenge arises from diverse investor perspectives and the impactful nature of numerous variables. To address this, a new sales forecasting algorithm has been introduced to enhance accuracy. The aim is to predict future sales through a comprehensive approach, leveraging technical analysis, time series modeling, machine learning, neural networks, and random forest techniques. The research methodology integrates various advanced techniques to improve sales forecasting for Mahram Food Industries. Technical analysis, time series modeling, machine learning, neural networks, and random forest methods are combined to create a robust framework. The focus is on predicting sales for a future period within the artificial intelligence-based machine learning domain. The study employs metrics such as Mean Absolute Deviation (MAD), MAD Percentage (MADP), and Mean Squared Error (MSE) to evaluate and compare the performance of the proposed neural network against traditional methods like multiple variable regression and time series modeling. The study's results highlight the superior performance of the neural network in sales forecasting for Mahram Food Industries. The Mean Absolute Deviation (MAD) for the neural network is 28.33, outperforming multiple variable regression (28.54) and time series modeling (29.45). Additionally, the neural network demonstrates a better MAD Percentage (MADP) with a value of 10.2%, surpassing the values associated with multiple variable regression (10.35%) and time series modeling (10.30%). The Mean Squared Error (MSE) further confirms the neural network's superiority with a value of 6452 compared to 6472 and 7865 for multiple variable regression and time series modeling, respectively. In conclusion, the study showcases the effectiveness of integrating advanced techniques, particularly the neural network, in enhancing the accuracy of sales forecasting for Mahram Food Industries. The comprehensive approach, combining technical analysis, time series modeling, machine learning, neural networks, and random forest, is a valuable strategy for predicting future sales. The superior performance of the neural network, as evidenced by lower MAD, MADP, and MSE values, suggests its potential for guiding informed decision-making in goal setting, hiring, budgeting, and other critical aspects of business management.

Phylometagenomics of cycad coralloid roots reveals shared symbiotic signals.

Cycads are known to host symbiotic cyanobacteria, including Nostocales species, as well as other sympatric bacterial taxa within their specialized coralloid roots. Yet, it is unknown if these bacteria share a phylogenetic origin and/or common genomic functions that allow them to engage in facultative symbiosis with cycad roots. To address this, we obtained metagenomic sequences from 39 coralloid roots sampled from diverse cycad species and origins in Australia and Mexico. Culture-independent shotgun metagenomic sequencing was used to validate sub-community co-cultures as an efficient approach for functional and taxonomic analysis. Our metanalysis shows a host-independent microbiome core consisting of seven bacterial orders with high species diversity within the identified taxa. Moreover, we recovered 43 cyanobacterial metagenome-assembled genomes, and in addition to Nostoc spp., symbiotic cyanobacteria of the genus Aulosira were identified for the first time. Using this robust dataset, we used phylometagenomic analysis to reveal three monophyletic cyanobiont clades, two host-generalist and one cycad-specific that includes Aulosira spp. Although the symbiotic clades have independently arisen, they are enriched in certain functional genes, such as those related to secondary metabolism. Furthermore, the taxonomic composition of associated sympatric bacterial taxa remained constant. Our research quadruples the number of cycad cyanobiont genomes and provides a robust framework to decipher cyanobacterial symbioses, with the potential of improving our understanding of symbiotic communities. This study lays a solid foundation to harness cyanobionts for agriculture and bioprospection, and assist in conservation of critically endangered cycads.

Holocene flood records and human impacts implied from the pollen evidence in the Daming area, North China Plain

Understanding the environmental significance of pollen and spores in alluvial plains is important for stratigraphic correlation and paleoenvironmental reconstruction. This paper presents palynological data from the North China Plain and explores their relationship with paleoflood records and human impacts since the Holocene. Our data reveal that pollen concentration and pollen assemblage vary in flood deposits (including overbank deposits and slackwater deposits) and inter-flood deposits (including sandy soils and lacustrine deposits). Flood deposits have higher fern percentages (28.6%) and lower herbaceous percentages (14.8%) compared to inter-flood deposits, though slackwater deposits share similar pollen concentrations and assemblages with sandy soils. Notably, overbank deposits are characterized by pollen-poor zones and aggregation of deteriorated pollen grains, especially in Unit III (755–385 cm, ca. 3.2–2.2 ka) and Unit V (190–0 cm, after ca. 0.6 ka). These findings suggest that overbank deposits correspond to strengthened hydrodynamic conditions at the flood-peak stage. Furthermore, the indicative pollen and spores provide compelling evidence for intensifying human impact in the North China Plain since the late Holocene. An aggregation of Selaginella sinensis at the depth of 640–610 cm indicates deforestation in the uplands since ca. 2.9 ka. Similarly, a sharp increase in Malvaceae percentage at the depth of 285–215 supports historical records of initial cotton planting in the Tang Dynasty (ca. 1.4–1.1 ka). The study underscores the value of palynological analysis for reconstructing paleoenvironment and human-environment interactions, providing a robust framework for understanding landscape evolution in the North China Plain.

Balancing pediatric device utility and harm: navigating the challenges for safer healthcare.

This article discusses the challenges in balancing the utility and harm associated with pediatric medical devices. Takashima et al. explored the prevalence and complications of invasive devices across three Australian pediatric tertiary hospitals and reported a high prevalence of complications, particularly with vascular access devices. We also highlight the need to pay attention to the use of these devices, particularly intravenous catheters, and the importance of a culture of patient safety in healthcare systems. We emphasize the need for a nuanced approach that carefully weighs the benefits against potential risks and encourages collaboration among stakeholders to establish robust regulatory frameworks and prioritize the safety and well-being of pediatric patients. Finally, we conclude by advocating the involvement of parents and families in gathering information about hospital safety incidents and fostering a culture of safety that supports reporting, flexibility, and learning. IMPACT: Reporting adverse events and their consequences is essential in the strategy to prevent them, allowing comparison with national or international standards, and identifying indicators to optimize and harbor the possibility of determining improvement measures that pursue a multifactorial approach and are cost-effective. Our comment advocates limiting the prevalence of medical invasive devices and their consequences by reviewing their epidemiology. Although advancements in medical technology have undoubtedly improved the diagnostic and therapeutic possibilities, the delicate balance between device utility and potential harm necessitates careful consideration.

Exploring the perspectives of cross-cultural instructors on integrating 21st century skills into EFL university courses

Integrating 21st century skills, including communication, collaboration, problem-solving, and critical thinking, into English as a Foreign Language (EFL) university courses is imperative. This study adopts a tripartite focus: (1) investigating cross-cultural EFL instructors’ perspectives on challenges linked to infusing 21st century skills into Saudi EFL university courses; (2) identifying contributory factors underpinning these challenges; and (3) elucidating proposed strategies for embedding 21st century skills in selected Saudi EFL university classrooms. Using narrative inquiry as a research method, empirical data was garnered through semi-structured interviews involving thirty EFL instructors representing mixed-gender and eight nationalities across multiple Saudi universities. The findings underscore instructors’ affirmative disposition towards incorporating 21st century skills in EFL classes, despite entrenched academic, institutional, collegial, and student-centered obstacles. Furthermore, the findings spotlight diverse factors exacerbating these challenges, encompassing technological underpinning, temporal constraints, and the absence of robust assessment frameworks, dwindling student engagement, conventional pedagogic paradigms, and administrative complexities. Addressing these challenges necessitates a comprehensive approach characterized by faculty development, curricular realignment, seamless technology integration, collaborative learning ecosystems, student-centric pedagogies, and judicious evaluation mechanisms. Through adopting this holistic approach, educational stakeholders can effectively surmount the intricacies linked with 21st century skill integration, fostering an enriched educational milieu aligned with evolving global imperatives.

A Comprehensive Study of Al2O3 Mechanical Behavior Using Density Functional Theory and Molecular Dynamics.

This study comprehensively investigates Al2O3's mechanical properties, focusing on fracture toughness, surface energy, Young's modulus, and crack propagation. The density functional theory (DFT) is employed to model the vacancies in Al2O3, providing essential insights into this material's structural stability and defect formation. The DFT simulations reveal a deep understanding of vacancy-related properties and their impact on mechanical behavior. In conjunction with molecular dynamics (MD) simulations, the fracture toughness and crack propagation in Al2O3 are explored, offering valuable information on material strength and durability. The surface energy of Al2O3 is also assessed using DFT, shedding light on its interactions with the surrounding environment. The results of this investigation highlight the significant impact of oxygen vacancies on mechanical characteristics such as ultimate strength and fracture toughness, drawing comparisons with the effects observed in the presence of aluminum vacancies. Additionally, the research underscores the validation of fracture toughness outcomes derived from both DFT and MD simulations, which align well with findings from established experimental studies. Additionally, the research underscores the validation of fracture toughness outcomes derived from DFT and MD simulations, aligning well with findings from established experimental studies. The combination of DFT and MD simulations provides a robust framework for a comprehensive understanding of Al2O3's mechanical properties, with implications for material science and engineering applications.

Thermal Conductivity of a Fluid-Filled Nanoporous Material: Underlying Molecular Mechanisms and the Rattle Effect.

Nanoporous materials are central to the energy and environmental crisis, with key applications in adsorption, separation, and catalysis. While confinement and surface effects on fluids severely confined in their porosity are well documented, the thermal behavior of nanoporous solids subjected to fluid adsorption remains puzzling in many aspects. With striking phenomena such as the so-called rattle effect, through which fluid/solid collisions decrease the overall thermal conductivity, the solid thermal conductivity and, more generally, heat transfer and dispersion in these complex systems challenge classical approaches (e.g., mixing rules including effective medium approaches fail to capture such effects as shown here). In particular, a robust molecular framework to describe the crossover between the decrease in thermal conductivity through the rattle effect in very narrow pores and the increase in thermal conductivity when replacing vacuum with a fluid phase in larger pores is still missing. Here, using a prototypical model of fluid-filled nanoporous materials (a Lennard-Jones phase confined in an all-silica zeolite), we perform a molecular simulation study to shed light on the parameters that govern the rattle effect in nanoporous solids. First, by varying the fluid/fluid, fluid/solid, and solid/solid interaction strengths as well as the fluid number density and mass density, we unravel the ingredients that lead to the essential coupling between fluid adsorption and phonon transport. Second, despite this complex interplay, inspired by pioneering molecular approaches on the rattle effect, we show that all data obey a simple statistical physics model that relies on the change in the speed of sound due to the fluid adsorbed density and the decrease in phonon lifetime due to scattering by fluid molecules. This framework, which provides a simple formalism to rationalize the thermal behavior of this class of solid/fluid composites, points to a decrease in thermal conductivity upon fluid confinement (up to 30% in some cases). Such an effect paves the way for the design of novel applications involving fluids in interaction with nanoporous materials.

Abstract A087: An ovarian cancer scRNA-seq atlas to dissect tumor-host interactions underlying metastatization and chemoresistance

Abstract The high tumor heterogeneity of high grade serous ovarian cancer (HGSOC) both intra-tumoral and inter-patient is one of the main factors hampering the identification of novel therapeutic treatment to improve survival of HGSOC patients. Single cell technologies are affording new inroads for the deep molecular characterization of such tumor heterogeneity, in both primary and metastatic samples. To this aim, we built a manually curated HGSOC transcriptomics atlas (~ 1,5 million cells, 79 patients) based on publicly available datasets and developed a data integration strategy specifically applicable to very heterogenous single cell data. scRNA-seq data integration is a central methodological challenge related to single cell data analysis, particularly in the context of highly heterogenous datasets. In this context, the main source of variability is the one existing among patients. Previously developed integration methods tend to perform badly on cancer data since they mainly rely on the assumption that cell populations present in different samples are similar, which might not apply for unknown, rare or patient-specific cell populations. Here we show a novel integration approach for patient-derived cancer data. To obtain a more faithful representation of the data, we performed identification of cell populations and metacells derivation separately for every sample to identify the distinct subpopulations characterizing each patient. In addition, to robustly represent the space describing the three main cell populations of the dataset, i.e., tumoral, immune and stromal, for each of the latter we selected the space identified by the union of highly variable genes, computed separately on every dataset, to preserve the variability of the system. Next, we fed a variational autoencoder with metacells data to integrate and characterize the different subpopulations that may be similar across samples. After integration, we were able to identify new robust gene signatures that would have been masked by datasets heterogeneity. The high degree of accuracy in the manual curation of metadata harmonization in the HGSOC atlas and the robustness and scalability of the data integration pipeline allowed us to obtain an in-depth characterization of the tumor subpopulations involved in therapy response by characterizing the chemotherapy induced transcriptional features. At the same time, thanks to the atlas-associated cell types labelling strategy, this atlas represents a transformative resource for the community by enabling the investigation of the interaction between tissue microenvironment (TME) cells and tumoral cells in shaping the metastatic process. This innovative HGSOC atlas will provide the scientific community of ovarian cancer research with a very powerful tool to investigate many aspects of this disease. The robust deep learning-based framework allows to expand the atlas easily and iteratively with newly generated datasets (out of sample extension), will constitute an invaluable resource for the HGSOC scientific field tackling HGSOC pathogenetic mechanism. Citation Format: Marta R. Sallese, Pietro Lo Riso, Carlo Emanuele Villa, Giuseppe Testa. An ovarian cancer scRNA-seq atlas to dissect tumor-host interactions underlying metastatization and chemoresistance [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr A087.

Youth violence and knife crime in ethnic minorities in the UK: A review of the literature.

Youth violence and knife crime is increasing dramatically, so much so it has been described as a global epidemic. The social, economic and political forces fuelling this rise mean that minority groups are particularly affected. This paper reviews the literature primarily from a UK perspective, and illustrates the disparate factors that are influencing the rise in youth violence and knife crime and illustrates the complexities of integrating the perspectives of different disciplines into coherent intervention strategies. We conducted a systematic review of the literature that explores both the causes of increasing youth violence and knife crime as well as some of the interventions that have attempted to deal with the problem. A complex interplay of social, economic, mental health and political factors underpin the increase in youth violence and knife crime. An uneasy tension exists between a traditional criminal justice system-based approach based upon deterrence and punishment, and a more liberal preventative model focusing on adolescent mental health. None of the interventions thus far have been particularly effective. Youth violence and knife crime is a global social issue that causes untold suffering to individuals, families and communities as well as fear that reverberates through society. Interventions have often been devised through the lens of particular disciplines or ideologies. Integrating these perspectives into a coherent approach that is actually effective demands greater co-operation, dialogue and mutual understanding between disciplines and agencies, as well as a robust framework for the evidence-based assessment of outcomes.