Abstract: Contemporary urban governance faces structural obsolescence in municipal solid waste management, particularly in the Global South. This study analyzes the implementation of the Zero Waste Cities Platform, a digital public infrastructure (DPI) designed to facilitate the transition from linear to circular economies, within the context of the first public pro-curement for innovation in Florianópolis, Brazil. Using an embedded case study and a mixed-methods approach, including a questionnaire administered to 125 citizens, the re-search evaluates the platform through four analytical lenses: institutional, political, terri-torial, and ecological. The implementation results demonstrate a 31% reduction in collec-tion route time and a 17% decrease in operational costs. Furthermore, cluster analysis of the questionnaire responses citizen a high latent potential for digital engagement, with 94% of respondents expressing willingness to use applications integrated into the nation-al governmental platform (Gov.br) to participate in recycling initiatives. The study con-cludes that “citizen digitalization,” when anchored in open DPIs and social innovation, acts as a systemic transition vector that reconfigures the roles of the state, cooperatives, and citizens. These findings provide empirically grounded insights for local governments seeking to combine emerging technologies, such as IoT and data intelligence, with demo-cratic experimentalism to accelerate ecological transitions and urban sustainability.

Abstract: CAPTCHA systems remain a widely deployed defense against automated abuse, but advances in machine learning have reduced the effectiveness of traditional challenge-based designs and exposed limitations in proprietary risk-scoring systems. This paper presents an adaptive, reinforcement learning-based CAPTCHA defense framework for high-security web applications. The proposed system formulates bot detection as a partially observable Markov decision process and uses a Proximal Policy Optimization agent with Long Short-Term Memory to analyze streamed behavioral telemetry, including mouse movements, clicks, keystrokes, and scrolling, over sequential interaction windows. Based on accumulated evidence, the agent can continue observing, deploy a honeypot, issue graded CAPTCHA challenges, allow a session, or block it. To complement the sequential agent, the framework also includes an XGBoost classifier that produces a session-level human-likelihood score as a supervised benchmark. Experiments on a simulated ticket-purchasing web application using human-generated sessions and multiple bot tiers, including scripted, replay-based, and LLM-powered agents, show strong preliminary performance. Among the evaluated reinforcement learning variants, Soft PPO achieved the best test performance with two reward structures, with one it reached 98.8% accuracy, 100% precision, and 0.987 F1 score, while with the revised reward structure it reached 96.4% accuracy, 100% precision, and 0.963 F1 score. The XGBoost classifier achieved 99.48% accuracy, 1.000 ROC-AUC, and 0.9919 F1 score. The results indicate that sequential reinforcement learning can support accurate and low-friction bot detection, while the accompanying classifier provides an interpretable and efficient benchmark. Compared with proprietary systems such as Google reCAPTCHA v3, the proposed framework emphasizes transparency, auditability, and explicit sequential decision-making rather than black-box risk scoring. Overall, this work introduces an open and adaptive CAPTCHA-defense framework that offers a promising alternative for studying and deploying behavior-based bot mitigation.

Abstract: Glycosylphosphatidylinositol-anchored proteins (GPI-APs) are a distinct class of eukaryotic cell-surface proteins characterized by a glycolipid anchor at their C-terminus. They display unique biophysical properties and play important roles in human diseases, including transmissible spongiform encephalopathies (TSEs), malaria, sleeping sickness, and rare disorders collectively termed inherited GPI deficiency (IGD). Because of their broad clinical relevance, GPI-APs have become a major focus of research, including their intracellular quality control (QC). Studies in diverse model organisms have revealed striking interspecies differences in GPI-AP QC pathways and notable distinctions from QC mechanisms and the degradation of misfolded species governing other secretory proteins. In this review, we summarize recent advances in the understanding of these cellular processes and propose that the observed variations in QC reflect distinct cellular strategies that balance protein quality control with membrane homeostasis across evolutionary contexts.

Abstract: Background: The renin-angiotensin system (RAS), traditionally known for its role in cardiovascular regulation, has also emerged as a key regulator of tumor progression and metastasis. Dysregulation of the RAS components has been implicated in breast cancer due to the significant presence of the RAS-related proteins in the breast tissue. This study aims to identify the dysregulated RAS components and investigate their potential as prognostic biomarkers. Methods: A pilot case-control study was carried out with 21 treatment-naïve breast cancer patients and 17 healthy controls. Plasma levels of Ang 1-7, Ang II, ACE2 and some cytokines were measured using LC-MS/MS and ELISA. ROC curves were used to assess changes in biomarker levels across the RAS components. Results: Breast cancer patients show significant dysregulation of the RAS components and Interleukin-10. The ratio of Ang 1-7/Ang II was reduced by over two-fold in breast cancer patients (p = 0.0442). While plasma ACE2 was significantly elevated in breast cancer patients (p = 0.0005), IL-10 was significantly suppressed (p = 0.0420). In exploratory logistic regression analysis, ACE2 showed potential as a classifier with improved discrimination when combined with Ang 1-7 and Ang II (AUC = 0.9396, accuracy = 92.59%). However, due to the small sample size and methodological limitations, these findings require further validation. Conclusions: Our hypothesis-generating study highlights the potential of RAS components as circulatory biomarkers, given their high accuracy in distinguishing breast cancer patients from healthy individuals. Despite promising results, external validation of this data in a larger, more diverse study cohort is recommended to generalize the findings.

Abstract: Phytohormones act as key endogenous factors and signalling molecules that mediate abiotic stress responses in plants, and are the integration centres of plant responses to environmental stimuli, playing an important role in plant resistance to drought, salt, cold and other stresses. Stress responses are finely regulated through a complex network of different classes of phytohormone signalling pathways. Many transcription factors are able to regulate the content of endogenous plant hormones by influencing hormone synthesis, metabolic gene and stress-related genes expression, which in turn affects plant growth and development and improves plant tolerance to abiotic stresses. Signaling molecules in plant stress responses, such as abscisic acid (ABA) ethylene (ETH), gibberellin (GA), jasmonic acid (JA) and salicylic acid (SA). Their roles in orchestrating plant responses to abiotic stresses. With global climate change, abiotic disasters have become increasingly frequent in recent years, severely hindering crop growth and development. Nanomaterials have attracted widespread attention from researchers because they can significantly alleviate abiotic stress in crops caused by factors such as salinity, drought, flooding, and heavy metals. This paper reviews recent research progress on the use of plant hormones and nanomaterials to alleviate abiotic stress in plants and elaborates on their underlying mechanisms of action. In the future, we will focus on investigating the roles of plant hormones and nanomaterials in modulating plant responses to abiotic stress, thereby enhancing plant tolerance to such stresses and increasing crop yields to address food security challenges.

Abstract: Background/Objectives: Molecular classification has become integral to endometrial cancer (EC) management, with mismatch repair deficiency (dMMR) representing a key biomarker for prognostication, Lynch syndrome screening, and immunotherapy eligibility. However, reported dMMR rates and their associations with histopathological features remain variable. This study aimed to determine the prevalence of dMMR and its correlation with clinicopathological characteristics in early-stage EC. Methods: In this prospective observational cohort study, 93 patients with early-stage EC undergoing primary surgical treatment between September 2022 and December 2025 were included. All patients were managed according to European Society of Gynaecological Oncology (ESGO) guidelines. Immunohistochemistry was performed on formalin-fixed, paraffin-embedded hysterectomy specimens to assess MLH1, PMS2, MSH2, and MSH6 expression. dMMR was defined as complete or subclonal loss of nuclear staining in ≥1 protein, while proficient MMR (pMMR) required preserved (> 90%) expression of all four proteins. Associations between MMR status and histology, grade, lymphovascular space invasion (LVSI), depth of myometrial invasion, FIGO 2023 stage, nodal status, recurrence, and survival were analyzed. Results: Median age was 66 years (range 35–86). Most tumors were endometrioid (86%), 27.9% were grade 3, 44% demonstrated deep myometrial invasion, and 23.6% showed substantial LVSI. dMMR was identified in 41.9% (39/93) of cases. MLH1 (38.0%) and PMS2 (39.8%) loss were most frequent, whereas MSH2 (3.2%) and MSH6 (4.3%) loss was uncommon. dMMR was significantly associated with endometrioid histology (p = 0.03) and deep myometrial invasion (p = 0.011). No significant correlations were observed with tumor grade, LVSI, FIGO stage, nodal involvement, recurrence, or overall survival during follow-up. Conclusions: dMMR was detected in approximately 40% of early-stage ECs and was significantly associated with endometrioid histology and deep myometrial invasion. Routine assessment of MMR status may refine risk stratification and support individualized therapeutic decision-making. Further studies with longer follow-up are warranted to clarify its prognostic impact.

Abstract: World models aim to enable agents to perceive states, predict future outcomes, and reason for decision-making by simulating real-world environments, and are widely regarded as a crucial pathway toward artificial general intelligence (AGI). Video, as one of the most accessible and intuitively representative media of dynamic environments, naturally contains rich implicit representations of the physical world. Consequently, learning world models from videos has become a prominent research direction. However, a significant gap remains between video data and the real physical world: videos capture only superficial visual phenomena and lack explicit representations of three-dimensional structure, physical properties, and causal mechanisms. This limitation severely constrains the physical consistency and practical applicability of world models. Motivated by this, the present work provides a prospective study of recent research in this domain, encompassing: (1) key challenges arising from the video–physical world gap and representative solutions; (2) three major construction paradigms of physical world models; and (3) future research directions and discussions. It is noteworthy that this study is the first to systematically examine video-driven world model research from the perspective of physical world. In contrast to prior study that primarily focus on generative modeling or provide broad overviews, this work emphasizes world models with tangible physical grounding, explicitly excluding generative tasks such as video synthesis or 3D/4D modeling that diverge conceptually from the goal of modeling the physical world. Adopting a problem-oriented perspective, this study aims to provide subsequent researchers with a systematic framework and decision-making guidance for understanding existing work, designing innovative methods, and facilitating the deployment of world models in real-world applications.

Abstract: Large Language Model (LLM) agents have demonstrated remarkable abilities to interact with external tools, functions, Model Context Protocol (MCP) servers, agents, and to take action on behalf of the user. Due to the fast-paced nature of the industry, existing literature does not accurately represent the current state of tool and agent selection. Furthermore, tool and agent selection in production has nuanced components not covered in experimental research. This work provides the first detailed examination of tool selection from a production perspective, distinguishing between the frontend layer where users interact with agents through buttons, slash commands, or natural language and the backend layer where retrieval, execution, orchestration, context engineering, and memory enable scalable reasoning. The paper contributes a unified taxonomy of modern tool and agent selection approaches spanning manual, UI-driven, retrieval-based, and autonomous methods. The backend covers dynamic tool retrieval, chunking, advanced RAG methods, context engineering, reinforcement learning, tool execution, human-in-the-loop processes, authentication, authorization, multi-turn tool calling, short- and long-term memory for tools, and evaluation. Finally, the paper identifies challenges in production components of both the backend and frontend and outlines promising avenues for research and development.

Abstract: Background/Objectives: Clinical phenotyping from narrative electronic health records (EHRs) often relies on multi-stage pipelines with span-level extraction, ontology mapping, and aggregation, which are complex to develop and maintain. Large language models (LLMs) may enable direct note-level abstraction of clinically meaningful features without intermediate extraction steps. We evaluated whether an LLM can approximate human inter-rater agreement for note-level multiple sclerosis (MS) phenotyping. Methods: We analyzed 100 de-identified MS neurology progress notes from a single academic medical center, each annotated for the presence or absence of 17 predefined neurological phenotype features (e.g., weakness, sensory, gait, pain, cognition, bladder). Two human annotators independently labeled all notes using a multi-label note-level framework in Prodigy, and discrepancies between the human annotators were adjudicated to create a gold standard. The same notes were annotated in a zero-shot setting by a large language model (GPT-5) and by the dictionary-based Doc2Hpo system. We computed percent agreement, Cohen’s κ, and precision, recall, and F1 scores for each annotator relative to the gold standard Results: Human–human agreement was substantial across most phenotype domains, with Cohen’s κ typically between 0.61 and 0.84, and lower agreement for infrequent features such as spasticity and hyperreflexia. Agreement between the LLM and human annotators was comparable to human inter-rater agreement across many features. Relative to the gold standard, the LLM showed recall that was equal to or higher than human annotators for most phenotypes, with overall F1 scores similar to human performance, whereas Doc2Hpo demonstrated lower precision and recall. Conclusions: In this note-level MS phenotyping task, a large language model achieved performance approaching expert human inter-rater agreement, with particularly strong recall across multiple phenotype domains. These findings suggest that direct note-level abstraction of clinically meaningful phenotypes from narrative neurology notes is feasible and may offer a scalable complement to traditional span-oriented extract–map–aggregate pipelines for population-level phenotyping and downstream machine learning applications.

Abstract: Efficient path planning and trajectory tracking are central to the safe and autonomous navigation of autonomous underwater vehicles (AUVs) in complex and unknown environments. To address the inherent challenges of safety, smoothness, and exploration efficiency in such settings, this paper presents an integrated framework that synergistically couples three enhanced core modules with complementary innovations. First, improved I-LazyTheta* and A-IRRT* algorithms are developed to incorporate safety margin collision detection and dynamic obstacle avoidance weight regulation, which enable efficient generation of collision-free paths that proactively maintain safe clearance in cluttered 3D spaces. Second, a trajectory tracking module based on a finite-state machine is designed, integrating B-spline optimization and a curvature-adaptive speed control mechanism to ensure high-precision following with guaranteed path smoothness and trackability. Third, a novel 3D autonomous exploration strategy tailored to underwater sonar constraints is constructed, combining frontier point clustering, multi-dimensional information gain evaluation, and traveling salesman problem (TSP) path optimization to achieve efficient unknown environment traversal while significantly reducing redundant detection and energy consumption. The proposed framework supports modular decoupling for independent reuse as well as integrated collaborative operation, offering flexible adaptability to diverse underwater robotic platforms. Simulations demonstrate that the integrated approach achieves superior performance in path safety and tracking accuracy, along with an exploration coverage of 79.08%, validating its effectiveness for robust AUV autonomy in complex underwater scenarios.

Abstract: Industrial transformation in resource-based regions (RBRs) is a global challenge. Shanxi is a typical resource-based province in China. The long-term exploitation of coal resources has posed huge challenges to its ecological protection and high-quality development. Breaking away from the single-city perspective, this study focuses on the regional scale and comparative analysis, and attempts to construct a novel three-dimensional analytical framework, namely “industrial characteristics, industrial layout, and industrial policies”, to explore the industrial transformation path of typical RBRs. The results indicate as follows: (1) Shanxi Province does not have obvious advantages in terms of resource endowment, with a severely heavy industrial structure and strategic emerging industries still in the initial stage of development. At the national strategic level, it is still necessary to strengthen the application of the “pioneer and pilot” policies and mechanism innovation. (2) Under the background of high-quality development, Shanxi needs to clarify the orientation and transformation direction of industrial development: for agricultural development, it should highlight characteristic and efficient development; for industrial development, it should focus on the upgrading of advantageous industries and the cultivation of emerging industries; for tertiary industry development, it should form a pattern of “new producer services + characteristic tourism”. It is also necessary to form a macro pattern of “four provincial clusters and four inter-provincial plates” in regional development layout to promote inter-regional coordinated development. (3) In the new period, Shanxi should accelerate the construction of a comprehensive transportation system as the backbone network to improve the convenience of inter-regional cooperation; increase investment in education and scientific research to enhance the overall social innovation capacity; and strengthen the supply of differentiated regional development policies to promote high-quality industrial development. Focusing on the regional scale, the new logical analysis paradigm can provide theoretical references for RBRs to clarify the direction of industrial transformation and formulate transformation policies.

Abstract: Optical spectroscopy provides several useful information about polymeric thin films by combining interferometric and optical absorption data contained in the UV-Vis-NIR spectra. In particular, the UV-Vis-NIR spectrum of a thin polymeric film contains information about the film thickness, structural disorder, bandgap energy, type of electron transition model (direct/indirect, allowed/forbidden), cut-on wavelength (i.e., the opaque/transparent switching wavelength), etc. Here, these properties have been determined for a model semi-crystalline polymer (polyethylene terephthalate, PET) in form of ultrathin film before and after a mild mechanical deformation treatment (manual stretching). It has been found that EU and Eg parameters are not strictly depending on mechanical deformation due to their main dependence on chemical composition/constitution of the polymer.

Abstract: The rapid advancement of AI is fundamentally disrupting research and engineering. While much attention is given to how AI may optimize wireless systems, this article explores a different question: how will AI impact the ecosystem and community developing future wireless technology? We trace this transformation across the entire lifecycle, from education and core research to technical publication and production-ready network deployments. As AI increasingly automates routine tasks, the primary value of the human researcher will shift from problem-solving to problem-finding, research orchestration, and oversight of trade-off management. By actively preserving spaces for deep, unplugged thinking and steering AI toward genuine discovery rather than mere recombination, we can navigate this profound shift to ultimately elevate human ingenuity and the future evolution of the researcher.

Abstract: Tuberculosis remains a major cause of mortality globally. Treatment of tuberculosis requires a long duration with multiple drug regimen. Unfortunately, tuberculosis drug resistance is emerging, resulting in a treatment failure rate of 14% in new cases. Bedaquiline, a poorly soluble second-line drug is used to treat multidrug-resistant tuberculosis in combination with first-line anti-tuberculosis drugs. Bedaquiline is often ad-ministered with rifampicin, as this combination has demonstrated additive intracellular bactericidal and faster onset of action compared to bedaquiline monotherapy. However, co-administration with rifampicin has been reported to increase bedaquiline clearance, reducing concentration of bedaquiline in the blood by up to 25%. There is a need for alternative pharmaceutical formulations to enhance bedaquiline bioavailability and treatment success of tuberculosis. Co-amorphous drug delivery systems have the potential to improve the water solubility and bioavailability of poorly soluble drugs. In an aim to enhance the solubility of bedaquiline when co-administered with rifampicin, we have prepared co-amorphous systems of rifampicin and bedaquiline fumarate. First, miscibility of the components was assessed using Hansen solubility parameters. Then, the solid co-amorphous drug was prepared by fast solvent evaporation, and characterized using PXRD, TGA-DSC, FTIR, dissolution rate, and accelerated stability study. Results show that the co-amorphous form exhibited better dissolution for bedaquiline without compromising rifampicin dissolution. Furthermore, the co-amorphous product remained stable under stress conditions for 30 days. These findings suggest that co-amorphous systems of rifampicin and bedaquiline fumarate may represent a viable strategy to improve treatment outcomes for patients with multidrug-resistant tuberculosis treated with these drugs and decrease pill burden.

Abstract: Low temperature photoluminescence (PL) has been studied under hydrostatic pressure and under varying excitation powers in three samples of single In0.17Ga0.83N quantum wells with different widths: 2.6 nm, 5.2 nm, and 10.4 nm. Transitions involving ground states were strong in the 2.6 nm well, weak in the 5.2 nm well, and absent in the 10.4 nm well. Pressure coefficients of PL lines have been used to estimate the electric field in the wells. In the widest well the field seems to be fully screened (at high excitation powers). Simulations involving Poisson and Schrodinger equations allowed to identify the experimental PL lines. Pressure evolution of the PL spectra agreed with the simula-tion.

Abstract: The study was developed in the context of the search for bioactive compounds of interest present in medicinal plants, among them phenolic compounds, recognized for their func-tional relevance. To this end, the extraction process was optimized using soursop leaves as a model, combining different particle sizes (< 2 mm, 2–6.3 mm, and 6.3–9.5 mm), mac-eration times (12, 24, and 48 h), and ethanol:water ratios (25:75, 50:50, and 75:25), which generated 27 extracts. The optimal process corresponded to an ethanol:water ratio of 50:50, fine particle size (< 2 mm), and 24 h of maceration, reaching 584.64 mg·L⁻¹ of gallic acid equivalents (GAE). Additionally, other plant species were evaluated: horsetail, kiswara, matico, muña, and thyme. Antioxidant capacity was determined using the DPPH method through IC₅₀ val-ues, where soursop, kiswara, and muña recorded the lowest values (0.52, 0.52, and 0.61 mg·L⁻¹), even lower than ascorbic acid (19.10 mg·L⁻¹). Thyme and horsetail showed inter-mediate activity, while matico presented the lowest response. The results indicate that these species have high potential as sources of functional bioac-tive compounds, highlighting the importance of medicinal plants for the development of natural products with antioxidant properties.

Abstract: Kidney transplantation for patients affected by end stage renal failure is considered the best therapeutic option, but this possibility is limited by deceased donor shortage. Living donor kidney transplantation (LDKD) is a valuable option, frequently limited by immunological incompatibility between donor and recipient. This review will consider the to date possibility of performing living donor kidney donation in the case of AB0 blood group incompatibility and the progresses that have been made in this field. Kidney paired donation is one possibility. Such technique offers the best possibility if the number of pairs available is wide. This is possible by performing national and also international registries. The technique more diffuse is the desensitization of the recipients. Desensitization may be obtained by several ways that are extensively treated in this review. In the recent period some published studies document the possibility to enzymatically convert A or B group from the cells of the donor to 0 group. This possibility is only on the beginning, but may represent the future eventually associated to a mild desensitization.

Abstract: This study addresses the role of cane density as a key agronomic factor in floricane raspberry production and explores its relationship with canopy structure assessed using UAV-based multispectral imaging. Although cane density is widely recognized as critical for yield formation, its interaction with remotely sensed vegetation indices remains insufficiently understood. The experiment was conducted in an open-field plantation in eastern Poland using four floricane raspberry cultivars and four cane-density treatments (two to five canes per plant). Yield components were measured throughout the harvest period, and multispectral data were acquired using an unmanned aerial vehicle to derive vegetation indices and assess spatial and temporal canopy variability. Yield per plant and fruit number increased consistently with cane density, while yield per cane decreased, indicating a trade-off between individual cane productivity and total yield. Fruit size remained relatively stable and was primarily influenced by harvest timing. Vegetation indices followed a common seasonal pattern, with moderate and variable responses to cane density. The integration of yield measurements with multispectral data revealed that cane density influences not only productivity but also canopy structure and its spatial uniformity. These findings highlight the potential of combining agronomic practices with remote sensing approaches to support data-driven optimization of raspberry production systems.

Abstract: Population aging is reshaping oral health systems in ways that are difficult to ignore. Such challenges become particularly complex in contexts suffering from strong social inequities, limited access to dental services and/or a growing dependency on informal healthcare. In spite of being largely preventable, oral and dental disease remain highly prevalent among older people. This results particularly worrisome since it is known that oral disease in the elderly has enormous consequences, well beyond the oral cavity and may affect nutrition, frailty and quality of life. It is in this context that mobile health technologies (mHealth) have emerged as potentially valuable tools to support, not only the promotion of oral health, but also its monitoring, education and the training of caregivers. However, it is not yet characterized, to what extent do existing mobile applications properly respond to the specific needs of the older population and their caregivers. This narrative review aims to critically examine the current landscape of mobile health applications designed to improve oral health in older people; with a strong focus on tools oriented to self-care, clinical monitoring, support to caregivers and training of primary attention personnel. We decided to follow the SANRA methodological framework, by summarizing the evidence published in the literature between the years 2000 and 2024 and further analyzing the application’s functionalities, their target users, their usability and the strength of the evidence supporting their development and implementation. Our findings reveal a substantial heterogeneity regarding scope, design and validation, as well as a persisting lack of culturally adapted solutions, focus on geriatrics and oriented towards caregivers. In spite of the potential, most applications show a limited clinical validation, a weak integration into health systems and an insufficient consideration of the cognitive, functional and social determinants of aging. Interpreted within global and regional policy frameworks, including the WHO Healthy Aging 2021–30 and the Global strategy and action plan on oral health 2023–30, these results highlight critical gaps and future directions for the development of equitable, evidence-based mHealth interventions in geriatric oral care.

Abstract: The social dimension of sustainability is increasingly recognized as essential to the aviation sector, yet systematic assessment of social impacts across aircraft systems and their associated design and production processes remains limited. This study applies Social Life Cycle Assessment (SLCA) principles, guided by the UNEP/SETAC guidelines and the ISO 14075:2024 standard, to perform a country-based screening that identifies, quantifies, and analyzes hotspot impacts associated with materials production and manufacturing in the aviation sector. A tailored SLCA framework is developed to reflect the specific characteristics of the aviation sector and to identify relevant stakeholder groups, including workers, local communities, consumers, value chain actors, and society. Aviation-specific social indicators are defined in line with industry needs and regulatory expectations, enabling socially informed decision-making during early design stages. The methodology is demonstrated through a comparative assessment of two major commercial aircraft, examining social impacts across global supply chains, identifying social hotspots and country-specific risk drivers, and evaluating targeted improvement measures. In addition, alternative component production locations are assessed to explore supply-chain configurations with lower social risks. The results provide actionable insights for policymakers and industry stakeholders and support holistic sustainability assessments by explicitly integrating the social dimension into sustainable aircraft design.