Abstract: This paper addresses the challenges of evidence dispersion, attention shift, and inference inconsistency in long text and multi-document reasoning scenarios. It investigates the problems that large language models often encounter under conditions of extremely long contexts, multi-source information conflict, and structural complexity, and proposes a hierarchical, curriculum-based fine-tuning algorithm framework. This framework organizes the input into a hierarchical structure of questions and multi-document contexts. At the representation level, it constructs a three-level convergence path of tokens, fragments, and documents to form structured memory and mitigate semantic drift caused by context expansion. At the reasoning level, it introduces a question-guided evidence scoring and weight aggregation mechanism to achieve differentiable selection across document fragments and global evidence vector construction, thereby strengthening the alignment of key evidence and suppressing redundant interference. At the training organization level, it employs a curriculum strategy, progressively scheduling samples according to difficulty levels, enabling the model's capabilities to gradually transition from local consistency to cross-document evidence integration and overall induction. Comparative experimental results show that this method exhibits more stable performance in multi-document evidence localization and inference consistency evaluation, validating the effectiveness of hierarchical modeling and curriculum scheduling in shaping multi-document reasoning capabilities.

Abstract: Artificial intelligence (AI) has rapidly advanced as a key analytical tool for processing complex datasets across disciplines, including environmental and health research. Meteorological data is increasingly used to understand and mitigate health risks, often linked to climate change. This rapid review aims to synthesise studies involving AI for meteorological data applications in health research to better understand its use. PubMed, Web of Science, and Scopus were systematically searched from 2020 to 2025 following a standardised framework in line with PRISMA-RR guidelines. Eligible studies included any empirical design involving human-related health research where AI techniques were applied to meteorological data. Two reviewers independently screened studies, extracted data, and synthesised findings narratively. Twelve studies met eligibility criteria. Despite heterogeneity in study design and sample size, most examined the impact of extreme pollution levels and temperature variations on the prevalence and severity of respiratory, bacterial, and other diseases. AI methods primarily included Random Forest models, as well as time-series and clustering analyses. Model performance was commonly evaluated using sensitivity; however, methodological justification was often insufficiently recorded. Overall, findings suggest that incorporating meteorological variables enhances the prediction of health outcomes, although detailed population characteristics were frequently underreported. This restricts the generalisability and applicability of AI-driven health models incorporating meteorological data, with stronger methodological rigour and clearer reporting standards needed to support reliable future development.

Abstract: Healthcare communication during crisis moments imposes profound responsibilities on providers, extending beyond clinical disclosure to encompass the psychosocial and ethical dimensions of patient interaction. The statement, "Go to Court if Not Satisfied," commonly deployed in institutional responses to adverse medical outcomes, raises serious ethical concerns that transcend mere legal defensibility. This paper argues that such language violates core bioethical principles of beneficence, nonmaleficence, and patient autonomy, while simultaneously reflecting a broader culture of defensive medicine that undermines therapeutic relationships. Drawing on the case of missing twins at the 37 Military Hospital in Ghana, we critically interrogate the ethical dimensions of adversarial language in bad-news delivery, foregrounding its psychological harm to already-vulnerable patients and relatives. Beyond existing critique, this paper offers a novel analytical framework: positioning the "Go to Court" discourse as an institutional manifestation of power asymmetry and defensive communication culture, antithetical to the values of patient-centred care. We propose that open disclosure frameworks, therapeutic alliance theory, and the patient-is-always-right principle together offer a more ethically robust alternative. The paper calls on healthcare institutions to move from legally motivated damage-limitation communication toward accountability-driven, empathic engagement.

Abstract: This review explores the modulation of the host cellular flexibility “kinome" (protein kinases) and "phosphatome" (protein phosphatases) by dietary nutrients and gut microbiota metabolites, proposing a potential paradigm in the strategies for healthy aging and metabolic disease prevention. While mainstream nutrition approaches focus on population-wide guidelines, precision nutrition exploits the innovations in personal molecular networks and systems medicine, integrating genomics and metabolomics to address "metabolic rigidity"—the cell inability to switch between fuel sources. The review examines how master molecular regulators like AMPK and mTOR, and "metabolic brakes" like PTP1B and PTEN, are affected by single nucleotide polymorphisms (SNPs) and microbial signals (SCFAs, secondary bile acids, indoles). Specifically, the "microbial kinomic interference" hypothesis is discussed, where gut metabolites act as remote ligands for host signaling enzymes. Finally, the potential role of a personalized phosphoproteomics strategy is highlighted as an effective functional readout to guide nutritional interventions, aiming to restore metabolic plasticity through a gut microbiota/multi-omics approach.

Abstract: In the introduction, a brief history of the body selection that would define the inertial frame of reference in mechanics (and electrodynamics) is given. At the same time, attention has been drawn to Einstein's opinion about the (unrealized) causal relationship which should exist between the frame of reference and the law that is formulated from it. Therefore, parts 2 to 5 gradually present and elaborate the idea that, instead of directly choosing a body of reference, the criterion which will give legitimacy to the selection result should be defined first. And the criterion is such that the reference body is ensured by causality just mentioned by Einstein. Such a criterion for the fields of mechanics and thermodynamics is defined here and called the "criterion of observer's non-involvement in the observed” by a reference body. In the conclusion, as the main result, significant changes in the formulations of mechanics and thermodynamics, which the application of the criterion leads to, are stated. In those improved formulations the contents related to the observer are separated from the contents related to laws of nature.

Abstract: In this study we show that on the basis of simple crystallographic rules applied to the sphere-in-contact model/theorem that we can predict that under ambient conditions of pressure and temperature that the most dense and stable form of lithium in GICs is LiC6 and that two distinct form of LiC8 are possible. We find that other empirical formulas such as MC2, MC3 and M3C8 are possible based on crystallography, but not stable based on intercalate repulsions. The results are based on the unit cell description of GICs with the sphere-in-contact model/theorem that is used to model the intercalation of an arbitrary atom within the AαAα stacking1 of two graphene layers in GICs. We calculate the density and the packing fraction of these materials. This approach offers a simple description of the structure of GICs in which the unit cell can be defined and the diffusion of ions can be estimated on the basis of the void space in these materials. We anticipate that this simple description of GIC will be useful for the rational design of new graphite-based materials that can find use in various energy storage applications such as ion-based batteries but also as an educational tool in which university level education in materials and surface chemistry is directly connected to basic laws in chemistry, physics and mathematics.

Abstract: The automation of Integrated Circuit (IC) physical layout optimization remains a critical challenge, primarily due to the complex interplay between electrical and physical constraints. We propose ChipForm, a framework that reframes this task as a constraint-driven, reinforcement learning-guided graph optimization problem. Unlike perception-based approaches, ChipForm directly processes circuit netlists using a Hierarchical Graph Encoder (HGE) to extract features and predict timing, power, and density constraints. Subsequently, a Reinforcement Learning Placement Agent (RLPA) performs sequential cell placement, optimizing for minimal wirelength while explicitly satisfying these predicted constraints. A key contribution is a unified, end-to-end training strategy that jointly optimizes constraint prediction and placement policy. Extensive experiments on the CircuitNet benchmark demonstrate state-of-the-art performance: ChipForm achieves an 85.2% physical executability rate (DRC/LVS pass) and reduces constraint prediction errors (e.g., 0.11 OOD timing criticality error) compared to prior methods. Ablation studies confirm the necessity of each component, showing that explicit constraint prediction heads improve OOD generalization by 5.7% in executability, and the RL agent outperforms a greedy baseline by 3.9%. ChipForm thus provides a robust, data-driven approach for generating high-quality, manufacturable chip layouts directly from netlist specifications.

Abstract: A decade has elapsed since the first recognized cluster of congenital anomalies associated with Zika virus (ZIKV) was reported in Brazil in 2015, culminating in the formal delineation of Congenital Zika Syndrome (CZS) as a specific pattern of birth defects. This narrative review examines the ten-year trajectory of CZS as a tropical infectious disease, from its initial emergence and public health emergency declaration by the World Health Organization (WHO) in February 2016, through evolving epidemiological, clinical, and scientific understanding. CZS is characterized by a spectrum of severe neurological manifestations—including microcephaly, subcortical calcifications, malformations of cortical development, ventriculomegaly, and corpus callosum abnormalities—alongside ophthalmic, auditory, and musculoskeletal complications. Transmitted primarily by Aedes aegypti mosquitoes in tropical and subtropical regions, ZIKV disproportionately affects low- and middle-income countries in Latin America, Africa, and Southeast Asia, underscoring its nature as a quintessential tropical disease linked to poverty, inadequate vector control, and health inequity. Over ten years, substantial advances have been made in understanding ZIKV pathogenesis, neurodevelopmental outcomes, diagnostic criteria, and multidisciplinary clinical management of affected children. In the therapeutic and preventive domain, over 45 vaccine candidates have been identified, with 16 reaching Phase 1 or 2 clinical trials by late 2025, though no licensed vaccine or specific antiviral therapy yet exists. This review contextualizes CZS within the broader framework of neglected tropical diseases, evaluates its global and family-level burden, and critically appraises progress and remaining gaps in clinical care, vaccination, and vector control over the past ten years.

Abstract: We develop a quantitative framework in which black holes function as erasure channels for exterior classical records at the horizon-side Landauer bound. At asymptotic infinity, greybody scattering turns the full radiation channel into a dissipative filter whose entropy efficiency η_∞ ≡ |dS_BH|/dS_rad is field-content dependent; for the spin-2 (graviton) channel in Schwarzschild evaporation, η_∞ ≈ 0.74 (Page 2005). Starting from the Cortês–Liddle result that Hawking evaporation saturates the Landauer principle, we make three contributions. First, we define a Landauer saturation ratio R_L as a bookkeeping diagnostic for horizon thermodynamics: Schwarzschild black holes yield R_L = 1 exactly, while the cosmological apparent horizon yields R_L = 1/2 in Trivedi's quasi-local energy accounting. Second, we show that within the standard Bekenstein–Hawking area law and the discrete transition model of Bagchi, Ghosh, and Sen, one-step Landauer-saturating area transitions select the Bekenstein–Mukhanov spacing ΔA = 4 ln 2 l_P², this discrete compatibility result complements, rather than derives, the continuous holographic scaling S ∝ M². Third, we argue that the black hole scrambling time t_* ~ (ℏ/2πk_BT_H) ln(S_BH/k_B) provides a partial gravitational analogue of the reversibility time τ_c in quantum measurement: for an old black hole it sets the delay after which newly injected information can begin to reappear in Hawking radiation. We formalize the horizon as an effective coarse-grained erasure channel within fixed-charge sectors via a semiclassical proposition that combines a strict exterior coarse-graining definition with GSL-compatible entropy bookkeeping and horizon-side first-law accounting. We check the supporting identities numerically across the Schwarzschild, Kerr, and Reissner–Nordström parameter spaces, and analyze robustness to the memory burden effect. The framework positions black holes as the thermodynamic complement to quantum measurement: measurement creates classical records by paying Landauer costs; horizons erase exterior access to those records at the quasi-static Landauer limit, while the asymptotic Hawking channel is greybody-dissipative.

Abstract: In this study, we compared the effects of microwave-assisted extraction (MAE) and ultrasound-assisted extraction (UAE) on the total phenolic content, antioxidant activity, morphological characteristics, and identification of the bioactive compounds in pomegranate seeds. We conducted a phytochemical characterization of the extracts by determining the total phenolic content and total flavonoids. Antioxidant activity was evaluated using ferric reducing antioxidant power (FRAP) and free radical inhibition methods (DPPH and ABTS). Morphological characteristics were analyzed via scanning electron microscopy, UV-Vis and FTIR of the extracts were recorded. Additionally, the main bioactive compounds were identified using HPLC-MS. Our results demonstrated that MAE was the most efficient technique, yielding a higher content of total phenols (35.47 mg GAE/g), total flavonoids (14.44 mg CAE/g) and antioxidant activity (0.19 and 0.41 mmol TEAC/g, as determined by FRAP and ABTS, respectively). In terms of morphological characteristics, UAE induced more changes in the structure of the plant material compared to MAE. According to HPLC-MS analysis, the extract obtained using MAE notably contained coumaric acid, cyanidin, and quercetin, whereas the UAE extract included coumaric acid, cyanidin, kaempferol, and epicatechin. In conclusion, this study demonstrated that MAE is a more efficient method than UAE for extracting bioactive compounds. Pomegranate seeds may represent a potential source of these compounds for application in various industrial areas.

Abstract: The retail fuel sector in urban areas presents significant environmental risks, requiring systematic sustainability assessments. This study aims to highlight the socio-environmental performance of fuel stations in Mossoró/RN using the Corporate Sustainability Index (ISE). It is a descriptive and exploratory study with a quantitative approach, based on questionnaires administered to managers of 12 licensed fuel stations. The ISE was calculated using 17 equally weighted environmental, legal, social, and operational indicators. The results indicated a predominance of high sustainable performance, with 91.7% of enterprises presenting an ISE above 75%, associated with operational organization, preventive practices, and compliance with legal requirements. However, some actions remain primarily tied to regulatory compliance, revealing a predominantly reactive environmental management profile. The study provides insights for enhancing strategic environmental management in the urban context of the Brazilian Semi-Arid region.

Abstract:

Mitochondrial dysfunctions are believed to contribute to the pathogenesis of tauopathies, a group of neurodegenerative diseases with abnormal accumulation of microtubule-associated protein tau. The combination of 5-aminolevulinic acid (5-ALA) and sodium ferrous citrate (SFC) is known to improve mitochondrial functions. Here, we report that 5-ALA combined with SFC (5-ALA/SFC) improves mitochondrial functions and mitigates neurodegeneration in transgenic Drosophila expressing human tau. We found that tau reduces ATP levels, decreases mitochondrial distribution to neurites, and increases mitochondrial reactive oxygen species (ROS). Expression of oxidative phosphorylation (OXPHOS) genes was upregulated and activities of complexes I and IV were elevated. Feeding 5-ALA/SFC to tau flies lowers oxidative damages without correcting OXPHOS activities or mitochondrial distribution. 5-ALA/SFC treatment suppressed pathological tau phosphorylation and mitigated tau-induced neurodegeneration. Our results suggest that 5-ALA/SFC decreases a neurodegenerative pathway involving tau, mitochondria, and ROS.

Abstract:

Background: Parkinson’s disease (PD) is clinically heterogeneous, yet the genetic architecture underlying this heterogeneity remains incompletely understood. We examined the genetic correlates of four complementary PD subtyping frameworks: clinical motor subtype (tremor-dominant [TD] vs. postural instability/gait difficulty [PIGD]), alpha-synuclein seed amplification assay status (SAA+ vs. SAA−), pathological subtype (brain-first vs. body-first, based on REM sleep behavior disorder), and data-driven subtype (diffuse malignant [DM] vs. mild-motor predominant [MMP] vs. intermediate [IM]). Methods: We analyzed 1,597 PD patients from the Parkinson’s Progression Markers Initiative (PPMI) with genetic testing for seven PD-associated genes (LRRK2, GBA, SNCA, PRKN, PINK1, PARK7, VPS35), including specific variant resolution (LRRK2 G2019S, R1441G/C/H; GBA N409S, severe variants; SNCA A53T), and APOE genotyping (ε2/ε3/ε4 alleles). Genetic variant frequencies were compared across subtypes using chi-square or Fisher’s exact tests with Benjamini–Hochberg false discovery rate (FDR) correction. Effect sizes were quantified using Cramér’s V. Multivariable logistic regression (statsmodels) estimated adjusted odds ratios with Wald-based 95% confidence intervals. Results: Among 1,390 genotyped PD patients, LRRK2 carriers constituted 13.7% (190/1,390; 170 G2019S, 18 R1441G/C/H), GBA 8.6% (119/1,390; 96 N409S, 23 severe), and SNCA 2.0% (28/1,390; all A53T). APOE ε4 carriers comprised 23.4% (323/1,380). SAA-negative patients were markedly enriched for LRRK2 variants (37.1% vs. 10.2%, P = 3.7 × 10⁻¹⁹, q < 0.001, V = 0.25), driven by G2019S (28.5% vs. 9.6%, P = 4.9 × 10⁻¹¹, q < 0.001) and R1441G/C/H (7.9% vs. 0.5%, P = 2.7 × 10⁻¹², q < 0.001). Body-first PD was enriched for GBA carriers (12.3% vs. 6.7%, P = 0.004, q = 0.021) and depleted for LRRK2 (7.9% vs. 15.0%, P = 0.002, q = 0.013). The DM subtype carried the highest GBA frequency (14.0% vs. MMP 5.9%, P < 0.001, q = 0.003). After FDR correction, 10 of 48 univariate tests remained significant. Clinical subtypes (TD vs. PIGD) showed only nominal LRRK2 differences that did not survive FDR correction. APOE genotype did not differ across any framework. Conclusions: PD subtypes defined by alpha-synuclein pathology (SAA), pathological onset pattern (brain-first/body-first), and data-driven classification (DM/MMP/IM) show distinct genetic profiles that survive multiple comparison correction. LRRK2 variants strongly associate with SAA-negativity (V = 0.25); GBA variants associate with the severe body-first onset and the diffuse malignant subtype.

Abstract: Large Reasoning Models (LRMs) face a fundamental challenge in balancing efficient "fast thinking" with accurate "slow thinking," often struggling to adaptively trigger deeper reasoning without incurring significant computational overhead. This paper introduces \( \textit{MetaThink (MT)} \), a novel inference-time adaptive refinement framework designed to imbue LRMs with conditional self-correction capabilities, without requiring any additional training. \( \textit{MetaThink} \) operates by an initial "fast thinking" phase, followed by a lightweight self-monitoring mechanism that assesses confidence through uncertainty markers. When low confidence or potential errors are detected, a refinement token triggers a targeted "slow thinking" phase, guided by domain-specific prompts. This allows the model to introspectively review and correct its reasoning, culminating in a more accurate final answer. Our comprehensive evaluation across diverse and challenging benchmarks—spanning mathematical reasoning, code generation, and scientific problem-solving tasks—demonstrates that \( \textit{MetaThink} \) consistently achieves substantial and robust improvements in Pass@1 accuracy. Crucially, these gains are realized while maintaining competitive or even improved inference efficiency, outperforming existing inference-time baselines. Our findings underscore that \( \textit{MetaThink} \) offers an effective, training-free approach to enhance the reliability and accuracy of LRMs in complex reasoning tasks by striking a superior balance between performance and efficiency.

Abstract: Background: Biosimilars represent a paradigm shift in access to biological therapies, yet physician understanding of their pharmacological basis, regulatory framework, and clinical implications remains heterogeneous. This review addresses the knowledge gap from a pharmacological perspective relevant to internists and clinical specialists. Objective: To provide a comprehensive, evidence-based narrative review of biosimilars for clinical physicians, covering definitions, the comparability exercise, interchangeability, therapeutic applications, global development, pharmacovigilance considerations, and the Latin American regulatory landscape. Methods: A narrative review was conducted using PubMed, WHO technical reports, FDA and EMA regulatory documents, and IQVIA market data. Studies published between 2009 and 2025 were included, with emphasis on systematic reviews, meta-analyses, WHO guidelines, and regional regulatory analyses. Results: The totality-of-evidence approach demonstrates that biosimilars exhibit no clinically meaningful differences from reference products in efficacy, safety, or immunogenicity. Meta-analyses of switching studies (>10,800 patients) confirm comparable outcomes. The FDA (2024) eliminated switching study requirements for interchangeability designation, while the EMA declared universal interchangeability in 2022. The global market reached USD 30.3 billion in 2024. In Latin America, regulatory heterogeneity, limited technical capacity, and prescriber misconceptions remain barriers despite advancing frameworks in Brazil, Argentina, Mexico, and Colombia. Conclusions: Biosimilars are pharmacologically and clinically equivalent therapeutic alternatives supported by robust evidence. Clinical physicians should integrate biosimilars into their prescribing decisions with confidence, while advocating for strengthened pharmacovigilance systems and regulatory harmonization in their regions.

Abstract: Objective: Soft tissue sarcomas (STS) are biologically heterogeneous malignancies with unpredictable clinical behavior. Although tumor size, histological grade, and surgical margin status remain the main determinants of prognosis, additional biomarkers that integrate tumor biology and host-related factors are needed. The hemoglobin × albumin × lymphocyte/platelet (HALP) score reflects systemic inflammation and nutritional status. This study aimed to evaluate the association between preoperative HALP score and oncological as well as surgical outcomes in patients undergoing curative resection for STS. Materials and Methods: A retrospective cohort study was conducted including 46 consecutive patients who underwent surgery for STS between 2017 and 2025. HALP scores were calculated using preoperative laboratory parameters, and patients were stratified into low and high HALP groups according to the cohort median (24.9). Overall survival (OS) and disease-free survival (DFS) were analyzed using the Kaplan–Meier method and Cox proportional hazards models. Surgical margin status and postoperative complications were also compared. Results: Patients with low HALP scores had significantly larger tumors, higher rates of non-R0 resection, and increased major complications (p<0.05). Recurrence and mortality were more frequent in the low HALP group. Kaplan–Meier analysis demonstrated significantly shorter OS (log-rank p=0.0034) and DFS (log-rank p=0.0318) in patients with low HALP scores. In univariate Cox analysis, HALP was significantly associated with survival outcomes; however, in multivariate analysis, histological grade and surgical margin status remained independent prognostic factors, while HALP lost independent significance. Conclusion: A low preoperative HALP score is associated with aggressive tumor characteristics, increased surgical morbidity, and poorer survival in STS patients. Although HALP did not retain independent significance in multivariable analysis, its strong association with tumor aggressiveness and survival suggests that it may reflect the systemic manifestation of high-risk tumor biology. As a simple and cost-effective biomarker derived from routine laboratory parameters, HALP may support preoperative risk stratification and help identify patients with biologically aggressive disease.

Abstract: Antimicrobial resistance has emerged as a significant global issue in combating bacterial diseases. Pseudomonas aeruginosa is one of the major opportunistic bacteria that cause acute, chronic, and nosocomial infections. The WHO has indicated P. aeruginosa as a member of ESKAPE group due to its high resistance rate to multiple existing treatments. The rapid rises in bacterial strains that are extensively drug-resistant (XDR), pan-drug-resistant (PDR), and multidrug-resistant (MDR) significantly increases the morbidity and mortality rates. In response to the escalating challenge of antimicrobial resistance (AMR), phage therapy has emerged as a promising alternative to the regular antibiotics. Lytic phages are specific viruses that infect and lyse bacterial cells, offering targeted antibacterial activity while minimizing disruption of normal microbiota. Recent progresses in specific bacteriophage isolation, optimized phage cocktail formulation, and combination therapy with antibiotics have demonstrated significant therapeutic potential in both laboratory and clinical studies. This review provides an overview of the current molecular mechanisms of antimicrobial resistance in P. aeruginosa and discusses the therapeutic potential of bacteriophages, highlighting their advantages, limitations, and future perspectives as an alternative therapy.

Abstract: Growing evidence suggests that insulin resistance (IR) might be a core, unifying mechanism linking various established risk factors for bladder cancer (BC). While factors like smoking, central obesity, sedentary lifestyle, and high-fat diets are known to increase BC risk, a common thread among them is their role in driving IR due to chronic hyperinsulinemia. Hyperinsulinemia promotes BC development in several ways. It acts as a potent growth factor, stimulating the proliferation and inhibiting the programmed cell death of malignant cells by activating the insulin/IGF signaling pathway. Furthermore, IR is closely associated with chronic low-grade inflammation and oxidative stress, both of which contribute to a pro-tumorigenic microenvironment. This convergence of growth-promoting and inflammatory signals highlights the central role of IR. While more research is needed to fully elucidate these complex interactions, the available data suggest that metabolic interventions aimed at improving insulin sensitivity could be a valuable, modifiable strategy for BC prevention.

Abstract: Smart city governance increasingly relies on AI-enabled planning systems, digital twins, vulnerability scoring tools, and capital investment prioritization platforms to allocate climate-resilient housing and infrastructure investments. Yet existing smart-urbanism and adaptation frameworks under-specify how such systems should encode (i) well-being, (ii) equity, and (iii) climate uncertainty in the decision logic that translates urban data into ranked projects and funded portfolios. This paper develops a governance-centered framework, Caring Urban AI, through a replicable conceptual synthesis that integrates research on (a) climate risk decision-making under deep un-certainty, (b) built-environment pathways relevant to psychosocial well-being, and (c) algorithmic accountability and fairness for public-sector decision infrastructures. The framework specifies a five-layer architecture linking (1) urban form and infrastruc-ture, (2) climate exposure and environmental resources, (3) psychosocial mediators of well-being, (4) algorithmic design choices (data, objective functions, equity constraints, uncertainty handling, documentation), and (5) institutional governance (procurement, auditing, participation, redress), with explicit feedback loops. The primary outputs are: (i) the five-layer Caring Urban AI architecture operationalized as auditable decision infrastructure; (ii) eight mechanism-based propositions that render the framework empirically testable via audits and quasi-experimental policy evaluations; and (iii) an operational specification guide illustrating objective-function forms, equity con-straints, robustness logic, and documentation artifacts for prioritization workflows. The analysis concludes that aligning Urban AI with SDG 11 requires treating well-being-supportive living conditions as a decision objective, constraining optimiza-tion with equity conditions, and institutionalizing auditability and contestability to prevent distributive and psychosocial harm in climate-resilient investment planning.

Abstract: To address the high computational complexity and insufficient real-time performance of traditional multi-radar trajectory planning methods in complex electromagnetic interference environments, this paper proposes an imitation learning-based trajectory planning method for multi-radar systems. This method designs a trajectory policy neural network architecture based on multiple semantic information. It proposes a training data construction method with coverage rate as the optimization objective. Then the trajectory policy neural network is trained by using an imitation learning algorithm with an auxiliary target. Simulation results show that the proposed method achieves an average coverage rate of 93.95%, and improves the single-step decision efficiency by a factor of 6.7 compared with heuristic-based trajectory optimization methods.