Abstract: Industry 4.0 quality control demands high-precision instance segmentation, yet such resource-intensive vision models require cost-prohibitive infrastructure, restricting adoption among Small and Medium Enterprises (SMEs). This study addresses this trade-off via a synchronized, three-layered Cyber-Physical System (CPS) optimized for low-cost edge nodes. In this cloud-to-edge framework, an ONNX-optimized YOLOv8s-seg model, trained on an augmented dataset of 1,331 industrial proxy images, is deployed onto an accessible $300 CPU-based edge computing node. To avert computational backlogs, the system decouples high-speed digital inference from an asynchronous n8n-powered IoT pipeline and an ESP32-actuated, 3D-printed mechanical sorting testbed. Results show the digital inference layer achieves peak bounding-box and mask accuracies of 93.8% and 93.7% mAP@0.5, with an average end-to-end latency of 605 ms. The physical actuation mechanism maintains a measured throughput of approximately 593 units per hour, consistent with the servo’s ~5,000 ms nominal recovery window, while the IoT layer records a 3.5 s cloud reporting latency. Built at a total hardware cost of approximately $1,543, this system offers a scalable blueprint for edge-AI deployment, showing that careful software-hardware orchestration can mitigate resource constraints and provide an accessible pathway for machine vision and digital traceability in resource-limited manufacturing under the tested proxy-based conditions.

Abstract: The office building sector concentrates a significant proportion of urban energy consumption and carbon emissions, while simultaneously facing the organizational reconfiguration driven by the COVID-19 pandemic and the growing demand for occupant well-being. The objective of this study was to comprehensively analyze design principles, environmental, economic and social impacts, certification frameworks and barriers to implementing sustainable architecture in office spaces. A qualitative approach of documentary review was adopted, inspired by the PRISMA-ScR protocol, through categorical thematic analysis of 63 sources indexed in Scopus and ScienceDirect, selected from an initial corpus of 152 records after the application of inclusion and exclusion criteria. The findings show that the integration of passive strategies, indoor vegetation and smart technologies (BIM, IoT, artificial intelligence) reduces operational energy consumption between 15% and 70%, while the life cycle assessment allows for a reduction of up to 50% in environmental impacts through recycled materials and envelope insulation. Certified buildings report productivity increases of between 2.5% and 26%, cognitive performance improvements of up to 61% and rent and market value premiums of between 10% and 12% in Europe and the United States, although in emerging economies adoption faces cost overruns of between 5% and 21% with payback periods of less than four years. Relevant gaps persist in the integration of social criteria within certification systems (DGNB, LEED, BREEAM) and financial, organizational and technical training barriers that limit the adoption of BIM and sustainable materials, particularly in Latin American contexts. It is concluded that the sustainability of contemporary offices requires a holistic framework that articulates technology, public policy and organizational culture, with direct implications for curricular updating in architecture and construction management programs.

Abstract: Traditional multi-objective evolutionary algorithms for small molecules suffer from structural homogenization and premature convergence, as they only optimize property space without controlling molecular topology diversity. This work proposes MOD-PCAC, a principal component analysis (PAC) and K-Means clustering based optimization framework. Normalized property vectors and dimension-reduced molecular fingerprints are fused to build a joint feature space that embeds structural information into evolutionary screening. A clustering-guided update strategy is designed: inter-cluster sparsity allocates retention quotas, while dynamic Tanimoto thresholds filter redundant structures; cross-cluster crossover broadens chemical space exploration. Six benchmark datasets and four quantitative metrics are adopted for evaluation. Comparative and ablation experiments verify that MOD-PCAC achieves superior convergence and richer scaffold diversity, effectively improving sampling efficiency for multi-property molecular computational optimization.

Abstract: The spatial distribution of Ground Control Points (GCPs) is a critical factor affecting the accuracy of UAV photogrammetry in hilly terrain. Existing studies primarily focus on the influence of GCP quantity on accuracy or discuss planar distribution uniformity in flat areas, with limited systematic analysis separating horizontal and vertical distributions as independent dimensions. This study utilizes a DJI Mavic 3E UAV to acquire aerial imagery in a typical hilly area of Mengyin County, Shandong Province, with 27 high-precision GCPs deployed. Four comparative experiments combining random/uniform distributions in both horizontal and vertical dimensions are designed to quantitatively analyze the impact of different distribution patterns on aerial triangulation and mapping accuracy. Results demonstrate that the dual-uniform distribution strategy achieves optimal accuracy, with horizontal RMSE of 0.045 m and vertical RMSE of 0.039 m, representing improvements of 32.8% and 17.0% respectively compared to the random distribution scheme. Furthermore, this paper proposes the Spatial Distribution Balance Index (SDBI), which integrates Planar Uniformity Index (PUI) and Vertical Uniformity Index (VUI) with a terrain-adaptive weighting mechanism. The VUI weight, calibrated as β=0.714 via a Sigmoid nonlinear amplification function (k=15,x0=0.15), enables the SDBI to adaptively reflect terrain sensitivity to vertical control. The enhanced SDBI shows a correlation coefficient of r=−0.93 with final accuracy, validating its effectiveness as a GCP layout optimization and evaluation tool. This research provides a quantifiable technical framework for GCP deployment in UAV photogrammetry in hilly regions.

Abstract: Fractional logistic maps introduce memory into a canonical route to chaos, but how memory shifts successive period-doubling bifurcation points is not yet fully understood. For the finite-memory Grünwald–Letnikov (GL) fractional logistic map, we formulate equations that determine exact periodic orbits and their period-doubling points, with the number of unknowns independent of memory length. The framework reproduces an independently derived exact fixed-point boundary and identifies a genuine period-4 to period-8 doubling, confirming its applicability beyond the fixed point. Across the first seven computed bifurcation levels, increasing memory length shifts the bifurcation points toward smaller control-parameter values, with stronger shifts at lower fractional orders. The GL memory tail decays more slowly at lower fractional order, helping explain why extending retained memory produces larger bifurcation-point shifts. At fixed memory length, decreasing the fractional order from its classical value first moves the bifurcation points toward smaller control-parameter values, before a turnover carries them toward larger values. As the order approaches zero, the GL memory terms vanish and limiting bifurcation points lie one unit above their classical counterparts, so branch continuity requires a turnover. Together, these results provide a validated basis for computing and interpreting how memory reorganizes the period-doubling route to chaos.

Abstract: Objective: To assess gut and endometrial microbiota simultaneously in women with unexplained recurrent implantation failure (RIF) and explore the hypothesis of a gut-endometrium microbial axis. Study design: This monocentric observational pilot study included women aged 38 years or younger with RIF and a normal uterine cavity. Paired fecal and endometrial samples were collected on the same day and analyzed by 16S rRNA gene sequencing. Relative abundances of major phyla and alpha-diversity indices were compared between compartments. Results: Twenty-one women were included. Chronic endometritis was documented hysteroscopically in all cases. Gut dysbiosis was observed in 20/21 patients (95.2%), whereas endometrial dysbiosis was found in 16/21 (76.2%); crude agreement between compartments was 71.4%. The intestinal microbiota was dominated by Bacteroidetes (52.8% ± 14.9%) and Firmicutes (33.3% ± 11.0%), whereas the endometrial microbiota was enriched in Firmicutes (64.5% ± 33.5%) and Proteobacteria (30.9% ± 32.3%). Bacteroidetes were significantly more abundant in fecal than endometrial samples (p < 0.001), while Firmicutes were significantly enriched in the endometrium (p = 0.001). All alpha-diversity indices were significantly higher in feces, indicating greater microbial richness and complexity in the gut. Conclusions: In women with RIF, gut and endometrial dysbiosis frequently coexist and show relevant concordance. These data support the hypothesis that endometrial dysbiosis may reflect a broader systemic microbial imbalance rather than an isolated uterine disorder.

Abstract: Background/Objectives: Plexiform neurofibromas (PNs) associated with Neurofibromatosis type 1 (NF1) can lead to spinal cord compression and motor dysfunction. However, there is a paucity of literature detailing the effects of selumetinib on dystonia or spastic gait in these patients. Methods: A retrospective analysis was conducted on a pediatric patient with NF1-associated PN, dystonia, and spastic gait who received selumetinib treatment. Additionally, two cases from Chinese-language literature were reviewed, sourced from the CNKI, Wanfang, and VIP databases. Results: Three pediatric cases were identified. All patients presented with café-au-lait macules, superficial nodules, and either gait disturbance or dystonia; two exhibited scoliosis, and all demonstrated both intra- and extraspinal PN involvement. Following selumetinib therapy, there was a reduction in tumor burden and an improvement in gait-related motor symptoms. In the case under review, tiptoe walking and frequent falls showed significant improvement after 180 days of treatment, with no adverse events reported during the follow-up period. Conclusions: Selumetinib appears to be a promising therapeutic option for children with symptomatic, unresectable NF1-associated PNs and compression-related dystonia or spastic gait. Nonetheless, further longitudinal studies and additional case reports are necessary to substantiate these findings.

Abstract: Alopecia is an emerging concern during tirzepatide or semaglutide therapy for weight management, but whether it is a consequence of weight loss or reflects incretin-specific biology remains unclear. Here we conduct an observational study comparing new users of tirzepatide (Zepbound, Mounjaro) or semaglutide (Wegovy, Ozempic, Rybelsus) with at least two prescriptions, no documented alopecia prior to the first prescription, and complete 12-month follow-up. Incident alopecia was defined using harmonized diagnosis codes and AI-curated clinical notes. Propensity-score matching was performed to balance tirzepatide and semaglutide users on age, race, sex, baseline BMI, and type 2 diabetes. In the matched cohorts (n=12,863 per arm), incident alopecia was more frequent with tirzepatide than with semaglutide (4.20% vs 2.88%; risk ratio [RR]: 1.46; 95% CI [1.28-1.66]; P<0.001). After additional matching on achieved weight loss (n=11,046 per arm), incident alopecia remained significantly higher after tirzepatide than semaglutide (4.24% versus 3.33%; RR: 1.27 [1.11-1.45]; P<0.001). By 6 months, incident alopecia had occurred in 1.55% of tirzepatide users versus 1.24% of semaglutide users, widening by 12 months to 4.20% versus 2.88%, respectively (hazard ratio [HR]: 1.46 [1.28-1.67]; log-rank P<0.001). Incident alopecia remained significantly more frequent with tirzepatide than semaglutide across clinically relevant strata (all P<0.001), including among patients with 20-to-30% weight loss, a range highlighted in pivotal obesity trials (RR: 1.44); in the low and high maximum-dose strata (RR: 1.76 and 1.39, respectively); and among patients with 4-6 prescriptions, a proxy for longer treatment duration (RR: 1.65). Analysis of newly initiated hair-loss therapies showed significantly higher minoxidil (Rogaine) initiation after tirzepatide than after semaglutide (RR, 2.04; P=0.002). Among 8,985 female patients, incident alopecia occurred in 5.44% of tirzepatide users versus 3.63% of semaglutide users (RR: 1.50 [1.31-1.72]; P<0.001), with significant differences in the 10-to-20% weight-loss band (RR: 1.41 [1.11-1.78]; P=0.004) and in the 20-to-30% weight-loss band (RR, 1.47; 95% CI, 1.06 to 2.04; P=0.018). Among tirzepatide users, incident-alopecia developers were more often female than non-developers (90.6% vs 68.9%, SMD: +0.56), and were enriched for underlying endocrine conditions including menstrual irregularity (20.0% vs 13.9%, SMD: +0.17), hypothyroidism (28.1% vs 21.6%, SMD: +0.15), and polycystic ovarian syndrome (6.7% vs 3.6%, SMD: +0.14). Repeated measurements of ferritin, iron, vitamin B12, folate, vitamin D, and zinc showed no significant nutrient decline accompanying incident alopecia. Exploratory single-cell RNA-seq analyses showed no GLP1R or GIPR expression in scalp follicular keratinocytes and identified multiple dermato-immune cell types that expressed GIPR but not GLP1R. Overall, in routine care, incident alopecia was higher after initiation of tirzepatide than semaglutide, motivating prospective comparative studies of incretin-based therapies.

Abstract: This study aims to forecast South Africa’s import demand for cassava starch using monthly data from April 2015 to December 2030. It applies Augmented Dickey-Fuller (ADF) to verify the stationarity of the series, while the Akaike criterion and Schwarz criterion were utilized to select the fitted model of ARIMA (p, d, q). The series were integrated of order one, and the best-fitted model was ARIMA (0,1,2). Thereafter, the Box-Jenkins estimation strategy was used to forecast the import demand for cassava starch. The forecasted import demand for cassava starch showed a steady increase in quantity, with an annual average import demand of 12 032 tons between 2025 and 2030. Given South Africa’s rising cassava starch imports, we endeavored to forecast demand thereof, considering the absence of commercial cassava production and processing in the country. This is the first empirical study to offer a thorough forecasting of the rising trend in Southern Africa’s demand for cassava starch imports. To manage the growing demand for cassava starch in South Africa, policymakers should consider medium- to long-term interventions to further develop the value chain. The most urgent and critical need is to fund research and development (R&D) for the advancement of the cassava value chain, as well as to establish phytosanitary-certified nurseries for producers to access high-quality planting materials.

Abstract: Currently, several LLMs (Large Language Models) assist users with specific tasks, such as summarizing texts or solving physics problems, to name a few. On the other hand, using scripts allows users to automate tasks and save time in their creation. This article evaluates an LLM for generating scripts for the Application Programming Interface (API) of the Fusion 360 CAD software application, to determine its capabilities for developing 3D modeling components used in the simulation of industrial robots or the automation of manufacturing systems. Among the results, it was found that the generated scripts enable modeling of industrial robots and automation elements using simple geometries that the user can later customize for a specific application. Furthermore, it is recommended that users be familiar with Fusion 360 in case they need to modify or assemble the components created by scripting. At the end, a Prompt Engineering Guide for Fusion 360 scripts focused on generating components for industrial robots is shared.

Abstract: Background: To evaluate the sociodemographic and clinical profiles of pregnant women with positive IgM anti‑T. gondii in the eastern Brazilian Legal Amazon, during the period from 2018 to 2023. Methods: This was an indirect observational study through the retrospective analysis of 1,049 medical records from pregnant women attended in the Obstetrics service of Basic Health Units. The dependent variable was the presence of IgM antibodies, and the independent variables included age brackets, ethnicity, schooling, occupation, and clinical complaints. Data were analyzed using Epi‑Info 3.3.2 software. Risk factors and odds ratio (OR) with 95% CI were calculated. Results: From the 1,049 medical records analyzed, 52 (4.97%) presented IgM anti‑T. gondii antibodies. There was a significant correlation between IgM presence and age older than 30 years (OR 2.17; CI: 1.60–7.12), being employed (OR: 8.42; CI: 1.05–22.12), being married (OR: 3.02; CI: 1.43–9.32), and having less than eight years of schooling (OR: 3.04; CI: 1.24–11.56). Conclusions: The prevalence of IgM antibodies in the studied population was considered high, associated with low schooling, employment, and age over 30 years. The asymptomatic characteristic of acute infection is highlighted, reinforcing the need for effective prenatal screening.

Abstract: The wider adoption of electric motorcycles has increased interest in their role in urban mobility. However, research on e-motorcycle adoption remains scattered across different disciplines, with no comprehensive overview of the field. Bibliometric analysis was utilised to identify the main research themes, and highlight future research directions. A total of 165 publications published between 2000 and June 2026 were retrieved from the Scopus and Web of Science databases and analysed using the Bibliometrix R package. The first publication appeared in 2005, but research grew rapidly after 2020 and reached its peak in 2025. China was the leading contributor to the field. The findings show that research has shifted from an early focus on battery technology and environmental performance to narrower issues, such as user behaviour, charging infrastructure, and sustainable urban mobility. Three main research themes were identified: sustainability and environmental policy; transport policy and economics; and behavioural and psychological factors. The review also identified important research gaps in governance, renewable energy, social inclusion, and studies from African cities. Future studies should give more attention to how policies, renewable energy-powered charging, and inclusive mobility initiatives can support e-motorcycle adoption in Africa.

Abstract: Spatial hearing is a key auditory function that enables the perception and interpretation of sound location and spatial cues in the environment. It plays a crucial role in everyday listening situations, particularly in complex acoustic settings. Despite its importance, spatial hearing remains under investigated in Brazil due to the limited availability of validated assessment instruments. Background/Objectives: Currently, few tools are available, including the Listening in Spatialized Noise Sentences (LiSN), the Speech, Spatial and Qualities of Hearing Scale (SSQ) and its short version (SSQ-12), and the Spatial Hearing Questionnaire (SHQ). While the SSQ has been adapted into Brazilian Portuguese, it is not exclusively focused on spatial hearing, and the LiSN adaptation is still in progress. Thus, there is a need for validated instruments specifically designed to assess spatial hearing in this population. Methods: This methodological study aimed to translate, cross-culturally adapt, and validate the Spatial Hearing Questionnaire (SHQ) into Brazilian Portuguese. The study was approved by the Research Ethics Committee of the Federal University of Minas Gerais (UFMG) (approval number 3,605,895). Data were collected at the Audiology Service of Hospital São Geraldo (UFMG). A total of 126 individuals with normal hearing thresholds (≤25 dB HL from 250 to 8000 Hz) and type A tympanometric curves participated. After signing informed consent, participants completed the Mini-Mental State Examination (MMSE), Visual Analog Scale (VAS) of the tinnitus magnitude, SSQ-12, and the Brazilian Portuguese version of the SHQ. Semantic validation, conducted with 30 individuals, showed comprehension rates above 80% for all items, with no need for modifications. Statistical analyses included the Shapiro–Wilk test, Wilcoxon signed-rank test, Cronbach’s alpha, and Spearman’s correlation. Conclusions: Internal consistency was high (Cronbach’s alpha: 0.97 for SHQ; 0.96 for retest; 0.88 for SSQ-12). Significant differences were observed in SHQ, VAS, MMSE, and SSQ-12 spatial scores between individuals with and without auditory complaints. No significant differences were found between SSQ-12 items 6–8 and total SHQ scores. The Brazilian Portuguese version of the SHQ demonstrated excellent reliability and validity, supporting its use as a robust instrument for assessing self-perceived spatial hearing abilities in Brazilian Portuguese-speaking populations.

Abstract: Polymeric membrane systems have emerged as an effective approach for water separa-tions due to their high separation efficiency, simplicity, and adaptability to a wide range of water treatment applications. However, traditional membrane fabrication processes often rely on toxic organic solvents, such as N-methyl-2-pyrrolidone (NMP) and dime-thylacetamide (DMAc), which pose environmental and health risks. Eco-friendly solvents have been investigated as an alternative to traditional toxic solvents. This study inves-tigates the fabrication and performance of polymeric membranes using eco-friendly solvent systems, with a focus on bilayer membranes designed to improve separation performance over traditional single-layer membranes. Membranes were fabricated using eco-friendly solvents, Rhodiasolv^© PolarClean and gamma-valerolactone in combination with polymers polysulfone (PSf) and poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP). Membranes were fabricated using non-solvent induced phase separation (NIPS) through doctor blade extrusion (DBE) and slot-die coating (SDC) methods in order to compare traditional laboratory-scale casting methods (DBE) with scalable fabrication techniques (SDC). Bilayer membranes were found to overcome the inherent limitations presented by single-layer membranes, such as mechanical stability, with polydopamine (PDA) being incorporated as an adhesion-promoting additive to enhance bonding be-tween polymer layers. Membrane characterization included scanning electron micros-copy (SEM), contact angle measurements, and tensile strength tests, along with per-meability and solute rejection tests to investigate membrane performance. Results showed that membranes fabricated at higher concentrations and through the SDC method per-formed at a higher level than the other membranes, exhibiting improved solute rejection rates and less variability in pore structure distribution.

Abstract: The catalytic hydrogenation of furfural is a key reaction to valorize lignocellulosic biomass into value-added chemicals. In this work, the influence of different hydrogen donors on furfural hydrogenation under continuous-flow conditions was evaluated using a 10% Pd/C catalyst in a Phoenix Flow Reactor. Molecular hydrogen (H₂), 2-propanol (transfer hydrogenation), and sodium borohydride (NaBH₄) were tested under identical conditions (150 °C, 20 bar, 0.1 mL·min¹). Each hydrogen source led to distinct reaction pathways and product distributions. H₂ promoted selective formation of furfuryl alcohol (FA) via a Langmuir–Hinshelwood mechanism, with transient formation of tetrahydrofurfuryl alcohol (THFA). 2-propanol favored carbonyl reduction while enabling gradual formation of 2-methylfuran (2-MF) at longer reaction times. NaBH₄ yielded FA exclusively through direct hydride transfer but showed operational limitations due to precipitation under flow conditions. Catalyst deactivation was observed in all systems, mainly associated with carbonaceous deposition. While H₂ and 2-propanol showed comparable catalytic efficiency, NaBH₄ exhibited lower efficiency and limited scalability. These results highlight the critical role of hydrogen donor selection in controlling reaction pathways, selectivity, and process feasibility in continuous-flow biomass valorization.

Abstract: Autism’s social signs vary widely: absence of declarative pointing, palm-reversed waving, reduced joint attention, and difficulty locating oneself relative to others. These are usually studied as separate deficits. I propose they share one upstream source.The entorhinal–hippocampal system encodes space through a hexagonal grid-cell code. Recent work shows this code also represents non-spatial and social information. I hypothesize that a distorted grid code in the entorhinal–hippocampal system is an upstream source of a coherent cluster of autism’s social features, supplying incorrect coordinates to downstream regions that depend on it, including the temporoparietal junction (TPJ), a central node in self–other distinction and perspective transformation. If the coordinate input is faulty, the TPJ cannot reliably compute this distinction. On this account, autism’s social signs are downstream effects of a single disrupted coordinate system rather than independent failures.I review the evidence linking grid codes to social and conceptual representation, connect it to reported TPJ differences in autism, and derive falsifiable predictions. Because this circuit is metabolically costly, I link the proposal to the energy-deficit framework of autism: a high-demand system is one that an energy shortage would compromise early. Identifying what disrupts the grid code is a key entry point to autism’s root etiology.

Abstract: Background: With the proliferation of repeated measures data from wearable technology and apps, researchers need diverse methods to assess change, beyond common approaches such as Generalized Estimating Equations and Latent Growth Curve Modeling. One such method is Area Under the Curve (AUC). The purpose of this study was to assess the efficacy of AUC with a larger number of repeated measures using simulated data. Methods: We generated two samples of 21 hypothetical cyclists with 30 and 100 repeated measures of performance data (speed, time, and power) based on a Strava app segment, and calculated AUC using the trapezoid rule and definite integrals with the best fitting linear and a six-degree (sextic) polynomial. We then assessed the relations between time, speed, and power with all three calculations using bivariate correlations, multiple regression analysis, and a mediation analysis whereby power was hypothesized to predict a reduction in time indirectly through speed. Results: There was little difference in relative performance comparing the three calculation methods in the two samples. However, there was a difference in mediation results when comparing the two samples with full mediation when using 100 replications and only partial mediation with 30 replications. Conclusions: The results of this study suggest that Area Under the Curve may be a viable method for assessing change when dealing with datasets including many repeated measures such as those acquired from wearable technology and apps. However, AUC may be more sensitive to performance changes with a larger number of replications. Future studies should assess AUC with larger numbers of repeated measures, additional equations, and non-simulated data.

Abstract: Despite significant advances in the measurement of state fragility, existing assessment frameworks remain primarily descriptive, identifying symptoms of institutional weakness without adequately explaining the systemic interactions that generate national vulnerability. Dominant approaches, including the Fragile States Index and state capacity measures, typically aggregate indicators of political instability, economic decline, insecurity, and governance deficits but provide limited guidance for diagnosing the underlying mechanisms that inhibit national adaptation and regeneration. This article introduces the Nationesis Diagnostic Matrix (NDM), a complexity-based diagnostic framework grounded in the theory of Nationesis. Rather than measuring fragility as a static condition, the NDM conceptualizes fragility as an emergent property of interacting failures across institutional, cognitive, social, ecological, economic, technological, and symbolic systems. The paper develops the theoretical foundations of the matrix, proposes its multidimensional architecture, explains its diagnostic methodology, and illustrates how it can support adaptive governance, institutional reform, and strategic policy design. The NDM complements the Nationesis Index by shifting analytical attention from measuring regenerative capacity to identifying the systemic constraints that prevent political communities from achieving long-term transformation.

Abstract: We develop a tensor-based dynamical framework for monetary flows in multi-sector, multi-agent economies and quantify the information destroyed when the monetary state is reduced to a scalar aggregate. The state is a third-order tensor encoding capital flows across sectors, agent classes, and time; deviations from equilibrium obey a tensorial Langevin equation with a coupling operator and sector-specific friction rates. We prove global asymptotic stability through a quadratic Lyapunov function, with a convergence bound valid for the non-normal system matrices typical of asymmetric economic coupling, and characterise the stochastically forced case in the mean-square sense. Shannon entropy, Kullback–Leibler divergence, and sector–agent mutual information measure the structural information discarded by scalar aggregation. In a 2008-inspired stylised scenario, Finance absorbs an 18.9% peak capital loss while Manufacturing and Services suffer 5.8% and 3.9% secondary drops; the scalar aggregate contracts by only 8.6% and, as measured by the Kullback–Leibler divergence, recovers about 21 quarters before the sectoral structure does. A targeted stimulus restores equilibrium in 13.7 quarters, versus 40.7 with no policy and 69.9 under an equal-budget uniform stimulus; the advantage persists under a symmetric exit rule. Scalar aggregation thus substantially underestimates sectoral heterogeneity, and tensor-based targeting produces quantitatively superior outcomes.

Abstract: Background/Objectives: Healthcare decision-making capacity is a critical component of autonomy in later life, yet its psychosocial determinants remain insufficiently understood. This study aimed to examine the relationship between healthcare decision-making capacity, cognitive functioning, depressive and anxiety symptoms, and sociodemographic factors in cognitively healthy older adults, as well as to identify psychosocial predictors of decisional capacity. Methods: A cross-sectional study was conducted with 60 community-dwelling older adults. Participants completed measures of healthcare decision-making capacity, cognitive performance, depressive symptoms, and anxiety symptoms. Spearman correlations and multiple linear regression analyses were performed to examine associations and predictors. Results: Healthcare decision-making capacity was positively associated with cognitive performance, but not significantly correlated with depressive or anxiety symptoms. However, regression analyses revealed that depressive symptomatology and age were significant negative predictors, with the overall model explaining 44.9% of the variance. Anxiety symptoms were not significant predictors. A strong positive association was observed between depressive and anxiety symptoms. Conclusions: These findings support a multidimensional model of healthcare decision-making capacity, highlighting the central role of cognitive functioning and the clinically relevant influence of depressive symptoms, even in cognitively healthy older adults. The results underscore the importance of integrating cognitive and emotional assessment in capacity evaluations and suggest that addressing depressive symptoms may contribute to preserving decisional autonomy. Future research should adopt longitudinal and clinically diverse samples to further elucidate these relationships.