River network size-dependent chemodiversity of dissolved organic matter and its driving factors in a multi-tributary mountain river.

The subject of the study is a wireless sensor network (WSN) with a mobile sink. The purpose of the work is to improve the performance of the WSN, increase its lifetime and functionality by reducing the data transmission delay time in the process of polling routers by optimizing the mobile sink route using the most efficient algorithm. To achieve this goal, the following tasks must be performed: optimize the route of the WSN mobile stock by solving the traveling salesman problem using the branch and bound method and comparing the conditional average route length of a set of solutions without optimization and with optimization using the Robbins–Monroe procedure; conduct a comparative analysis of the exact solution of the traveling salesman problem obtained by the branch and bound method and the approximate solution obtained by heuristic methods; formulate practical recommendations for the selection of algorithms for optimizing the mobile flow route depending on the size of the sensor network. The following methods were used: simulation modeling, optimization methods, mathematical data processing. Results achieved. The solution of the mobile flow route optimization problem in BSM using heuristic algorithms was investigated in order to formulate practical recommendations for selecting mobile flow route optimization algorithms depending on the size of the sensor network. A comparative analysis was performed of the exact solution of the traveling salesman problem, performed using the branch and bound method, and the approximate solution, performed using heuristic methods. To obtain an approximate solution, two heuristic algorithms were implemented: the ant colony optimization (ACO) algorithm and the simulated annealing (SA) algorithm. These algorithms were implemented for the traveling salesman problem with specific coordinates for each problem. The effectiveness of the algorithms is evaluated on networks of various sizes, from 10 to 500 nodes. The simulation results show that ACO is highly effective on small and medium-sized networks (up to 50 nodes), providing shorter routes and faster computation times. SA is determined to be the best scalable on large networks (100 nodes and more), offering stable performance under high computational load. Conclusions. It has been demonstrated that introducing optimization in the selection of the mobile flow route in BSM leads to a reduction in the length of the mobile flow bypass contour in the range of 30–40% depending on the network size and the distances between routers. Reducing the polling time of routers in a sensor network leads to an increase in the residual power of power supplies, and thus extends the life of the network. It has been proven that the use of heuristic algorithms is only appropriate when a high speed of calculating a new mobile flow route is required. If the speed of calculating a new route is not critical, then it is better to use accurate calculation algorithms. For each algorithm, parameters must be selected depending on the task at hand, since these parameters affect the speed of the algorithm and can reduce the range of possible routes that can be obtained during calculations. The study proves the importance of individual parameter tuning of algorithms to improve the accuracy and adaptability of solutions in mobile flow routing tasks.

The objective of the present review is to discuss the involvement of altered mitochondrial quality control in Mn-induced neurotoxicity. Existing data demonstrate that mitochondrial autophagy (mitophagy) and brain mitochondrial unfolded protein response (mtUPR) are activated in response to Mn exposure to counteract the Mn-induced mitochondrial dysfunction. Both mitophagy and mtUPR have significant overlap and mechanistic intersections with the integrated stress response (ISR). Increased Mn exposures impair mitochondrial dynamics, further aggravating Mn-induced mitochondrial dysfunction. Specifically, Mn suppresses PTEN-induced kinase 1 (PINK1)-Parkin-dependent mitophagy through a variety of mechanisms, including nitric oxide synthase 2 (NOS2)-dependent PINK1 S-nitrosylation, inhibition of transcription factor EB (TFEB) signaling, and mammalian target of rapamycin complex 1 (mTORC1) activation. In addition, Mn promotes mitochondrial fission by up-regulating dynamin-1-like protein (Drp1) expression and phosphorylation via the activation of c-Jun N-terminal kinase (JNK) and inhibition of sirtuin 1 (SIRT1)/peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α) pathways. Concomitantly, Mn impairs mitochondrial fusion by inhibiting mitofusin (Mfn) 1/2 and dynamin-like 120kDa protein (Opa1) expression, leading to a reduction in mitochondrial size and disruption of the mitochondrial network. High-dose Mn exposure results in inhibition of peroxisome proliferator-activated receptor gamma coactivator 1α (PGC-1α)/nuclear factor erythroid 2-related factor 2 (NRF2)-dependent mitochondrial biogenesis. The latter may be mediated by inhibition of SIRT1/SIRT3 activity, as well as modulation of PINK1/ zinc finger protein 746 (ZNF746)/PGC-1α axis. Alterations in the mitochondrial quality control system may contribute to Mn-induced neuronal damage and neuroinflammation, indicating that dysregulation of the brain mitochondrial dynamics is an important mechanism by which Mn induces its neurotoxicity.

The relevance of this research stems from the increasing vulnerability of global supply chains to various disruptions caused by pandemics, geopolitical conflicts, and natural disasters. Recent global events have demonstrated the critical need for digital technology implementation to enhance logistics network adaptability and resilience. The purpose of this article is to develop a comprehensive approach to managing supply chain resilience through digital logistics solutions implementation. To achieve this aim, the following objectives were addressed: systematization of knowledge on five digital logistics solution categories (Transportation Management Systems TMS, Warehouse Management Systems WMS, cloud-based SaaS platforms, ERP systems, and supply chain visibility platforms); development of the DLS-SCR (Digital Logistics Solutions for Supply Chain Resilience) conceptual framework establishing relationships between digital solutions and resilience components; proposal of an original mathematical risk assessment model based on the Composite Resilience Index (CRI) integrating four network structural parameters: density (D), centralization (C), connectivity (K), and network size (N). The methodology is based on systematic review of 17 peer-reviewed publications from 2020-2025 and comparative analysis of digital solutions. Scenario modeling results demonstrate that staged digital solution implementation increases CRI from baseline 0.382 to 0.700 with full digital stack deployment, representing 83.2% improvement. Methodological caveat: quantitative improvement estimates (35-45% visibility enhancement, 40-60% response time reduction, 25-35% operational risk decrease) represent scenario modeling outputs and literature synthesis rather than direct empirical measurements, requiring further empirical validation. The scientific novelty lies in integrating network structural parameters with digital solution impact coefficients within a unified mathematical model. Practical significance is determined by the framework's applicability for evidence-based digital solution selection based on quantitative resilience impact assessment. Key findings indicate that visibility platforms provide strongest anticipation support, while cloud SaaS platforms offer fastest implementation with highest scalability. Limitations relate to the theoretical nature of the model. Article type: theoretical.

The transformer architecture and its attention-based modules have become quite popular recently and are used for solving most computer vision tasks. However, there have been attempts to explore whether other modules can perform equally well with lower computational costs. In this paper, we introduce a nonlinear convolution structure composed of learnable polynomial and Fourier features, which allows better spectral representation with fewer parameters. The solution we propose is in principle feasible for many CNN application fields, and we present its theoretical motivation. Next, to demonstrate the performance of our architecture, and we exploit it for a paradigmatic task: image translation in driving-related scenarios such as deraining, dehazing, dark-to-bright, and night-to-day transformations. We use specific benchmark datasets for each task and standard quality parameters. The results show that our network provides acceptable or better performances when compared to transformer-based architectures, with a major reduction in the network size due to the use of such a nonlinear convolution block.

SummaryRett syndrome is a debilitating neurodevelopmental disorder with cerebral processing impairments caused byMECP2loss-of-function mutations. Mecp2-deficient mouse models reveal disruptions of microscale cortical circuits. Yet how cellular-scale information processing is altered in Mecp2-deficient microscale functional networks is unknown. We investigated the development of functional connectivity, network topology, and dynamics in microelectrode array (MEA) recordings of primary cortical cultures from Mecp2-deficient and wild-type mice. Mecp2-deficient cortical networks developed more slowly and showed decreased functional connectivity compared to wild-type, leading to smaller network size, density, and strength of connectivity. Altered network topological features in Mecp2-deficient microscale circuits predicted decreased efficiency and information-sharing capacity. This reveals developmental deficits in microscale functional networks, which may in turn underlie the cortical decline and severe cognitive disability in Rett syndrome. These findings also offer circuit-level targets and an in-vitro approach for evaluating new therapeutic products for restoring microscale network function.

The HIV epidemic in the United States disproportionately affects sexual and gender minority (SGM) communities. Social networks shape health behaviors. Understanding the structure and dynamics of SGM networks could enhance engagement with HIV-related services. We analyzed egocentric social networks of SGM individuals in Charlotte, North Carolina, who engaged in HIV-related services. Participants (egos) identified social connections (alters) from the past 6 months. Networks were stratified based on the size of the ego's sexual network (0-1 vs. ≥2), defined as the subset of alters with reported sexual partnerships, to assess differences in structure and support dynamics. Among 51 participants, 294 alters were reported. Networks with 0-1 sexual partners were smaller, more connected, and exhibited greater demographic diversity. These egos had stronger ties with their alters (n = 141), who were more likely to provide social and health-related support. By contrast, networks with ≥2 sexual partners (n = 153 alters) were less cohesive, exhibited lower network density and global efficiency (capacity for efficient information sharing), and sexual partners were less likely to offer health support. Network-based interventions must account for pre-existing social support structures. Tight-knit, supportive networks may reinforce health norms and serve as conduits for intervention messaging, while individuals with larger, less connected sexual networks may require additional support to mobilize their networks effectively. Tailoring interventions to network dynamics could enhance HIV service uptake and improve health outcomes in SGM populations.

Addressing two types of supply chain disruptions—frequent short-duration disruptions (e.g., minor natural disasters) and infrequent long-duration disruptions (e.g., geopolitical conflicts, public health crises)—while considering their impact on logistics capacity, this paper proposes a multi-echelon inventory management optimization framework based on the Proximal Policy Optimization (PPO) algorithm. Unlike traditional inventory control models with simplistic assumptions, this study integrates factors such as the frequency, duration, and impact of disruptions into the inventory optimization process. It is designed to coordinate replenishment decisions at the warehouse while reacting to local retailer states. Since retailers share the same cost parameters and demand dynamics, their decision problems are structurally symmetric, which allows us to use a shared policy across retailers and thus keep the learning model compact and scalable. Numerical experiments compare the PPO policy with classical inventory heuristics under various network sizes and disruption types. The results show that PPO consistently achieves lower total costs than the benchmarks, and its relative advantage becomes more pronounced under severe or longer disruptions. These findings suggest that modern policy-gradient methods, combined with simple forms of structural symmetry, can provide an effective and scalable tool for managing disrupted multi-echelon supply chains.

ContextFamily and Group Conferencing (FGC) is a strengths-based approach to social work, originating from New Zealand and now used internationally. Previous research on FGC has focused largely on the context of children’s services but, FGC also aligns with the principle of the Care Act in England to prevent, reduce or delay the need for long-term (and potentially costly) adult care services. Limited previous research has tended to explore potential cost savings associated with FGC, without accounting for the cost of the intervention itself, risking biased results.ObjectiveThis paper aims to identify resource use and associated monetary costs associated with FGC services in English adult social care and mental health settings.MethodsFramework development was informed by previously published work establishing programme theory for FGC, extended by expert opinion and published sources of monetary costs. The framework used scenario-based analysis and a bottom-up costing approach, with sensitivity analysis.ResultsEstimated costs of conducting a standard full FGC (excluding referral) range from £1,455 to £2,043 (adjusted from 2022–2023–2025 prices) from a local authority and National Health Service (NHS) perspective. Costs can vary depending on the involvement of an advocate or interpreter, network size and the complexity of issues being addressed. We report overall costs with and without resource use specifically related to referral.DiscussionHigher staff costs account for slightly higher intervention costs in an NHS mental health setting, compared to adult social care settings.ConclusionReallocating scarce public resources with the intention of preventing, reducing or delaying use of costly future care must be evidence-based as pressures build to meet acute needs. Accurate per-case costing of FGC is a necessary preliminary step towards exploring the cost-effectiveness of FGC. A full economic evaluation will account for costs, outcomes, and alternative options (uses of limited resources).

Abstract— Wireless Sensor Networks (WSNs) frequently face challenges in locating detector bumps directly while conserving limited energy coffers. This work explores how three well- known optimization styles inheritable Algorithm (GA), flyspeck mass Optimization (PSO), and Grey Wolf Optimizer (GWO) can ameliorate localization in WSN surroundings. A simulation model was created to observe how different figures of anchor bumps affect network performance. Several parameters, including energy use, outturn, packet delivery, detention, jitter, continuance, connectivity, content, and delicacy, were recorded for analysis. Among the tested algorithms, the GWO system showed more harmonious localization delicacy and balanced energy operation under varying network sizes. The study highlights that mass- grounded optimization can give a dependable path toward erecting energy-effective and adaptive WSNs suitable for long- term deployment. Keywords—Wireless sensor networks, Node localization, Metaheuristic algorithms, Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA)

Blockchain technologies underpin an expanding ecosystem of decentralized applications, financial systems, and infrastructure. However, the fundamental networking layer that sustains these systems, the peer-to-peer (P2P) layer, of all but the top few ecosystems remains largely opaque. In this paper, we present the first longitudinal, cross-network measurement study of 36 public blockchain networks. Over 9 months (since late 2024), we deployed 15 active crawlers, sourced data from two additional community crawlers, and conducted hourly connectivity probes (e.g., pings and protocol-level handshakes) to observe the evolving state of these networks. Furthermore, by leveraging Ethereum's discovery protocols, we inferred metadata for an additional 19 auxiliary networks that utilize the Ethereum peer discovery protocol. We also explored Internet-wide scans, which only require probing each protocol's default ports with a simple, network-specific payload. This approach allows us to rapidly identify responsive peers across the entire address space without having to implement custom discovery and handshake logic for every blockchain. We validated this method on Bitcoin and similar networks with known ground truth, then applied it to Cardano, which we could not crawl directly. Our study uncovers dramatic variation in network size from under 10 to more than 10,000 active nodes. We quantify trends in IPv4 versus IPv6 usage, analyze autonomous systems and geographic concentration, and characterize churn, diurnal behavior, and the coverage and redundancy of discovery protocols. These findings expose critical differences in network resilience, decentralization, and observability. Beyond characterizing each network, our methodology demonstrates a general framework for measuring decentralized networks at scale. This opens the door for continued monitoring, benchmarking, and more transparent assessments of blockchain infrastructure across diverse ecosystems.

Motivated by the need to distinguish connectivity from robustness and to enable comparability across networks of varying scales, this work introduces a new measure termed the spectral connectivity extent . is defined as the average of the second and third smallest Laplacian eigenvalues, normalized by network size and rescaled to the interval [0, 100]. Higher values of correspond to stronger network connectivity. Several networks are analyzed to evaluate the proposed measure. By analyzing a real-world network, it is found that when the network undergoes a critical change, the relative variation of and the global efficiency are 16.485 and 0.037, respectively, whereas under an unimportant change, the relative variations of and are 0.2 and 0.162, respectively. These findings indicate that, compared with existing measures, more effectively captures significant structural changes within the network. Since emphasizes the efficiency of interactions among nodes, it has potential applications in the design of communication and transportation networks, the analysis of disease spreading, and the assessment of infrastructure resilience.

Poly(vinyl alcohol) cryogels: Effect size of polymer concentration, number of cycles and thawing rate on material properties and dermal drug delivery.

BackgroundApproximately one in four older Americans with cognitive impairment or dementia lives alone, but there is limited understanding of who provides their care and how their care is structured. Current research often relies on binary classifications of living alone or with others, which miss critical nuances. Among multi‐person households, living with a spouse versus living with other cohabitants can distinctly affect caregiving dynamics. This study examines how dementia care networks differ based on these living arrangements.MethodsWe utilized data from the 2022 National Health and Aging Trends Study, categorizing older adults with possible or probable dementia by living situation: living alone (N = 265), spouse present (N = 369), or with others only (N = 328). We analyzed caregiving network size, relationship composition, and care tasks, employing chi‐square tests and ANOVAs for comparisons, with sensitivity analyses for non‐dementia groups.ResultsIndividuals living alone with dementia had significantly more helpers (M = 1.92, similar to those living with others, M = 1.88, p = .127) compared to those living with a spouse (M = 1.48, p < .001). Despite larger networks, individuals living alone with dementia had significantly less help across care domains such as household, medical, and transportation than other groups (ps< .001), except mobility and self‐care (living alone v. spouse, p = .448). In terms of relationship composition, the presence of a child caregiver was similar across living arrangements. However, living alone was associated with a higher number of non‐family caregivers (e.g., paid helpers or friends) compared to those living with a spouse (p < .001). The presence of any non‐immediate and non‐family member as a caregiver was notably lower for those living with a spouse versus alone or with others (ps< .001).ConclusionThe findings reveal that while those living alone with dementia have larger networks, these helpers, often including non‐kin, provide less practical support, indicating potential unmet needs. In contrast, spousal caregivers shoulder a substantial care burden with limited additional support. Diversified networks in multi‐person households distribute care but may complexify coordination. These insights underscore the need for tailored interventions to support caregivers and enhance care for older adults based on living arrangements. Future research should investigate how these network dynamics influence health outcomes and service utilization.

Introduction: Social isolation is a major barrier to recovery for individuals with schizophrenia. Although prior studies examined social networks in relation to symptom severity and quality of life, few have integrated structural and relational dimensions of connectedness within a framework relevant to occupational therapy. This study investigated factors associated with social networks among individuals with schizophrenia in Japan, using Wang et al.’s conceptual framework and social network analysis. Method: A cross-sectional study was conducted with 31 individuals attending a psychiatric day-service center. Social isolation was assessed across four domains: network quantity, network quality, emotional appraisal (mattering and loneliness), and resource appraisal (helping and being helped). Multiple regression analysis identified factors related to network size, with significance set at p &lt; 0.05. Results: Participants had extremely small networks, averaging fewer than one close contact. Regression analysis showed that only being helped by others was significantly associated with larger network size (β = 0.84, p &lt; 0.0005). Loneliness, helping others, and mattering were not significant predictors. Conclusion: Individuals with schizophrenia may experience profound social isolation, with networks limited to relationships where they are recipients of help. Occupational therapy should promote opportunities for reciprocal, empowering connections to support recovery and community integration.

ABSTRACT In many countries, insurers build preferred provider networks to reduce costs. In this paper, we address the issue of the optimal size of such networks. Considering the case of medical services whose price is not regulated, we assume that the insurer sets a number of in‐network providers and organizes price competition to select the preferred providers among suppliers. Policyholders benefit from a higher reimbursement when they choose an in‐network provider. We show that both in‐network and off‐network prices decrease with the number of in‐network providers, that the largest possible network is optimal for the representative policyholder and for a not‐for‐profit insurer while a network with only one provider is preferred by a for‐profit insurer.

BackgroundPeers are known to influence the health behaviors and attitudes of adolescents, yet recruitment of these networks is challenging. Previous studies have used web-based respondent-driven sampling methods to recruit this population, yet none have experimentally investigated the impact of financial reimbursements.ObjectiveThis study aimed to (1) compare the effectiveness of two financial reimbursement strategies for recruiting adolescents and their peer networks and (2) explore factors associated with successfully recruiting peers.MethodsA parallel-design randomized controlled trial was conducted in which participants (seeds) were randomly allocated to a fixed cash reimbursement (control) or scaled reimbursement (experimental) group as a strategy to be recruited into a web-based peer network study. Seeds aged 16 to 18 years were recruited through social media advertisements and an online student panel. They completed a web-based survey, which assessed eligibility and included questions about their friends (peers). Allocation occurred through a survey platform using a simple randomization method. In the fixed group, all participants in a peer network received AUD $5 (US $3.29); in the scaled group, all participants in a peer network received an additional AUD $5 (US $3.29) per peer who successfully completed the survey (up to AUD $30 each [US $19.72]). Participants and researchers were not blinded to intervention groups. The primary outcome was recruitment of peers to complete the web-based survey (proportion of nominated peers). The number of peers recruited was a secondary outcome. In secondary analyses, we identified peer-, relationship-, and seed-level variables associated with successfully recruiting peers.ResultsOf 463 seeds allocated to an intervention (scaled n=221 and fixed n=242), 319 (68.9%) had complete data for analysis (scaled n=157, 71% and fixed n=162, 67%). A total of 11.9% of seeds successfully referred peers (18.5% scaled group and 5.6% fixed group). Those in the scaled reimbursement intervention were 3.80 times more likely to successfully recruit their peers than those in the fixed reimbursement intervention (proportion ratio 3.80, 95% CI 1.78‐8.09). Similarly, the average number of peers recruited differed by 0.19 (95% CI 0.11‐0.28) per seed between the scaled and fixed intervention groups. Peer recruitment success was similar regardless of the gender, age, education level, and network size of seeds or the gender, age, and closeness of peers. Seeds recruited through social media were more likely to successfully recruit their nominated peers than those recruited through a research panel (proportion ratio 2.20, 95% CI 1.06‐4.55).ConclusionsScaled reimbursements resulted in significantly greater recruitment of peers than fixed reimbursements; however, the total number of peers recruited was low. Greater-value incentives and stronger initial recruitment through social media may be needed to recruit large numbers of friend networks.

Abstract: The rapid expansion of the Internet of Things (IoT) has enabled pervasive sensing, automation, and data-driven decision-making. However, privacy and security challenges remain critical in Wireless Sensor Networks (WSNs), where limited computational and energy resources render traditional routing protocols vulnerable to traffic analysis, identity spoofing, and data manipulation. Existing routing schemes emphasize performance or energy efficiency but lack adaptive, privacy-aware mechanisms capable of responding to dynamic threats. This paper presents Dynamic Privacy-Aware Routing (DyPAR), an adaptive probabilistic routing protocol that balances privacy preservation, energy efficiency, and computational feasibility for large-scale IoT networks. DyPAR incorporates entropy-based relay selection, dynamic adjustment of forwarding probabilities, and context-aware weighting to reduce adversarial traceability while maintaining efficient routing. The protocol integrates lightweight privacy-preserving components, including Efficient Key Management (EfKM), Privacy-Aware Data Aggregation (PrADA), and an Adaptive Privacy Parameter Change Mechanism (A2PCM) for real-time adjustment based on network conditions and data sensitivity. Extensive simulations across heterogeneous network sizes and attack models show that DyPAR achieves high privacy compliance, strong resilience against Sybil, eavesdropping, and data-tampering attacks, and improved packet delivery performance relative to established privacy-aware routing baselines. While DyPAR maintains low energy consumption in benign scenarios, computational and energy overhead increase under multi-vector adversarial conditions, highlighting the need for further optimization in ultra–resource-constrained environments. Future work will explore (i) lightweight cryptographic integration to reduce energy cost, (ii) federated learning–based adaptive routing to enhance real-time privacy decisions, (iii) real world and energy-efficient clustering for large-scale deployments, and (iv) blockchain-enabled distributed trust frameworks to mitigate identity spoofing and coordinated attacks. Overall, DyPAR offers a scalable, adaptive, and privacy-preserving routing solution for next-generation IoT systems, providing a strong foundation for secure and resilient sensor network communication.

Abstract This study examines the impact of Information and Communication Technologies (ICT) on students’ travel and social behaviors in three European cities: Lisbon, Granada, and Zagreb. Drawing on a sample of 2,455 university students, the research examines the interplay between ICT usage (ICTU), social network size (SNS), perceived usefulness (PU) of ICT, patterns of interaction behavior (IB) (both online and face-to-face), and overall satisfaction (SA) with social relationships. The study employs structural equation modelling to capture both direct and indirect relationships among these factors, and incorporates a MIMIC approach to account for socio-demographic and contextual heterogeneity. The results show that ICT usage has mostly indirect effects on perceived usefulness, interaction behavior, and satisfaction, operating through social network size and interaction behavior. Social network size acts as a key mediator, linking ICT use to other variables, and is also the strongest predictor of interaction behavior. Similarly, perceived usefulness and traditional social networks emerge as the most influential factors shaping satisfaction. The findings confirm that online interactions tend to reinforce, rather than replace, face-to-face contact. Furthermore, a larger traditional network is associated with a larger online network, though not vice versa. Overall, the study highlights the complex, mediated pathways through which ICT use shapes young people’s social engagement and satisfaction, offering valuable insights for transport researchers and policymakers seeking to understand evolving mobility patterns.

Building upon theories of capital conversion, we examine relationships between one form of social capital (intergenerational closure, defined as the number of connections among parents in a school) and both simple and complex forms of cultural capital (school-oriented home activities and concerted cultivation) across socioeconomic status (SES). Using nationally representative data from the Early Childhood Longitudinal Study-Kindergarten Class of 2010 to 2011, the results show that intergenerational closure is significantly and positively associated with both home activities and concerted cultivation, having a stronger association with the latter. Families with lower SES report higher rates of engaging in everyday home activities, whereas families with higher SES concentrate more of their efforts into concerted cultivation. Furthermore, network size positively affects home activities across socioeconomic contexts but has stronger associations with concerted cultivation in socioeconomically disadvantaged families. We interpret these findings as suggesting that parental networks can be key mechanisms for converting forms of capital in socioeconomically disadvantaged contexts.