Search Results (146)

Youth-led STEM programming depends on skilled adult facilitators who can support authentic teen leadership, yet professional learning for developing these specialized skills remains understudied. Through three cycles of design-based research, we iteratively developed and studied a professional development model that trained informal educators from museums, libraries, afterschool programs, and schools to launch Teen Science Café programs—a youth-led model where teens organize STEM events. Analysis of data from trainer reflections, trainee interviews, trainee surveys, and implementation tracking across three iterative design cycles revealed six interconnected principles essential for effective professional development: focusing on a committed adult leader; personalized training characterized by mutual respect; learning by doing; establishing accountability that builds momentum; enabling learning from peers and near-peers; and recognizing success to nurture professional pride. Implementing these principles to prepare educators to center youth voice requires substantial, coordinated investment across stakeholders—commensurate with the complexity of developing youth agency and STEM identity in informal settings. From our findings, we contrast this approach with the “efficiency trap,” in which scaled training without sustained support wastes resources when many educators are trained but youth-centered programs fail to materialize. Full article

Medical drone delivery (MDD), defined as the use of uncrewed aerial vehicles to transport medical products, is an emerging technological innovation responding to persistent health supply chain challenges in rural and low-resource settings. Within sub-Saharan Africa, MDD systems have demonstrated large-scale success in improving key health outcomes, health supply chain efficiency, and reductions in medical product stockouts and wastage. However, the existing evidence base on the effectiveness of this technology is dominated by quantitative, performance-based evaluations, with limited emphasis on the community-driven mechanisms that shape such outcomes. Drawing on original qualitative research, this article presents a qualitative secondary analysis (QSA) of interview data collected as part of a larger case study on MDD in Madagascar. The QSA, guided by socio-technical systems theory, analyzes a subset of 18 interviews with 23 community-level stakeholders to understand how MDD affects healthcare services in remote regions of the country. Participants reported that MDD led to downstream healthcare improvements in vaccination coverage and malaria-related health outcomes. These improvements were enabled through four interconnected socio-technical mechanisms: (1) improved medical product availability through the mitigation of geographic and transportation barriers, (2) stabilization of vaccine and cold chain transportation, (3) building trust and healthcare-seeking behaviours through predictable service delivery, and (4) reduced physical, mental, and financial burdens experienced by healthcare workers. A final, cross-cutting theme emphasized was the criticality of MDD program continuity, with participants noting that operation disruptions or withdrawals risked reversing benefits and breaking communities’ trust in the health system. By centering lived realities, perceptions, and social processes, this article bridges the gap between predominantly quantitative evidence on MDD systems and the experiences of the communities they are intended to serve. Full article

Animal-assisted services (AAS) involve providers working in partnership with specially trained animals to deliver therapeutic, educational, and supportive benefits that promote human well-being. Although research shows dog-assisted AAS benefits recipients, little is known about handlers’ motivations and how this work impacts handlers and their dogs. This study explores why individuals engage in AAS with their dogs and the relational benefits and challenges involved. A convenience sample of adult AAS handlers was recruited through various organizations via newsletters and social media. Participants (N = 247), predominantly older, white, and highly educated women, responded to three open-ended survey questions. Data were analyzed using reflexive thematic analysis, which revealed several interconnected themes. Handlers described profound joy, pride, and purpose derived from sharing their dogs with others, and an enhanced capacity to support recipients. Participants reported using AAS to augment professional roles, to pay forward acts of kindness, and to enrich their dogs through social interaction. While overwhelmingly meaningful, this work also involves challenges, underscoring the need for organizational support for AAS teams. Training programs should equip handlers to recognize signs of animal stress, navigate demanding contexts, and engage in proactive conversations about rest, retirement, and loss. Normalizing these experiences may promote handler well-being and sustain ethical, relationship-centered AAS practice. Full article

This study explores the interconnection between the variables of ChatGPT usage, energy consumption, water consumption, and carbon dioxide CO₂ emissions. A new integer and fractional order model using the Caputo derivative is proposed to comprehend the long-term dependencies of these variables. Boundedness, and global and local stability are examined for the fractional order model. The equilibrium points of these variables are shown to determine the stability of the model. The Runge–Kutta 7 numerical method is employed for the integer order model, whereas the semi-implicit linear interpolation (L1) method is used for the fractional order model. The parameter sensitivity is conducted on the system’s parameters to understand the variables’ impact by varying the relevant parameters for the system. To increase the efficacy of our analysis, we used machine learning approaches to model and predict the dynamics of CO₂ emissions, energy and water consumption, and ChatGPT usage. The Prophet ML model stood out among the other methods because it is adept at identifying long-term growth trends, seasonal changes, and the impact of outside variables in intricate time-series data. It is extremely beneficial for research centered on sustainability, where accurate projections are essential for wellinformed decision-making, because it can produce robust, interpretable forecasts against missing values and outliers. Using the Prophet ML model, our research guarantees precise and expandable predictions and provides valuable information that can direct tactics to balance environmental sustainability and technological progress. Full article

Based on a bibliometric analysis of 2680 publications (1962–2024), this study elucidates the knowledge structure and intellectual evolution of research on climate change-driven nitrogen migration and transformation in inland waters, a critical issue for water security and global climate stability. The field has experienced accelerated growth since 2016, led by the United States and China. Analysis reveals a research framework centered on climate change, nitrogen, and water quality, interconnected with processes like eutrophication and denitrification. The intellectual focus has evolved from early investigations into fundamental chemical mechanisms towards a contemporary emphasis on human–climate interactions (e.g., land use), model-based predictions, and regional management solutions for nonpoint source pollution. A key finding is the bidirectional climate–nitrogen feedback, where climate alters nitrogen pathways and transformations, which in turn release greenhouse gases. The findings underscore a pivotal shift from theoretical understanding to applied, solution-oriented research. Future work must prioritize integrated multi-technique approaches, cross-ecosystem comparisons, and data-driven modeling to advance predictive capabilities and support effective nitrogen management in inland waters under a changing climate. Full article

Edge, fog, and cloud continuum architectures that interconnect resource-constrained devices, intermediate edge servers, and remote cloud data centers face persistent challenges in handling heterogeneous and latency-sensitive workloads while reducing energy consumption and improving resource utilization. Classical task offloading approaches either rely on static heuristics, which lack adaptability to dynamic conditions, or on metaheuristic optimizers, which often incur high computational overhead and centralized coordination. This paper proposes LITO, a lemur-inspired task offloading algorithm for edge, fog, and cloud continuum systems that models the infrastructure as a social system in which computing nodes assume distinct roles that mirror lemur social hierarchies. Building on an abstracted model of lemur group behavior, LITO incorporates two key lemur-inspired mechanisms: an energy-aware task assignment mechanism based on sun basking, a thermoregulation behavior in which lemurs seek favorable warm spots, mapped here to selecting energetically efficient execution nodes, and a cooperative scheduling policy based on huddling, group clustering under stress, mapped here to sharing load among overloaded nodes. These mechanisms are combined with a continual supervised policy-learning layer with contextual bandit feedback that refines offloading decisions from online feedback. The resulting multi-objective formulation jointly minimizes energy consumption and deadline violations while maximizing resource utilization and throughput under high-load conditions in the edge and fog segment of the continuum. Simulations under diverse workload regimes and task complexities show that LITO outperforms representative multi-objective offloading baselines in terms of energy consumption, resource utilization, latency, Service Level Agreement (SLA) violations, and throughput in congested scenarios. Full article

LGBTQ+ individuals continue to experience substantial barriers to accessing affirming healthcare, including discrimination, structural inequities, and provider-level limitations. This study aimed to develop an emergent grounded theory model of constructing wholeness in healthcare. Methods: This study employed a secondary constructivist grounded theory analysis of qualitative data from The Rainbow Connections Study, a community-based system dynamics project. Data were collected through eight group model-building sessions conducted via Zoom with 28 LGBTQ+ participants, including older adults, youth, transgender and gender-diverse individuals, and staff from the LGBTQ+ community center who also held service and practitioner roles; analytic claims are framed to reflect this mixed-role sample. Sessions were audio- and video-recorded, transcribed verbatim, and analyzed using open, axial, and selective coding procedures. Constant comparative methods, reflexive memoing, and member checking were used to support analytic rigor and trustworthiness. Results: Analysis revealed a dynamic process in which LGBTQ+ individuals encounter external forces within healthcare systems that alternately support or fragment their sense of self. In response, participants engaged in four interconnected internal processes—interconnecting selves, intra-community support, self-determined care, and meaning-finding—that facilitated movement toward wholeness. These processes were non-linear, iterative, and present across diverse identities and life stages. Conclusions: The emergent theory of Constructing Wholeness in Connecting to Healthcare highlights that LGBTQ+ healthcare experiences extend beyond access and utilization to include identity integration, community reliance, and meaning making. Supporting LGBTQ+ health requires healthcare approaches that affirm wholeness, reduce structural harm, and recognize the central role of community in navigating care. Full article

Design of large-scale power systems is getting increasingly complex nowadays from an operational and reliability standpoint due to the uncertainties associated with the injection of renewables and consumption of load. These uncertainties pose a great challenge in gauging and subsequently obtaining reliable system-level assurances from subsystem-level guarantees, particularly in mission-critical systems such as those seen in data centers. We propose a formal and modular framework of probabilistic assume–guarantee contracts (PAGCs) for compositional reasoning and control of uncertain power systems, motivated by the need for resilient and verifiable operation in data center power networks. In contrast to classical contracts, which require absolute satisfaction of assumptions and guarantees, PAGCs allow for high-probability satisfaction under system uncertainty and variability. We formalize the syntax and semantics of PAGCs, develop soundness and compositionality theorems, and demonstrate their applicability to power grid components such as generators, transformers, circuit breakers, and loads. Given the current approval bottlenecks in interconnection requests, a growing number of data center operators are opting for islanded generation configuration. A case study on such a modular islanded data center power system is presented to validate the proposed theory. The proposed PAGC application in power networks is promising in several aspects to solve several existing open problems in distributed systems, particularly in future large-scale smart power networks. Full article

Maternal and child health (MCH) disparities remain a critical public health concern in the United States, with the Southern region experiencing some of the nation’s highest maternal mortality rates. Black and Latinx women are disproportionately affected, reflecting persistent structural and systemic inequities. This review examines maternal health disparities among Black and Latinx women in the Southern United States and identifies structural, social, and policy-related contributors to these inequities. A narrative review approach was used to synthesize epidemiological data, policy reports, and peer-reviewed literature published between 2000 and 2025, drawing on national surveillance systems such as CDC WONDER and the National Center for Health Statistics, as well as state-level reports and policy analyses relevant to maternal health in Southern states. Across the region, maternal mortality rates frequently exceeded the national average of 23.2 deaths per 100,000 live births, with several states reporting rates above 38 deaths per 100,000. Structural contributors included limited access to maternity care, rural hospital closures, Medicaid non-expansion, chronic disease burden, and experiences of discrimination within healthcare systems, while social determinants such as poverty, housing instability, language barriers, and immigration-related fears further compounded risks for Black and Latinx women. In the post-Roe context, restrictive reproductive health policies intensified existing inequities. Overall, maternal health disparities among Black and Latinx women in the Southern United States are driven by interconnected structural, social, and policy factors, underscoring the need for coordinated policy reforms, expansion of culturally responsive care models, and targeted investment in Southern communities disproportionately affected by maternal mortality. Full article

Background: The humanization of care is increasingly recognized as a core component of healthcare quality; however, its meaning remains complex and strongly shaped by organizational, professional, and educational contexts. Nursing students, as future healthcare professionals, play a crucial role in the development and transmission of humanized care values, making their representations of the humanized hospital particularly relevant for understanding how these values are constructed during professional education. Aim: To explore how undergraduate nursing students conceptualize the humanized hospital. Methods: A qualitative exploratory study was conducted involving 742 undergraduate nursing students enrolled in a Bachelor of Science in Nursing program in Italy. Data were collected through a single open-ended written question inviting students to describe how they imagine a humanized hospital. Textual data were analyzed using Automatic Analysis of Textual Data within an Exploratory Multidimensional Data Analysis framework, enabling the identification of shared lexical patterns, discursive clusters, and latent semantic dimensions within a large textual corpus. Findings: Students articulated the humanized hospital as an integrated and system-oriented care environment in which relational, organizational, professional, and holistic dimensions are deeply interconnected. Humanization was associated not only with empathy, respect, and emotional engagement, but also with organizational functioning, teamwork, adequate resources, and professional competence. Two latent dimensions structured these representations: the first highlighted organizational systems as enabling conditions for person-centered care, while the second framed professional operability and technical competence as foundations for a holistic understanding of patients’ physical, psychological, and social well-being. Conclusions: Undergraduate nursing students’ discourse revealed an articulated and multidimensional representation of hospital humanization, conceptualizing it as an emergent property of healthcare environments rather than as a function of individual attitudes alone. These findings underscore the importance of addressing hospital humanization simultaneously at relational, educational, and organizational levels and highlight the need for nursing education programs and healthcare institutions to foster structural and professional conditions that sustainably support humanized care in clinical practice. Full article

The connectivity demands of high-performance computing (HPC), artificial intelligence (AI) and data centers are driving the development of a new generation of multimode optical components. This paper discusses the vertical cavity surface emitting laser (VCSEL) bandwidth and noise performance needed to support 106 Gbd line rates with PAM4 modulation for 200 Gbps per lane multimode optical links. A −3 dB bandwidth greater than 35 GHz and a RIN of less than −152 dB/Hz are demonstrated. No uncorrectable errors were observed over 50 m of OM4 fiber, demonstrating good link stability. VCSEL device performance and the associated wear-out life are presented. Leveraging good device reliability and low power consumption of VCSEL-based links, a novel VCSEL near-packaged optics (NPO) concept is proposed for optical interconnects in AI scale-up network applications. Optical interconnects allow for longer reaches, compared to copper interconnects, which facilitate larger AI clusters with network disaggregation. The proposed VCSEL NPO can achieve an energy efficiency of ~1 pJ/bit, which is the highest among optical interconnects. Full article

In the context of the digital transformation of industrial production, the need for intelligent maintenance and repair systems capable of ensuring reliable operation of machine-tool equipment without operator involvement is growing. This present study reviews the current state and future development of diagnostic and condition-monitoring systems for metalworking machine tools. A review of international standards and existing solutions from domestic and international vendors in vibration diagnostics has been conducted. Particular attention is paid to non-intrusive vibration diagnostics, digital twins, multiparametric analysis methods, and neural network approaches to failure prediction. The architecture of the developed system is presented. The concept of the system is developed in full compliance with Russian and international standards of vibration diagnostics. At its core, the comprehensive digital twin relies on machine learning methods. The proposed architecture is a predictive-maintenance system built on interconnected digital twin realizations: the dynamic machine passport of a unit, operational data, and a comprehensive digital twin of the machine-tool equipment. The potential of neuromorphic computing on a hardware platform is being considered as a promising element for local-condition classification and emergency protection. At the current development stage, the operating principle has been demonstrated along with the integration into the control loop. The system is now at the beginning of laboratory testing. It demonstrates capabilities for comprehensive assessment of the equipment’s technical condition based on multiparametric data, short-term vibration trend forecasting using a Long Short-Term Memory network, and state classification using a Multilayer Perceptron model. The results of the system’s testing on a turning machining center have been analyzed. Full article

The explosive growth of optical interconnection service traffic urgently necessitates the evolution of optical transponders and fibers. The core-division-multiplexed (CDM) transmission technique using weakly coupled multi-core fibers (MCFs) and beyond-1T optical transport network (OTN) transponders has emerged as an attractive solution to meet the bandwidth demands of future networks. In this paper, we demonstrate an ultra-high-speed OTN system using C+L-band 1.2 Tb/s OTN transponders with a weakly coupled seven-core fiber. The OTN transponders support two configurable modulation rates of 135 Gbaud and 155 Gbaud, along with a probability constellation-shaping 64-ary quadrature amplitude modulation (PCS-64QAM) format. The MCF exhibits characteristics comparable to those of SMFs and negligible inter-core crosstalk, providing a superior physical channel for ultra-high-speed CDM transmission. Fiber length and low-noise EDFAs are also chosen to enhance the transmission distance under the limited optical signal-to-noise ratio (OSNR) budget when using 1.2 Tb/s OTN transponders. Benefiting from the high-performance OTN transponders and MCF, we achieve real-time 0.672 Pb/s and 0.571 Pb/s 4 × 89 km CDM transmissions using 135 Gbaud and 155 Gbaud modulation rates, respectively. The performance of the two modulation configurations is also compared and discussed. This work demonstrates the feasibility of implementing 1.2 Tb/s OTN transponders with weakly coupled MCFs to achieve ultra-high-speed metro–regional transmission, presenting a promising solution for next-generation inter-city data center interconnections. Full article

The current refugee crisis has revealed flaws in existing systems. Factors such as socioeconomic background, access to housing, and urban policies influence refugees’ abilities to fully participate in city life. The research objective is to analyze the interplay between housing access for adult refugees residing in Curitiba, Brazil, and the city’s targeted public policies and strategies for refugees. The research methodology adopts a case study approach centered on Curitiba, Brazil, with the city shown as a key destination for refugees in Brazil. This study combines qualitative and quantitative techniques, following a structured research protocol that guides the processes of data collection and analysis. The innovation and originality lie in offering a new perspective on how urban strategies intersect with the rights and inclusion of refugees, exploring the relationship between refugees’ housing access and its interconnection with the strategic digital city framework. The results highlight the importance of a multifaceted approach to addressing housing access challenges for refugees, which includes safeguarding their rights, promoting stability, integration, and ensuring their participation in shaping public policies. The conclusion outlines the urgent need to promote integration by reassessing housing affordability, ensuring access to services, engaging refugees in decision-making processes, and improving their social welfare. Full article

Background/Objectives: Triple-negative breast cancer (TNBC) exhibits high immune infiltration yet remains clinically aggressive. Although immune checkpoint blockade benefits a subset of patients, the molecular programs enabling concurrent immune activation and immune evasion in TNBC are not fully defined. This study aimed to identify TNBC-specific tumor-intrinsic and tumor-extrinsic molecular features that may explain this paradox. Methods: Publicly available single-cell RNA-sequencing data from primary breast tumors were analyzed to characterize subtype-specific transcriptional programs across epithelial and stromal compartments. Tumor-intrinsic findings were independently validated using bulk transcriptomic and clinical data from the METABRIC cohort. Tumor microenvironment remodeling was evaluated using multiplexed tissue imaging of TNBC tumors. Functional analyses were done included Gene Ontology enrichment, Hallmark gene set enrichment analysis, and SERPINB3-centered protein–protein interaction network analysis using STRING. Results: Single-cell analysis identified SERPINB3 as a TNBC-enriched epithelial gene relative to ER+ and HER2+ tumors. This subtype-restricted pattern was validated in the METABRIC cohort and associated with pathways related to epithelial–mesenchymal transition, interferon signaling, and antigen presentation. TNBC tumors also displayed a humoral immune signature characterized by B-cell and plasmablast enrichment, as well as ectopic immunoglobulin gene expression in cancer-associated fibroblasts, endothelial cells, and myeloid populations. Multiplex imaging revealed coordinated associations between immune suppression, stromal activation, and tumor proliferation. Network analysis placed SERPINB3 within interconnected immune-regulatory and stromal signaling modules. Conclusions: Together, these data indicate that TNBC exhibits co-existing immune activation and immune-suppressive features. The identified epithelial and stromal signatures represent candidate biomarkers that may inform future studies of immune regulation and therapeutic stratification in TNBC. Full article