Search Results (2,488)

To enhance PharmD student leadership and advocacy skills, combat the paucity of trained pre-health advisors for pharmacy admissions, augment community relationships, and increase pharmacy admissions volume, we designed, implemented, and assessed PAALS, a Pre-health Academic Advising and Leadership System. PAALS was grounded in Astin’s Theory of Student Involvement and evaluated using the RE-AIM implementation science framework. RE-AIM measured outcomes across Reach, Effectiveness, Adoption, Implementation, and Maintenance as indicators of PAALS’s scale, fidelity, sustainability, and institutional embedding. Analysis of PAALS using the RE-AIM framework demonstrated the following outcomes: (1) Reach: 42 P1-P3 PharmD students participated as mentors; external partnerships expanded from 2 to 8 regional high schools and community programs; and more than 25 mentored learners successfully matriculated into the PharmD program. (2) Effectiveness: students enacted sustained leadership, advocacy, and mentoring roles. (3) Adoption: voluntary uptake of mentoring and governance roles by PharmD students occurred with repeated engagement by external partner institutions. (4) Implementation: Core program components were delivered consistently using existing institutional resources. (5) Maintenance: PAALS remained operational across five academic years despite student turnover, with leadership succession and institutional embedding sustained across cohorts. Our findings demonstrate that student-led advising and advocacy ecosystems address critical gaps in pharmacy-specific pre-health advising models. Full article

Global warming threatens sustainable human development, and carbon emission rights trading (CERT) has emerged as a key market-based tool for reducing emissions. Yet evidence on how CERT affects corporate green innovation—especially high-quality, substantive innovation—remains mixed and fragmented. Using unbalanced panel data on Chinese A-share listed firms from 2007 to 2016 and applying fixed-effect, DID, and PSM-DID models, this study examines the impact of China’s CERT pilot policy on quota-managed firms’ green innovation. The results show that the policy primarily stimulates substantive green innovation, reflected in green invention patents, with limited influence on strategic, low-novelty patents. Its effects are stronger for firms in central and western pilot regions, in non-high-tech industries, and at more mature stages of development, and differ between firms that anticipated regulation and those brought under quota management unexpectedly. Overall, the findings indicate that a well-designed carbon trading mechanism can reallocate resources to incentivize high-quality green innovation, offering micro-level support for Coasian market-based approaches to environmental externalities and informing the further development of China’s national carbon market. Full article

In the context of small, battery-powered systems, a lightweight, reusable architecture is needed for integrated measurement, visualization, and cloud telemetry that minimizes hardware complexity and energy footprint. Existing solutions require high resources. This limits their applicability in Internet of Things (IoT) devices with low power consumption. The present work demonstrates the process of design, implementation and experimental evaluation of a single-cell lithium-ion battery monitoring prototype, intended for standalone operation or integration into other systems. The architecture is compact and energy efficient, with a reduction in complexity and memory usage: modular architecture with clearly distinguished responsibilities, avoidance of unnecessary dynamic memory allocations, centralized error handling, and a low-power policy through the usage of deep sleep mode. The data is stored in a cloud platform, while minimal storage is used locally. The developed system combines the functional requirements for an embedded external battery monitoring system: local voltage and current measurement, approximate estimation of the State of Charge (SoC) using a look-up table (LUT) based on the discharge characteristic, and visualization on a monochrome OLED display. The conducted experiments demonstrate the typical U(t) curve and the triggering of the indicator at low charge levels (LOW − SoC ≤ 20% and CRITICAL − SoC ≤ 5%) in real-world conditions and the absence of unwanted switching of the state near the voltage thresholds. Full article

Background: Food insecurity among college students is a multidimensional challenge shaped by individual, interpersonal, institutional, community, and policy factors. Although many campuses require or provide meal plans, students may experience food insecurity when barriers related to agency (choice and autonomy), utilization (nutrition security), and availability persist. This study explored how undergraduate students at a private, urban U.S. university experience and navigate the multiple dimensions of food insecurity. Methods: We conducted in-depth, semi-structured interviews via Zoom between December 2024 and January 2025 with n = 22 undergraduate students recruited based on food security status, determined by a Fall 2024 longitudinal survey using the USDA Six-Item Short Form. Transcripts were double-coded by trained research assistants in ATLAS.ti using an inductive codebook. Thematic analyses followed a phronetic, iterative approach, organizing findings within a socio-ecological determinants framework and comparing themes by food security status. Results: We identified nine themes across four domains (individual, interpersonal, institutional and community, and political). At the individual level, constrained personal resources for groceries and cooking, time scarcity leading to skipped meals, and health impacts that detracted from academics emerged as key themes. Interpersonally, reliable family financial support was protective and informal support from peers/coaches filled gaps sporadically for some. At the institutional and community level, dining hall hours misaligned with student schedules, perceived limited variety and nutrition quality reduced food agency and utilization, and transportation impeded use of the sole grocery partner accepting university meal plan benefits. Notably, meal plans including unlimited meal swipes provided stable access but did not guarantee food security when food agency and utilization barriers persisted. Many students relied on campus events for free food; formal assistance (e.g., food pantry) was largely underused. At the policy level, Supplemental Nutrition Assistance Program (SNAP) awareness and enrollment was limited among our sample. Conclusions: Meal plan access alone is insufficient to ensure food security. Campus strategies should extend beyond access to prioritize flexibility, variety, and alignment with students’ schedules and preferences, while strengthening communication and eligibility support for external benefits. Future work should design and evaluate interventions that integrate all dimensions of food security and address institutional policies affecting students’ basic needs. Full article

Non-AIDS-defining cancers (NADCs) have emerged as an increasingly prominent cause of non-AIDS-related morbidity and mortality among people living with HIV (PLWH). However, the scarcity of NADC clinical samples, compounded by privacy and security constraints, continues to present formidable obstacles to advancing pathological and clinical investigations. In this study, we adopted a joint analysis strategy and deeply integrated and analyzed transcriptomic data from 12,486 PLWH and cancer patients to systematically identify potential key regulators for 23 NADCs. This effort culminated in NADCdb—a database specifically engineered for NADC pathological exploration, structured around three mechanistic frameworks rooted in the interplay of immunosuppression, chronic inflammation, carcinogenic viral infections, and HIV-derived oncogenic pathways. The “rNADC” module performed risk assessment by prioritizing genes with aberrant expression trajectories, deploying bidirectional stepwise regression coupled with logistic modeling to stratify the risks for 21 NADCs. The “dNADC” module, synergized patients’ dysregulated genes with their regulatory networks, using Random Forest (RF) and Conditional Inference Trees (CITs) to identify pathogenic drivers of NADCs, with an accuracy exceeding 75% (in the external validation cohort, the prediction accuracy of the HIV-associated clear cell renal cell carcinoma model exceeded 90%). Meanwhile, “iPredict” identified 1905 key immune biomarkers for 16 NADCs based on the distinct immune statuses of patients. Importantly, we conducted multi-dimensional profiling of these key determinants, including in-depth functional annotations, phenotype correlations, protein–protein interaction (PPI) networks, TF-miRNA-target regulatory networks, and drug prediction, to deeply dissect their mechanistic roles in NADC pathogenesis. In summary, NADCdb serves as a novel, centralized resource that integrates data and provides analytical frameworks, offering fresh perspectives and a valuable platform for the scientific exploration of NADCs. Full article

This paper investigates how external shocks propagate through fiscal transmission mechanisms in a commodity-dependent economy within a dynamic macroeconomic framework. The study contributes to the literature on macroeconomic fluctuations by examining the interaction between external revenue volatility, fiscal behavior, and institutional features in shaping short-run dynamics and medium-term outcomes. A Dynamic Stochastic General Equilibrium (DSGE) model is developed and calibrated to the Ecuadorian economy. The framework explicitly incorporates procyclical fiscal behavior, public capital accumulation, and endogenous spending efficiency, allowing for a structural analysis of fiscal transmission channels under external and productivity shocks. Counterfactual simulations are employed to assess the role of fiscal policy design and institutional constraints. The results show that while productivity shocks remain a key driver of output fluctuations, external revenue shocks significantly influence macroeconomic volatility through fiscal channels. Procyclical fiscal responses amplify fluctuations by reducing public investment and spending efficiency, slowing public capital accumulation and prolonging output contractions. Alternative fiscal configurations mitigate short-run volatility, although their effectiveness depends critically on institutional features governing spending efficiency. Overall, the analysis highlights that macroeconomic dynamics in resource-dependent economies are shaped not only by external shocks, but also by the interaction between fiscal policy design and institutional capacity. Integrating these elements into DSGE models provides a more comprehensive understanding of fiscal transmission mechanisms and macroeconomic volatility. Full article

This study diagnoses the level and profile of university lecturers’ digital competence at a Chilean higher education institution, drawing on the DigCompEdu and OpenEdu frameworks. A non-experimental correlational design was used, based on a self-perception questionnaire adapted from the DigCompEdu Check-In tool and administered to 569 lecturers through the Qualtrics platform. The instrument underwent external expert validation and demonstrated excellent internal consistency (Cronbach’s α = 0.96). Results indicate that 44% of lecturers position themselves at the “Integrator” level, 22% at the “Explorer” level, and 19% at the “Expert” level, with three clearly differentiated competence profiles. These findings informed the development of a structured training programme centred on three components: the pedagogical use of digital technologies, the incorporation of open educational practices aligned with OpenEdu, and the strengthening of students’ digital competence. The programme includes modular workshops, mentoring led by high-competence lecturers, and the creation of open educational resources. Overall, the study provides empirical evidence to guide institutional policies and to foster a reflective, ethical, and pedagogically grounded integration of digital technologies in university teaching. Full article

The global space economy, valued at approximately USD 400–630 billion (depending on definitional scope), is projected to expand rapidly, crossing USD 1 trillion as early as 2032 and reaching up to about USD 1.8 trillion by 2035. This growth has been driven by a surge (a roughly twelvefold increase) in satellite launches over the past decade, transforming Earth’s orbits into an increasingly congested domain plagued by space debris. The proliferation of space junk poses an escalating threat to orbital sustainability, yet effective governance mechanisms remain limited. This paper examines why conventional solutions for managing common-pool resources (command-and-control regulation, Pigouvian taxes, private property rights, allocation of tradable permits, and horizontal governance regimes) are not fully effective or are difficult to implement in addressing the orbital debris problem. Using a system dynamics perspective, the study qualitatively maps hypothesized feedback mechanisms shaping orbital expansion and space debris accumulation. It suggests that, under the assumed causal structure, reinforcing growth loops associated with geopolitical rivalry and commercial cost reductions linked to the New Space paradigm currently dominate over delayed balancing effects arising from the finite nature of orbital space, whose regenerative capacity is progressively degraded. There exists a threshold of exploitation beyond which orbital space effectively behaves as a non-renewable resource. The analysis suggests that, without binding international coordination, meaningful intervention may require the occurrence of a catalyzing crisis—e.g., a localized cascade of orbital object collisions that could transform stakeholder perceptions and enables active debris removal deployment. Full article

Background/Objectives: Earlier initiation of palliative care improves clinical outcomes, including better symptom relief, enhanced quality of life, and decreased use of healthcare resources in advanced disease. This systematic review aimed to identify and critically appraise existing tools, both conventionally developed and based on artificial intelligence, designed to identify patients eligible for early palliative care interventions. Methods: Six electronic databases were examined for primary research studies published between 2000 and 2025. Studies that described or evaluated screening instruments developed to support the early identification of adult patients with palliative care needs underwent dual reviewer screening and data extraction. Results: A total of 35 studies were included. Of these, 13 reported the development of screening tools and 22 focused on the external validation of these instruments. Nine tools were developed using traditional methods, and four instruments were created using artificial intelligence techniques. Significant heterogeneity was observed in tool design and target populations. Most screening tools used death prediction as a proxy, with limited integration of psychological and spiritual dimensions. External validation studies primarily focused on predicting mortality. Overall, all the tools showed moderate predictive ability. Conclusions: The ability of current screening tools to identify patients with advanced diseases who are likely to have palliative care needs remains limited. Further research is needed to develop standardized screening processes that address not only mortality prediction but also disease trajectory and functional decline. Full article

Accurately identifying drug off-targets is essential for reducing toxicity and improving the success rate of pharmaceutical discovery pipelines. However, current deep learning approaches often struggle to fuse chemical structure, protein biology, and multi-target context. Here, we introduce HDPC-LGT (Hybrid Dual-Prompt Cross-Attention Ligand–Protein Graph Transformer), a framework designed to predict ligand binding across sixteen human translation-related proteins clinically associated with antibiotic toxicity. HDPC-LGT combines graph-based chemical reasoning with protein language model embeddings and structural priors to capture biologically meaningful ligand–protein interactions. The model was trained on 216,482 experimentally validated ligand–protein pairs from the Chemical Database of Bioactive Molecules (ChEMBL) and the Protein–Ligand Binding Database (BindingDB) and evaluated using scaffold-level, protein-level, and combined holdout strategies. HDPC-LGT achieves a macro receiver operating characteristic–area under the curve (macro ROC–AUC) of 0.996 and a micro F1-score (micro F1) of 0.989, outperforming Deep Drug–Target Affinity Model (DeepDTA), Graph-based Drug–Target Affinity Model (GraphDTA), Molecule–Protein Interaction Transformer (MolTrans), Cross-Attention Transformer for Drug–Target Interaction (CAT–DTI), and Heterogeneous Graph Transformer for Drug–Target Affinity (HGT–DTA) by 3–7%. External validation using the Papyrus universal bioactivity resource (Papyrus), the Protein Data Bank binding subset (PDBbind), and the benchmark Yamanishi dataset confirms strong generalisation to unseen chemotypes and proteins. HDPC-LGT also provides biologically interpretable outputs: cross-attention maps, Integrated Gradients (IG), and Gradient-weighted Class Activation Mapping (Grad-CAM) highlight catalytic residues in aminoacyl-tRNA synthetases (aaRSs), ribosomal tunnel regions, and pharmacophoric interaction patterns, aligning with known biochemical mechanisms. By integrating multimodal biochemical information with deep learning, HDPC-LGT offers a practical tool for off-target toxicity prediction, structure-based lead optimisation, and polypharmacology research, with potential applications in antibiotic development, safety profiling, and rational compound redesign. Full article

Wireless sensor networks comprise many resource-constrained nodes that must protect both local readings and routing metadata. The sensors collect data from the environment or from the individual to whom they are attached and transmit it to the nearest gateway node via a wireless network for further delivery to external users. Due to wireless communication, the transmitted messages may be intercepted, rerouted, or even modified by an attacker. Consequently, security and privacy issues are of utmost importance, and the nodes must be protected against unauthorized access during transmission over a public wireless channel. To address these issues, we propose the Probabilistic Bit-Similarity-Based Key Agreement Protocol (PBS-KAP). This novel method enables two nodes to iteratively converge on a shared secret key without transmitting it or relying on pre-installed keys. PBS-KAP enables two nodes to agree on a symmetric session key using probabilistic similarity alignment with explicit key confirmation (MAC). Optimized Garbled Circuits facilitate secure computation with minimal computational and communication overhead, while Secure Sketches combined with Fuzzy Extractors correct residual errors and amplify entropy  producing reliable and uniformly random session keys. The resulting protocol provides a balance between security, privacy, and usability, standing as a practical solution for real-world WSN and IoT applications without imposing excessive computational or communication burdens. Security relies on standard computational assumptions via a one-time elliptic–curve–based base Oblivious Transfer, followed by an IKNP Oblivious Transfer extension and a small garbled threshold circuit. No pre-deployed long-term keys are required. After the bootstrap, only symmetric operations are used. We analyze confidentiality in the semi-honest model. However, entity authentication, though feasible, requires an additional Authenticated Key Exchange step or malicious-secure OT/GC. Under the semi-honest OT/GC assumption, we prove session-key secrecy/indistinguishability; full entity authentication requires an additional AKE binding step or malicious-secure OT/GC.  Full article

Background/Objectives: Swimming requires precise motor control, sustained attention, and optimal cognitive–motor integration, making it an ideal model for investigating neural efficiency—the phenomenon whereby expert performers achieve optimal outcomes with reduced neural resource expenditure, operationalized as lower activation, sparser connectivity, and enhanced functional integration. This systematic review examined cognitive performance and neural adaptations in swimming athletes, investigating neuroimaging and behavioral outcomes distinguishing swimmers from non-athletes across performance levels. Methods: Following PRISMA 2020 guidelines, seven databases were searched (1999–2024) for studies examining cognitive/neural outcomes in swimmers using neuroimaging or validated assessments. A total of 24 studies (neuroimaging: n = 9; behavioral: n = 15) met the inclusion criteria. Risk of bias assessment used adapted Cochrane RoB2 and Newcastle–Ottawa Scale criteria. Results: Neuroimaging modalities included EEG (n = 4), fMRI (n = 2), TMS (n = 1), and ERP (n = 2). Key associations identified included the following: (1) Neural Efficiency: elite swimmers showed sparser upper beta connectivity (35% fewer connections, d = 0.76, p = 0.040) and enhanced alpha rhythm intensity (p ≤ 0.01); (2) Cognitive Performance: superior attention, working memory, and executive control correlated with expertise (d = 0.69–1.31), with thalamo-sensorimotor functional connectivity explaining 41% of world ranking variance (r² = 0.41, p < 0.001); (3) Attention: external focus strategies improved performance in intermediate swimmers but showed inconsistent effects in experts; (4) Mental Fatigue: impaired performance in young adult swimmers (1.2% decrement, d = 0.13) but not master swimmers (p = 0.49); (5) Genetics: COMT Val158Met polymorphism associated with performance differences (p = 0.026). Effect sizes ranged from small to large, with Cohen’s d = 0.13–1.31. Conclusions: Swimming expertise is associated with specific neural and cognitive characteristics, including efficient brain connectivity and enhanced cognitive control. However, cross-sectional designs (88% of studies) and small samples (median n = 36; all studies underpowered) preclude causal inference. The lack of spatially quantitative synthesis and visualization of neuroimaging findings represents a methodological limitation of this review and the field. The findings suggest potential applications for talent identification, training optimization, and mental health promotion through swimming but require longitudinal validation and development of standardized swimmer brain atlases before definitive recommendations. Full article

Accurate model aggregation is pivotal for the efficient dispatch and control of massive distributed photovoltaic (PV) and energy storage (ES) resources. However, the lack of unified standards across equipment manufacturers results in inconsistent data formats and resolutions. Furthermore, external disturbances like noise and packet loss exacerbate the problem. The resulting data are massive, multi-source, and heterogeneous, which poses severe challenges to building effective aggregation models. To address these issues, this paper proposes a hierarchical aggregation method based on multi-source multi-scale data fusion. First, a Multi-source Multi-scale Decision Table (Ms-MsDT) model is constructed to establish a unified framework for the flexible storage and representation of heterogeneous PV-ES data. Subsequently, a two-stage fusion framework is developed, combining Information Gain (IG) for global coarse screening and Scale-based Trees (SbT) for local fine-grained selection. This approach achieves adaptive scale optimization, effectively balancing data volume reduction with high-fidelity feature preservation. Finally, a hierarchical aggregation mechanism is introduced, employing the Analytic Hierarchy Process (AHP) and a weight-guided improved K-Means algorithm to perform targeted clustering tailored to the specific control requirements of different voltage levels. Validation on an IEEE-33 node system demonstrates that the proposed method significantly improves data approximation precision and clustering compactness compared to conventional approaches. Full article

During a late-winter field survey in Baihuashan National Nature Reserve, Beijing, several noctuid moths were observed flying during the daytime at low temperatures and being actively preyed upon by Marsh tits, which removed the heads and wings of captured individuals. These observations indicate that adults of this noctuid lineage are active in late winter, providing a critical nutritional resource for insectivorous birds during the ecologically constrained, food-limited winter period. Here, we formally describe this lineage as a new genus, Shoudus gen. nov., based on a new species, S. baihuashanus sp. nov., collected from Baihuashan reserve, including three specimens retrieved during active interception of tit predation, along with detached wings and heads recovered from the snow. The new genus is placed in the tribe Orthosiini Guenée, 1837, primarily based on adult external morphology, including large compound eyes with long interfacetal hairs and bipectinate male antennae, as well as forewing patterning similar to certain orthosiine genera such as Perigrapha and Clavipalpula. Notably, the dark reddish-brown forewings with sharply contrasting pale markings, as seen in the new genus and these related genera, appear well adapted for camouflage against bark, leaf litter, and exposed soil in their habitats—potentially functioning as both background matching and disruptive coloration. To further assess its phylogenetic placement, we conducted a molecular analysis based on mitochondrial COI sequences (13 newly generated and 6 retrieved from BOLD/NCBI). The resulting maximum likelihood and Bayesian trees consistently support the monophyly of the new genus and reveal a close phylogenetic relationship with Orthosia, the type genus of Orthosiini. This integrative evidence strongly supports the recognition of Shoudus as a distinct lineage within Orthosiini. Full article

Diagnostic errors have been a critical concern in healthcare, leading to substantial financial burdens and serious threats to patient safety. The Improving Diagnosis in Health Care report by the National Academies of Sciences, Engineering, and Medicine (NASEM) defines diagnostic errors, focusing on accuracy, timeliness, and communication, which are influenced by clinical knowledge and the broader healthcare system. This review aims to integrate existing literature on diagnostic error from a systems-based perspective and examine the factors across various domains to present a comprehensive picture of the topic. A narrative literature review was structured upon the Systems Engineering Initiative for Patient Safety (SEIPS) model that focuses on six domains central to the diagnostic process: Diagnostic Team Members, Tasks, Technologies and Tools, Organization, Physical Environment, and External Environment. Studies on contributing factors for diagnostic error in these domains were identified and integrated. The findings reveal that the effectiveness of diagnostics is influenced by complex, interconnected factors spanning all six SEIPS domains. In particular, socio-behavioral factors, such as team communication, cognitive bias, and workload, and environmental pressures, stand out as significant but difficult-to-capture contributors in traditional and commonly used data resources like electronic health records (EHRs), which limits the scope of many studies on diagnostic errors. Factors associated with diagnostic errors are often interconnected across healthcare system stakeholders and organizations. Future research should address both technical and behavioral elements within the diagnostic ecosystem to reduce errors and enhance patient outcomes. Full article