Search Results (1,974)

How AI can contribute to green sustainable development (GSD) in China is a critical yet underexplored question. Leveraging the staggered implementation of the National New Generation Artificial Intelligence Innovation and Development Pilot Zone (AIPZ) as a quasi-natural experiment, this study employs a difference-in-differences approach with panel data from 285 prefecture-level cities (2017–2022). The main findings are threefold. First, AI directly promotes GSD and, more importantly, indirectly enhances GSD by upgrading new-quality productivity (NQP)—a novel mechanism that distinguishes this study from conventional environmental policy evaluations. Second, the facilitating effect is not uniform: significant positive effects are detected in the western, eastern, and central regions, but not in the northeastern region; among major urban agglomerations, the Pearl River Delta, Chengdu-Chongqing, and Yangtze River Deltaexhibit significant effects, whereas the Middle Reaches of the Yangtze River and Beijing-Tianjin-Hebei region does not. Third, spatial spillover analysis reveals that AI’s favorable effect on GSD spreads primarily through intercity similarity in economic development level. These findings provide actionable insights for policymakers aiming to harness AI for sustainable development, highlighting the importance of fostering NQP and designing regionally differentiated strategies. Full article

Traditional Peruvian cuisine has become a globally recognized experience, but its impact on visitors to the Caral Supe archaeological site—one of the oldest centers of civilization in South America and a UNESCO World Heritage Site—has not been studied. The main objective was to explain the constructs of the perceived authenticity of traditional food, loyalty to traditional food, service quality at traditional restaurants, and tourist satisfaction with visits to archaeological sites, based on the experience economy theory. An explanatory study was conducted using a structural equation modeling approach (PLS-SEM), applied to a sample of 381 tourists who visited the archaeological site and consumed local cuisine at restaurants in the destination of Barranca. The findings confirmed significant relationships among the model’s constructs (p < 0.01). It is suggested that the perception of authenticity of traditional food is a determining factor for loyalty (R² = 0.743) and a driver of satisfaction with the visit to the archaeological site (R² = 0.617), which constitutes the study’s contribution. However, the R² value for the construction of the tourist experience at the destination (R² = 0.301), the model does not fully capture the complexity of experiential processes at this particular heritage destination, which may depend on emotional, cultural, or contextual variables not included in this study. Satisfaction with the visit to the archaeological site is primarily related to staff attentiveness, the quality of guide explanations, and safety. It is concluded that the interplay between satisfaction with the visit to the archaeological site, the perceived authenticity of traditional food, and the quality of service in restaurants is fundamental to enhancing the experience at the heritage destination, thereby positioning traditional food and archaeotourism. It is recommended that the public and private sectors design strategies aimed at generating authentic and sustainable experiences for visitors, strengthening factors such as the destination’s reputation, the positive image of the site, satisfaction with the trip at the destination, and the positive experience. Full article

Polymer-based product use is rapidly increasing worldwide, resulting in critical social, environmental, ecological, economic, and health effects. Worldwide efforts have increasingly focused on solutions to the equilibrium consumption, production, and disposal of plastics to tackle these issues. The frontiers of biodegradable and bio-based polymers are continually advancing in pursuit of sustainability. Therefore, designing ecological bioplastics made of both biodegradable and bio-based polymers reveals chances to overcome plastic pollution and resource depletion. Polymeric materials are mainly used to manufacture different products at the beginning of their lifespans and which become waste after usage. Numerous sustainability strategies and polymer recycling methods are described and mostly classified into chemical, mechanical, and thermal recycling processes. This manuscript presents a New Polymers Frontier in Recycling and Sustainability Using an Ensemble of Deep Learning with a Heuristic Search Algorithm (NPFRS-EDLHSA). This work is devoted to computational polymer typology, which is based on machine learning algorithms applied to data on physicochemical properties. Although polymer classification can facilitate downstream materials research, the present study does not directly simulate recycling, environmental impacts, or sustainability. The main contributions made by this work include (i) an exploratory analysis of ensemble deep learning models to classify polymers by type on a small and unbalanced dataset; (ii) an evaluation of the effect of feature selection with a heuristic optimization methodology; and (iii) a comparison of the effects on classification performance under limited data conditions. This research sets out to provide a methodological explanation, not arguments for industrial-scale applicability. For the polymer-type classification process, the proposed NPFRS-EDLHSA model designs an ensemble of deep learning techniques, namely a bidirectional recurrent neural network (BiRNN) model, a bidirectional gated recurrent unit (BiGRU) method, and a graph autoencoder (GAE) technique. Finally, the grasshopper optimization algorithm (GOA) adjusts the hyperparameter values of the ensemble models optimally and results in an improved classification performance. A wide-ranging set of experiments was conducted to validate the performance of the NPFRS-EDLHSA method. The experimental results indicated that the NPFRS-EDLHSA technique achieved a better performance than an existing model. Full article

This study investigates the influence of process parameters on dimensional accuracy and microhardness in fiber laser cutting of low-carbon steel. A full factorial design of experiments (DOE) with three factors—cutting speed, focal position, and assist gas pressure—was applied to evaluate their effects on dimensional deviations and microhardness in the heat-affected zone (HAZ). The results showed that focal position is the most significant factor affecting all evaluated dimensional responses, while cutting speed has a strong influence on circular and linear dimensions. The effect of assist gas pressure was found to be response-dependent, being insignificant for inner diameter deviation but significant for selected linear features and through interaction effects with focal position. Statistical analysis confirmed the presence of significant interaction effects between process parameters. Microhardness measurements revealed a substantial increase in hardness from the base material toward the cut edge, indicating microstructural transformations caused by rapid thermal cycles during laser cutting. While this increase in hardness may enhance wear resistance, it may also lead to increased brittleness and reduced toughness. The findings provide a detailed insight into the relationship between process parameters and dimensional accuracy, highlighting the importance of parameter optimization and interaction effects in contributing to improved quality of laser-cut components. Full article

Urban festivals attract substantial numbers of tourists, which consequently imposes significant strain on host cities through spatial overcrowding, uneven pressure on infrastructure, and diminished quality of the visitor experience. Destination management organizations (DMOs) require effective tools to redistribute tourist flows; however, the influence of social media on tourists’ actual destination choices remains insufficiently understood. We ask whether social media discussion intensity (“buzz”) causally influences tourists’ destination choices and whether the effect grows stronger during festivals when information asymmetry is at its peak. Combining 95,692 taxi GPS trajectories with 5995 geotagged Twitter records from the 2019 Songkran Festival in Bangkok, we constructed an exponentially weighted moving average (EWMA) buzz variable with a temporal lag that establishes causal ordering. A conditional logit model shows that district-level buzz significantly raises destination choice probability and that the effect is amplified during the festival. Causal identification rests on a triangulated strategy that combines temporal lag, placebo permutation, and Bartik shift-share instrumental variables. The festival-period IV-corrected estimate (${\widehat{\beta }}^{\mathrm{IV}}=+0.019$, $p<0.001$) is 51% larger than the within-period OLS estimate (${\widehat{\beta }}^{\mathrm{OLS}}=+0.012$, $p<0.001$), a gap consistent with classical measurement-error attenuation in sparse social-media data, and a panel 2SLS analysis at the district–day level isolates a causal visitation channel confirming that cascades reinforce spatial concentration at the tourist-flow level. The aggregate Gini coefficient of spatial concentration declines over the study window in a statistically significant monotonic trend. The positive district-level correlation between buzz and congestion does not survive district and date fixed effects, which indicates that it reflects underlying differences in attractiveness across districts rather than a direct within-district channel. These findings provide an empirical foundation for information-based visitor flow management by identifying the underlying behavioral mechanism rather than evaluating a designed intervention. Full article

Organized after-school activities can play a vital role in supporting historically marginalized youth in science, technology, engineering, and mathematics (STEM), yet less is known about how culturally responsive practices—which are practices that integrate youths’ cultural backgrounds and lived experiences into learning—are enacted in math-focused learning spaces. Drawing on empowerment theory and critical youth empowerment frameworks, this qualitative study examines how culturally responsive practices foster empowerment among middle school students participating in a university-based after-school math enrichment program. Ninety-two students (M_age = 12.26 years; 47% girls; 86% Latine) from three under-resourced schools in Southern California participated in semi-structured interviews about moments when they felt empowered and what contributed to those experiences. Thematic analysis revealed that all four domains of culturally responsive practices helped promote empowerment: structured opportunities for contribution and leadership, caring relationships, cultural affirmation, and efforts to make real-world connections. In particular, students most frequently described structured opportunities for contribution and leadership, practices that centered their knowledge and voices, and relational climates characterized by care and high expectations. The findings suggest that in after-school STEM contexts, empowerment does not arise as an isolated individual trait but is part of a relational and context-dependent process that is supported by culturally responsive practices. These findings highlight how intentional, culturally responsive program design can advance both youth empowerment and equity-oriented STEM education. Full article

Wildfires are increasing in frequency, intensity, and duration, driving up suppression and damage costs and motivating a more coordinated use of aerial firefighting assets. Within this context, we extend the COLOSSUS Project’s X-Challenge System-of-Systems (SoS) simulation toolkit with an integrated aircraft sizing and fleet assessment methodology that links conceptual aircraft design with tactic selection. Two platforms are sized under 2035 technology assumptions—a 2000 kg payload electric Vertical Takeoff Landing (eVTOL) and a 3000 kg payload Single Engine Air Tanker (SEAT) using physics-based performance and parametric cost models. A Design of Experiments (DoE) workflow coupled with the SoS toolkit evaluates mixed fleets and tactic assignments in three representative regions. Effectiveness is quantified via a weighted, normalized Measure of Effectiveness that aggregates burnt area, emissions, and cost metrics into a single scalar. Results show that acquisition cost dominates overall effectiveness and that location-specific fleet compositions can outperform a single fixed fleet without degrading suppression outcomes, motivating future work on adaptive, region-specific fleet design and sensitivity analyses. Full article

Background: Due to α-Ketoglutaric acid (AKG)’s anti-inflammatory and antioxidant properties, it may serve as a beneficial feed supplement for broilers. This study aimed to evaluate the impact of AKG supplementation in broiler diets on growth efficiency, blood indicators, and histological status. Methods: Two hundred sixteen unsexed, one-day-old broilers were randomly assigned to three experimental groups utilizing a completely randomized design. Each treatment was then subdivided into six replicates, each consisting of twelve chicks. In the experiment, the 1st group received a control (basal diet); the 2nd group received the basic diet supplemented with 0.5% AKG; and the 3rd group received the basal diet supplemented with 1.0% AKG. Results: The findings indicate that the addition of 0.5% and 1% AKG to broiler feed significantly (p < 0.05) improved BW, BWG, and FCR, particularly during the early stages of growth. The 0.5% AKG group had better feed efficiency and less FI, which means they grew faster and used nutrients better. AKG administration significantly (p < 0.05) increased TP and albumin levels in avian subjects, while simultaneously decreasing MDA and elevating CAT concentrations. Adding AKG to the broiler diet raises the levels of RBC, Hb, and PCV, but lowers RDW_CV. The results indicate that AKG lowers inflammation by raising IL-10 levels and lowering IL-1β levels. It also raises levels of antioxidant enzymes like SOD and CAT. Microscopic analysis revealed normal jejunal mucosa, submucosa, and muscular layers across all groups, including those receiving AKG supplements. The jejunal villus height, crypt depth, and their ratio were unchanged. The structure and function of the intestinal lining showed no significant changes when compared to the control. Conclusions: Adding AKG to broiler feed helps their growth, biochemical, and immune system indicators, but it does not hurt their histological conditions. Full article

Historically, pharmaceutical formulation development relied heavily on trial-and-error experimentation, which was useful for empirical progress but often provided limited mechanistic understanding and insufficient efficiency for increasingly complex drug products. The introduction of Design of Experiments (DoE) and Quality by Design (QbD) established a more systematic framework for studying formulation variables, manufacturing parameters, and Critical Quality Attributes (CQAs). Approaches such as factorial designs, response-surface methodology, and mixture designs have therefore become central to modern pharmaceutical development because they improve experimental efficiency and support the definition of design space. However, as formulations become more nonlinear, high-dimensional, and multi-objective, these classical approaches may no longer be sufficient on their own. This review examines the evolution of experimental design in pharmaceutical research, from one-factor-at-a-time experimentation to structured DoE/QbD strategies, and then to emerging intelligent optimization methods. Its central objective is to clarify when conventional DoE/QbD remains appropriate and when it should be complemented by machine learning, Bayesian optimization, digital twins, and closed-loop experimental systems. The review first summarizes the foundations and strengths of classical experimental design; then, it discusses its practical limitations in complex formulation settings, and finally evaluates how data-driven and hybrid approaches can extend pharmaceutical development. Evidence from tablets, capsules, nanocarriers, transdermal patches, and biotherapeutic systems suggests that intelligent optimization can improve predictive performance and experimental efficiency when used alongside, rather than instead of, established pharmaceutical development principles. Full article

To enhance the utilization efficiency of limited ecological water, this study conducted field ecological irrigation experiments in a typical desert riparian forest in the lower reaches of the Tarim River. Based on the experimental data, a soil water transport model under the overflow irrigation mode was constructed using the HYDRUS-2D (version 2.04) model. Based on the model, numerical simulation scenarios of different irrigation schemes were designed to provide key evidence for the scientific utilization of water resources in the ecological restoration of desert riparian forests. Simulation results indicate that (1) more irrigation water does not necessarily yield better results. When the total irrigation volume is the same, conducting overflow irrigation in two separate applications significantly outperforms a single concentrated irrigation in terms of soil moisture replenishment and maintenance, with an optimal interval of 20 h between applications. (2) For single overflow irrigation, the optimal water depth is 5 cm. (3) For two-stage irrigation, the available water resources and core objectives must be considered. When water is plentiful, and it is necessary to replenish moisture in the lower soil layers, the 5 cm + 5 cm scheme is optimal; if irrigation water is limited, the 3 cm + 3 cm scheme is more efficient. These schemes can effectively activate the seed bank in the surface soil while supplying water to the root systems of desert riparian vegetation, thereby promoting the restoration and growth of desert vegetation and achieving the goal of ecological sustainability. Full article

Effective scientific literature retrieval requires moving beyond surface-level term matching toward structured semantic reasoning. This paper presents a controlled empirical study of multi-stage retrieval for scientific literature, integrating lexical matching, dense semantic modeling, hybrid fusion, and cross-encoder re-ranking within a unified evaluation framework. The study is designed to analyze the interactions, trade-offs, and failure modes of these components in claim-based scientific search. Experiments on the SciFact benchmark demonstrate that dense models capture semantic similarity but remain insufficient when used in isolation. Hybrid fusion broadens the candidate pool but does not consistently outperform the best standalone dense retriever, as RRF-based fusion can dilute strong dense rankings when lexical and semantic signals diverge. Cross-encoder re-ranking proves to be the primary driver of final performance gains, with the best configuration, Hybrid (SciNCL + BM25) + Cross-Encoder, reaching NDCG@10 of 0.523, MAP@10 of 0.479, Recall@10 of 0.642, and MRR@10 of 0.497. Ablation analysis shows that lexical pseudo-relevance feedback (RM3) introduces query drift in claim-focused retrieval, and that passage-level max pooling weakens effectiveness by fragmenting document-level evidence. Cross-domain evaluation on SciFact, PubMedQA, and SciDocs demonstrates that the relative ranking of retrieval paradigms remains stable across datasets with varying difficulty levels, while also revealing that the RRF dilution effect intensifies on harder retrieval tasks. These findings suggest that effective scientific retrieval benefits from integrated multi-stage pipelines, and that understanding component-level interactions is essential for designing robust retrieval systems. Full article

Introductory Java programming requires learners to reason about abstract computational concepts such as program state, control flow, and execution order, which often present substantial difficulties for novice programmers. These challenges may be further intensified for collegiate student athletes when programming instruction remains disconnected from the domain knowledge that shapes their prior experiences. This paper proposes a wrestling-inspired, state-based pedagogical framework that leverages the rule system of National Collegiate Athletic Association (NCAA) wrestling as an analogical knowledge domain for introducing foundational Java programming concepts. Within this framework, wrestling match states and scoring actions are systematically mapped to core programming constructs, which include variable assignment, conditional branching, loops, method invocation, and program termination. This paper is positioned as a conceptual and pedagogical framework study rather than an empirical intervention study. It focuses on the theoretical rationale, conceptual alignment, instructional mappings, and classroom implementation possibilities of a wrestling-inspired approach. This paper does not report participant data, learning assessments, or comparative outcome measures. Instead, it illustrates how sport-specific mental models can be transformed into structured instructional representations that may support learners’ reasoning about program execution. By integrating domain-aligned cognitive schemas with programming instruction, the proposed framework offers a structured knowledge scaffolding approach that is designed to support novice understanding of computational processes in introductory programming education. Full article

Contactless transmission of mechanical power, which is characteristic of coaxial magnetic gears (CMGs), offers significant advantages over conventional mechanical gears, in particular, reduced maintenance frequency and inherent overload protection. At the same time, there is a lack of design methodologies for this type of gear based on the analysis and systematization of experience gained from already implemented designs. This paper presents a method for determining the maximum magnetic torques of CMGs on the basis of an equivalent magnetic-circuit model. The error associated with the proposed methodology does not exceed ±15%, which enables the influence of geometric parameters and the magnetic properties of materials on the key performance indicators of the gear to be assessed already at the preliminary design stage. A mathematical model of CMG dynamics was also developed, based on a quasi-stationary two-dimensional approximation of the magnetic field, accounting for the geometry of the magnetic circuit, the spatial distribution of the magnetic vector potential, and magnetic-circuit saturation. The proposed mathematical model was verified using the results of physical experiments. The discrepancy between the calculated and experimental values of the torque on the low-speed shaft in the steady state does not exceed 5.5%. Based on the optimization procedure, the dependence of the maximum linear torque density on the outer diameter of the CMG, the number of poles of the high-speed rotor, and the transmission ratio was determined. It was shown that, as the number of poles increases, the linear torque density also increases and, for example, for diameters of approximately 800 mm, it may exceed 100 N·m/m. Full article

Self-efficacy—one’s belief in their ability to take the actions necessary to succeed—is a critical determinant of student success and retention, particularly during the transitional first two years of undergraduate study. Learning communities that incorporate peer mentors have been identified as promising structures for fostering self-efficacy, yet the mechanisms by which intentional peer mentoring within structured career development contexts shapes students’ self-efficacy beliefs remain underexplored. This study examined the following research questions: (1) How does participation in a career-focused Learning community course shape first- and second-year STEM students’ sense of self-efficacy regarding academic and career decision-making? (2) In what ways do peer mentors and peer support networks within the learning community contribute to students’ self-efficacy development? (3) How do students describe feeling empowered—or not—to pursue their career goals as a result of this experience? Using a mixed-methods design that combined pre- and post-course surveys, semi-structured focus groups, and phenomenological one-on-one interviews, we investigated the self-efficacy development of first- and second-year STEM students (N = 53) enrolled in a semester-long learning community course at a large, predominantly White public institution in the Midwest. Of these, 25 students completed both the pre- and post-course Career Self-Efficacy surveys and were included in matched statistical analyses. Three major findings emerged: (1) the learning community class environment created a space where self-efficacy was prioritized and developed; (2) peer support groups and peer mentors positively impacted students’ self-efficacy; and (3) students felt empowered by the experience in pursuing their chosen career goals. These findings have practical implications for the design of learning communities in STEM, highlighting the value of intentional peer mentoring structures and career-focused activities as tools for promoting student confidence, retention, and long-term academic success. Full article

The scientific pursuit of understanding human “emotion” has historically been plagued by a fundamental lack of conceptual consensus. Researchers, clinicians, and the lay public frequently utilize terms such as “emotion,” “feeling,” “affect,” and “mood” as interchangeable synonyms, creating a linguistic ambiguity that hampers both experimental precision and diagnostic validity. In response to this “umbrella term” crisis, the Walla Emotion Model (WEM), also referred to as the ESCAPE Model (Emotions Convey Affective Processing Effects), introduces a redefined and distinct terminology designed to disentangle the neurophysiological, experiential, and behavioral components of affective phenomena. The essence of this new model is the removal of the umbrella aspect from the term emotion and defining “emotion” strictly as behavioral output, and “feeling” as the conscious perception of released neurochemicals, both resulting from non-conscious affective processing. By doing so, the WEM provides a logical, clear, and easy-to-apply terminological lens for diagnosing, communicating, and treating clinical conditions that include what has previously been termed “emotion” dysregulation. When “emotion” is used as an umbrella term, it depends on the school one follows how one would explain such clinical conditions. The most critical argument for introducing the WEM is that each prior school has had its focus on another set of phenomena that generate an “emotion”. The WEM terminology provides a clear separation of brain activity, subjective experience, and expression regarding affective phenomena. Various clinical conditions that include “emotion” dysregulation exist; however, to highlight the potential benefits of the WEM, the current essay has its focus on two of the most frequent conditions, namely Borderline Personality Disorder (BPD) and Major Depressive Disorder (MDD). The goal is to provide an analysis of the WEM architecture, evaluating its utility in clinical neuropsychology, and delineating its theoretical advantages when combined with traditional categorical and dimensional models. However, it is important to emphasize that this essay is only theoretical. It does not include any direct empirical support, but it suggests the replacing of existing terminology with WEM terminology. Full article