Search Results (345,743)

This work explores the application of a Category Theory model, advocating a paradigm for comprehending human experience and the communication process of a complex system from the perspective of a living Anticipatory System. Following the principles created by Robert Rosen for the anticipatory system and associated models—models that respect the principles of impredicativity, anticipation, and closure to efficient cause (CLEF)—we propose the Performance–Resilience–Sustainability (PRS) model. This new model introduces a new way to explain how anticipatory systems can elucidate the portions of variability observed in practice and research. Anticipatory system theory suggests that models such as PRS have significant potential to complement and explain dynamic phenomena observed in communication and experience development research, as well as in practical applications, underscoring the transformative potential for both fields. This class of models for complex systems may introduce a new dimension of emergent causality and its impact on current behavior, which was not previously considered. Full article

This study investigates the methodologies for robust parameter estimation within the context of the parameters of the inverted Kumaraswamy model using data derived from step-stress partially accelerated life testing with Type-I generalized hybrid censoring. We formulate estimation procedures within both frequentist (maximum likelihood) and Bayesian frameworks, including the construction of asymptotic and credible intervals. Subsequently, we provide a formal derivation of the associated asymptotic and bootstrap confidence intervals. To address the analytical intractability of the Bayesian estimation, we employ Markov Chain Monte Carlo techniques. The proposed methods are illustrated through an illustrative example, an application to real-world precipitation data, and a simulation study. Full article

To enhance the rheological properties and engineering applicability of fully solid waste filling slurry, this study uses iron tailings sand as aggregate and slag, steel slag, and desulfurization ash as cementing materials. Through a central composite design experiment, the synergistic regulatory effects of steel slag (10~30%) and desulfurization ash (10~30%) on the slurry’s rheological and strength properties were systematically investigated. The yield stress and plastic viscosity of the slurry were quantified based on the Bingham fluid model, using expansion tests and L-tube models, while isothermal calorimetry analysis and microscopic image processing revealed the underlying micro-mechanisms. The results show that when both steel slag and desulfurization ash contents are 20%, the cured specimen prepared from the slurry achieves an optimal 28-day uniaxial compressive strength of 5.90 MPa at 28 days, with yield stress and plastic viscosity of 146.71 Pa and 3.04 Pa·s, respectively. Micro-mechanistic analysis revealed that desulfurization ash effectively reduced the yield stress by up to 38% (from 196.04 Pa to 90.01 Pa) and increased the fractal dimension of flocculated structures to 1.906, thereby optimizing initial flowability. Conversely, steel slag increased the yield stress but decreased plastic viscosity, enhancing structural stability, and regulating the later hydration process. The loop tests confirmed the good transport performance and engineering adaptability of the optimized mix, achieving a cost reduction of up to 65% compared to cement-based systems. Full article

Hosta ‘So Sweet’, a shade-tolerant Asparagaceae species, displays remarkable high-light tolerance in open-field full-sun cultivation without photoinhibition symptoms. To clarify its growing-season photosynthetic dynamics and adaptive strategies, this study measured diurnal photosynthetic variations from May to September, determined chlorophyll fluorescence parameters and pigment contents in May, July and September, and analyzed the data alongside the light and CO₂ response curves for July. The results showed that the high temperatures combined with high-light conditions in July lowered P_n relative to May and September, but the light saturation point (LSP) reached 1508.99 μmol m⁻² s⁻¹, and the CO₂ compensation point (CCP) was 75.46 μmol mol⁻¹, highlighting the robust light energy utilization and carbon assimilation potential. Meanwhile, PSII maximum photochemical efficiency (Fv/Fm) remained stable under these conditions, indicating undamaged photosystems. Mechanistically, its photosynthetic limitation strategies showed seasonal plasticity: a tight coupling between P_n, stomatal conductance, and humidity in May shifted to a strong association between P_n and photoprotective dissipation (qN) in July, followed by an optimization of light capture linked to increased chlorophyll content and adjusted Chl a/b ratios in September. Taken together, H. ‘So Sweet’ synergistically adapts to growing-season light and temperature fluctuations by integrating light utilization potential, photosystem stability and pigment adjustment strategies. This study preliminarily delineated its photosynthetic physiological profile, revealed core light-adaptive strategies, and provided a theoretical basis for the ecological application of this excellent ornamental cultivar. Full article

Despite the growing body of literature on pro-environmental behavior (PEB) across various domains, a systematic review that synthesizes the application of theoretical frameworks to examine the internal and external factors influencing PEB within the hospitality domain remains noticeably absent. Therefore, this study aims to examine the progress of the current literature in exploring the concept of PEB by answering three primary questions: (1) How has PEB been defined in the hospitality literature? (2) What theories have various authors adopted? (3) What future research recommendations have been identified in the literature? A total of 104 peer-reviewed articles were analyzed using thematic analysis. The analysis indicated that the view of employee-focused studies on the definition of PEB has changed to be conceptualized through the lens of workplace–environmental citizenship, while the current guest-focused studies have acknowledged the psychological effect of hotel guests on their engagement in environmental practices. Traditional theories, such as the theory of planned behavior and socially oriented theories, remain dominant, and most studies recommend future research to integrate constructional and psychological factors to expand current research models. Overall, this review can serve as a useful tool for future studies to identify the right definition and theoretical lens. Additionally, the review calls for the use of diverse frameworks to deepen our understanding of how PEB unfolds within hospitality settings. Full article

High-performance difference patterns (DPs) are critical for compact and integrated microwave array systems, particularly in monopulse tracking and beam-scanning applications. However, the design of monopulse phased arrays with steep slopes, high directivity, low sidelobes, and symmetric main lobes remains challenging due to constraints imposed by the array aperture and radome structure. In this paper, a novel design method is proposed to maximize the DP directivities for monopulse linear and planar phased arrays composed of microstrip patch antennas. The DP synthesis problem is first formulated as a nonconvex optimization model for directivity maximization. By fixing the reference phase of the DP slope and applying a first-order Taylor expansion of the quadratic function, the original problem is decomposed into a sequence of convex subproblems that can be solved efficiently. The proposed method fully exploits the flexibility of the phased array feed network, enabling directivity enhancement without altering the geometric configuration of the monopulse array. Finally, three numerical examples employing a radome-enclosed linear array, a uniform planar array, and a radome-enclosed planar array are presented to demonstrate the effectiveness of the proposed method in achieving the monopulse array DP synthesis with high directivity and symmetric main lobes. Full article

Freshwater scarcity limits agricultural production in southern Xinjiang, China, while saline groundwater utilized for direct irrigation adversely affects soils and crops. Excessive nitrogen fertilizer is often applied to compensate for these adverse effects, potentially jeopardizing soil environmental quality. A two-year field experiment was conducted to assess the impact of decreased nitrogen application on cotton growth, nitrogen use efficiency, and yield under different irrigation water salinity levels, with the addition of biofertilizer. The experiment was undertaken on drip-irrigated cotton fields in southern Xinjiang, China, during 2021 and 2022. Three salinity concentrations of irrigation water were quantified: W₁ (1 g L⁻¹), W₂ (3 g L⁻¹), and W₃ (7 g L⁻¹). Under all three salinity levels, conventional fertilization (F1) served as the control, and F0, a no-nitrogen treatment, was also utilized. A total of 18 treatments were assessed using four nitrogen fertilizer application rates in conjunction with biofertilizer: no nitrogen (B₀), 100% conventional nitrogen rate (B₁), 85% conventional nitrogen rate (B₂), and 70% conventional nitrogen rate (B₃). The findings showed that adding biofertilizer considerably increased cotton output under both freshwater and brackish water irrigation regimes when compared to traditional nitrogen fertilization. In just two years, the yield of seed cotton grew by 6.15–10.56% (W₁) and 6.49–11.81% (W₂). In 2021, lint yield climbed by 11.79% (W₁), and in two years, it increased by 6.69–15.51% (W₂). Although internal nitrogen use efficiency (iNUE) initially rose and subsequently fell with escalating nitrogen rates, the application of lower nitrogen combined with biofertilizer significantly enhanced agronomic nitrogen use efficiency (aNUE) and diminished soil nitrogen residue. Recommended nitrogen application rates for cotton, utilizing 1200 kg ha⁻¹ of biofertilizer, were established for diverse irrigation water qualities to achieve optimal nitrogen reduction, maximum iNUE, and peak yield: 283.21–322.95 kg ha⁻¹ under freshwater irrigation (W₁), 281.00–328.14 kg ha⁻¹ under brackish water (W₂) irrigation, and ≥326.28 kg ha⁻¹ under saline irrigation (W₃). These findings recommend the optimization of fertilizers across various irrigation conditions and facilitate the efficient utilization of saline water resources. Full article

In this paper, we present approximation results for a generalized $\alpha$-non-expansive multi-valued mapping using a four-step iteration scheme introduced in the context of a convex metric space. We extend some recent results about generalized $\alpha$-non-expansive multi-valued mappings from the Banach space setting to a convex metric space. Two examples of generalized $\alpha$-non-expansive multi-valued mappings are presented, and it is numerically shown that our iteration scheme enables faster convergence than other well-known schemes in the literature. To demonstrate the application of one of our results, we provide the solution of a non-linear integral equation. Full article

Sulfonamide antibiotics (SAs) are ubiquitous and persistent organic contaminants in aquatic and soil ecosystems due to their extensive application and high structural stability, causing rising environmental hazards. Conventional treatment approaches, generally based on physical adsorption or biological processes, remain limited in achieving efficient and stable removal as well as deep molecular modification of SAs. In recent years, modified biochar has developed as a flexible environmental functional material incorporating adsorption and reaction regulation capabilities, owing to its customizable pore structure, surface chemistry, and electronic characteristics. This study comprehensively highlights current achievements in the adsorption and degradation of sulfonamide antibiotics by modified biochar, with specific emphasis on modification techniques, structural modulation, structure–performance connections, and interfacial reaction processes. Through physical activation, heteroatom doping, defect engineering, and metal integration, biochar has developed from a traditional adsorbent into a carbon-based interfacial reactor capable of pollutant adsorption, molecular activation, and directed transformation. Surface-confined reaction interfaces, where π–π interactions, hydrogen bonding, electrostatic interactions, and metal coordination cooperatively control adsorption and transformation processes, are primarily responsible for the elimination of SAs. Moreover, the dual functions of modified biochar in driving both radical and non-radical pathways are explored, showing the vital importance of interfacial electronic structure modulation and electron-transfer mechanisms in influencing reaction efficiency and selectivity. The impact of sulfonamide molecular configurations, ambient circumstances, and concomitant chemicals on removal performance are also explored. Unlike previous reviews that mainly summarize adsorption efficiency or oxidant activation systems separately, this work integrates structural modulation, interfacial electronic regulation, and bond-selective transformation mechanisms into a unified structure–chemistry–reactivity framework. By correlating sulfonamide molecular configuration with biochar electronic structure, this review provides a mechanistic roadmap for the rational design of next-generation catalytic biochar systems. Finally, key challenges related to structural controllability, long-term stability, and engineering scalability are identified, and future research directions are proposed to support the rational design of high-performance biochar materials and the practical control of sulfonamide antibiotic pollution. Full article

Yeast β-glucans are bioactive polysaccharides derived primarily from the cell walls of Saccharomyces cerevisiae. They are widely recognized for their immunomodulatory, antioxidant, and anti-inflammatory actions as well as for their probiotic effects. Their addition to food products has gained growing interest owing to their ability to promote health as well as to enhance sensorial and technological attributes of foods. The aim of this narrative review is to present the health benefits of yeast β-glucans according to the mechanisms taking place, compare them to other biomolecules with analogous health-promoting effects, and summarize the existing knowledge on their incorporation into various food matrices. Focus is also given to clinical trials using foods enriched with yeast β-glucans as well as in vitro digestion studies of such foods. In addition, research interest extends to the methods of yeast β-glucan assessment in food products. Despite the promising results so far, significant challenges remain, including variability in study design, limited translational evidence from in vitro studies, and the lack of standardized protocols for determination across various food categories. Overall, the reviewed literature supports their growing potential as valuable components in the design of functional foods. Ongoing research and advancement should prioritize well-designed human trials, standardized production protocols and deeper structure–function relationship investigation in order to further reveal their contribution across a wide range of applications, reinforcing both consumer health and innovation within the food industry. Full article

This study examined the role of e-modules and learning applications in enhancing scientific literacy among Grade 11 Life Sciences learners in a South African secondary school. Grounded in constructivist and connectivist learning theories, the research responded to persistent challenges in learners’ conceptual understanding, scientific reasoning, and application of scientific knowledge. A mixed-methods case study design was employed, combining quantitative pre- and post-test data with qualitative classroom observations and semi-structured learner interviews. Thirty learners participated in a technology-enhanced instructional intervention using curriculum-aligned e-modules delivered through Binogi and Google Classroom. Quantitative findings revealed a statistically significant improvement in scientific literacy following the intervention. Learners’ mean scores increased from 39.20% (pre-test) to 63.07% (post-test), representing a gain of 23.87 percentage points. A paired-samples t-test confirmed that this improvement was extremely significant (t (29) = 11.58, p < 0.0001), with a very large effect size (Cohen’s d = 2.11). Qualitative findings indicated that learners experienced enhanced engagement, improved conceptual clarity, and greater motivation when using digital learning tools, particularly through visualisations, animations, and self-paced learning. However, persistent difficulties with graph interpretation were also identified. The study concludes that the intentional integration of e-modules and learning applications can substantially enhance scientific literacy in Life Sciences by supporting conceptual understanding, reasoning, and learner engagement. These findings highlight the importance of pedagogically guided digital integration and teacher professional development to strengthen science learning outcomes. Full article

Background: Extreme weather events, such as storms, may evoke intense fear in individuals and impair their daily functioning, resulting in significant distress. In Brazil, recent climate-related disasters have highlighted the need to assess storm fear in the population. Objective: This study aimed to translate, adapt, and validate the Storm Fear Questionnaire (SFQ) for the Brazilian context. Methods: Translation and adaptation were conducted, followed by back-translation, review by an expert panel, and acceptability assessment by the target population. For the psychometric evaluation, a sample of 268 postpartum women exposed to a flood in southern Brazil completed the SFQ and the following questionnaires: the Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5), Beck Depression Inventory II (BDI-II), and the Pregnancy Experience Scale—Brief Version (PES-Brief). Results: The instrument showed excellent acceptability in the target population and good content validity. Regarding criterion validity, Pearson correlations indicated high convergence between the SFQ and PCL-5 and moderate convergence with the BDI-II. Regarding construct validity, SFQ scores were significantly higher among postpartum women who had to leave their homes due to the flood or had their houses affected by floodwaters. The first factor generated in the factor analysis explained 35.2% of the variance, with 14 out of 15 items presenting loadings greater than 0.40. Internal consistency was high (α = 0.88). Conclusions: The Brazilian version of the SFQ is a valid and reliable instrument for assessing fear of storms. Future studies are needed to evaluate the instrument’s applicability in diverse populations across the country. Full article

Artificial intelligence (AI) has been increasingly applied to leukemia research, spanning diagnostic, prognostic, therapeutic, and translational domains. However, the rapid growth and methodological diversity of this literature present challenges for existing reviews, which are often constrained by limited scope, narrow clinical focus, or reliance on either manual or purely bibliometric approaches. As a result, cross-domain relationships, evolving methodological trends, and the interaction between data modalities and clinical objectives remain insufficiently understood. This paper presents a systematic, AI-assisted literature analysis of AI applications in leukemia, combining scalable machine-driven discovery with author-led qualitative interpretation. Using a PRISMA-guided screening process, a corpus of 2338 peer-reviewed publications retrieved from Scopus (1990–2024) is analyzed through semantic text representation and unsupervised clustering. An iterative human–machine process is employed to identify and refine 23 analytical parameters grouped into five macro-parameters, enabling structured organization of the research landscape across diagnostic, prognostic, therapeutic, genetic, and methodological dimensions. Building on this structured representation, in-depth qualitative analysis is conducted by the authors across parameters and macro-parameters, synthesizing methodological developments, data usage patterns, application domains, and commonly used datasets. The resulting analysis provides a coherent, interpretable mapping of AI-driven leukemia research, supporting cross-domain comparison and identification of research concentrations, fragmentation, and emerging directions. By integrating large-scale automation with domain-informed qualitative analysis in a reusable analytical pipeline, this work contributes a rigorous and transferable framework for structured literature analysis in leukemia and related biomedical domains. Full article

Coastal and marine systems are governed by fragile water-quality dynamics, where disturbances can trigger harmful algal blooms with significant ecological and societal consequences. These pressures have intensified interest in forecasting systems that can anticipate bloom development and support environmental management. This study presents a systematic review of simulation-based and predictive environmental modeling approaches used for marine forecasting of water quality and harmful algal bloom phenomena. Following PRISMA guidelines, 11,185 records were identified, 127 articles were screened in full text for eligibility, and 40 peer-reviewed studies published between 2015 and 2025 were included and synthesized using a structured extraction framework capturing modeling paradigms, forecast targets, data inputs, spatial and temporal scope, validation practices, operational context, and reported limitations. The reviewed literature indicates the dominance of predictive and hybrid modeling approaches, with forecasting efforts primarily focused on coastal systems and short-term applications. Harmful algal blooms and chlorophyll-a emerge as dominant forecast targets, commonly supported by satellite observations, in situ measurements, and environmental forcing variables. Despite substantial methodological advances, persistent challenges related to data availability and quality, validation rigor, system integration, and operational deployment remain evident across modeling paradigms. Overall, the findings suggest that while marine forecasting models have become increasingly sophisticated, their translation into reliable and operational systems remains uneven, highlighting the need for closer alignment. Full article

Forest fires are a major concern due to their significant impact on the environment, economy, and wildlife habitats. Efficient early detection systems can significantly mitigate their devastating effects. This paper provides a comprehensive review of forest fire detection (FFD) techniques and traces their evolution from basic lookout-based methods to sophisticated remote sensing technologies, including recent Internet of Things (IoT)- and Unmanned Aerial Vehicle (UAV)-based sensor network systems. Historical methods, characterized primarily by human surveillance and basic electronic sensors, laid the foundation for modern techniques. Recently, there has been a noticeable shift toward ground-based sensors, automated camera systems, aerial surveillance using drones and aircraft, and satellite imaging. Moreover, the rise of Artificial Intelligence (AI), Machine Learning (ML), and the IoT introduces a new era of advanced detection capabilities. These detection systems are being actively deployed in wildfire-prone regions, where early alerts have proven critical in minimizing damage and aiding rapid response. All FFD techniques follow a common path of data collection, pre-processing, data compression, transmission, and post-processing. Providing sufficient power to complete these tasks is also an important area of research. Recent research focuses on image compression techniques, data transmission, the application of ML and AI at edge nodes and servers, and the minimization of energy consumption, among other emerging directions. However, to build a sustainable FFD model, proper sensor deployment is essential. Sensors can be either fixed at specific geographic locations or attached to UAVs. In some cases, a combination of fixed and UAV-mounted sensors may be used. Careful planning of sensor deployment is essential for the success of the model. Moreover, ensuring adequate energy supply for both ground-based and UAV-based sensors is important. Replacing sensor batteries or recharging UAVs in remote areas is highly challenging, particularly in the absence of an operator. Hence, future FFD systems must prioritize not only detection accuracy but also long-term energy autonomy and strategic sensor placement. Integrating renewable energy sources, optimizing data processing, and ensuring minimal human intervention will be key to developing truly sustainable and scalable solutions. This review aims to guide researchers and developers in designing next-generation FFD systems aligned with practical field demands and environmental resilience. Full article