Search Results (6,026)

This article examines the integration of artificial intelligence (AI) into qualitative psychological research, focusing specifically on AI-assisted data analysis and its epistemological and ethical implications. While recent publications highlight AI’s potential to support analysis, such approaches risk undermining the reflexive, situated, and culturally sensitive foundations of qualitative inquiry. Drawing on relational and social constructionist epistemologies, as well as examining risks inherent in AI technologies, this work critiques the superficial outsourcing of analytical and interpretive processes to AI models. This trend reflects a broader tendency to regard AI as a neutral and objective research tool, rather than as an active participant whose outputs are shaped by, and in turn shape, the social, cultural, and technological contexts in which it operates. An alternative framework is proposed for integrating AI into qualitative inquiry, particularly in psychological research, where data are often sensitive, situated, and ethically complex. A list of best practices is also included and discussed. Key ethical concerns, such as data privacy, related algorithmic affordances, and the need for comprehensive informed consent, are examined. The article concludes with a call to nurture a qualitative research culture that embraces relational and reflective practices alongside a critical and informed use of AI in research. Full article

MicroRNAs are closely associated with various physiological and pathological processes, making their in situ fluorescence imaging crucial for functional studies and disease diagnosis. Current methods for the in situ fluorescence imaging of microRNA predominantly rely on linear signal amplification, resulting in relatively weak imaging signals. This study introduces a Y-shaped cascade assembly (YCA) method for high-brightness microRNA imaging in cells. Triggered by target microRNA, catalytic hairpin assembly forms double-stranded DNA (H). Through annealing and hybridization, a Y-shaped structure (P) is created. These components assemble into DNA nanofluorescent particles with multiple FAM fluorophores, significantly amplifying fluorescence signals. Optimization experiments revealed that a 1:1 ratio of P to H and an assembly time of 60 min yielded the best results. Under these optimal conditions, the resulting fluorescent nanoparticles exhibited diameters of 664.133 nm, as observed by DLS. In Huh7 liver cancer cells, YCA generated DNA nanoparticles with a fluorescence intensity increase of 117.77%, triggered by target microRNA-21, producing high-intensity fluorescence images and enabling qualitative detection of microRNA-21. The YCA in situ imaging method offers excellent imaging quality and high efficiency, providing a robust and reliable analytical tool for the diagnosis and monitoring of microRNA-related diseases. Full article

Perovskia atriplicifolia Benth., a perennial aromatic plant widespread in Iran’s Sistan and Baluchestan region, is known for its essential oil composition, rich in aromatic and non-aromatic sesquiterpenes. To the best of our knowledge, limited information exists on the composition of its non-volatile extracts. Herein, the phytochemical investigation of the EtOH extract of P. atriplicifolia aerial parts was performed, guided by an analytical approach based on LC-(-)ESI/QExactive/MS/MS. This led to the identification of phenolics, flavonoids, diterpenes (mainly carnosic acid derivatives), and triterpenes. Structural elucidation was performed via NMR and HRMSMS analysis. Furthermore, considering the occurrence of diterpenes closely related to carnosic acid and carnosol, known for their antioxidant properties, the antioxidant activity of the extract (0.5–5.0 μg/mL) and selected pure compounds (0.5–25 μM; compounds 5, 7, 9, 10, 12, 16) was evaluated in Caco-2 intestinal cells, showing significant reduction in free radical levels. The quantitative results highlighted that the above cited compounds occurred in concentrations ranging from 1.73 to 520.21 mg/100 g aerial parts, with carnosol (12) exhibiting the highest concentration (520.21 mg/100 g aerial parts), followed by 1α-hydroxydemethylsalvicanol (9) (91.73 mg/100 g aerial parts) and carnosic acid (16) (88.16 mg/100 g aerial parts). Full article

This study investigates the severity of injuries sustained by over seven million participants involved in road traffic incidents in Poland between 2015 and 2024, with a view to supporting sustainable mobility and the United Nations Sustainable Development Goals. Road safety is a crucial dimension of sustainable development, directly linked to public health, urban liveability, and the socio-economic costs of transportation systems. Using a harmonised participant-level dataset, this research identifies key demographic, behavioural, and environmental factors associated with injury outcomes. A novel five-level injury severity variable was developed by integrating inconsistent records on fatalities and injuries. Descriptive analyses revealed clear seasonal and weekly patterns, as well as substantial differences by participant type and driving licence status. Pedestrians and passengers faced the highest risk, with fatality rates more than five times higher than those of drivers. An XGBoost classifier was trained to predict injury severity, and SHAP analysis was applied to interpret the model’s outputs at the feature level. Participant role emerged as the most important predictor, followed by driving licence status, vehicle type, lighting conditions, and road geometry. These findings provide actionable insights for sustainable road safety interventions, including stronger protection for pedestrians and passengers, stricter enforcement against unlicensed driving, and infrastructural improvements such as better lighting and safer road design. By combining machine learning with interpretability tools, this study offers an analytical framework that can inform evidence-based policies aimed at reducing crash-related harm and advancing sustainable transport development. Full article

This study is devoted to solving the nonlinear inhomogeneous time-fractional FitzHugh–Nagumo differential problem ($\mathsf{TFFNDP}$) using the spectral modified collocation method. The proposed algorithm uses non-symmetric polynomials, namely shifted Legendre polynomials ($\mathsf{SLPs}$), which are orthogonal. The orthogonality property of $\mathsf{SLPs}$ and certain relations facilitate the acquisition of accurate spectral approximations. Comprehensive convergence and error studies are conducted to validate the accuracy of the suggested shifted Legendre expansion. Several numerical examples are presented to demonstrate the method’s effectiveness and accuracy. The proposed scheme is benchmarked against known analytical solutions and compared with other algorithms to ensure the applicability and efficiency of the proposed algorithm. Full article

Muscle dysmorphia (MD) is a body image disorder characterized by an obsessive preoccupation with muscularity and compulsive behaviors such as excessive exercise, rigid dieting, and frequent body checking. MD has been linked to obsessive–compulsive traits and the use of anabolic–androgenic steroids (AASs), yet these associations have not been comprehensively synthesized. This systematic review and meta-analysis examined the relationships between MD, obsessive–compulsive symptomatology, and AASs or performance-enhancing drug use. Following PRISMA 2020 guidelines and PROSPERO preregistration (CRD42025640206), we searched four major databases for peer-reviewed studies published between 2015 and 2025. Ten studies (five quantitative, five qualitative) met the inclusion criteria. Meta-analytic findings revealed a moderate positive correlation between MD symptom severity and obsessive–compulsive traits (r ≈ 0.24), and significantly higher MD symptoms among AAS users compared to non-users (Cohen’s d ≈ 0.45). Odds of MD were markedly higher in steroid-using populations. Thematic synthesis of qualitative studies highlighted compulsive training routines, identity conflicts, motivations for AAS use, and limited engagement with healthcare services. These findings suggest that MD exists at the intersection of obsessive–compulsive psychopathology and substance-related behavior, warranting integrated interventions targeting both dimensions. The study contributes to understanding MD as a complex, multi-faceted disorder with significant clinical and public health relevance. Full article

Forests in the Beijing–Tianjin–Hebei region have played an important role in wind prevention, sand fixation, and carbon emission reduction in China. This study uses scenario analysis to assess the region’s potential for carbon emission reduction through forest carbon sinks under low-carbon development scenarios. The findings suggest that, by 2030, when carbon emissions are expected to peak, the maximum projected cumulative carbon reduction from forest carbon sinks in the Beijing–Tianjin–Hebei region will be 25.572 million tons, contributing 6.26% to carbon emission reduction. By 2060, when the region aims to achieve carbon neutrality, the maximum projected cumulative carbon reduction from forest carbon sinks will be 366.207 million tons, with a contribution to carbon neutrality exceeding 17%. In the medium-to-long term, the forest carbon sink mechanism is anticipated to become a primary pathway for carbon emission reduction in the Beijing–Tianjin–Hebei region. This study expands the analytical framework for carbon emission reduction pathways under various scenarios and recommends that relevant government departments in the Beijing–Tianjin–Hebei region enhance coordination of “carbon-related” policies across cities and actively explore cross-regional ecological compensation models for forest carbon sinks, etc. Full article

Single-particle inductively coupled plasma mass spectrometry (spICP-MS) offers the unprecedented advantage of sensitive and selective detection of individual particles based on their constituent elements. It has been applied to the qualitative/quantitative evaluation of nonporous/mesoporous particles ranging from the nanoscale to the microscale and, recently, targeted proteins bound to particles. However, lipid membranes bound to particles have not been explored as potential targets for spICP-MS, despite its analytical potential. To address this, we investigated the applicability of spICP-MS for evaluating the binding states of two different types of lipid membranes (liposomes, i.e., phospholipid bilayer-based spherical vesicles, and nanodiscs comprising a disc-shaped phospholipid bilayer and membrane scaffold protein) to mesoporous silica microspheres (SBA24). The presence of bound liposomes and nanodiscs was confirmed using spICP-MS, which selectively monitored the derived P as a marker element. The presence of bound liposomes was confirmed by confocal laser Raman microscopy. Our findings demonstrate that spICP-MS can be used to qualitatively evaluate the binding states of lipid membranes to mesoporous SiO₂ microspheres. This method offers a new platform for evaluating the effectiveness of particles as carriers of biomolecules (lipid membranes) and provides valuable insights into biomedical research and quality control in related industries. Full article

This paper explores a class of analytic functions defined in the open unit disk by means of a symmetric q-differential operator. In the first part, we derive sufficient conditions for functions to belong to a subclass associated with this operator, using inequalities involving their coefficients. Additionally, we establish several inclusion relations between these subclasses, obtained by varying the defining parameters. In the second part, we focus on differential subordination and superordination for functions transformed by the operator. We provide sufficient conditions under which such functions are subordinate or superordinate to univalent functions, and we determine the best dominant and best subordinant in specific cases. These results are complemented by several corollaries that highlight particular instances of the main theorems. Furthermore, we present a sandwich-type result that brings together the subordination and superordination frameworks in a unified analytic statement. Full article

This paper presents an analytical forecasting exercise about the criticality of an important collection of materials, namely copper, cobalt, nickel, lithium, and rare earth, recognized today as fundamental materials for the energy transition. The criticality of this set of materials is scrutinized not only in terms of the comparison of production/reserves but also in terms of geopolitical and environmental aspects related to their exploration. For this quantitative forecasting exercise, a logistic equation tool was used to estimate future accumulated production considering the logistic extrapolation of the current level of production. Conclusions are drawn about the possibility of the depletion of this set of materials, as well as other environmental and/or geopolitical risks involved in their massive mining explorations. Full article

This study investigates how the integration of artificial intelligence (AI) transforms job practices within a leading European infrastructure company. Grounded in the Feeling Economy framework, the research explores the shift in task composition following AI implementation, focusing on the emergence of new roles, required competencies, and the ongoing reconfiguration of work. Using a qualitative, single-case study methodology, data were collected through semi-structured interviews with ten employees and company documentation. Thematic analysis revealed five key dimensions: the reconfiguration of job tasks, the improvement of efficiency and quality, psychological and adaptation challenges, the need for AI-related competencies, and concerns about dehumanisation. Findings show that AI systems increasingly assume repetitive and analytical tasks, enabling workers to focus on strategic, empathetic, and creative responsibilities. However, psychological resistance, fears of job displacement, and a perceived erosion of human interaction present implementation barriers. The study provides theoretical contributions by empirically extending the Feeling Economy and task modularisation frameworks. It also offers managerial insights into workforce adaptation, training needs, and the importance of ethical and emotionally intelligent AI integration. Additionally, this study highlights that the Feeling Economy must address AI’s epistemic risks, emphasising fairness, transparency, and participatory governance as essential for trustworthy, emotionally intelligent, and sustainable AI systems. Full article

Small and medium-sized enterprises (SMEs) often face severe resource constraints and operational fragility during crises. However, little is known about how managerial resilience (MR) translates into performance through time-related psychological resources and innovation—two capabilities that are both scarce and critical under such conditions. Drawing on Temporal Motivation Theory (TMT), this study develops and tests a dual-mediation model in which employee temporal psychological capital (TPC) and employee innovative behavior (EIB) transmit the effects of MR on performance. As a core methodological innovation, we adopt a multi-method analytical strategy to provide robust and complementary evidence rather than a hierarchy of results: Partial Least Squares Structural Equation Modeling (PLS-SEM) is used to examine sufficiency-based causal pathways and quantify the mediating mechanisms; Support Vector Machine (SVM) classification offers a non-parametric predictive validation of how MR and its mediators distinguish high- and low-performance cases; and Necessary Condition Analysis (NCA) identifies non-compensatory conditions that must be present for high performance to occur. These three methods address different research questions—sufficiency, classification robustness, and necessity—therefore serving as parallel, equally important components of the analysis. A total of 455 SME managers and employees were surveyed, and results show that MR significantly enhances all three dimensions of TPC (temporal control, temporal fit, time pressure resilience) and EIB (idea generation, idea promotion, idea realization), which in turn improve employee performance. SVM classification confirms that high MR, strong TPC, and active innovation align with high performance, while NCA reveals temporal control, idea generation, and idea realization as necessary bottleneck conditions. By integrating sufficiency–necessity logic with predictive classification, our findings suggest that SMEs should prioritize leadership resilience training to strengthen managers’ adaptive capacity, while simultaneously implementing time management interventions—such as temporal control workshops, workload balancing, and innovation pipeline support—to enhance employees’ ability to align tasks with organizational timelines, execute ideas effectively, and sustain performance during crises. Full article

This study examines how Industry 4.0 (I4.0) technologies enhance supply chain resilience (SCR) in manufacturing firms by testing the mediating roles of supply chain agility (SCAG), supply chain adaptability (SCAD) and the moderating effect of customer integration (CI). Grounded in the Resource-Based View (RBV) and Dynamic Capabilities View (DCV), the research conceptualizes digital technologies—such as the Internet of Things (IoT), big data analytics, and artificial intelligence (AI)—as both strategic resources and enablers of dynamic capabilities in turbulent environments. Survey data were collected from 273 manufacturing firms in Turkey, a context shaped by geopolitical and economic disruptions, and analyzed using structural equation modeling (SEM). The results indicate that I4.0 technologies positively affect SCR directly and indirectly through SCAG and SCAD. However, while agility consistently strengthens resilience, adaptability shows a negative mediating effect, suggesting context-specific constraints. CI significantly amplifies the positive impact of I4.0 on SCR, underscoring the importance of external relational capabilities. Theoretically, this research advances supply chain literature by integrating RBV and DCV to explain how digital transformation drives resilience through distinct dynamic capabilities. Practically, it offers guidance for managers to combine digital infrastructure with collaborative customer relationships to mitigate disruptions and secure long-term performance. Overall, the study provides an integrated framework for building resilient supply chains in the digital era. Full article

Unsafe behavior among air traffic controllers is a significant causal factor in civil aviation safety incidents. To explore the risks and pathways associated with controller-induced aviation accidents, this study develops an analytical model of controller unsafe behavior based on association rules and fault tree Bayesian networks. First, the Human Factors Analysis and Classification System (HFACS) was applied to identify and categorize aviation incident reports attributed to controller errors. Next, association rule algorithms were employed to uncover potential associations between controller unsafe behaviors and related risk factors, and a fault tree Bayesian network (FT-BN) model of controller unsafe behaviors was constructed based on these associations. The results revealed that the most likely unsafe behaviors were: improper allocation of aircraft spacing (30.5%), failure to take necessary intervention measures (28.4%), and improper transfer of control (27.8%). Backward analysis of the FT-BN indicated that improper allocation of aircraft spacing was most likely triggered by failure to provide adequate controller training, failure to take necessary intervention measures was most often caused by forgotten information, and improper transfer of control was most frequently associated with controller fatigue and failure to put risk management efforts in place. This study provides an important framework for the analysis and evaluation of controller behavior management and offers key insights for improving air traffic safety. Full article

Fossils are portals to the past, providing researchers with vital information about the evolution of life on Earth throughout the geological eras. The present study synthesizes the recent trends in fossil research, emphasizing the most common techniques found in the specialized literature over the past 20 years. The bibliographic survey revealed that destructive methods continue to play a significant role in scientific production related to this topic, particularly in studies on 3D morphologies, diagenesis, nutritional ecology, dating, elucidating dietary or habitat preferences, or understanding the physiology of extinct species. However, noninvasive tools, such as Raman spectroscopy, are rapidly rising, particularly when integrated with imaging techniques. As such, fossil research continues to advance even beyond the borders of our planet, exploring extraterrestrial samples in a quest to unlock the universal mystery of life. At the same time, the advent of advanced AI methods—particularly model chatbots that rival the capabilities of experienced scientists—has facilitated and enhanced data interpretation and classification. As fossil research evolves, upcoming technological advancements in spatial resolution, penetration depth, and detection sensitivity will integrate state-of-the-art spectroscopic tools. This will undoubtedly take fossil research to new heights, generating breakthroughs that optimize analysis while preserving invaluable specimens. Overall, the present study offers a holistic overview of analytical techniques through meta-analysis and bibliometric mapping, including a critical assessment of commonly used methods and offering a glimpse into the integration of machine learning and AI tools in fossil research. Full article