Search Results (12,904)

Archaeological research on Santiago Island (Cape Verde) offers a strategic framework for investigating ceramic material culture shaped by Iberian and African interactions during the early modern period. This study presents first-stage results from a non-destructive archaeometric analysis of pottery fragments recovered from early colonial sites and curated at the Museu de Arqueologia in Praia. Using portable X-ray fluorescence spectroscopy (pXRF), low-fired, handmade vessels associated with African technological traditions were analysed to determine their elemental composition and potential provenance. The work also focused on sugar moulds, containers used in the refining of this product, one of the most important in Atlantic colonisation. The resulting geochemical data is compared with established reference groups from the Iberian Peninsula, Atlantic Africa, and Macaronesia. Elemental variability indicates the use of diverse clay sources and production techniques, reflecting hybrid technological practices shaped by cultural interaction and provisioning constraints. These results contribute to ongoing research within the CERIBAM (Iberian Atlantic Expansion in North Africa and Macaronesia) and Palarq-funded projects, which aim to reconstruct early colonial ceramic networks and sociotechnical dynamics. By integrating archaeometric data with archaeological and historical perspectives, this study aims to demonstrate the utility of non-invasive analytical protocols for understanding ceramic technology, intercultural exchange, and Atlantic material connectivity in early Creole formations while preserving the integrity of the collections. Full article

Volatile organic compounds (VOCs) play a central role in plant communication and ecology, acting as a chemical language that mediates interactions with other organisms and responses to environmental stimuli. Analyzing changes in the plant volatilome enables the effective differentiation between biotic and abiotic stresses. Consequently, monitoring VOC emissions offers valuable insights into plant signaling pathways and health status. These insights position this approach as a promising strategy for improving crop protection. Direct infusion (DI) online analytical techniques, such as proton transfer reaction mass spectrometry (PTR-MS) and adduct ionization mechanism mass spectrometry (AIM-MS), have been developed to detect and characterize VOCs in real time. Here, we evaluated the suitability of PTR-MS and AIM-MS for monitoring VOC emissions in pea plants (Pisum sativum L.). Comparative analysis revealed that AIM-MS, a recently developed technology, detected a higher number of distinct signals than PTR-MS. Annotation of detected and significant AIM-MS signals indicated a predominance toward those that were putative lipids-derived and amino acids-derived, whereas PTR-MS signals were primarily associated with putative phenolic compounds. These findings suggest that the newly developed AIM reactor offers a broader detection range and may enhance our ability to monitor plant VOC emissions. Consequently, AIM-MS emerges as a promising tool for the real-time assessment of pea plant health and stress responses. Further efforts are needed to improve the portability of DI-MS techniques and to integrate them with GC-MS techniques. Overall, these efforts will allow this technology to be exploited for plant protection in compromised environments. Full article

This study evaluates China’s National Smart Education Platform (NSEP) as a national digital reform aligned with SDG 4 (quality education) and SDG 10 (reduced inequalities), yet evidence remains limited on whether such platforms reduce urban–rural gaps in real-world use and outcomes. A quantitative, stratified, random survey of students, teachers, and administrators used validated scales to measure perceived ease of use (PEOU), perceived usefulness (PU), user satisfaction (US), behavioral intention (BI), engagement level (EL), learning outcomes (LO), and system quality (SQ). The measures demonstrated strong reliability. Hierarchical regression analyses supported an extended technology acceptance model (TAM): SQ, PEOU, and PU significantly predicted US and BI, with PU showing the strongest effect. Interaction effects indicated context-sensitive adoption and the results suggested a persistent rural disadvantage in adoption even after accounting for key predictors. Mediation analyses further showed that US and BI transmitted technology beliefs to LO. Nevertheless, urban–rural gaps remained evident, particularly in PEOU and SQ, and teachers consistently reported a lower PEOU than students and administrators. These findings suggest that NSEP has the potential to support SDG-oriented digital equity, but closing urban–rural gaps requires teacher-centered design, improved usability and system reliability, and targeted infrastructure and capacity-building support in rural contexts. Full article

Sepsis is a life-threatening syndrome characterized by marked clinical heterogeneity and complex host–pathogen interactions. Although traditional mechanistic studies have identified key molecular pathways, they remain insufficient to capture the highly dynamic, multifactorial, and systems-level nature of this condition. The advent of high-throughput omics technologies—particularly integrative multi-omics approaches encompassing genomics, transcriptomics, proteomics, and metabolomics—has profoundly reshaped sepsis research by enabling comprehensive profiling of molecular perturbations across biological layers. However, the unprecedented scale, dimensionality, and heterogeneity of multi-omics datasets exceed the analytical capacity of conventional statistical methods, necessitating more advanced computational strategies to derive biologically meaningful and clinically actionable insights. In this context, artificial intelligence (AI) has emerged as a powerful paradigm for decoding the complexity of sepsis. By leveraging machine learning and deep learning algorithms, AI can efficiently process ultra-high-dimensional and heterogeneous multi-omics data, uncover latent molecular patterns, and integrate multilayered biological information into unified predictive frameworks. These capabilities have driven substantial advances in early sepsis detection, molecular subtyping, prognosis prediction, and therapeutic target identification, thereby narrowing the gap between molecular mechanisms and clinical application. As a result, the convergence of AI and multi-omics is redefining sepsis research, shifting the field from descriptive analyses toward predictive, mechanistic, and precision-oriented medicine. Despite these advances, the clinical translation of AI-driven multi-omics approaches in sepsis remains constrained by several challenges, including limited data availability, cohort heterogeneity, restricted interpretability and causal inference, high computational demands, difficulties in integrating static molecular profiles with dynamic clinical data, ethical and governance concerns, and limited generalizability across populations and platforms. Addressing these barriers will require the establishment of standardized, multicenter datasets, the development of explainable and robust AI frameworks, and sustained interdisciplinary collaboration between computational scientists and clinicians. Through these efforts, AI-enabled multi-omics research may progress toward reproducible, interpretable, and equitable clinical implementation. Ultimately, the synergy between artificial intelligence and multi-omics heralds a new era of intelligent discovery and precision medicine in sepsis, with the potential to transform both research paradigms and bedside practice. Full article

Empathic behavior is increasingly incorporated into socially assistive robots, yet little is known about how older adults’ personality-based self-regulatory processes shape responses to such designs. The present study examined a recognition-based “mirror effect” framework of narcissistic self-regulation, referring to the ways individuals maintain a valued self-image through social feedback and acknowledgment. We focused on two core dimensions: narcissistic admiration, characterized by self-promotion and the pursuit of affirmation, and narcissistic rivalry, characterized by defensiveness, antagonism, and sensitivity to threat. Community-dwelling older adults (N = 527; M_age = 72.73) were randomly assigned to view a video of a socially assistive robot interacting in either an empathic or a cold manner. Participants reported their perceived recognition by the robot, defined as the subjective experience of feeling seen, acknowledged, and valued, as well as multiple robot evaluations (anthropomorphism, likability, perceived intelligence, safety, and intention to use). At the mean level, empathic robot behavior increased perceived recognition, anthropomorphism, and likability but did not improve perceived intelligence, safety, or intention to use. Conditional process analyses revealed that narcissistic admiration was positively associated with perceived recognition, which in turn predicted more favorable robot evaluations, regardless of robot behavior. In contrast, narcissistic rivalry showed a behavior-dependent pattern: rivalry was associated with reduced perceived recognition and less favorable evaluations primarily in the empathic condition, whereas this association reversed in the cold condition. Importantly, once perceived recognition and narcissistic traits were accounted for, the cold robot was evaluated as more intelligent, safer, and more desirable to use than the empathic robot. Studying these processes in older adults is theoretically and practically significant, as later life is marked by shifts in social roles, autonomy concerns, and sensitivity to interpersonal evaluation, which may alter how empathic technologies are experienced. Together, the findings identify perceived recognition as a central psychological mechanism linking personality and robot design and suggest that greater robotic empathy is not universally beneficial, particularly for users high in rivalry-related threat sensitivity. Full article

The predominant approach in the realm of industrial process monitoring and control involves the utilization of HMI (Human–Machine Interface) interfaces and conventional SCADA (Supervisory Control and Data Acquisition) systems. This limitation restricts user mobility, interaction with industrial equipment, and process status assessment. In the context of Industry 4.0, the ability to monitor and control industrial processes in real time is paramount. The present paper designs and implements a system for monitoring and controlling an industrial assembly line based on mixed reality. The technology employed to facilitate communication between the system and the industrial line is S7.Net. These elements facilitate direct communication with the industrial process equipment. The system facilitates the visualization of operating parameters and the status of the equipment utilized in the industrial process and its control. All data is superimposed on the physical environment through virtual operational panels. The system functions independently, negating the necessity for intermediate servers or other complex structures. The system’s operation is predicted on a series of algorithms. These instruments facilitate the automated analysis of industrial process parameters. These devices are utilized to ascertain the operational dynamics of the industrial line. The experimental results were obtained using a real industrial line. These models are employed to demonstrate the performance of data transmission, the identification of the system’s operating states, and the system’s ability to shut down in the event of operating errors. The proposed system is designed to function in a variety of industrial environments within the paradigm of Industry 4.0, facilitating the utilization of multiple virtual interfaces that enable user interaction with various elements through which the assembly process is monitored and controlled. Full article

This paper uses a systematic approach to constructing a mathematical description of the technological process of aluminum production, aimed at addressing control challenges and improving energy sustainability through a comprehensive analysis of technological parameters. Using expert assessment and correlation–regression analysis methods, the most significant technological parameters were identified, and quantitative relationships among them were established. Based on available statistical data from the current supply subsystem, a regression model was constructed that describes the influence of subsystem parameters on the voltage drop across the straight section of the bus and confirms the key role of transition resistances in welded joints in energy loss formation. Using the obtained dependencies, a conceptual model of the electrolysis process and its mathematical representation describing interactions among the electrical, thermal, and physicochemical subsystems of the electrolyzer was developed. The developed model is applicable to the analysis and prediction of technological modes, the construction of digital twins, and the development of automated control systems. In future work, the model is planned to be experimentally verified using a laboratory aluminum electrolysis setup in order to refine model parameters and assess applicability under industrial electrolyzer conditions. Full article

This study examines the relationship between Emotional Intelligence (EI) and Immediate Auditory Memory (IAM) in primary-school students aged 10–12 years. Through a neuroeducational perspective, it explores how emotional competencies, particularly emotional meta-knowledge, interact with cognitive retention processes. Standardized instruments were administered to a sample of 175 students from schools in Southern Spain. The findings indicate a positive association between Emotional Clarity—a key subdimension of EI—and IAM, with Emotional Clarity emerging as a modest predictor of auditory retention. No notable associations were observed for Emotional Attention or Emotional Repair. These results suggest that the ability to understand one’s emotions may subtly facilitate the processing and retention of auditory information. From neuroscientific and technological viewpoints, the study highlights the potential benefits of integrating emotional education and digital tools in the classroom to enhance student well-being and cognitive development, while calling for cautious interpretation due to the multifaceted nature of these variables. Full article

The rise in antimicrobial resistance (AMR) among the most clinically significant bacteria presents a global threat. The coexistence of resistance mechanisms to both carbapenems and colistin is particularly concerning, as these are last-line treatments, specifically reserved for the most challenging infections caused by clinically multidrug-resistant Enterobacterales. Natural aquatic environments have become environmental reservoirs for the transmission of AMR, particularly concerning mechanisms against these two types of critically important drugs. The crucial role of environmental settings as a driving force for the spread and evolution of AMR associated with these drugs is underestimated, and scientific knowledge on this topic is limited. This review aims to fill an important gap in the scientific literature and comprehensively consolidate the available data on carbapenem- and colistin-associated AMR in the aquatic environment. This study provides a comprehensive synthesis of the current knowledge by integrating bibliographic data with a detailed genomic analysis of 278 bacterial genomes sourced from natural waters. It explores the distribution of carbapenemase and mobile colistin resistance (mcr) genes, identifying their hosts, geographical spread, and complex gene–plasmid–host associations. This review distinguishes two critical host groups for genes that provide resistance to last-resort drugs, Enterobacterales and autochthonous aquatic microbiota, highlighting both confirmed and potential interactions between them. Crucially, genomic analysis highlights the alarming co-occurrence of carbapenem and colistin resistance in single cells and on single plasmids, contributing to the spread of multidrug resistance phenotypes. These findings clearly indicate that aquatic environments are not merely passive recipients but active, evolving hubs for high-risk AMR determinants. Future research should focus on the interplay between allochthonous vectors and autochthonous microbiota to better understand the long-term stabilization of carbapenemase and mcr genes. Such efforts, combined with advanced sequencing technologies, are essential to ensure that carbapenems and colistin remain viable treatment options in clinical settings. Full article

In fish gut microbiome studies, there are no standardized protocols regarding sampling region or post-feeding time, nor clear consensus on whether analyses should target resident (autochthonous) or transient (allochthonous) bacteria. This study examined the dynamics and interactions of both microbial communities in the anterior and posterior intestine of farmed gilthead sea bream and evaluated the resident microbiome at 24 and 48 h post-feeding. Microbial DNA was sequenced using the Oxford Nanopore Technology platform. Data were analyzed through statistical and discriminant approaches, as well as a Bayesian network framework to assess bacterial interactions. Transient communities showed higher richness and diversity, regardless of intestinal section, suggesting a more specialized and stable microbial environment in the mucus layer. The two communities differed markedly in structure and composition. Variations associated with intestinal region were less pronounced, particularly for autochthonous bacteria, and post-feeding fluctuations in the resident microbiome were minimal. Functionally, results indicated relevant synergies between communities. Protein metabolism pathways were enriched in autochthonous bacteria, whereas allochthonous microorganisms contributed mainly to bile acid and carbohydrate metabolism. Overall, resident and transient bacteria constitute distinct communities in the gut of gilthead sea bream, with numerous genera present in both but most being differentially represented and interconnected. Full article

The growing need to foster inclusive education in university environments has driven the development of technological solutions aimed at improving the academic experiences of students with disabilities. These individuals often face barriers to autonomy and participation, especially on large and complex campuses. This article presents the performance evaluation of a LoRaWAN network specifically designed for monitoring people with disabilities on a university campus. The system aims to provide equitable access to campus resources and real-time support to students with disabilities. Leveraging the advantages of Low-Power Wide-Area Networks (LPWAN), particularly LoRaWAN, the proposed system enables real-time tracking with broad coverage and minimal power consumption, without requiring any active user interaction. Each student receives a wearable LoRa-enabled device that wirelessly communicates with a network of gateways strategically installed throughout the campus. To evaluate the system’s performance, this work conducts link-level experiments focusing on the communication between the LoRa end devices (nodes) and the central gateway. The analysis focuses on the network coverage, signal strength (RSSI), signal-to-noise ratio (SNR), and packet reception rate (PRR). The experimental results confirmed that the proposed system is technically robust and operationally effective under real campus conditions. Beyond its technical contributions, the proposed solution represents a concrete step toward building safer and more accessible academic environments that reinforce the autonomy and inclusion of students with disabilities. Full article

Cytokine and chemokine profiling is central to understanding inflammatory processes and the mechanisms driving diverse diseases. We introduce InCytokine, an open-source tool for semiquantitative analysis of cytokine and chemokine data generated by protein array technologies. InCytokine features robust and modular image-processing workflows, including automated spot detection, template alignment, normalization, quality control measures, and quantitative intensity summarization to deliver consistent and reliable readouts from profiling assays. We evaluated InCytokine by profiling wild-type microglia, TREM2 knockout, and Alzheimer’s disease-associated TREM2 R47H variant cells in response to lipopolysaccharide and sulfatide exposure. Differential expression analysis revealed unique sulfatide-specific and genotype-specific cytokine signatures in TREM2 variants. We also report an intriguing modulation of DPP4 and a divergent expression pattern of ENA-78 in TREM2 variants in response to lipopolysaccharide and sulfatide treatment. Such distinct expression signatures raise the possibility that TREM2 variants may play a role in modulating inflammatory signaling relevant to cardio-metabolic and Alzheimer’s disease. These signatures were corroborated using transcriptional profiling of the same microglia cells, revealing also a good concordance between protein array and RNA sequencing technologies. Taken together, InCytokine is an interactive, user-friendly web application for rapid, reproducible, and scalable analysis of protein array data, proven to generate meaningful insights for drug and biomarker discovery campaigns in pharmaceutical settings. Full article

The combined implementation of land consolidation and smart agriculture is crucial for food security and agricultural carbon neutrality. Using 2010–2024 panel data from 279 Chinese prefecture-level cities, this study constructs an integrated assessment system and examines impact mechanisms and spatial effects using dual machine learning, mediation analysis, and dynamic spatial models. Results show that the interaction between land consolidation and smart agriculture significantly enhances food security at the 10% significance level and promotes agricultural carbon neutrality. Mechanism analysis indicates that agricultural industrial agglomeration positively contributes to both outcomes, while technological innovation significantly promotes carbon neutrality but temporarily suppresses food security. Spatial analysis reveals limited direct effects on local food security but positive indirect and total effects on neighboring regions, with carbon neutrality showing positive direct, indirect, and total effects. After controlling for city fixed effects and quadratic terms, the synergy remains significant, indicating robustness. The study suggests strengthening coordinated governance and innovation-driven regional development to jointly advance food security and agricultural carbon neutrality. Full article

Megacity clusters are the key battlegrounds for carbon emission reduction in China, and the polycentric spatial structure of these clusters has a profound impact on their carbon emission intensity. This paper focuses on five major megacity clusters: the Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD), the middle reaches of the Yangtze River (MRYR), and the Chengdu–Chongqing (CY) City Clusters. We construct an inter-period panel dataset spanning from 2002 to 2023 and utilize an index of polycentric spatial structure, which equally considers both morphology and functionality. A fixed-effects model is employed, and the Lind–Mehlum U-shape test is applied to identify the nonlinear relationship. Additionally, a two-step approach is used to examine the mediating effect of industrial agglomeration, while interaction terms help identify the moderating effects of technological innovation and transport infrastructure. The results indicate a significant U-shaped relationship between the polycentric structure of megacity clusters and carbon emission intensity. When the polycentric spatial structure index reaches a specific threshold, carbon emission intensity is minimized, suggesting that a moderate degree of polycentricity is most conducive to carbon reduction. Mechanism analysis reveals that industrial agglomeration functions as a significant mediator, whereas technological innovation and transport infrastructure serve as critical moderators in this relationship. Based on these findings, we propose several policy recommendations: to guide the moderate adjustment of the polycentric structure of city clusters with stage-specific targets, optimize the mechanism of industrial synergy and transfer, differentiate the allocation of innovation resources, and achieve a fine-tuned alignment between the transport system and spatial structure. These measures will support the high-quality, low-carbon transformation of city clusters. Full article

The escalating global water crisis necessitates the development of efficient, sustainable, and cost-effective remediation technologies. This review highlights bentonite–sawdust-derived carbon composites as a promising class of adsorbents for the removal of diverse water pollutants. The synthesis strategies, physicochemical properties, key interfacial adsorption mechanisms, and adsorption performance toward different pollutant categories are systematically discussed. These hybrid materials exhibit synergistically enhanced properties, including increased surface area, optimized porosity, abundant functional groups, tunable surface charge, and improved structural stability, often outperforming the individual components. Their effectiveness has been demonstrated for both heavy metals (e.g., Cd and Pb) and organic contaminants (e.g., dyes and pharmaceuticals), governed by a combination of ion exchange, electrostatic attraction, π–π interactions, and pore-filling mechanisms. Current challenges related to large-scale production, long-term stability, and regeneration are critically evaluated, and future research directions for the sustainable application of these composites in advanced water treatment systems are outlined. Full article