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Smart safety helmets equipped with vision systems are emerging as powerful tools for industrial infrastructure inspection. This paper presents a comprehensive state-of-the-art review of such VSLAM-enabled (Visual Simultaneous Localization and Mapping) helmets. We surveyed the evolution from basic helmet cameras to intelligent, sensor-fused inspection platforms, highlighting how modern helmets leverage real-time visual SLAM algorithms to map environments and assist inspectors. A systematic literature search was conducted targeting high-impact journals, patents, and industry reports. We classify helmet-integrated camera systems into monocular, stereo, and omnidirectional types and compare their capabilities for infrastructure inspection. We examine core VSLAM algorithms (feature-based, direct, hybrid, and deep-learning-enhanced) and discuss their adaptation to wearable platforms. Multi-sensor fusion approaches integrating inertial, LiDAR, and GNSS data are reviewed, along with edge/cloud processing architectures enabling real-time performance. This paper compiles numerous industrial use cases, from bridges and tunnels to plants and power facilities, demonstrating significant improvements in inspection efficiency, data quality, and worker safety. Key challenges are analyzed, including technical hurdles (battery life, processing limits, and harsh environments), human factors (ergonomics, training, and cognitive load), and regulatory issues (safety certification and data privacy). We also identify emerging trends, such as semantic SLAM, AI-driven defect recognition, hardware miniaturization, and collaborative multi-helmet systems. This review finds that VSLAM-equipped smart helmets offer a transformative approach to infrastructure inspection, enabling real-time mapping, augmented awareness, and safer workflows. We conclude by highlighting current research gaps, notably in standardizing systems and integrating with asset management, and provide recommendations for industry adoption and future research directions. Full article

Streptococcus pyogenes (Group A Streptococcus, StrepA) is a human pathogen responsible for hundreds of millions of infections each year and remains one of the most prevalent bacterial causes of upper respiratory and skin infections worldwide. Despite its global impact, there is no approved vaccine, and the optimal protective immune response is still not fully understood. In particular, the role of Th17 T cells in immunity against StrepA remains to be explored. We have previously shown that Th17 T cells are induced in humans following StrepA infection. In this study, we investigated the role of Th17 T cells during skin and upper airway StrepA infections. To generate StrepA-specific Th17 T cells, we utilized a novel cationic liposomal adjuvant system. We demonstrated that vaccine-induced Th17 T cells are recruited to the skin and upper airways upon StrepA infection. In the airways, Th17 T cells and IgA correlate with protection, whereas Th1 T cells and IgG do not. To further characterize the recruited Th17 T cells, we used an IL-17 fate-reporter mouse model to track Th17 T cells. Our results show that Th17 T cells outnumber bona fide Th1 T cells in both StrepA-infected skin and upper airways. Surprisingly, most Th17 T cells lose expression of IL-17, and do not express TNFα, IFNγ, and IL-2. Initial single-cell sequencing data suggest the existence of multiple Th17 T cell subsets with distinct expression profiles. We discuss the functional relevance of these subsets in the context of a StrepA infection. Full article

In scientific research and engineering practice, the design of deep spiking neural network (DSNN) architectures remains a complex task that heavily relies on the expertise and experience of professionals. These architectures often require repeated adjustments and modifications based on factors such as the DSNN’s performance, resulting in significant consumption of human and hardware resources. To address these challenges, this paper proposes an innovative evolutionary membrane algorithm for optimizing DSNN architectures. This algorithm automates the construction and design of promising network models, thereby reducing reliance on manual tuning. More specifically, the architecture of DSNN is transformed into the search space of the proposed evolutionary membrane algorithm. The proposed algorithm thoroughly explores the impact of hyperparameters, such as the candidate operation blocks of DSNN, to identify optimal configurations. Additionally, an early stopping strategy is adopted in the performance evaluation phase to mitigate the time loss caused by objective evaluations, further enhancing efficiency. The optimal models identified by the proposed algorithm were evaluated on the CIFAR-10 and CIFAR-100 datasets. The experimental results demonstrate the effectiveness of the proposed algorithm, showing significant improvements in accuracy compared to the existing state-of-the-art methods. This work highlights the potential of evolutionary membrane algorithms to streamline the design and optimization of DSNN architectures, offering a novel and efficient approach to address the challenges in the applications of automated parameter optimization for DSNN. Full article

Most research on domestic dog (Canis familiaris) behavior has focused on pets with restricted movement. However, free-ranging dogs exist in diverse cultural contexts globally, and their interactions with humans are less understood. Tourists can facilitate unrestricted dog movement into wilderness areas, where they may negatively impact wildlife. This study investigated which stimuli—namely, voice, touch, or food—along with inherent factors (age, sex, sociability) motivate free-ranging dogs to follow a human stranger. We measured the distance (up to 600 m) of 129 free-ranging owned and stray dogs from three villages in southern Chile as they followed an experimenter who presented them one of the above stimuli or none (control). To evaluate the effect of dog sociability (i.e., positive versus stress-related or passive behaviors), we performed a 30 s socialization test (standing near the dog without interacting) before presenting a 10 s stimulus twice. We also tracked whether the dog was in the company of other dogs. Each focus dog was video-recorded and tested up to three times over five days. Generalized linear mixed-effects models revealed that the food stimulus significantly influenced dogs’ motivation to follow a stranger, as well as a high proportion of sociable behaviors directed towards humans and the company of other dogs present during the experiment. Juveniles tended to follow a stranger more than adults or seniors, but no effects were found for the dog’s sex, whether an owner was present, the repetition of trials, the location where the study was performed, or for individuals as a random variable. This research highlights that sociability as an inherent factor shapes dog–stranger interactions in free-ranging dogs when food is given. In the context of wildlife conservation, we recommend that managers promote awareness among local communities and tourists to avoid feeding dogs, especially in the context of outdoor activities close to wilderness. Full article

Forests play a crucial role in storing excess carbon released into the atmosphere. By mitigating climate change, forest carbon stocks play a vital role in achieving green transitions. However, limited information is available regarding the factors that affect forest carbon stocks. The primary objective of this analysis is to investigate the impact of green agricultural technologies and bioenergy on forest carbon stocks. The empirical investigation was conducted using the method of moments quantile regression (MMQR) technique. Results using the MMQR approach indicate that bioenergy is beneficial in augmenting forest carbon stores at all levels. A 1% increase in bioenergy is associated with an increase in forest carbon stocks ranging from 3.100 at the 10th quantile to 1.599 at the 90th quantile. In the context of developing economies, similar findings are observed; however, in developed economies, bioenergy only fosters forest carbon stocks at lower and middle quantiles. In contrast, green agricultural technologies have an adverse effect on forest carbon stocks. Green agricultural technologies have a significant negative impact on forest carbon stocks, particularly between the 10th and 80th quantiles, with their influence declining in magnitude from −2.398 to −0.619. This negative connection is observed in both developed and developing countries at most quantiles, except for higher quantiles in developed economies. Gross domestic product (GDP) has an adverse effect on forest carbon stores only in developing countries, whereas human capital diminishes forest carbon stocks in both developed and developing nations. Governments should provide support for the creators of bioenergy and agroforestry technologies so that forest carbon stocks can be increased. Full article

This contribution aims to honour Guido Barbiellini’s profound interest in the interpretation and impact of quantum mechanics by examining the implications of the so-called before–before Experiment on quantum entanglement. This experiment was inspired by talks and discussions with John Bell at CERN. This was during the years when John and Guido co-worked, promoting the mission of the laboratory: “to advance the boundaries of human knowledge”. As the experiment uses measuring devices in motion, it can be considered a complement to entanglement experiments using stationary measuring devices, which have meanwhile been awarded the 2022 Nobel Prize in Physics. The before–before Experiment supports the idea that the quantum realm exists beyond space and time and that the quantum state is a real mental entity concerning choices. As it also leads us to a better understanding of the ‘quantum collapse’ and the measurement process, we pay homage to Guido’s work on detectors, such as his collaborations on the DELPHI experiment at CERN, on cosmic ray detection at the International Space Station, and gamma-ray astrophysics during a large NASA space mission. Full article

Wildfires have become increasingly frequent and intense due to climate change, posing severe threats to ecosystems, infrastructure, and human lives. As a result, accurate wildfire spread prediction is critical for effective risk mitigation, resource allocation, and decision making in disaster management. In this study, we develop a deep learning model to predict wildfire spread using remote sensing data. We propose LinU-Mamba, a model with a U-Net-based vision Mamba architecture, with light spatial attention in skip connections, and an efficient linear attention mechanism in the encoder and decoder to better capture salient fire information in the dataset. The model is trained and evaluated on the two-dimensional remote sensing dataset Next Day Wildfire Spread (NDWS), which maps fire data across the United States with fire entries, topography, vegetation, weather, drought index, and population density variables. The results demonstrate that our approach achieves superior performance compared to existing deep learning methods applied to the same dataset, while showing an efficient training time. Furthermore, we highlight the impacts of pre-training and feature selection in remote sensing, as well as the impacts of linear attention use in our model. As far as we know, LinU-Mamba is the first model based on Mamba used for wildfire spread prediction, making it a strong foundation for future research. Full article

This paper examines the effect of digital innovation on cost stickiness in manufacturing firms, focusing on the underlying mechanisms and contextual factors. Using data from Chinese A-share listed manufacturing firms from 2012 to 2023, we find that, first, for each one-unit increase in the level of digital technology, the cost stickiness index of enterprises decreases by an average of 0.4315 units, primarily through digital process innovation and digital business model innovation, whereas digital product innovation does not exhibit a statistically significant impact. Second, manufacturing servitization and the optimization of human capital structure are identified as key mediating mechanisms. Digital innovation promotes servitization by transitioning firms from product-centric to service-oriented business models, thereby reducing fixed costs and improving resource flexibility. It also optimizes human capital by increasing the proportion of high-skilled employees and reducing labor adjustment costs. Third, the effect of digital innovation on cost stickiness is found to be heterogeneous. Firms with high financing constraints benefit more from the cost-reducing effects of digital innovation due to improved resource allocation efficiency. Additionally, mid-tenure executives are more effective in leveraging digital innovation to mitigate cost stickiness, as they balance short-term performance pressures with long-term strategic investments. These findings contribute to the understanding of how digital transformation reshapes cost behavior in manufacturing and provide insights for policymakers and firms seeking to achieve sustainable development through digital innovation. Full article

Joint attention, the capacity of two or more individuals to focus on a common event simultaneously, is fundamental to human–human interaction, enabling effective communication. When considering the field of social robotics, emulating this capability might be necessary for promoting natural interactions and thus improving user engagement. Responding to joint attention (RJA), defined as the ability to react to external attentional cues by aligning focus with another individual, plays a critical role in promoting mutual understanding. This study examines how RJA impacts user engagement during human–robot interaction. The participants play a turn-taking game against a social robot under two conditions: with our RJA system active and with the system inactive. Auditory and visual stimuli are introduced to simulate real-world dynamics, testing the robot’s ability to detect and follow the user’s focus of attention. We use a twofold approach to evaluate the system’s impact on the user’s experience during the interaction. On the one hand, we use head pose telemetry to quantify attentional aspects of engagement, including measures of distraction and focus during the interaction. On the other hand, we use a post-experimental questionnaire incorporating the User Engagement Scale Short Form to assess engagement. The results regarding telemetry data reveal reduced distraction and improved attentional consistency, highlighting the system’s ability to maintain attention on the current task effectively. Furthermore, the questionnaire responses show that RJA significantly enhances self-reported engagement when the system is active. We believe these findings confirm the value of attentional mechanisms in promoting engaging human–robot interactions. Full article

Due to climate effects and human influences, wildfire regimes in boreal forests are changing, leading to profound ecological consequences, including shortened fire return intervals and elevated tree mortality. However, a critical knowledge gap exists concerning the spatiotemporal dynamics of fire-induced tree mortality specifically within the vast North American boreal forest, as previous studies have predominantly focused on Mediterranean and tropical forests. Therefore, in this study, we used satellite observation data obtained by the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra MCD64A1 and related database data to study the spatial and temporal variability in burned area and forest mortality due to wildfires in North America (Alaska and Canada) over an 18-year period (2003 to 2020). By calculating the satellite reflectance data before and after the fire, fire-driven forest mortality is defined as the ratio of the area of forest loss in a given period relative to the total forest area in that period, i.e., the area of forest loss divided by the total forest area. Our findings have shown average values of burned area and forest mortality close to 8000 km²/yr and 40%, respectively. Burning and tree loss are mainly concentrated between May and September, with a corresponding temporal trend in the occurrence of forest fires and high mortality. In addition, large-scale forest fires were primarily concentrated in Central Canada, which, however, did not show the highest forest mortality (in contrast to the results recorded in Northern Canada). Critically, based on generalized linear models (GLMs), the results showed that fire size and duration, but not the burned area, had significant effects on post-fire forest mortality. Overall, this study shed light on the most sensitive forest areas and time periods to the detrimental effects of forest wildfire in boreal forests of North America, highlighting distinct spatial and temporal vulnerabilities within the boreal forest and demonstrating that fire regimes (size and duration) are primary drivers of ecological impact. These insights are crucial for refining models of boreal forest carbon dynamics, assessing ecosystem resilience under changing fire regimes, and informing targeted forest management and conservation strategies to mitigate wildfire impacts in this globally significant biome. Full article

The increasing prevalence of antibiotic resistance and the limited availability of antiviral therapeutics for pathogens such as human respiratory syncytial virus (hRSV) underscore the need for novel, plant-derived antimicrobial substances. In this study, we evaluated the antiproliferative, antibacterial, and antiviral activities of aqueous leaf extracts from two plants commonly found in North America, Osage orange (M. pomifera) and spearmint (M. spicata). Both extracts exhibited no significant cytotoxic or morphologic impact on HEp-2 human cancer cells up to 25 mg/mL. However, both extracts demonstrated strong dose-dependent antibacterial activity, significantly inhibiting replication of E. coli and S. aureus at concentrations ≥ 1 mg/mL. Antiviral assays revealed that both extracts inhibited hRSV infectivity, with spearmint extract showing higher potency (EC₅₀ = 1.01 mg/mL) compared to Osage orange (EC₅₀ = 3.85 mg/mL). Gas chromatography–mass spectrometry (GC-MS) identified three major extract constituents: 3-hydroxybenzyl alcohol, 4-hydroxybenzyl alcohol (Osage orange), and R-(-)-carvone (spearmint). Among these, only carvone significantly inhibited hRSV in vitro, suggesting its key role in spearmint’s antiviral activity. These findings highlight the therapeutic potential of Osage orange and spearmint leaf extracts, particularly as sources of water-soluble compounds with antimicrobial properties, and support further investigation into their mechanisms of action and broader clinical relevance. Full article

Macrophage-mediated inflammation is known to be involved in the epithelial–mesenchymal transition (EMT) of various types of cancer. This makes macrophage-derived inflammatory factors prime targets for the development of new treatments. This study uncovers the therapeutic potential and action mechanism of DAB-2-28, a small-molecule derived from para-aminobenzoic acid, in the treatment of breast cancer. The luminal MCF-7 and the triple-negative MDA-MB-231 cancer cell lines used in this study represent, respectively, breast cancers in which the differentiation states are related to the epithelial phenotype of the mammary gland and breast cancers expressing a highly aggressive mesenchymal phenotype. In MCF-7 cells, soluble factors from macrophage-conditioned media (CM-MØ) induce a characteristic morphology of mesenchymal cells with an upregulated expression of Snail1, a mesenchymal marker, as opposed to a decrease in the expression of E-cadherin, an epithelial marker. DAB-2-28 does not affect the differential expression of Snail1 and E-cadherin in response to CM-MØ, but negatively impacts other hallmarks of EMT by decreasing invasion and migration capacities, in addition to MMP9 expression and gelatinase activity, in both MCF-7 and MDA-MB-231 cells. Moreover, DAB-2-28 inhibits the phosphorylation of key pro-EMT transcriptional factors, such as NFκB, STAT3, SMAD2, CREB, and/or AKT proteins, in breast cancer cells exposed to different EMT inducers. Overall, our study provides evidence suggesting that inhibition of EMT initiation or maintenance is a key mechanism by which DAB-2-28 can exert anti-tumoral effects in breast cancer cells. Full article

The construction industry is a major consumer of raw materials and a significant contributor to environmental emissions. Life cycle assessment (LCA) using digital models is a valuable tool for conducting a science-based analysis to reduce these impacts. However, transferring data from environmental product declarations (EPDs) to BIM for the purpose of sustainability assessment requires significant resources for its interpretation and integration. This study is founded on a comprehensive review of the scientific literature and standards, an analysis of published digital EPDs, and a thorough evaluation of IFC (industry foundation classes), identifying twenty gaps for the automated incorporation of LCA data from construction products into BIM. The identified limitations were assessed using the digital model of a building pilot, applying simplifications to incorporate actual EPD data. This paper presents the identified barriers to the automated incorporation of digital EPDs into BIM, and proposes eleven concrete actions to improve IFC 4.3. While prior studies have analyzed the environmental data in IFC, this research is significant in two key areas. Firstly, it focuses on the direct machine interpretation of environmental information without human intervention. Secondly, it is intended to be directly applicable to a revision of the IFC standards. Full article

This review explores the biofilm architecture and drug resistance of Enterococcus faecalis in clinical and environmental settings. The biofilm in E. faecalis is a heterogeneous, three-dimensional, mushroom-like or multilayered structure, characteristically forming diplococci or short chains interspersed with water channels for nutrient exchange and waste removal. Exopolysaccharides, proteins, lipids, and extracellular DNA create a protective matrix. Persister cells within the biofilm contribute to antibiotic resistance and survival. The heterogeneous architecture of the E. faecalis biofilm contains both dense clusters and loosely packed regions that vary in thickness, ranging from 10 to 100 µm, depending on the environmental conditions. The pathogenicity of the E. faecalis biofilm is mediated through complex interactions between genes and virulence factors such as DNA release, cytolysin, pili, secreted antigen A, and microbial surface components that recognize adhesive matrix molecules, often involving a key protein called enterococcal surface protein (Esp). Clinically, it is implicated in a range of nosocomial infections, including urinary tract infections, endocarditis, and surgical wound infections. The biofilm serves as a nidus for bacterial dissemination and as a reservoir for antimicrobial resistance. The effectiveness of first-line antibiotics (ampicillin, vancomycin, and aminoglycosides) is diminished due to reduced penetration, altered metabolism, increased tolerance, and intrinsic and acquired resistance. Alternative strategies for biofilm disruption, such as combination therapy (ampicillin with aminoglycosides), as well as newer approaches, including antimicrobial peptides, quorum-sensing inhibitors, and biofilm-disrupting agents (DNase or dispersin B), are also being explored to improve treatment outcomes. Environmentally, E. faecalis biofilms contribute to contamination in water systems, food production facilities, and healthcare environments. They persist in harsh conditions, facilitating the spread of multidrug-resistant strains and increasing the risk of transmission to humans and animals. Therefore, understanding the biofilm architecture and drug resistance is essential for developing effective strategies to mitigate their clinical and environmental impact. Full article

Diabetes mellites (DM) is a chronic disease with increasing prevalence worldwide and multiple health implications. Among them, sarcopenia is a metabolic disorder characterized by loss of muscle mass and function. The two age-related diseases, DM and sarcopenia, share underlying pathophysiological pathways. This narrative literature review aims to provide an overview of the existing evidence on metabolomic studies evaluating DM associated with sarcopenia. Advancements in targeted and untargeted metabolomics techniques could provide better insight into the pathogenesis of sarcopenia in DM and describe their entangled and fluctuating interrelationship. Recent evidence showed that sarcopenia in DM induced significant changes in protein, lipid, carbohydrate, and in energy metabolisms in humans, animal models of DM, and cell cultures. Newer metabolites were reported, known metabolites were also found significantly modified, while few amino acids and lipids displayed a dual behavior. In addition, several therapeutic approaches proved to be promising interventions for slowing the progression of sarcopenia in DM, including physical activity, newer antihyperglycemic classes, D-pinitol, and genetic USP21 ablation, although none of them were yet validated for clinical use. Conversely, ceramides had a negative impact. Further research is needed to confirm the utility of these findings and to provide potential metabolomic biomarkers that might be relevant for the pathogenesis and treatment of sarcopenia in DM. Full article