Smart home devices are becoming more popular over the years. A diverse range of appliances is being created, and Ambient Intelligence is growing in homes. However, there are various producers of these gadgets, different kinds of protocols, and diverse environments. The lack of interoperability reduces comfort of the user and turns into a barrier to smart home adoption. Matter is growing by constructing an open-source application layer protocol that can be compatible with all smart home ecosystems. In this article, a Matter overview is provided (namely, of the Commissioning stage), and a Matter Accessory using ESP32-S3 is developed referring to the manufacturer’s SDKs and is inserted into an existent household ecosystem. Its behavior on the network is briefly analyzed, and interactions with the device are carried out. The simplicity of these tasks demonstrates accessibility for developers to create products, especially when it comes to firmware. Additionally, device commissioning and control are straightforward for the consumer. This capacity of gadget incorporation into diverse ecosystems using Matter is already on the market and might result in higher device production and enhanced smart home adoption. Full article

This paper examines bilingual communications between family doctors and patients in Galicia (Spain). The study adopts a sociolinguistic and sociopragmatic approach to analyze how language choice and code-switching (CS) impact their interactions. The main objectives are understanding language dynamics within a speech community and identifying factors influencing language use in healthcare, focusing on language alternation, associated with language choice, and language accommodation. The research utilized a twofold dataset. First, a corpus of 586 audio-recorded medical consultations and eight semi-structured interviews with all participating doctors offered real-life linguistic dynamics between doctors and patients. Additionally, a second dataset with 208 questionnaires and 15 semi-structured interviews provided insights into language preferences and attitudes, inside and outside the doctor’s office. The findings indicate that doctors and patients strategically select and switch between Spanish and Galician to achieve communication goals. Approximately half of the consultations are bilingual, either with or without code-switching, showing that language negotiation is key in the Galician healthcare system. The insights gained can inform healthcare professionals, policymakers, and language planners in developing strategies to improve communication. The study also provides contextual data for understanding the role of language in shaping social dynamics beyond the healthcare setting. Full article

In the present work, we investigate the potential of modified barium titanate (BaTiO₃), an inexpensive perovskite oxide derived from earth-abundant precursors, for developing efficient water oxidation electrocatalysts using first-principles calculations. Based on our calculations, Rh doping is a way of making BaTiO₃ absorb more light and have less overpotential needed for water to oxidize. It has been shown that a TiO₂-terminated BaTiO₃ (001) surface is more promising from the point of view of its use as a catalyst. Rh doping expands the spectrum of absorbed light to the entire visible range. The aqueous environment significantly affects the ability of Rh-doped BaTiO₃ to absorb solar radiation. After Ti→Rh replacement, the doping ion can take over part of the electron density from neighboring oxygen ions. As a result, during the water oxidation reaction, rhodium ions can be in an intermediate oxidation state between 3+ and 4+. This affects the adsorption energy of reaction intermediates on the catalyst’s surface, reducing the overpotential value. Full article

Already used in the food, pharmaceutical, cosmetic, and agrochemical industries, encapsulation is a strategy used to protect active ingredients from external degradation factors and to control their release kinetics. Various encapsulation techniques have been studied, both to optimise the level of protection with respect to the nature of the aggressor and to favour a release mechanism between diffusion of the active compounds and degradation of the barrier material. Biopolymers are of particular interest as wall materials because of their biocompatibility, biodegradability, and non-toxicity. By forming a stable hydrogel around the drug, they provide a ‘smart’ barrier whose behaviour can change in response to environmental conditions. After a comprehensive description of the concept of encapsulation and the main technologies used to achieve encapsulation, including micro- and nano-gels, the mechanisms of controlled release of active compounds are presented. A panorama of natural polymers as wall materials is then presented, highlighting the main results associated with each polymer and attempting to identify the most cost-effective and suitable methods in terms of the encapsulated drug. Full article

Recent advancements in applications of deep neural network for bearing fault diagnosis under variable operating conditions have shown promising outcomes. However, these approaches are limited in practical applications due to the complexity of neural networks, which require substantial computational resources, thereby hindering the advancement of automated diagnostic tools. To overcome these limitations, this study introduces a new fault diagnosis framework that incorporates a tri-channel preprocessing module for multidimensional feature extraction, coupled with an innovative diagnostic architecture known as the Lightweight Ghost Enhanced Feature Attention Network (GEFA-Net). This system is adept at identifying rolling bearing faults across diverse operational conditions. The FFE module utilizes advanced techniques such as Fast Fourier Transform (FFT), Frequency Weighted Energy Operator (FWEO), and Signal Envelope Analysis to refine signal processing in complex environments. Concurrently, GEFA-Net employs the Ghost Module and the Efficient Pyramid Squared Attention (EPSA) mechanism, which enhances feature representation and generates additional feature maps through linear operations, thereby reducing computational demands. This methodology not only significantly lowers the parameter count of the model, promoting a more streamlined architectural framework, but also improves diagnostic speed. Additionally, the model exhibits enhanced diagnostic accuracy in challenging conditions through the effective synthesis of local and global data contexts. Experimental validation using datasets from the University of Ottawa and our dataset confirms that the framework not only achieves superior diagnostic accuracy but also reduces computational complexity and accelerates detection processes. These findings highlight the robustness of the framework for bearing fault diagnosis under varying operational conditions, showcasing its broad applicational potential in industrial settings. The parameter count was decreased by 63.74% compared to MobileVit, and the recorded diagnostic accuracies were 98.53% and 99.98% for the respective datasets. Full article

The emerging digitalization in today’s industrial environments allows manufacturers to store online knowledge about production and use it to make better informed management decisions. This paper proposes a multi-agent framework enhanced with digital twin (DT) for production scheduling and optimization. Decentralized scheduling agents interact to efficiently manage the work allocation in different segments of production. A DT is used to evaluate the performance of different scheduling decisions and to avoid potential risks and bottlenecks. Production managers can supervise the system’s decision-making processes and manually regulate them online. The multi-agent system (MAS) uses asset administration shells (AASs) for data modelling and communication, enabling interoperability and scalability. The framework was deployed and tested in an industrial pilot coming from the bicycle production industry, optimizing and controlling the short-term production schedule of the different departments. The evaluation resulted in a higher production rate, thus achieving higher production volume in a shorter time span. Managers were also able to coordinate schedules from different departments in a dynamic way and achieve early bottleneck detection. Full article

An experiment-supported simulation process chain is set up to perform numerical forced response analyses on a transonic high-pressure compressor front stage at varying operating conditions. A wake generator is used upstream of the rotor to excite a specific resonance within the operating range of the compressor. Thereby, extensive aerodynamic and structural dynamic experimental data, obtained from state-of-the-art rig testing at the Transonic Compressor Darmstadt test facility at the Technical University of Darmstadt, are used to validate numerical results and ensure realistic boundary conditions. In the course of this, five-hole-probe measurements at steady operating conditions close to the investigated resonance enable a validation of the steady aerodynamics. Subsequently, numerically obtained aeroelastic quantities, such as resonance frequency, and damping, as well as maximum alternating blade stresses and tip deflections, are compared to experimental blade tip timing data. Experimental trends in damping can be confirmed and better explained by considering numerical results regarding the aerodynamic wall work density and secondary flow phenomena. The influence of varying loading conditions on the resonance frequency is not observed as distinctly in numerical, as in experimental results. Generally, alternating blade stresses and deflections appear to be significantly lower than in the experiments. However, similar to the aerodynamic damping, numerical results contribute to a better understanding of experimental trends. The successive experimental validation shows the capabilities of the numerical forced response analysis setup and enables the highlighting of challenges and identification of potential further adaptations. Full article

Cynara scolymus L. is an herbaceous plant originally from the western Mediterranean area, with Italy, Spain and France the main being producers. Both the edible flowering head and the by-products generated during processing (outer bracts, leaves and stem) are characterized by a high content of essential vitamins, minerals and bioactive compounds. In particular, the leaves represent a great source of phenolic acids derived from caffeoylquinic acid or flavonoids such as luteonin and apigenin, while the head and stem contain a high content of soluble and insoluble dietary fiber, especially inulin and pectins. Its high content of bioactive compounds provides artichoke a high antioxidant power due to the modulation effect of the transcription factor Nrf2, which may lead to protection against cardiovascular, hepatic and neurological disorders. The potential use of artichoke as a functional ingredient in the food industry may be promising in terms of improving the nutritional value of products, as well as preventing oxidation and extending the shelf-life of processed foods due to its antimicrobial activity. This review aims to provide an overview of the nutritional qualities of Cynara scolymus L. and its by-products, focusing on the possible health effects and potential applications in food products as a higher-value-added alternative ingredient. Full article

Different approaches have been suggested for the waste heat recovery of high-temperature exhausted gas of a solid oxide fuel cell (SOFC). In such systems, mostly gas turbine (GT) and organic Rankine cycle (ORC) are added as bottoming systems to the SOFC (Configuration 1). However, the SOFC-GT-ORC has a considerable amount of waste energy which can be recovered. In the present research, the waste energy of ORC in the heat rejection stage and the residual exhausted gas of the system were recovered by a thermoelectric generator (TEG) and a hot water unit, respectively. Then, the extra produced power in the TEG was directed to a proton exchange membrane electrolyzer and a reverse osmosis desalination unit (RODU) for hydrogen and potable water outputs. The performance of SOFC-GT, Configuration 1, and Configuration 2 was compared through a 4E (energy, exergy, exergy-economic, and environmental) analysis. In the best performance point, the exergy efficiency and unit cost of product (UCOP) of SOFC-GT were obtained as 69.41% and USD 26.53/GJ. The exergy efficiency increased by 2.56% and 2.86%, and the UCOP rose by 0.45% and 12.25% in Configurations 1 and 2. So, the overall performance of Configuration 1 was acceptable and Configuration 2 led to the highest exergy efficiency, while its economic performance was not competitive because of the high investment cost of RODU. Full article

There is a growing interest in the use of flexible substrates for label-free and in situ Surface-enhanced Raman Spectroscopy (SERS) applications. In this study, a flexible SERS substrate was prepared using self-assembled Au/Ti₃C₂ nanocomposites deposited on a cellulose (CS) paper. The Au/Ti₃C₂ nanocomposites uniformly wrapped around the cellulose fibers to provide a three-dimensional plasma SERS platform. The limit of detection (LOD) of CS/Au/Ti₃C₂ was as low as 10⁻⁹ M for 4-mercaptobenzoic acid(4-MBA) and crystal violet (CV), demonstrating good SERS sensitivity. CS/Au/Ti₃C₂ was used for in situ SERS detection of thiram on apple surfaces by simple swabbing, and a limit of detection of 0.05 ppm of thiram was achieved. The results showed that CS/Au/Ti₃C₂ is a flexible SERS substrate that can be used for the detection of thiram on apple surfaces. These results demonstrate that CS/Au/Ti₃C₂ can be used for the non-destructive, rapid and sensitive detection of pesticides on fruit surfaces. Full article

A novel single-step method has been developed for the synthesis of 1-(1-isocyanoethyl)adamantane from 1-(1-adamantylethyl)amine, chloroform, and t-BuOK, in a dichloromethane/tert-butanol (1:1) medium, yielding 92%, which is 27% higher compared to the known method, without the use of highly toxic compounds. The product was characterized using ¹H and ¹³C NMR spectroscopy, GC-MS, and elemental analysis. Full article

Tree species with high ecological plasticity are recommended for use in urban green infrastructures. This study explores the genetic diversity of Tilia cordata, Tilia platyphyllos, and Tilia argentea, highlighting their contribution to urban landscapes. In this respect, the genetic variability of individuals from four populations of the Tilia genus has been analyzed using Inter Simple Sequence Repeats (ISSR) molecular markers, and some of their phenotypic characters were naked-eyed observed or determined by dendrometric measurements. Significant variability between the populations studied was determined. Cluster analysis identified two main groups: Tilia cordata I and Tilia cordata II populations formed the first cluster, and Tilia platyphyllos and Tilia argentea populations formed the second cluster. The results of the phenotypic analysis confirmed the genetic results separating the two populations of Tilia cordata from the populations of Tilia platyphyllos and Tilia argentea in relation to their visible morphological characters. Results obtained from dendrometric measurements analysis represent the foundation for further investigations on urban ecology. An essential potential practical use of the results: the establishment of more efficient ISSR primers to identify the Tilia species analyzed: UBC864, A12, UBC840, and A7 for Tilia cordata, A13 for Tilia platyphyllos and A7 for Tilia argentea has been demonstrated. Full article

Recombinant protein expression is a fundamental aspect of both synthetic biology and biotechnology as well as a field unto itself. Microalgae, with their eukaryotic cellular machinery, high lipid content, cost-effective cultivation conditions, safety profile for human consumption, and environmentally friendly attributes, are a promising system for protein expression or metabolic engineering for sustainable chemical production. Amongst the incredible diversity of microalgae species, Chlorella spp. are heavily studied due to their high growth efficiency, potential for low-cost cultivation, and well-characterized scale-up process for large-scale cultivation. This review aims to comprehensively examine the ongoing advancements in the bioengineering of Chlorella spp. for recombinant protein production and its biotechnological applications. This includes genetic elements such as promoters, terminators, reporters and markers, enhancers, and tags successfully used in Chlorella spp. Full article

The methods of artificial intelligence (AI) have been used in the planning and operation of electric power systems for more than 40 years. In recent years, due to the development of microprocessor and data storage technologies, the effectiveness of this use has greatly increased. This paper provides a systematic overview of the application of AI, including the use of machine learning (ML) in the electric power system. The potential application areas are divided into four blocks and the classification matrix has been used for clustering the AI application tasks. Furthermore, the data acquisition methods for setting the parameters of AI and ML algorithms are presented and discussed in a systematic way, considering the supervised and unsupervised learning methods. Based on this, three complex application examples, being wind power generation forecasting, smart grid security assessment (using two methods), and automatic system fault detection are presented and discussed in detail. A summary and outlook conclude the paper. Full article

This study delves into the relationships between the perception of corporate social responsibility (PCSR), organizational commitment and employee innovation behavior, as well as the multiple mediating roles of affective, normative and continuance commitment in the relationship between the perception of CSR and innovation behavior. This research involved 419 employees from 15 artificial intelligence (AI) enterprises in Shenzhen, China. This study’s hypotheses were tested using structural equation modeling. The findings indicate that PCSR significantly impacts innovation behavior, and affective, continuance and normative commitments also positively influence innovation behavior. Moreover, these three commitments play a partial mediating role in the relationship between PCSR and innovation behavior. This study enriches and expands the understanding of the multiple mediating mechanisms between PCSR and employee innovation behavior, providing a theoretical basis and guidance for management to comprehensively understand the role of employees’ PCSR in enhancing organizational commitment and fostering innovation behavior. Full article

In shaft mining, monitoring the deformation of the roadway due to mining pressure is of great significance to the safe production of coal mines. For this reason, a distributed high-density anchor (cable) support force monitoring system was designed by developing a low-cost anchor (cable) stress monitoring device, which consists of an anchor (cable) stress sensor and a data acquisition device. The whole system consists of an anchor bar (cable) stress monitoring device and a mine roadway deformation monitoring substation. The signals collected by the anchor force sensors are processed by the data acquisition device and sent to the self-developed mine roadway deformation monitoring substation through Long Range Radio (LoRa) wireless communication. All data from the monitoring substation are transmitted to the ground control center in real time via the Message Queuing Telemetry Transport (MQTT) network transmission protocol. The distributed high-density arrangement of monitoring nodes reflects the deformation trend of the whole section of the roadway by monitoring the anchor bar (cable) support force data of multiple sections, which effectively ensures the safety of the roadway. Full article

A tremendous increase in the green synthesis of metallic nanoparticles has been noticed in the last decades, which is due to their unique properties at the nano dimension. The present research work deals with synthesis mediated by the actinomycete Streptomyces tendae of silver nanoparticles (AgNPs), isolated from Little and Greater Rann of Kutch, India. The confirmation of the formation of AgNPs by the actinomycetes was carried out by using a UV-Vis spectrophotometer where an absorbance peak was obtained at 420 nm. The X-ray diffraction pattern demonstrated five characteristic diffraction peaks indexed at the lattice plane (111), (200), (231), (222), and (220). Fourier transform infrared showed typical bands at 531 to 1635, 2111, and 3328 cm⁻¹. Scanning electron microscopy shows that the spherical-shaped AgNPs particles have diameters in the range of 40 to 90 nm. The particle size analysis displayed the mean particle size of AgNPs in aqueous medium, which was about 55 nm (±27 nm), bearing a negative charge on their surfaces. The potential of the S. tendae-mediated synthesized AgNPs was evaluated for their antimicrobial, anti-methicillin-resistant Staphylococcus aureus (MRSA), anti-biofilm, and anti-oxidant activity. The maximum inhibitory effect was observed against Pseudomonas aeruginosa at (8 µg/mL), followed by Escherichia coli and Aspergillus niger at (32 µg/mL), and against Candida albicans (64 µg/mL), whereas Bacillus subtilis (128 µg/mL) and Staphylococcus aureus (256 µg/mL) were much less sensitive to AgNPs. The biosynthesized AgNPs displayed activity against MRSA, and the free radical scavenging activity was observed with an increase in the dosage of AgNPs from 25 to 200 µg/mL. AgNPs in combination with ampicillin displayed inhibition of the development of biofilm in Pseudomonas aeruginosa and Streptococcus pneumoniae at 98% and 83%, respectively. AgNPs were also successfully coated on the surface of cotton to prepare antimicrobial surgical cotton, which demonstrated inhibitory action against Bacillus subtilis (15 mm) and Escherichia coli (12 mm). The present research integrates microbiology, nanotechnology, and biomedical science to formulate environmentally friendly antimicrobial materials using halotolerant actinomycetes, evolving green nanotechnology in the biomedical field. Moreover, this study broadens the understanding of halotolerant actinomycetes and their potential and opens possibilities for formulating new antimicrobial products and therapies. Full article

The fabrication of zinc oxide-based nanomaterials (including natural and synthetic polymers like sulfated polysaccharide, chitosan, and polymethyl methacrylate) has potential to improve oral cancer treatment strategies. This comprehensive review explores the diverse synthesis methods employed to fabricate zinc oxide nanomaterials tailored for oral cancer applications. Several synthesis processes, particularly sol–gel, hydrothermal, and chemical vapor deposition approaches, are thoroughly studied, highlighting their advantages and limitations. The review also examines how synthesis parameters, such as precursor selection, the reaction temperature, and growth conditions, influence both the physicochemical attributes and biological efficacy of the resulting nanomaterials. Furthermore, recent advancements in surface functionalization and modification strategies targeted at improving the targeting specificity and pharmaceutical effectiveness of zinc oxide-based nanomaterials in oral cancer therapy are elucidated. Additionally, the review provides insights into the existing issues and prospective views in the field, emphasizing the need for further research to optimize synthesis methodologies and elucidate the mechanisms underlying the efficacy of zinc oxide-based nanoparticles in oral cancer therapy. Full article

Fibromyalgia, a chronic pain condition marked by abnormal pain processing, impacts a significant part of the population, leading to reduced quality of life and function. Hallmark symptoms include widespread persistent pain, sleep disturbances, fatigue, cognitive dysfunction, and mood changes. Through this updated review, we aim to contribute to the evolving understanding and management of fibromyalgia, offering insights into the diverse tools available to improve the lives of those affected by this challenging condition. Management begins with educating patients to ultimately relieve them of unnecessary testing and provide reassurance. Treatment emphasizes a comprehensive approach, combining nonpharmacological interventions such as aforementioned education, exercise, and psychotherapy, alongside pharmacologic management—namely duloxetine, milnacipran, pregabalin, and amitriptyline—which have consistent benefits for a range of symptoms across the spectrum of fibromyalgia. Notably, drugs like nonsteroidal anti-inflammatory drugs (NSAIDs) and acetaminophen are generally not recommended due to limited efficacy and associated risks. Lastly, a variety of other medications have shown promise, including NMDA-receptor antagonists, naltrexone, and cannabinoids; however, they should be used with caution due to a small amount of evidence and potential for adverse effects. Full article

This study aims to describe the prevalence of malocclusion and identify associated factors in preschool children. Completed in 2022–2023, this cross-sectional study included 523 children aged 26 to 80 months in municipal schools in Salvador. An oral examination was carried out on the children, and a questionnaire was self-administered by the parents. Descriptive analyses and multivariate logistic regression (the backward method, p-value ≤ 0.05, 95% CI) were conducted. The majority of children were female (51.82%), over 54 months old (52.2%), Black or mixed race (90.63%), and not affected by COVID-19 (92.35%). The prevalence of malocclusion was 43.21%, with open bite as the most common condition. There was a significant association between malocclusion and screen time (OR: 1.34; p: 0.116; CI: 1.0–1.94), physical/psychological aggression (OR: 2.55; p: 0.031; CI: 1.0–5.98), consumption of ultra-processed foods (OR: 1.77; p: 0.003; CI: 1.22–2.57), digital suction (OR: 3.1; p: 0.001; CI: 1.56–6.16), and the habit of biting objects (OR: 1.56; p: 0.121; CI: 1.0–2.73). The promotion of comprehensive health in early childhood and psychosocial interventions are recommended, aiming to reduce screen time, aggression, consumption of ultra-processed foods, thumb sucking, and the habit of biting objects to prevent malocclusion. Full article

The Women’s March on 21 January 2017, opened a new social and political landscape for Muslim women to engage in Islamic liberatory activism. I locate Muslim women’s participation in the marches following the 2017 ‘Muslim travel ban policy’ as a site for discovering the link between the politics of resistance and the utility of Islam as a source for liberation. I argue that Muslim women living in minority and post-secular contexts resort to faith as a source of agentival liberation to address the political rhetoric of anti-Islamic sentiments and policies. The outcome of this research demonstrates (1) how Muslim women activists challenge the Western narratives of being oppressed and explore the ways they want to represent themselves; (2) how Islam serves as a catalyst for theological resistance and how this enhances the role of Muslim women as moral and spiritual agents in transforming their political and social conditions; (3) how the Islamic liberation in the US context historically intersects with Black churches’ resistance toward White racism; and (4) how Muslim women’s agency as spiritual beings is linked to the promotion of justice in the Western liberatory movements. Overall, the article shows how Muslim women resort to their spiritual journey and use such narratives to confront unjust political rhetoric and policies. Full article

In recent years, Artificial Intelligence has been used to assist healthcare professionals in detecting and diagnosing neurodegenerative diseases. In this study, we propose a methodology to analyze functional Magnetic Resonance Imaging signals and perform classification between Parkinson’s disease patients and healthy participants using Machine Learning algorithms. In addition, the proposed approach provides insights into the brain regions affected by the disease. The functional Magnetic Resonance Imaging from the PPMI and 1000-FCP datasets were pre-processed to extract time series from 200 brain regions per participant, resulting in 11,600 features. Causal Forest and Wrapper Feature Subset Selection algorithms were used for dimensionality reduction, resulting in a subset of features based on their heterogeneity and association with the disease. We utilized Logistic Regression and XGBoost algorithms to perform PD detection, achieving 97.6% accuracy, 97.5% F₁ score, 97.9% precision, and 97.7%recall by analyzing sets with fewer than 300 features in a population including men and women. Finally, Multiple Correspondence Analysis was employed to visualize the relationships between brain regions and each group (women with Parkinson, female controls, men with Parkinson, male controls). Associations between the Unified Parkinson’s Disease Rating Scale questionnaire results and affected brain regions in different groups were also obtained to show another use case of the methodology. This work proposes a methodology to (1) classify patients and controls with Machine Learning and Causal Forest algorithm and (2) visualize associations between brain regions and groups, providing high-accuracy classification and enhanced interpretability of the correlation between specific brain regions and the disease across different groups. Full article