Deep defects in the long-wave infrared (LWIR) HgCdTe heterostructure photodiode were measured via deep-level transient spectroscopy (DLTS) and photoluminescence (PL). The n⁺-P⁺-π-N⁺ photodiode structure was grown by following the metal–organic chemical vapor deposition (MOCVD) technique on a GaAs substrate. DLTS has revealed two defects: one electron trap with an activation energy value of 252 meV below the conduction band edge, located in the low n-type-doped transient layer at the π-N⁺ interface, and a second hole trap with an activation energy value of 89 meV above the valence band edge, located in the π absorber. The latter was interpreted as an isolated point defect, most probably associated with mercury vacancies (V_Hg). Numerical calculations applied to the experimental data showed that this V_Hg hole trap is the main cause of increased dark currents in the LWIR photodiode. The determined specific parameters of this trap were the capture cross-section for the holes of σ_p = 10⁻¹⁶–4 × 10⁻¹⁵ cm² and the trap concentration of N_T = 3–4 × 10¹⁴ cm⁻³. PL measurements confirmed that the trap lies approximately 83–89 meV above the valence band edge and its location. Full article

According to our recent studies on hydrophobicity, this work is aimed at understanding the dependence of hydrophobic interactions on the shape of a solute’s surface. It has been observed that dissolved solutes primarily affect the structure of interfacial water, which refers to the top layer of water at the interface between the solute and water. As solutes aggregate in a solution, hydrophobic interactions become closely related to the transition of water molecules from the interfacial region to the bulk water. It is inferred that hydrophobic interactions may depend on the shape of the solute surface. To enhance the strength of hydrophobic interactions, the solutes tend to aggregate, thereby minimizing their surface area-to-volume ratio. This also suggests that hydrophobic interactions may exhibit directional characteristics. Moreover, this phenomenon can be supported by calculated potential mean forces (PMFs) using molecular dynamics (MD) simulations, where different surfaces, such as convex, flat, or concave, are associated with a sphere. Furthermore, this concept can be extended to comprehend the molecular packing parameter, commonly utilized in studying the self-assembly behavior of amphiphilic molecules in aqueous solutions. Full article

To determine the optimal salinity of irrigation water for fruit radish cultivated in peat, five levels of salinized water were used to evaluate their effect on the growth and quality of fruit radish (Raphanus sativus L.). Results showed that with an increase in salinity, the leaf growth was somehow inhibited, but the fleshy root growth increased, and quality improved with more soluble solids, sugar, protein, and Vitamin C substances in fleshy roots. With an increase in water salinity up to 4.2 dS/m, the weight of fleshy roots increased by 51.10% with a high increment in the root/shoot ratio. With the same salt concentration, the content of soluble solids in both root peal and pulp was the highest and improved by 11.06% and 6.70%, respectively. The soluble sugar content was the highest in root peals with the 4.2 dS/m treatment and in fleshy roots with the 7.4 dS/m treatment, with a 55.85% and 32.30% increase, respectively. The content of both soluble protein and vitamin C with the 4.2 dS/m treatment increased by 11.99% and 113.36%, respectively. Strong evidence derived from the study has indicated that 4.2 dS/m salinized irrigation water is optimal for growing ‘ice-cream’ fruit radishes and maintaining ultimate root weight and quality. Full article

Zinc deficiency is a common nutritional disorder with detrimental health consequences. Whether parental zinc deficiency induces intergenerational effects remains largely unknown. We investigated the effects of a combined maternal and paternal zinc deficiency on offspring’s metabolic outcomes and gene expression changes in Drosophila melanogaster. The parent flies were raised on zinc-deficient diets throughout development, and their progeny were assessed. Offspring from zinc-deprived parents exhibited a significant (p < 0.05) increase in body weight and whole-body zinc levels. They also displayed disrupted glucose metabolism, altered lipid homeostasis, and diminished activity of antioxidant enzymes. Gene expression analysis revealed significant (p < 0.05) alterations in zinc transport genes, with increases in mRNA levels of dZIP1 and dZnT1 for female and male offspring, respectively. Both sexes exhibited reduced dZnT35C mRNA levels and significant (p < 0.05) increases in the mRNA levels of DILP2 and proinflammatory markers, Eiger and UPD2. Overall, female offspring showed higher sensitivity to parental zinc deficiency. Our findings underscore zinc’s crucial role in maintaining health and the gender-specific responses to zinc deficiency. There is the need for further exploration of the underlying mechanisms behind these intergenerational effects. Full article

The emergence of construction 5.0 marks a shift toward a human-centric approach to digitization within the construction industry. Along with diverse digital innovations related to this shift, blockchain technology offers vast opportunities for the construction industry, including streamlining project management processes, enhancing transparency in payment processes, and improving contract administration. This paper systematically reviews 109 articles using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol to examine the applications of blockchain in construction, identifying twenty-three topics across eight thematic areas. These areas were further mapped using VOSviewer Online version 1.2.3 to identify interrelationships among the themes and highlight their broad impact. Key features like immutability, security, transparency, and traceability show promise in contract administration, supply chain logistics, facilities management, and sustainability. However, the study also describes the challenges of adopting blockchain in construction, emphasizing the need for enhanced stakeholder education, improved regulatory frameworks, and the creation of industry-specific blockchain platforms to support its acceptance in the construction industry. Emphasizing emerging blockchain applications and the adoption challenges equips researchers and practitioners with the knowledge of these applications and their significance to construction practices. Full article

Understanding the modification mechanism of C₉ petroleum resin (C₉PR) on styrene–butadiene–styrene (SBS) polymer modified asphalt properties is of significant importance. In this paper, dynamic shear rheometer (DSR), storage stability, fluorescence morphology (FM), scanning electron microscope (SEM), Fourier transform-infrared (FTIR) spectrometer, and molecular dynamic (MD) simulation were adopted to evaluate the rheological, chemical, and microstructure molecular motion state of C₉PR and SBS composite modified asphalt at different aging states. The DSR storage results indicate that the addition of C₉PR could improve the high-temperature property, storage stability, and temperature susceptibility. FM and SEM results indicate that the network microstructure was enhanced and the roughness between polymer resins and virgin asphalt was improved at the microscopic scale. The MD results indicate that the heterogeneity between C₉PR and SBS modified asphalt was demonstrated, and the bonding energies were enhanced with the addition of C₉PR. Moreover, the FTIR results indicate that new function groups were generated in addition to C₉PR. In general, the addition of C₉PR is a good approach to promote high-quality polymer modified asphalt (PMA) for pavement engineering. Full article

To strengthen major transportation infrastructure projects’ (MTIPs’) organizational resilience and fortify their capacity for crisis management and project risk prevention. In this paper, based on the resilience theory development process, the connotation of organizational resilience of MTIPs is defined, and 20 influencing factors of organizational resilience of MTIPs are extracted from four categories of stability, redundancy, adaptability, and rapidity according to the literature analysis and case study method. The significance, causality, and multilevel recursive order structure of the influencing factors were investigated by the fuzzy DEMATEL-ISM approach, and their driving and dependent characteristics were analyzed through MICMAC. The results indicate that risk warning and prediction, human resources management, inter-organizational synergies, resource reserve situations, organizational leadership, and organizational learning are the crucial factors of organizational resilience in MTIPs. There are three levels and five ranks in the multilevel recursive rank structure of the factors affecting MTIPs’ organizational resilience. Among them, risk warning and prediction, equipment condition and performance, human resources management, and organizational leadership have the deepest impact on organizational resilience in MTIPs. The findings can clarify ideas for subsequent research on organizational resilience in this area and inform project decision-makers in developing strategies for optimizing organizational resilience. Full article

Due to huge investments by both the public and private sectors, artificial intelligence (AI) has made tremendous progress in solving multiple real-world problems such as disease diagnosis, chatbot misbehavior, and crime control. However, the large-scale development and widespread adoption of AI have been hindered by the model-centric mindset that only focuses on improving the code/architecture of AI models (e.g., tweaking the network architecture, shrinking model size, tuning hyper-parameters, etc.). Generally, AI encompasses a model (or code) that solves a given problem by extracting salient features from underlying data. However, when the AI model yields a low performance, developers iteratively improve the code/algorithm without paying due attention to other aspects such as data. This model-centric AI (MC-AI) approach is limited to only those few businesses/applications (language models, text analysis, etc.) where big data readily exists, and it cannot offer a feasible solution when good data are not available. However, in many real-world cases, giant datasets either do not exist or cannot be curated. Therefore, the AI community is searching for appropriate solutions to compensate for the lack of giant datasets without compromising model performance. In this context, we need a data-centric AI (DC-AI) approach in order to solve the problems faced by the conventional MC-AI approach, and to enhance the applicability of AI technology to domains where data are limited. From this perspective, we analyze and compare MC-AI and DC-AI, and highlight their working mechanisms. Then, we describe the crucial problems (social, performance, drift, affordance, etc.) of the conventional MC-AI approach, and identify opportunities to solve those crucial problems with DC-AI. We also provide details concerning the development of the DC-AI approach, and discuss many techniques that are vital in bringing DC-AI from theory to practice. Finally, we highlight enabling technologies that can contribute to realizing DC-AI, and discuss various noteworthy use cases where DC-AI is more suitable than MC-AI. Through this analysis, we intend to open up a new direction in AI technology to solve global problems (e.g., climate change, supply chain disruption) that are threatening human well-being around the globe. Full article

The growing importance of environmental efficiency in reducing carbon emissions has prompted scientists around the world to intensify their efforts to prevent the destructive effects of a changing climate and a warming planet. Global carbon emissions rose by more than 40% in 2021, leading to significant variations in the planet’s weather patterns. Nevertheless, a significant proportion of natural resources continue to be exploited. To prepare for this challenge, it is essential to implement a sustainable approach in the construction industry. Biobased materials are made primarily from renewable raw materials like hemp, straw, miscanthus, and jute. These new materials provide excellent thermal and acoustic performance and make optimum use of local natural resources such as agricultural waste. Nowadays, cement is one of the most important construction materials. In an attempt to meet this exciting challenge, biobased materials with low-carbon binders are one of the proposed solutions to create a more insulating and less polluting material. The aim of this review is to investigate and to analyze the impact of the incorporation of different types of biobased materials on the mechanical, thermal, and hygric performance of a mix using different types of binder. Full article

Renewable energy resources are playing a crucial role in minimizing fossil fuel emissions. Integrating machine learning techniques with tidal power forecasting could greatly enhance the accuracy and reliability of predictions, which is crucial for efficient energy production and management. A hybrid approach combining different methods often yields better results than relying on individual techniques. The accuracy of tidal current power is very important, especially for smart grid applications. This work proposes hybrid adaptive neuro-fuzzy inference system (ANFIS) with the Kalman filter (KF) and a neuro-wavelet (WNN) for tidal current speed, direction, and power forecasting. The turbine used in this study is driven by a direct drive permanent magnet synchronous generator (DDPMSG). The predictions of individual and hybrid models including the ANFIS, the Kalman filter, and the WNN for tidal current speed and the power it generates are compared with another dataset as a way of validation which is the tidal currents direction. Also, other published work results in the literature are compared to the proposed work. Different hybrid models are proposed for smart grid integration. The results of this work indicate that the hybrid model of the WNN and the ANFIS for tidal current power or speed forecasting has the highest performance compared to all other models. Full article

Robotic manipulators are critical for industrial automation, boosting productivity, quality, and safety in various production applications. Key factors like the payload, speed, accuracy, and reach define robot performance. Optimizing these factors is crucial for future robot applications across diverse fields. While 6-Degrees-of-Freedom (DoF)-articulated robots are popular due to their diverse applications, this research proposes a novel 5-DoF robot design for industrial automation, featuring a combination of three prismatic and two revolute (2R) joints, and analyzes its workspace. The proposed techno-economically efficient design offers control over the robot manipulator to achieve any reachable position and orientation within its workspace, replacing traditional 6-DoF robots. The kinematic model integrates both parallel and serial manipulator principles, combining a Cartesian mechanism with rotational mechanisms. Simulations demonstrate the end effector’s flexibility for tasks like welding, additive manufacturing, and material inspections, achieving the desired position and orientation. The research encompasses the design of linear and rotational actuators, kinematic modeling, Human–Machine Interface (HMI) development, and welding application integration. The developed robot demonstrates a superior performance and user-friendliness in welding. The experimental work validates the design’s optimized joint trajectories, efficient power usage, singularity avoidance, easy access in application areas, and reduced costs due to fewer actuators. Full article

The aim of the present research was to determine if the developed ovo−vegetarian sausage (SO), which was made with 15% chickpea flour, 51% albumin and 34% soy protein concentrate, exhibited improved physicochemical and sensory characteristics compared to vegetarian sausages available on the local market (classic vegan sausage, SC; vegan fine herb sausage, SH; and quinoa sausage, SQ). According to the physicochemical results, the developed sample, SO, presented significant differences (p < 0.05) compared to the others, including higher protein content, lower pH and a higher a* value. Three types of sensory analyses were conducted—flash profile, overall liking and purchase intention (to determine consumers’ willingness to purchase the product)—with the first involving 15 consumers and the second and third involving 60 participants each. Descriptors for each sample were determined using the vocabulary provided by consumers in the flash profile analysis. Descriptors for SO included ‘elastic’, ‘smell of cooked corn’, ‘characteristic flavor’, ‘pasty’, ‘soft’ and ‘pastel color’, contributing to its greater overall liking and purchase intention compared to the others. Through the hierarchical multiple factor analysis, a positive correlation was observed between the texture and sensory descriptors of the flash profile. Conversely, a correlation was found between the physicochemical characteristics (pH, a_w, color) and overall liking and purchase intention. Full article

Making plant breeding programs less expensive, fast, practical, and accurate, especially for soybeans, promotes the selection of new soybean genotypes and contributes to the emergence of new varieties that are more efficient in absorbing and metabolizing nutrients. Using spectral information from soybean genotypes combined with nutritional information on secondary macronutrients can help genetic improvement programs select populations that are efficient in absorbing and metabolizing these nutrients. In addition, using machine learning algorithms to process this information makes the acquisition of superior genotypes more accurate. Therefore, the objective of the work was to verify the classification performance of soybean genotypes regarding secondary macronutrients by ML algorithms and different inputs. The experiment was conducted in the experimental area of the Federal University of Mato Grosso do Sul, municipality of Chapadão do Sul, Brazil. Soybean was sown in the 2019/20 crop season, with the planting of 103 F2 soybean populations. The experimental design used was randomized blocks, with two replications. At 60 days after crop emergence (DAE), spectral images were collected with a Sensifly eBee RTK fixed-wing remotely piloted aircraft (RPA), with autonomous takeoff control, flight plan, and landing. At the reproductive stage (R1), three leaves were collected per plant to determine the macronutrients calcium (Ca), magnesium (Mg), and sulfur (S) levels. The data obtained from the spectral information and the nutritional values of the genotypes in relation to Ca, Mg, and S were subjected to a Pearson correlation analysis; a PC analysis was carried out with a k-means algorithm to divide the genotypes into clusters. The clusters were taken as output variables, while the spectral data were used as input variables for the classification models in the machine learning analyses. The configurations tested in the models were spectral bands (SBs), vegetation indices (VIs), and a combination of both. The combination of machine learning algorithms with spectral data can provide important biological information about soybean plants. The classification of soybean genotypes according to calcium, magnesium, and sulfur content can maximize time, effort, and labor in field evaluations in genetic improvement programs. Therefore, the use of spectral bands as input data in random forest algorithms makes the process of classifying soybean genotypes in terms of secondary macronutrients efficient and important for researchers in the field. Full article

Saffron (Crocus sativus) floral by-products are a source of phenolic compounds that can be recovered and used in the nutraceutical, pharmaceutical, or cosmetic industries. This study aimed to evaluate the phenolic compounds’ extraction using green extraction techniques (GETs) in saffron floral by-products and to explore the influence of selected extraction techniques on the phytochemical composition of the extracts. Specifically, ultrasound-assisted extraction (UAE), subcritical water extraction (SWE), and deep eutectic solvents extraction (DESE) were used. Phenolic compounds were identified with (HR) LC-ESI-QTOF MS/MS analysis, and the quantitative analysis was performed with HPLC-PDA. Concerning the extraction techniques, UAE showed the highest amount for both anthocyanins and flavonoids with 50:50% v/v ethanol/water as solvent (93.43 ± 4.67 mg/g of dry plant, dp). Among SWE, extraction with 96% ethanol and t = 125 °C gave the best quantitative results. The 16 different solvent mixtures used for the DESE showed the highest amount of flavonoids (110.95 ± 5.55–73.25 ± 3.66 mg/g dp), while anthocyanins were better extracted with choline chloride:butane-1,4-diol (16.0 ± 0.80 mg/g dp). Consequently, GETs can be employed to extract the bioactive compounds from saffron floral by-products, implementing recycling and reduction of waste and fitting into the broader circular economy discussion. Full article

Evaluating the access to and quality of healthcare services from the users’ perspective is an important assessment process to identify priorities. This study assessed the profile of health service usage and the views of the Unified Health System (SUS) users about the access to and quality of SUS public health services. A cross-sectional study was conducted with participants from the Coastal Lowlands Region of the Rio de Janeiro State/Brazil, between August and November 2023. The association between categorical variables was analyzed using the Pearson Chi-Square test, using R software 4.3. A total of 200 individuals were interviewed using a 66-question survey instrument. Participants who reported using SUS services more frequently rated this system as essential (p-value = 0.031). However, overall, 64% of participants rated the quality of care to be very bad/bad and 34.9% rated access as very bad/bad. Access was considered poor by respondents who used public services rarely or sometimes (p-value = 0.002). In terms of accessing SUS services consultations provided by specialists (e.g., neurologists), these were available only in another municipality (p-value = 0.001). Many participants were SUS dependent for health services, and gaps and weaknesses were observed regarding users’ perspectives of the access to and quality of SUS health care. Policymakers should prioritize evaluations and dialogue with the community to make SUS services responsive and to optimize value-for-money in health service planning. Full article

This manuscript analyses changes in the optical parameters of a commercial alumina nanoporous structure (Anodisc^TM or AND support) due to surface coverage by the ionic liquid (IL) AliquatCl (AlqCl). XPS measurements were performed for chemical characterization of the composite AND/AlqCl and the AND support, but XPS resolved angle analysis (from 15° to 75°) was carried out for the homogeneity estimation of the top surface of the ANDAlqCl sample. Optical characterization of both the composite AND/AlqCl and the AND support was performed by three non-destructive and non-invasive techniques: ellipsometry spectroscopy (SE), light transmittance/reflection, and photoluminescence. SE measurements (wavelength ranging from 250 nm to 1250 nm) allow for the determination of the refraction index of the AND/AlqCl sample, which hardly differs from that corresponding to the IL, confirming the XPS results. The presence of the IL significantly increases the light transmission of the alumina support in the visible region and reduces reflection, affecting also the maximum position of this latter curve, as well as the photoluminescence spectra. Due to these results, illuminated I–V curves for both the composite AND/AlqCl film and the AND support were also measured to estimate its possible application as a solar cell. The optical behaviour exhibited by the AND/AlqCl thin film in the visible region could be of interest for different applications. Full article

Background: Microaggressions are subtle slights that can cause significant psychological distress among marginalized groups. Few studies have explored interventions that might mitigate these effects. Objective: This study aimed to investigate if and how humor-infused immersive storytelling via virtual reality (VR) could reduce identity-related psychological distress caused by microaggressions. Methods: Using a community-based participatory research approach, we developed a 7-min 360-degree VR film depicting scenarios of microaggressions across various identities. Forty-six college students participated in a controlled study where they were exposed to this immersive VR experience. We measured identity-related psychological anxiety, character identification, perceived humor, and perceived psychological presence. Results: The findings demonstrated a significant anxiety reduction following the VR intervention, supporting the efficacy of humor-infused storytelling in alleviating the impact of microaggressions. Character identification significantly predicted anxiety reduction, while perceived humor and psychological presence did not directly influence anxiety reduction but indirectly contributed through enhanced character identification. Conclusions: Humor-infused immersive storytelling, facilitated by VR, effectively reduces identity-related psychological distress primarily through character identification. The structural equation modeling results emphasize the importance of integrating humor and psychological presence to enhance character connection, advocating for a balanced approach that combines traditional narrative elements with technological innovations in health interventions aimed at combating the adverse psychological effects of microaggressions. Full article

Utility as-built plans, which typically provide information about underground utilities’ position and spatial locations, are known to comprise inaccuracies. Over the years, the reliance on utility investigations using an array of sensing equipment has increased in an attempt to resolve utility as-built inaccuracies and mitigate the high rate of accidental underground utility strikes during excavation activities. Adapting data fusion into utility engineering and investigation practices has been shown to be effective in generating information with improved accuracy. However, the complexities in data interpretation and associated prohibitive costs, especially for large-scale projects, are limiting factors. This paper addresses the problem of data interpretation, costs, and large-scale utility mapping with a novel framework that generates probabilistic inferences by fusing data from an automatically generated initial map with as-built data. The probabilistic inferences expose regions of high uncertainty, highlighting them as prime targets for further investigations. The proposed model is a collection of three main processes. First, the automatic initial map creation is a novel contribution supporting rapid utility mapping by subjecting identified utility appurtenances to utility inference rules. The second and third processes encompass the fusion of the created initial utility map with available knowledge from utility as-builts or historical satellite imagery data and then evaluating the uncertainties using confidence value estimators. The proposed framework transcends the point estimation of buried utility locations in previous works by producing a final probabilistic utility map, revealing a confidence level attributed to each segment linking aboveground features. In this approach, the utility infrastructure is rapidly mapped at a low cost, limiting the extent of more detailed utility investigations to low-confidence regions. In resisting obsolescence, another unique advantage of this framework is the dynamic nature of the mapping to automatically update information upon the arrival of new knowledge. This ultimately minimizes the problem of utility as-built accuracies dwindling over time. Full article

Good manufacturing practices play an important role in obtaining safe food and preventing foodborne diseases. To achieve this goal, food handlers must receive appropriate training to be aware of their responsibilities. In this work, compliance with specific personal hygiene requirements by food handlers was assessed in a cross-sectional study of traditional small retail establishments, namely butcher (n = 56) and fishmonger (n = 17) shops in Almada, Portugal. Food handlers (n = 140, of which 113 worked in butcher shops, and 27 worked in fishmonger shops) were interviewed for data collection, and retail establishments were audited considering specific hygiene requisites. In fishmonger shops, most food handlers are women (89%), aged 18 to 45 years (70%), with a high school degree, having worked for less than 5 years in this activity, while in butcher shops most food handlers are men (90%) over 45 years old (58%), with a basic education level, and more than 26 years of experience. Most food handlers (>95%) attended recent food safety and hygiene training courses and were able to recognize that hand sanitizers cannot replace a proper hand wash, and to identify Staphylococcus aureus transmission routes to food. However, approximately 23% of retail establishments failed to provide hot water in the handwashing basin and exhibited improper placement of handwashing instructions. Furthermore, these establishments did not implement corrective actions following non-conforming microbiological results of hand hygiene monitoring. These findings reinforce the need for consistent management commitment, and for providing food handlers with regular training, which is crucial for maintaining a strong food safety and hygiene culture in these traditional small retail establishments. Full article

This research explores the utilization of powder fused filament fabrication (PFFF) for producing tungsten carbide-cobalt (WC-10Co) hardmetals, focusing on binder formulations and their impact on extrusion force as well as the influence of printing variables on the green and sintered density of samples. By examining the interplay between various binder compositions and backbone contents, this study aims to enhance the mechanical properties of the sintered parts while reducing defects inherent in the printing process. Evidence suggests that formulated feedstocks affect the hardness of the sintered hardmetal—not due to microstructural changes but macrostructural responses such as macro defects introduced during printing, debinding, and sintering of samples. The results demonstrate the critical role of polypropylene grafted with maleic anhydride (PP-MA) content in improving part density and sintered hardness, indicating the need for tailored thermal debinding protocols tailored to each feedstock. This study provides insights into feedstock formulation for hardmetal PFFF, proposing a path toward refining manufacturing processes to achieve better quality and performance of 3D printed hardmetal components. Full article

The effects of exposure to airborne particulate matter with a size of 10 μm or less (PM₁₀) on C57BL/6 mouse corneas, their response to Pseudomonas aeruginosa (PA) infection, and the protective effects of SKQ1 were determined. C57BL/6 mouse corneas receiving PBS or SKQ1 were exposed to control (air) or PM₁₀ for 2 weeks, infected, and the disease was documented by clinical score, PMN quantitation, bacterial plate count, RT-PCR and Western blot. PBS-treated, PM₁₀-exposed corneas did not differ at 1 day postinfection (dpi), but exhibited earlier (3 dpi) corneal thinning compared to controls. By 3 dpi, PM₁₀ significantly increased corneal mRNA levels of several pro-inflammatory cytokines, but decreased IL-10, NQO1, GR1, GPX4, and Nrf2 over control. SKQ1 reversed these effects and Western blot selectively confirmed the RT-PCR results. PM₁₀ resulted in higher viable bacterial plate counts at 1 and 3 dpi, but SKQ1 reduced them at 3 dpi. PM₁₀ significantly increased MPO in the cornea at 3 dpi and was reduced by SKQ1. SKQ1, used as an adjunctive treatment to moxifloxacin, was not significantly different from moxifloxacin alone. Exposure to PM₁₀ increased the susceptibility of C57BL/6 to PA infection; SKQ1 significantly reversed these effects, but was not effective as an adjunctive treatment. Full article

This paper explores the economic trends and identifies speculative bubbles within the emerging metaverse, based on the specific example of Decentraland, which is represented by its underlying native token MANA.For comparability, we consider three further tokens: SAND, ETH, and BTC.The study shows price prediction and provides further insight into bubble behavior to provide a deeper insight into the real trend and situation of the metaverse. When comparing all considered tokens, evidence of comovement and positive as well as negative bubbles is identified. This paper makes use of proven modeling techniques, such as SARIMA, for price prediction and LPPLS for financial bubble identification, visualization, and time stamping. Full article