The objective of this study was to investigate the transcriptomic profiling of differentially ex-pressed genes between a commercial layer breed and an indigenous Indian breed. RNA was ex-tracted from NDV-infected and uninfected embryo lung tissue samples of Aseel, Kadaknath, and White Leghorn chickens. RNA-Seq was performed, and reads were aligned to the chicken refer-ence genome (Galgal7). A total of 594 genes were significantly differentially expressed, with 264 upregulated and 330 downregulated in infected embryos. Highly overexpressed genes in infect-ed conditions included C8A, FGG, PIT54, FETUB, APOC3, FGA, SPIA1, AvBD13, APOH, AMBP, TTR, PLA2G12B. Additionally, 29 novel transcripts and 20 lncRNAs were upregulated. White Leghorn embryos showed distinct expression patterns for CAV1 and GATA3, while Kadaknath healthy embryos had higher levels of CYTB, HBBA, ND4, ND4L, and ND5, indicating breed uniqueness. The analysis revealed significant gene involvement through Gene Ontology and KEGG pathways, highlighting alterations in gene expression related to immune function, me-tabolism, cell cycle, nucleic acid processes, and mitochondrial activity due to NDV infection. ALDOB, PRPS2, XDH, DCK2, and TK1 genes, which were involved in several metabolic path-ways, clearly displayed an altered expression pattern in the infected embryo. Additionally, ALB, TLR4, TLR2, TLR21, IL1R2, IL22RA2, HSP90AA, HSPB9(p60), and HSPB8 were significantly up-regulated in infected lung samples, while CXCR4, CXCL14, GATA3, IL17REL, and IL22RA1 were downregulated. Notably, higher expression of HSPs in Kadaknath suggests heat tolerance. This research provides insights into the genetic differences between commercial and local breeds and reveals the impact of NDV infection on metabolic processes, potentially identifying candidate genes for breed improvement.

Although social sustainability plays a crucial role in the sustainable development agenda, there is a scarcity of research that clearly defines and fully operationalizes this concept. This study seeks to fill this gap in the literature by creating a comprehensive measurement scale to evaluate social sustainability in the context of urban parks. According to the literature review, social sustainability is identified as a multidimensional concept, encompassing eight key dimensions in urban parks: identity, place attachment, cohesion, safety, equity, facilities, comfort, and accessibility. The validity, reliability, and dimensions of the social sustainability scale for urban parks are examined using factor analysis. To explore the idea that user evaluations of urban parks can be employed as a method to ensure social sustainability, user feedback was collected for the study. The article evaluates data from 120 participants in Alaeddin Hill Park, located in Konya, Turkey—a site selected for its potential to best reflect the identified dimensions. This study reveals the relationships between different dimensions of social sustainability that influence the park's social sustainability performance at the urban park scale. Additionally, it provides insights aimed at improving park design quality. The findings of the study indicate that the park’s social sustainability is at a moderate level, with identity identified as the most influential dimension. On the other hand, the dimension that most negatively impacts sustainability performance is safety, primarily due to low perceptions of safety caused by inadequate lighting, the predominant use of the park by people from lower socioeconomic backgrounds, and a general lack of security.

This study examines a case involving a 7-year-old child who developed dengue symptoms following Qdenga vaccination. Despite initial negative diagnostic results, molecular analysis confirmed an infection with DENV4. Next-generation sequencing detected viral RNA from both DENV2 and DENV4 serotypes, which were identified as vaccine-derived strains using specific primers. Phylogenetic analysis further confirmed that these sequences belonged to the Qdenga vaccine rather than circulating wild-type viruses. This case underscores the critical need for precise diagnostic interpretation in vaccinated individuals to avoid misdiagnosis and to strengthen public health surveillance. A comprehensive understanding of vaccine-induced viremia is essential for refining dengue surveillance, improving diagnostic accuracy, and informing public health strategies in endemic regions.

In terahertz communication systems, lens antennas used in transceivers are basically plano-convex dielectric lenses. As the aperture increases and the focal length decreases, the thickness and mass of the plano-convex lens increase rapidly. Through theory and simulation, we designed a Fresnel lens suitable for the terahertz band to meet the requirements of large aperture and short focal length, and simulated the performance, advantages and disadvantages of the terahertz Fresnel lens. A 300GHz terahertz wireless communication system was built to verify the gain effect of the Fresnel lens antenna. The experimental results show that the Fresnel lens can amplify the signal in the terahertz band. The theoretical gain of a 30 cm Fresnel lens is 48.83dB, while the actual measured gain is approximately 45dB.

Understanding the interplay between infections and severe acute malnutrition is critical in attaining good clinical outcomes in managing malnourished children. However, review studies describing the profile of the associated pathogens in the malnourished African pediatric population are sparse in the literature. We aimed to identify the spectrum of pathogens from studies reporting infections in malnourished African children, as well as the antibiotic resistance pattern and clinical outcomes. A systematic literature of the PubMed database was conducted following PRISMA guidelines from January 2001 to June 2024. The search algorithm was ((marasmus) OR (kwashiorkor) OR (severe acute malnutrition) OR (protein energy malnutrition)) AND (Africa). For a more comprehensive retrieval, an additional search algorithm was deployed: (HIV OR tuberculosis) AND (severe acute malnutrition). We included 67 studies conducted between 2001 and 2024. Most of the studies were from East Africa (n=53, 79.1%) and Southern Africa (n=5, 7.4%). A total of 7,056 pathogens were identified comprising 3,030 Viruses, 2,381 bacteria, 1,452 parasites and 193 fungal pathogens. The predominant pathogens were HIV, Mycobacterium tuberculosis, and malaria parasites accounting for 42.5%, 25.2%, and 17.1% respectively. Antibiotic susceptibility testing was documented in only three studies. Fatality rates were reported in 49 studies and ranged from 2% to 56% regardless of the category of pathogen. This review affirms the deleterious effect of infections in malnourished patients and suggests a gross underdiagnosis as studies were found from only 17 (31.5%) African countries. Moreover, data on fungal infections in malnourished African children was nearly absent despite being at risk. There is also a need to prioritize research investigating African children with severe acute malnutrition for fungal infections besides other opportunistic pathogens and improve the availability of diagnostic tools and the optimized usage of antibiotics through the implementation of antimicrobial stewardship programmes.

Endocrine-disrupting chemicals (EDCs) significantly damage biological systems related to reproductive, neurological, and metabolic functions. Approximately 1,000 chemicals are known to possess endocrine-acting properties, including bisphenol A (BPA) and di(2-ethylhexyl) phthalate (DEHP). This study primarily focuses on the potential effects of EDCs on the transcriptional levels of innate immune prophenoloxidase (proPO) system-related genes to oxidative stress in the gonad and stomach of the mud crab Macrophthalmus japonicus, an indicator species for assessing coastal benthic environments, when exposed to BPA or DEHP. After EDC exposure, the expression of lipopolysaccharide and β-1,3-glucan-binding protein (LGBP), a pattern recognition protein that activates the proPO system, was upregulated in the stomach of M. japonicus, whereas LGBP gene expression was downregulated in the gonad. In the gonad, which is a reproductive organ, EDC exposure mainly induced the transcriptional upregulation of trypsin-like serine protease (Tryp) at relatively low concentrations. In the stomach, which is a digestive organ, LGBP expression was upregulated at relatively low concentrations of EDCs over 7 days, whereas all proPO system-related genes (LGBP, Tryp, serine protease inhibitor (Serpin), and peroxinectin (PE)) responded to all concentrations of EDCs. These results suggest that the antioxidant and immune defense responses of the proPO system to EDC toxicity may vary, causing different degrees of damage depending on the tissue type in the mud crab.

The growing use of drones in precision agriculture highlights the need for enhanced operational efficiency. Despite the ability of computer vision based on deep learning has made remarkable progress in the past ten years, when it comes to segmentation task on UAVs, there is always a conflict between the demand of high precision and low inference latency. Due to such a dilemma, we propose the IRB-YOLO, an efficiency model based on Inverted Residual Block, devoting to provide constructive strategies in real-time detection tasks of UAV camera. The working details of this paper are as follows: (1) This paper innovates with a IR-Block(Inverted Residual Block), integrated into a refined YOLOv8-seg structure to create IRB-YOLO. This model specializes in pixel-level classification of UAV-acquired RGB images, facilitating the creation of exact maps to guide agricultural strategies. (2)When it comes to the experiments on a Vatica dataset with any other light-weight segmentation model, IRB-YOLO achieve at least a 3.3% increase in mAP. Further validation using a diverse species dataset confirms its robust generalization. (3)Without overloading the complex attention mechanism and deeper and deeper network, a stem that incorporates efficient feature extraction components, inverted residual block, can still possess outstanding modeling capabilities. IRB-YOLO builds a bridge between academic research and edge deployment of drones, making it applicable in real-world scenarios.

The month of May 2024 was characterized by solar energetic particles events directed towards the Earth, especially the big event causing a strong terrestrial geomagnetic storm during the night from 10 to 11 May 2024, with auroras observed everywhere in Europe. This was the strongest storm for the last 20 years with a Dst < -400 nT. In the present work, we show with observations of GOES, PROBA-V/EPT and MetOP/MEPED that this exceptional event was associated with the injection of energetic protons in the proton radiation belt, with important consequences for the South part of the South Atlantic Anomaly (SAA). In addition, the geomagnetic storm caused by the solar eruption has had tremendous impacts on the electron radiation belts. Indeed, we show that for 0.3 to 1 MeV electrons, the storm led to a long lasting four belts configuration which was not observed before with EPT launched in 2013, until a smaller geomagnetic storm on June 15 took place. Moreover, for the first time since its launch, observations of the EPT show that ultra-relativistic electrons with E>2 MeV have been injected into the inner belt until L=2.4, violating the impenetrable barrier previously estimated to be located at L=2.8.

Large Language Models (LLMs) have emerged as powerful tools in the domain of artificial intelligence, specifically enhancing natural language processing capabilities. These models leverage extensive datasets and sophisticated algorithms to automate and enhance tasks traditionally performed by humans, thereby revolutionizing innovation and entrepreneurship. LLMs accelerate product development, streamline business operations, and enable precise and rapid decision-making, all of which are crucial for maintaining competitiveness in dynamic markets. This paper categorizes the applications of LLMs in innovation and entrepreneurship into three main areas: technological innovation, strategic decision-making, and process automation. By exploring various integrations of LLMs in entrepreneurial ventures, this work offers both theoretical insights and practical examples, underscoring the transformative impact of LLMs in shaping modern business landscapes.

This review aims to show, by an analysis of the literature on the topic, that hyperinsulinemia associated with insulin resistance is a multiple risk factor for type 2 diabetes, cardiovascular diseases, cellular senescence and cancer, and neurodegenerative diseases. This condition is progressively increasing in developed and developing countries, and its prevalence has now exceeded 50% of the general population. Since it is asymptomatic or poorly symptomatic, it can last for many years before manifesting itself in the form of type 2 diabetes mellitus, cardiovascular disease, neoplasm, cognitive impairment or dementia, therefore determining enormous social and health care costs. For these reasons, we strongly believe that a screening plan for this pathology should be implemented, in order to identify affected subjects and promptly start them on preventive treatment.

Aim: This study aims to analyze the collaborative networks and identify key players in autoimmune pancreatitis (AIP) research from 2000 to 2023. Using data from the Web of Science (WoS) Core Collection, I assess the structure and evolution of co-authorship networks to understand collaboration trends and influential researchers in the field.Method: I conducted a network analysis of co-authorship patterns in AIP research using Python (Version 3.10.5) in the PyCharm development environment (Software Version 2022.1.3). The analysis included macro-level indicators: network density, clustering coefficient, components, and average distance, as well as micro-level indicators: degree centrality, closeness centrality, and betweenness centrality. These metrics were used to evaluate the connectivity, clustering, and key nodes within the network across three distinct periods: 2000-2009, 2010-2019, and 2020-2023.Result: The analysis revealed that the co-authorship network in AIP research evolved from a fragmented structure with limited collaboration in the early 2000s to a more interconnected but still dispersed network in recent years. Despite an increase in network density and a decrease in the number of disconnected components over time, the overall structure remains highly clustered, with many researchers operating within isolated groups. Key figures, such as Kazuichi Okazaki, Terumi Kamisawa, and Shigeyuki Kawa from Japan, consistently ranked high in centrality measures, highlighting their significant influence and leadership in the field. The findings suggest that while local collaborations are strong, broader international connections are still developing.Conclusion: This study provides a comprehensive overview of the collaborative landscape in AIP research, identifying central researchers and highlighting persistent fragmentation within the network. The prominent role of Japanese researchers in shaping the field underscores the importance of fostering greater international collaboration to bridge gaps between research clusters. Enhancing global partnerships could lead to more integrated and impactful research outcomes, ultimately advancing the understanding and management of autoimmune pancreatitis.

Globally, the fuel demand is increasing rapidly and the conventional method to produce fuel is toxic which causes environmental degradation and climate crisis. Therefore, biodiesel emerges as the substitute to overcome the conventional fuel problems because biodiesel is renewable and non-toxic. Biodiesel synthesis is the process of using fats and oils to produce biodiesel ester and glycerol. Biodiesel can be produced using edible and non-edible feedstock but using the edible feedstocks problems like fuel vs food, nutritional value and economic considerations were raised. Therefore, non-edible feedstocks are being used to overcome problems arising from edible feedstock. As non-edible feedstocks can easily be grown anywhere on land, doesn’t cause imbalance to food economy and has low cost. Many non-consumable seeds like Mahua, Jatropha, Castor, Neem etc. are used to produce biodiesel. Now a days, researchers are using nanoparticles to enhance the stability, quality, and properties of biodiesel. In future, researchers can use algae and microbial oils for production of biodiesel. Research can also use biojet fuel in such a way that it reduces the carbon emission. In future, production of biodiesel can be integrated into circular economy models to reduce environmental waste and impact. In this review we will discuss the production methods for biodiesel, catalysts for biodiesel production, and non-edible feedstock properties of biodiesel.

The agricultural soil maps (ASMs) showing the agricultural land of Poland were prepared in 1:5000 scale in 1960s’ and 1970s’. These maps show land suitability group, soil type and soil texture (ST) to a depth of about 150cm. Nowadays, these maps are being digitalized and might be a basis for the preparation of modern soil maps at local, regional, national and international level. The agreement between the ST of topsoil derived from ASMs and recently evaluated for eleven fields located in three voivodeships (regions) of Poland was studied. The study of soil considered the examination of soil profiles or augerings and laboratory analysis of the ST. The agreement between soil status in the field and according to maps was field-specific. Complete agreement (purity) within the field was assessed for 5-79% of ST classes and for 23-100% of agronomic categories (ACs) – a groupings of similar ST classes. However, the averaged agreement, which treated adjacent ST classes as having partial agreement, varied from 37 to 88% for ST classes and from 61 to 100% for the ACs among studied fields. These results indicate on the variable quality of the information shown on the ASMs and on the necessity of improvement of these maps.

Loop-Mediated Isothermal Amplification (LAMP) is a widely used technique for nucleic acid amplification due to its high specificity, sensitivity, and rapid results. Advances in microfluidic lab-on-chip (LOC) technology have enabled the integration of LAMP into miniaturized devices, known as μ-LAMP, which require precise thermal control for optimal DNA amplification. This paper introduces a novel thermal bed design using PCB copper traces and FR−4 dielectric materials, providing a reliable, modular, and repairable heating platform. We present the design, fabrication, and control mechanisms of the thermal bed, demonstrating its ability to maintain accurate temperatures essential for μ-LAMP applications. The thermal bed’s performance is validated through finite element method (FEM) simulations, confirming its effectiveness in diverse applications such as disease diagnostics, biological safety, and forensic analysis.

The objective of this study was to evaluate the antifungal activity of eugenol against the genomic DNA of Candida albicans. A quantitative experimental study was conducted with the C. albicans INCQS 40006 strain. Eugenol was tested in concentrations of 12.5%, 25%, 50%, 75% and 100%. The test substance remained in contact with the fungus for 24 hours in a BHI medium in a microbio-logical greenhouse, then the DNA extraction was performed and part of the sample obtained was washed with TE to remove the Eugenol. The fungus was placed in BHI again and placed for 24 hours in a microbiological greenhouse. At the end, the samples with 100% eugenol went through seeds in Sabouraud Dextrose for confirmatory tests. Thus, from the electrophoresis, which admits evaluating the quality of the extracted material, preserving its characteristics and integrity, it was observed in the eugenol concentration test (test 1), in which the eugenol was in direct contact with the pathogen, that in concentrations of equal to or greater than 75% of the test substance the pathogen had no proliferative power. In which the fungus's ability to recover is evaluated (test 2), eugenol was removed from the sample, noting that in 100% of the test substance the fungus did not have the ability to recover, resulting in definitive cell death.

As a method of lightweight design, multi-material design aims to make targeted use of materials in order to reduce CO2 emissions. In this context, it can be described as one of the product development methods used to meet the challenges of climate change. However, since the design of structures in multi-material design is complex, topology optimization can be used to support the product developer. In this article, a multi-material topology optimization method is developed that combines the Velocity Field Level Set method with the Reconciled Level Set method. Furthermore, the current design is explicitly resolved in each iteration by means of multi-material remeshing. The edge collapse phase in the remeshing process is achieved by applying the producer consumer pattern. The developed method is then validated using known examples from the state of research and the influence of parameters of the method on the result is analyzed by means of studies.

Context: The advent of new strains of resistant microbes and the concomitant growth in multidrug resistance have made antimicrobial resistance an urgent public health concern. New antimicrobials are desperately needed to boost the success rates of treating infectious diseases and save lives. There are many intriguing biomolecules with antibacterial action, which are mostly unexplored in microorganisms. Objectives: The objective of the present review article is to describe the importance of natural compounds against microorganisms using advanced techniques to protect people from diseases. Method: We have searched and incorporated many online literature to gather information from SCOPUS, SCI, PUBMED, ScienceDirect and Medline. We have discussed several microorganism sources for antimicrobials and antifungal drugs. In addition, describes microculturing techniques and applications of microbial-based microsystems. Results and Conclusion: Every kind of higher trophic life depends on microorganisms for sustenance. The unseen majority is essential to understanding how humans and other living forms can survive anthropogenic climate change. Antimicrobial substances and the latest methods, techniques, and strategies for developing effective treatments have been discussed in this article. Novel model systems and cutting-edge biomolecular and computational methodologies could help researchers enhance antimicrobial resistance by completely capitalizing on lead antimicrobials.

Anxiety-based cognitive distortions pertaining to somatic perception (ABCD-SP) - primarily catastrophizing, fear avoidance and kinesiophobia- have been repeatedly linked to worsening chronic, non-cancer pain (CNCP) outcomes of increased disability, amplified pain, ineffective opioid use, and opioid misuse. Several studies have suggested that treating ABCD-SP can improve pain outcomes, yet identification and targeting of ABCD-SP is not part of the standard medical pain assessment and treatment plan. Utilizing a narrative review of proposed mechanisms, published patient perspectives, and study correlates connecting these cognitive distortions with CNCP outcomes, a better practice approach to the delivery of standard medical CNCP care can be deduced and formulated into a Belief and Behavior Action Plan (BBAP) for medical clinicians treating CNCP to implement into initial and maintenance care planning. These recommendations require relatively few resources to implement and have the potential to disseminate more effective CNCP treatment on a large scale now, and into the new frontier of cognitive computing within medicine.

Event detection provides a crucial foundation for intelligent text analysis and supports numerous downstream applications. However, existing detection models often fail to meet the high accuracy requirements and typically require a human–machine collaborative detection mode involving “machine detection followed by manual audit and correction.” Analysis reveals that the recall rate of machine detection is a major factor limiting the efficiency of human–machine collaboration. To address this at its root and ensure high recall, an event trigger recommendation task is introduced, and a span-based recommendation model for Chinese, SpETR (Span-based Event Trigger Recommender), is developed. This model integrates confidence information from both trigger identification and event classification stages to recommend candidate triggers and their event types for each event. To enhance recall, a semantic boundary smoothing method is proposed, which assigns soft labels to spans around gold triggers based on semantic completeness and part-of-speech, combined with a modified focal loss function to make the model predictions smoother. Experiments on DuEE and a self-built dataset show that SpETR is most effective when the number of recommendations is set to 3, achieving recall rates of 97.76% and 98.70%, respectively, comprising improvements of 4.30% and 5.22% over the optimal baseline models. In terms of human–machine collaborative detection, SpETR saved 30.6% and 25.8% of time compared to traditional detection models on two datasets, and the average F1 score of the annotation results was also improved by 5.35% and 3.48%, respectively.

While significant efforts have been made to recycle metals like lithium or cobalt for sustainable development in the battery industry, other high-value materials in the renewable energy sector, such as fluorinated carbon nanotubes (FCNTs), have not been adequately explored for re-utilization after their quality has been downgraded. FCNTs are expensive and energy-intensive to produce, making their sustainable use crucial. This study explores the potential of reusing quality-downgraded fluorinated carbon nanotubes (QD-FCNTs) as eco-friendly nano-additives in microgel composites for water retention applications. By employing a surface treatment process that avoids additional energy costs and environmentally harmful chemicals, we successfully enhanced the interaction between QD-FCNTs and microgels. This treatment significantly improved water retention time by reducing rapid evaporation, enabling sustained water release. Our findings demonstrate a promising avenue for the sustainable application of quality-downgraded fluorinated carbon nanotubes in agricultural and environmental technologies.

We investigated the potential of Mucuna pruriens and Canavalia ensiformis as phytoremediation candidates for Tebuthiuron (TBT)-contaminated soils. Crotalaria juncea was the bioindicator specie for tebuthiuron and Lactuca sativa used in ecotoxicological bioassays, both analyses aim to verify the herbicide presence in soil samples as TBT is persistent with environmental and health risks due to its tendency to bioaccumulate. We conducted a 140-day greenhouse experiment associated with bioaugmentation by microbial inoculants - solid or liquid. Both C. ensiformis (CE) and M. pruriens (MP) exhibited distinct growth patterns, with MP demonstrating a faster growth rate than CE, evident from its higher growth apex (α = 84.52) and specific growth rate (k = 0.113). While soil samples with microbial inoculants demonstrated significant biomass increase with tebuthiuron absence, the introduction this herbicide notably hindered phytomass production, particularly in MP + TBT treatments. Liquid inoculant notably contributed to biomass accumulation in MP, whereas microbial bioaugmentation did not substantially enhance phytomass in CE under tebuthiuron presence. Furthermore, C. juncea exhibited initially slow development in the reference treatment but displayed accelerated growth and greater height with liquid inoculant compared to solid inoculant. Despite the presence of tebuthiuron, C. juncea in soil with TBT alone reached a maximum height exceeding 100 cm. However, tebuthiuron significantly reduced biomass accumulation in C. juncea, indicating its high sensitivity to this herbicide. Liquid inoculant facilitated the highest dry biomass accumulation in C. juncea, yet the introduction of tebuthiuron led to a considerable reduction in biomass, underscoring the plant's susceptibility to it. While bioaugmentation showed potential in mitigating herbicide phytotoxicity, its efficacy varied among tested inoculants. In bioassays with L. sativa, bioaugmentation with liquid inoculant resulted in a higher final germination index (GI) compared to solid inoculant and the reference treatment. Conversely, soil samples containing only tebuthiuron demonstrated a higher final GI and growth velocity compared to the reference treatment, implying the natural attenuation of toxicity over time. Additionally, previous cultivation of Mucuna pruriens positively influenced the development of test-organism, resulting in a higher GI and growth rate compared to the reference treatment. These findings provide valuable insights for various stakeholders, including the scientific community, industries, and bioengineers. They offer a strong incentive to develop advanced bioremediation strategies to address persistent pesticide contamination, especially in intensive agroecosystems like sugarcane-producing fields. Implementing our biological solution holds the potential to usher in a cleaner and safer era in agriculture, thereby protecting both society and the environment.

This scoping review explores the intersection of Extended Mind Theory and Extended Reality (XR) technologies, focusing on how Virtual Reality, Augmented Reality, and Mixed Reality reshape human cognition and interaction. XR enables users to offload cognitive tasks and engage in embodied experiences, extending cognition beyond the brain into digital environments. The review highlights a wide range of XR applications, from immersive learning in STEM education and medical training, neuropsychological assessment to therapeutic interventions, arts and entertainment, professional skills development, retail and e-commerce, remote work, sports training, architecture and urban planning, and cultural heritage preservation. XR's integration with modalities like haptics, eye-tracking, face- and body-tracking, and brain-computer interfaces further enhances cognitive extension and user engagement. However, alongside these advancements come significant ethical, psychological, and societal challenges, such as data privacy concerns, the psychological effects of prolonged immersion, and social inequality arising from disparate access to XR technologies. This review emphasizes the need for robust ethical frameworks that address these challenges, ensuring that XR technologies enhance human development while maintaining autonomy, privacy, and mental well-being. As XR continues to evolve and integrate with artificial intelligence and other emerging technologies, its role in expanding human cognition will depend on responsible implementation and governance.

Cotton (Gossypium spp.) is the most important fibre crop worldwide. Black root rot and Fusarium wilt are two major diseases of cotton caused by soil-borne Berkeleyomyces rouxiae and Fusarium oxysporum f. sp. vasinfectum (Fov), respectively. Phenotyping plant symptoms caused by soil-borne pathogens has always been a challenge. To increase the uniformity of infection, we adapted a seedling screening method that directly uses liquid cultures to inoculate the plant roots and the soil. Four isolates each of B. rouxiae and Fov were collected from cotton fields in Australia and were characterised for virulence on cotton under controlled plant growth conditions. While the identity of all four B. rouxiae isolates were confirmed by multi-loci sequencing, only two of them were found to be pathogenic on cotton, suggesting variability in the ability of isolates of this species to cause disease. The four Fov isolates were phylogenetically clustered together with the other Australian Fov isolates and displayed both external and internal symptoms characteristic of Fusarium wilt on cotton plants. Furthermore, the isolates appeared to induce varied levels of plant disease severity indicating differences in their virulence on cotton. To contrast the virulence of the Fov isolates, four putatively non-pathogenic Fusarium oxysporum (Fo) isolates collected from cotton seedlings exhibiting atypical wilt symptoms were assessed for their ability to colonise cotton host. Despite the absence of Secreted In Xylem genes (SIX6, SIX11, SIX13 and SIX14) characteristic of Fov, all four Fo isolates retained the ability to colonise cotton and induce wilt symptoms. This suggests that slightly virulent strains of Fo may contribute to the overall occurrence of Fusarium wilt in cotton fields. Findings from this study will allow better distinction to be made between plant pathogens and endophytes and allow fungal effectors underpinning pathogenicity to be explored.

The peptide hormone kisspeptin attenuates liver steatosis, metabolic dysfunction-associated stea-tohepatitis (MASH) and fibrosis in mouse models, by signaling via the kisspeptin 1 receptor (KISS1R). However, whether kisspeptin impacts fibrogenesis in the human liver is not known. We investigated the impact of a potent kisspeptin analog (KPA) on fibrogenesis using human precision cut liver slices (hPCLS) from fibrotic livers from male patients, in human hepatic stellate cells (HSCs), LX-2 and in primary mouse HSCs. In hPCLS, 48 h and 72 h of KPA (3 nM, 100 nM) treatment decreased collagen secretion, and lowered expression of fibrogenic and inflammatory markers. Immunohistochemical studies revealed that KISS1R is expressed and localized to HSCs in MASH/fibrotic livers. In HSCs, KPA treatment reduced transforming growth factor beta (TGFb)-induced expression of fibrogenic and inflammatory markers, in addition to decreasing TGFb induced collagen secretion, cell migration, proliferation and colony formation. Mechanistically, KISS1R signaling downregulated TGFb signaling by decreasing SMAD2/3 phosphorylation, via activation of protein-phosphatases PP2A, that dehosphorylates SMAD 2/3. This study revealed for the first time that kisspeptin reverses human hepatic fibrogenesis thus identifying it as a new therapeutic target to treat hepatic fibrosis.

Staphylococcus aureus, found in the oral cavity as an opportunistic bacterium, has been reported as a cause of infective endocarditis and pneumonia. Salt is an essential mineral component for maintaining human body cells and has attracted attention as a promoter of S. aureus biofilms. This study was conducted to clarify how salt affects biofilm formation by S. aureus and S. mutans, which is a pathogen implicated in the dental caries. Bacteria were cultivated with various concentration of sodium chloride (NaCl) in Toryptic Soy Broth with 0.25% sucrose and 0.25% glucose (TSBs and TSBg) for 16 hours. Contribution of GTF on the membrane vesicles (MVs) from S. mutans and extracellular DNA (eDNA) was also analyzed in the biofilm formation. S. aureus biofilms were induced by the addition of 0.004–0.25 M NaCl but decreased by NaCl concentrations greater than 0.25 M, in TSBs. The biofilm formation of mixed bacteria (S. aureus and S. mutans) gradually increased in a NaCl concentration-dependent manner in TSBs and TSBg. Moreover, the biofilm was dependent on glucan formation by GTF in MVs at high salinity. The mixed-species biofilm of S. aureus and S. mutans was upregulated by the collaboration of eDNA and MVs. Therefore, eDNA and MVs are developmental factors for S. aureus biofilm formation under harsh conditions. The intake of sucrose and glucose may be a risk factor for infection by opportunistic pathogens such as S. aureus in persons who consume high concentrations of salt in foods and drinks in custom-type foods.

In 2004, a significant portion of the spruce-dominated forests in the Tatra National Park (northern Slovakia) was destroyed by an exceptionally strong windstorm. Our study focused on the status of deadwood and its influence on post-disturbance forest regeneration 18 years after the destruction. Since some disturbed areas were salvaged and others were not, we could compare the situations between these two management approaches. In total, 40 research sites (20 salvaged and 20 unsalvaged) were measured, each represented by four satellite-like plots with a radius of 4 m. We measured the diameters of deadwood and its decay status (classes 1 – 5). At the same time, we recorded the sizes (stem base diameter and height) and species of young trees. The results showed that while salvage conditions and contact with soil stimulated deadwood decomposition, the diameter of logs was not a significant factor. The highest decay class (3.83) was found in deadwood on salvaged areas and attached to the soil, while the lowest decay class (3.10) was found in deadwood on unsalvaged areas and in the case of logs unattached to the soil. While carbon content (based on percentage of wood mass) did not change with decay class, wood density decreased sharply with decay class. Water moisture showed an increasing tendency with decay class. Moreover, our results showed that post-disturbance management influenced forest regeneration. Significantly higher tree diversity was observed in salvaged areas (5.40 species per plot) compared to unsalvaged areas (3.85 species per plot). While areas with logging were predominantly covered by broadleaved species, those without logging were typically dominated by Norway spruce (Picea abies L. Karst). We concluded that although salvage logging decreased the total carbon and water content in deadwood, it can stimulate tree species diversity. Thus, the currently prevailing opinion about the exclusively negative ecological effects of salvage logging should be accepted with caution, and local conditions must be considered before making broad judgments.

Cervical Cancer affects a large part of female population that makes the prediction of this disease based on Machine Learning (ML) by outmost importance. ML algorithms can be integrated in complex intelligent agent-based systems that can offer decision support to the resident medical doctors or even to experienced medical doctors. For instance can be mentioned the situation when an experienced medical doctor diagnose a case but he/she needs expertise support that is related to another medical specialty. Data imbalance is frequent in healthcare data and has a negative influencing effect in making predictions using ML algorithms. Cancer data generally and cervical cancer data particularly are frequently imbalanced. Based on this fact the study of data imbalance impact on diverse state-of-the-art ML prediction algorithms is important. This research subject is also motivated by the fact that in many research are presented experimental evaluations of algorithms without characterization of the data on that they have been applied. Such characterizations could give clear indication to other researchers regarding the applicability of the algorithms on their specific data. Specifically, if the data have the respective characteristics than are expectable the same performance evaluation results like those in the reported research. For the study we chosed a messy real-life Cervical Cancer dataset available in a recognized data repository included a large amount of missing and noisy values. To identify the best imbalanced technique for this medical dataset, it is compared the performance of eleven important resampling methods combined with the following state-of-the-art ML models: K-Nearest Neighbours (with k values of 2 and 3), binary Logistic Regression, and Random Forest, that are frequently applied in prediction types of researches in healthcare. The studied resampling methods includes seven undersampling methods namely Condensed Nearest Neighbour, Tomek Links, Edited Nearest Neighbours, Repeated Edited Nearest Neighbours, All K-Nearest Neighbours, NearMiss, Neighbourhood Cleaning Rule, and Instance Hardness Threshold, and four oversampling methods namely Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling Approach for Imbalanced Learning SMOTE, Support Vector Machine SMOTE and Borderline SMOTE. In the case of the dataset for the confidence interval with 95% confidence level was between 9.23 and 16.22, while the imbalance ratio is 12.73. The obtained results show that resampling methods help the learning models to improve the classification ability of cervical cancer. The applied oversampling techniques generally showed better results than undersampling methods. In the case of Logistic Regression classifier had the highest impact on balanced techniques, while Random Forest had promising performance, even before balancing techniques and KNN2 was generally better than KNN3.

The 21st century's booming population and escalating energy demands have driven significant efforts to enhance the Energy Hub (EH). The goal is to create a more intelligent and responsive system that can effectively cater to consumer needs while simultaneously improving the reliability and efficiency of contemporary energy infrastructure. The Internet of Things (IoT) has emerged as a key enabling technology for Smart Energy Hubs (SEH). While IoT offers a plethora of innovative solutions across various sectors, including critical infrastructure, it also introduces new security challenges. Since IoT devices inherently connect to the internet, SEH systems become susceptible to a wider range of cyberattacks. This vulnerability heightens as the number of integrated IoT devices increases. A single compromised device can expose the entire hub to cyber threats. Such attacks on energy sources have the potential to cripple entire cities, resulting in significant economic devastation. Therefore, robust security measures must be implemented before the widespread adoption of IoT-based devices in energy systems. This research delves into the architecture and infrastructure of IoT-enabled SEH. Following this, the focus shifts to analyzing the primary challenges and security concerns associated with their deployment. Finally, the research emphasizes cutting-edge approaches and tools that can bolster the security and resilience of IoT-enabled SEH against contemporary physical and cyber threats. Implementing secure and advanced data transmission systems based on blockchain technology holds promise in safeguarding the entire EH from cyber-physical attacks in the future.

The multi-UAV target search problem is crucial in the field of autonomous Unmanned Aerial Vehicle (UAV) decision-making. The algorithm design of Multi-agent Reinforcement Learning (MARL) methods has become integral to research on multi-UAV target search, due to its adaptability to the rapid online decision-making required by UAVs in complex, uncertain environments. In non-cooperative target search scenarios, targets may have the ability to escape, complicating UAVs' search efforts and hindering the convergence of the MARL algorithm training. This paper investigates the multi-UAV target search problem in scenarios involving static obstacles and dynamic escape targets, modeling the problem within the framework of Decentralized Partially Observable Markov Decision Process. Based on this model, a Spatio-Temporal Efficient Exploration network and a Global Convolutional Local Ascent mechanism are proposed. Subsequently,we introduce a Multi-UAV Escape Target Search Algorithm Based on MAPPO(ETS-MAPPO) for addressing the escape target search difficulty problem. Simulation results demonstrate that the ETS-MAPPO algorithm outperforms five classical MARL algorithms in terms of the number of target searches,the area coverage rate, and other metrics.

The availability of petroleum asphalt, derived from non-renewable sources, is steadily declining alongside diminishing petroleum reserves. To reduce reliance on petroleum, alternative renewable natural sources are being explored as both modifiers and replacements for petroleum asphalt, particularly in asphalt binders. This study explores the chemical, rheological, and mechanical properties of Gopal, a bio-asphalt derived from Gondorukem (gum rosin) and crude palm oil (CPO). Two Gopal variants, Gopal-GEM130 and Gopal-GEG90, were analyzed using FTIR and EDX methods, with Pen 60 petroleum asphalt as a control. The results reveal that Gopal-GEG90 and Gopal-GEM130 exhibit 86% chemical similarity to Pen 60 asphalt. Gopal-GEM130 is less toxic and less alkaline compared to Pen 60, while Gopal-GEG90 is less toxic but more alkaline. Both variants fall under the same classification as Pen 60 in terms of rheological properties, with Gopal-GEG90 demonstrating slightly better stripping resistance but lower aging resistance. Gopal-GEM130 showed better stripping resistance and rutting performance, though with lower fatigue resistance and faster aging compared to Pen 60 asphalt. Both bio-asphalt variants align with performance grade PG64S, signifying their potential as alternatives to conventional petroleum asphalt.

The land use land cover (LULC) map is extensively employed for different purposes. The LULC maps provided by the Portuguese National Geographic Information System (SNIG) are not ideal for continuous assessment and regular scrutiny. Machine learning (ML) algorithms applied in remote sensing (RS) data has been proven effective in image classification, object detection, and semantic segmentation. Previous studies have shown that Random Forest (RF) and Support Vector Machine (SVM) consistently achieved high accuracy for land classification. In this study, the application of RF and SVM classifiers, and object-based (OBIA) and pixel-based (PBIA) approaches with Sentinel-2A imagery was evaluated using Google Earth Engine (GEE) platform for land cover classification of a burned area in the Serra da Estrela Natural Park (PNSE), Portugal. This aimed to detect the land cover change and closely observe the burned area and vegetation recovery after the 2022 wildfire. The combination of RF and OBIA achieved the highest accuracy in all evaluation metrics. At the same time, a comparison with the Normalized Difference Vegetation Index (NDVI) map and Conjunctural Land Occupation Map (COSc) of 2023 year indicated that the SVM and PBIA map resembled the maps better.

Aural rehabilitation with hearing aids can decrease the attentional requirements of cognitive resources by amplifying the deteriorated-frequency sound in hearing loss patients and improve the auditory discrimination ability like speech-in-noise perception. As aural rehabilitation with an intelligible-hearing sound also can be hopeful, the aim of this study was to evaluate the effectiveness of the aural rehabilitation with intelligible-hearing sound for hearing loss patients. Adult native Japanese speakers (17 males and 23 females, 68.43±9.23 years) showed over 30 dB at 125, 250, 500, 1000, 2000, 3000, 4000, 8000, 10000, and 12000Hz on any side, were recruited and underwent Mini-Mental State Examination Japanese. we conducted self-evaluation questionnaire for hearing problems of voice, gap detection test, fast speech test, and speech-in-noise test, pure tone audiogram, speech perception test using Japanese 67-S, cortical auditory evoked fields, and magnetic mismatch negativity before and after the non-intelligible-hearing (N=20) and intelligible-hearing (N=20) sound therapy with listening to music for one hour a day for 35 days. Better hearing ear was defined using a four-frequency pure-tone average at the thresholds of 500, 1000, 2000 and 4000 Hz. After the sound therapy, speech-in-noise test with signal to noise ratio +10 in better hearing ear showed significant improvement (p&lt;0.05), and N1m-P2m amplitudes showed the significant increase in Lt superior temporal gyrus at the stimulus from better hearing ear (p&lt;0.05). The significant enhancement of the magnetic mismatch negativity amplitude at Lt superior temporal gyrus was exhibited after the sound therapy (p&lt;0.01). Intelligible-hearing sound therapy can improve the ability of speech-in-noise perception in better hearing ear and the enhancement of central cortex response, which reflect the ability of working memory, was proved by cortical auditory evoked fields and magnetic mismatch negativity. Intelligible-hearing sound therapy can be a valuable aural rehabilitation method for sensory neural hearing loss as same as hearing aids.

This study investigates the urban ecology of the Red-Whiskered Bulbul (Pycnonotus jocosus) in New Barrackpore, West Bengal, India. We examined the feeding behavior, breeding success, and behavioral patterns of the species in urban habitats. Our results show that the Red-Whiskered Bulbul is adapting to urban environments, with a moderate breeding success rate (64%) and specific behavioral patterns. The species exhibits a significant preference for urban habitats, particularly mango and bamboo trees, and a significant increase in singing activity during the breeding season. Our findings highlight the importance of urban ecology in understanding the behavior and conservation of this species. We suggest that urban green spaces with native tree species and sustainable urban planning practices can support the conservation of the Red-Whiskered Bulbul and other urban-adapted species. This study contributes to our understanding of urban wildlife ecology and conservation in India and has implications for urban planning and biodiversity conservation globally.

A new tetracyclic morpholine-fused [5,1-c]-triazole, (4aS,5S,6aR,10aR,10bR)-5-methoxy-9,9-dimethyl-4a,5,6a,7,10a,10b-hexahydro-12H-[1,3]dioxino[4',5':5,6]pyrano[4,3-b][1,2,3]triazolo[1,5-d][1,4]oxazine, was synthesized via a five-step sequence starting from methyl α-D-glucopyranoside by using as a key step an intramolecular copper(I) cat-alyzed alkyne-azide cycloaddition (CuAAC). The synthesized compound was fully characterized by 1H and 13C NMR, FT-IR, and HRMS.

A complete understanding of groundwater dynamics and its interaction with surface water underthe impact of agricultural activities is vital for local agriculture, ecology, and residents of dry regions, which is not commonly recognized in arid areas. This research outlines the geochemical characteristics, recharge sources, and potential factors impacting groundwater quality in a new land reclamation located in the small basin of Abu Mina, which is part of the Western Nile Delta region.1 Thirty‑one groundwater samples and two surface water samples were collected in 2021 to represent the Pleistocene aquifer and were subjected to multivariate statistical, hydrochemical, and stable isotope analyses. Data analysis demonstrates that Na+ > Ca2+ > Mg2+ > K+ and SO4 2– > Cl– > HCO3 – > NO3 – are the predominant cations and anions, respectively. Groundwater salinity ranged from 465.60 to 6455.18 mg/l, with slightly alkaline. Most of the water samples fall into one of three types of facies: Ca–Cl, Na–Cl, and Mixed Ca–Mg–Cl, in decreasing order. The meteoric genesis index (r2) indicates that deep meteoric water percolation dominates the Pleistocene aquifer. The aquiline diagrams, correlation matrix, and different ionic ratios indicate that evaporation, reverse ion exchange reactions, and the dissolution of carbonate and silicate minerals are the main processes governing groundwater chemistry. Factor analysis (FA) indicated that three factors explain groundwater hydrochemistry, accounting for 71.98% of the total variance. According to the rotating components matrix (F1–F3), the chemistry of the Quaternary aquifer is principally affected by evaporation, ion exchange reactions, and anthropogenic influences. Additionally, salinity increases due to the return flow of irrigation activities and mixing between old and recent water. The stable isotopes (δ18O and δ2H) indicate that the Quaternary aquifer receives groundwater recharge through the return flow of excess irrigation and canal seepage. Under desert reclamation conditions, groundwater salinization processes should be given special consideration. All groundwater samples are appropriate for agricultural irrigation based on the Sodium Adsorption Ratio (SAR), Permeability Index (PI), Percent Sodium (%Na), and Residual Sodium Carbonate (RSC).

Background: Regular moderate-vigorous intensity exercise is recommended for adults as it can improve longevity and reduce health risks associated with a sedentary lifestyle. However, there are barriers to achieving intense exercise that may be addressed using virtual reality (VR) as a tool to promote exercise intensity and adherence, particularly through visual feedback and competition. The purpose of this work is to compare visual feedback and competition within fully-immersive VR to enhance exercise intensity and user experience of exercise for young adults; and to describe and compare visual attention during each of the conditions. Methods: Young adults (21- 34 years old) bicycled in three 5-minute VR conditions (visual feedback, self-competition and competition against others). Exercise intensity (cycling cadence and % of maximum heart rate) and visual attention (derived from a wearable eye tracking sensor) were measured continuously. User experience was measured by an intrinsic motivation questionnaire, perceived effort and participant preference. A repeated measures ANOVA with paired t-test post-hoc tests was conducted to detect differences between conditions. Results: Participants exercised at a higher intensity and had higher intrinsic motivation in the two competitive conditions compared to visual feedback. Further, participants preferred the competitive conditions and only reached a vigorous exercise intensity during self-competition. Visual exploration was higher in visual feedback compared to self-competition. Conclusion: For young adults bicycling in VR, competition promoted higher exercise intensity and motivation compared to visual feedback.

NLR genes are crucial components of the effector-triggered immunity (ETI) system, which recognizes pathogens and initiates immune responses. While NLR genes in many plants have been extensively studied, the diversity of NLR genes in citrus remains largely unknown. In this study, we systematically identified 1,585 NLR genes across the genomes of 10 citrus species. Significant variations in NLR gene copy numbers were observed among these citrus species, with gene duplication and recombination identified as major driving forces. Citrus NLR genes were classified into TIR-NBS-LRR (TNL), CC-NBS-LRR (CNL), RPW8-NBS-LRR (RNL), and NL categories. It is believed that TNL, RNL, and CNL genes originated from NL genes through the acquisition of TIR, RPW8 domains and CC motifs, respectively, followed by the random loss of the corresponding domains. Phylogenetic analysis revealed that citrus NLR genes originated with the species and underwent adaptive evolution, potentially playing crucial roles in the global colonization of citrus. This study is importance for comprehensively understanding citrus NLR genes and provides a foundational dataset for research aimed at breeding disease-resistant citrus varieties.

Plastic pollution is a pressing environmental challenge, necessitating the development of biodegradable alternatives like polyhydroxybutyrate (PHB). This study focuses on optimizing PHB production by Neobacillus niacinii GS1, a bacterium isolated from a municipal dumping site. By utilizing agricultural residues such as corn flour, wheat bran, and peptone as substrates, we aimed to establish an eco-friendly method for biopolymer production, contributing to sustainable waste management and bioplastic innovation. The bacterium was identified using morphological, biochemical, and molecular techniques. The optimization process involved adjusting variables such as inoculum age, inoculum size, incubation time, agitation rate, incubation temperature, pH of the medium, carbon sources and nitrogen sources. Response surface methodology (RSM) was employed to identify optimal conditions, with the highest PHB yield of 61.1% achieved under specific conditions: 37°C, pH 7, and an agitation rate of 150 rpm. These findings underscore the potential of Neobacillus niacinii GS1 in converting agro-industrial residues into valuable biopolymers, promoting sustainable bioplastic production, and advancing waste valorization efforts through the use of eco-friendly materials.

The growing energy consumption in the cryptocurrency and artificial intelligence (AI) industries is becoming an increasingly critical issue. This paper discusses methods for addressing this problem using data compression algorithms and the optimization of computational processes. Specifically, we examine research results that demonstrate the potential for significantly reducing energy consumption through mathematical solutions, such as the Riemann Hypothesis. We also present the notarized test results of an image compression program developed by the author. The work was presented at a UN panel session and an ECO webinar, receiving recommendations for implementation in ECO member countries. Additionally, modern technologies for accelerating cryptographic methods and algorithms are considered.The growing energy consumption in the cryptocurrency and artificial intelligence (AI) industries is becoming an increasingly critical issue. This paper discusses methods for addressing this problem using data compression algorithms and the optimization of computational processes. Specifically, we examine research results that demonstrate the potential for significantly reducing energy consumption through mathematical solutions, such as the Riemann Hypothesis. We also present the notarized test results of an image compression program developed by the author. The work was presented at a UN panel session and an ECO webinar, receiving recommendations for implementation in ECO member countries. Additionally, modern technologies for accelerating cryptographic methods and algorithms are considered.

Rift Valley Fever (RVF), is a viral zoonotic disease predominant in East Africa and transmitted by Aedes mosquitoes carrying the virus. Using the systematic literature review approach, the present study evaluated machine learning techniques and time series approaches to find literature on the impact of climatic changes on RVF outbreaks published between 1930 and 2024. The literature search involved databases including PubMed, PLOS ONE, JSTOR, Web of Science, Google Scholar, and SCOPUS (Kenmoe et al., 2023).The results show that most of the articles were published between 2018 and 2022, and most of the articles were from United States, France, and Kenya. We conducted a detailed review of the articles using the PRISMA 2020 flow chart, screening and qualifying 10,015 articles. Some articles revealed significant gaps in both internal and external validation. Therefore, future research should focus on developing multi-disciplinary models that incorporate climatic condition, geographical, biological, and social factors.

The forest is a complex and dynamic ecosystem that requires integrated planning and management, considering economic, social, environmental, and ecological dimensions. Different stakeholders often have conflicting goals, making optimal management decisions challenging. This study aims to balance increasing economic benefits for forest users with reducing negative environmental impacts, aligning the interests of managers, government institutions, and policymakers. To achieve this, multi-objective optimization methods and game theory were used. The objectives were to maximize the net present value of wood harvesting and the amount of carbon sequestration. The study was conducted in the Shafarood forest in northern Iran. Data collected included stumpage prices, variable harvesting costs, tree density per ha, volume per ha, growth rates, interest rates, carbon sequestration, and labor costs. Using this data, models were developed for the expected mean price of various species, stored carbon, logistics growth, and labor. After adjusting the objective functions and model constraints, the range of Pareto optimal solutions for the multi-objective model and the Nash equilibrium for the game theory model were determined using the epsilon-constrain method. The results indicate that both modeling approaches can identify optimal points for balancing economic and environmental goals. The Pareto optimal range and Nash equilibrium allow for informed decision-making in managing the Hyrcanian forests, accommodating different user goals. Therefore, both models are effective tools for forest management planning and provide a framework for achieving sustainable management practices.

Background: Obesity and Type 2 diabetes (T2D) promote inflammation, increasing the risk of colorectal cancer (CRC). High-fat diet (HFD)-induced obesity plays a key role in these diseases through biological mechanisms. This study examined the effect of genetic background on the multimorbidity of intestinal cancer, T2D, and inflammation due to HFD-induced obesity. Methods: A cohort of 357 Collaborative Cross (CC) mice from 15 lines was fed either a control chow diet (CHD) or HFD for 12 weeks. Body weight was tracked biweekly, and blood glucose was assessed at weeks 6 and 12 via intraperitoneal glucose tolerance tests (IPGTT). At the study's endpoint, intestinal polyps were counted, and cytokine profiles were analyzed to evaluate the inflammatory response. Results: HFD significantly increased blood glucose levels and body weight, with males showing higher susceptibility to T2D and obesity. Genetic variation across CC lines influenced glucose metabolism, body weight, and polyp development. Mice on HFD developed more intestinal polyps, with males showing higher counts than females. Cytokine analysis revealed diet-induced variations in pro-inflammatory markers like IL-6, IL-17A, and TNF-α, differing by genetic background and sex. Conclusions: Host genetics plays a crucial role in susceptibility to HFD-induced obesity, T2D, CRC, and inflammation. Genetic differences across CC lines contributed to variability in disease outcomes, providing insight into the genetic underpinnings of multimorbidity. This study supports gene-mapping efforts to develop personalized prevention and treatment strategies for these diseases.

It aims to utilize more solar energy, which is both environmentally friendly and easily accessible, to satisfy the energy needs of developing countries. Therefore, maximum power generation should be provided from photovoltaic panels. Several maximum power point tracking(MPPT) methods are utilized for the maximum power generation in photovoltaic panels systems under different weather conditions. In this paper, a novel hybrid and clever maximum power point monitoring fuzzy logic controller maximum power point tracking algorithm established on the perturb and observe (PO) algorithm is presented. The proposed fuzzy logic controller algorithm and the incremental conductivity maximum power point tracking algorithm were simulated in MATLAB/SIMULINK environment and evaluated by comparing the results. Four Sharp ND-F4Q295 solar panels, two in series and two in parallel were used for the simulation. The hybrid method proposed here was found to reduce the voltage and current ripple compared to the classical incremental conductivity method. The proposed intelligent method has been shown to stabilise the system more quickly in the event of sudden weather changes.

The context of this research is given by a socio-economic situation in which new and more demanding challenges appear. Using wood, a naturally occurring, renewable, and biodegradable material, in construction is becoming more and more necessary due to the awareness of the negative impact on the environment of the construction industry and the energy crisis limitations. The purpose of this study is to analyze what do we really know about the use of wood as a building material. The search for an answer to the research question implies the use of a scientometric literature analysis, followed by an in-depth and expert analysis. The research results indicate a major preoccupation for studying engineering wood structures, like the Cross laminated Timber, the structural behaviour of the structures, the implementation of new IT discoveries in wood construction technology and a more recent approach regarding the environmental impact of wood construction, mostly the possibility of using wood waste to prolong the life of wood as a building material. The results also show a need to create new models of understanding the structural behaviour of wood that consider the fibrous and cellular nature of the material. Wood as a construction material is an extremely complex material and for a better understanding of it, there is a need to have an interdisciplinary approach in which to combine the knowledge of construction with that of forestry, ecology, biology and others.

The problem can be stated as over 3 billion choices to improve 14 disease states with 9 optimization goals (some of the optimization goals are diametrically opposed) to improve dyspnea, shortness of breath, fatigability, exercise intolerance, edema, swelling, fluid retention, and arrhythmias. The goal is to increase Left Ventricular Outflow Integral, reduce mitral regurgitation, increase longitudinal conduction velocities and restore synchrony of the septum to the ventricle that needs it the most. A method to accomplish this task for the spectrum of cardiac disease is presented.

Falls are a major public health problem among older adults, therefore predicting the risk of having falls in the near future is of great importance for health and social systems worldwide. Nowadays, prospective fall risk assessment relies on clinical and functional mobility assessment tools, such as the Timed Up and Go (TUG) test. Recently, wearable inertial measurement unit (IMU) sensors measurements have been proposed for fall detection. We hypothesize that the IMU readings captured during TUG test realizations can provide prospective fall risk prediction. In this study, analysis by deep learning convolutional neural networks (CNN) and recurrent neural networks (RNN) were used for fall prediction based on features extracted from IMU data acquired during TUG test realizations. Data is obtained from a cohort of 106 older adults wear-ing a wireless inertial sensor with a sampling frequency of 100 Hz while performing the TUG test. Prospective fall incidence was obtained in a six-month follow-up period and used as the ground truth for the supervised training and validations of the deep learning and competing machine learning approaches. Further data collection could lead to potential fall risk biomarker. A repeated hold out cross-validation process using 75 subjects for training and 31 subjects for testing was carried out. Best results were achieved by a bidirectional long short-term memory (BLSTM), obtaining an accuracy of 0.83 and AUC of 0.73 with good sensitivity and specificity values.

In-situ production of PC components is the same process as in a factory, with the order of installing processed rebar, pouring concrete, curing, and storage. In-situ production not only reduces environmental impact by more than 14.58% compared to factory production, but also reduces costs by up to 39.4% compared to factory production when increasing the number of in-situ produced PC components. The stock yard area is more than 5 times larger than the production area, which accounts for a significantly higher proportion. Research on in-situ production of PC components is mainly focused on the production area. PC component yard layout planning for in-situ production can effectively reduce carbon dioxide emissions and improve construction efficiency. Therefore, the purpose of this study is to develop an environmental impact minimization model for in-situ production of PC components. As a result of applying the developed model, the improved dung beetle optimization algorithm optimization improved the adjacent correlation by 22.79% and reduced carbon dioxide emissions by 18.33% compared to the dung beetle optimization algorithm, which verifies its efficiency. The proposed environmental impact minimization model can support the construction, reconstruction, and functional upgrade of logistics centers, contributing to low carbon dioxide in the logistics industry.

Amidst the Covid-19 pandemic, open spaces provided solace for people during social isolation but also heighten risks, particularly in areas lacking proper management. This was especially critical in regions already facing challenges like low human development, extreme poverty, limited healthcare access, as seen in Pará Coastal Zone (NE Brazil). To address this, this study introduced the Pandemic Risk Index (PRI), evaluating Covid-19 risk using demographic, social, economic, and epidemiological indicators. Applied to municipalities in the area, PRI highlighted preexisting vulnerabilities shaping the region’s pandemic response and overall risk. PRI analysis revealed temporal and spatial variations in risk levels, identifying areas where the use of open spaces like beaches, presented a higher transmission risk. The results emphasized the significance of effective governance, improved healthcare, and containment measures, lowers risk levels. affect risk levels, and becomes one of the most important challenges, especially in remote areas with limited services and access.

Unevenly distributed rainfall leads to reduced potato water productivity (WP) under rainfed production. Understanding practices that can increase WP is vital. Objectives were to (i) understand seasonal variables that influence WP under rainfed conditions and (ii) the effect of the integration of cultivar, locality, mulch on potato WP. The study was under two agroecological zones Appelsbosch (Mbalenhle locality), and Swayimane (Stezi, and Mbhava locality), under smallholder. A split plot, in a randomized complete block design experiment, consists of mulching (mulch and not mulch), and selected cultivars. Soil water content (SWC), yield, and climatic conditions were collected, water use (ET) and WP were calculated. Rainfall, ET, and SWC had a significant influence on seasonal WP. Cultivar x mulch x locality had an insignificant effect on WP, however, locality x cultivars significantly altered potato WP. Localities that had lower vapor pressure deficit (VPD), low relative humidity, and sandy soil had higher potato WP of 14.53 kg m-3. The findings suggest that localities that have less atmospheric dryness and cultivars that show stability of yield across seasons can be an easy-to-apply practice for increasing potato WP under a resource-limited environment. Mulch is important when the distribution of intra-annual rainfall does not match crop water requirements.

The objective of this research is to show the family farms as an energy consumers and producers from renewable sources. It is a possibility for them to reduce operating costs. Time scope of the study covers the period 2014-2022. The data source is the Farm Accountancy Data Network. On the basis of the value of energy output and energy costs the rankings were prepared. After the ranks were awarded, the strong position of the Netherlands and Germany was indicated. It has been shown that the production and consumption of energy depend on the economic size of the farm, and do not depend on the type of production. As such, panel models were built for energy output and costs. The determinants that affect them were indicated. Energy output depends on total inputs – the higher they are, the higher the output. The energy costs are related to utilized agricultural area, total output and family farm income. The results obtained are discussed within the framework of recommendations of the European Commission.

Previously, we described that Adenine, Thymine, Cytosine, and Guanine nucleobases were superconductors in a quantum superposition of phases on each side of the central Hydrogen bond acting as a Josephson Junction. Genomic DNA has two strands wrapped helically around one another, but during transcription, they are separated by the RNA polymerase II to form a molecular condensate called the transcription bubble. Successive steps involve the bubble translocation along the gene body. This work discusses how quantum information transfer and entanglement can be achieved by modeling DNA as a combination of ? nonperturbative circuits quantum electrodynamics with nine Radio-Frequency- Superconducting Quantum Interference Devices (SQUIDs) inside. A bus can be coupled capacitively to a single-mode microwave resonator. The cavity mode and the bus can mediate long-range, fast interaction between neighboring and distant DNA SQUID qubits. RNA polymerase II produces decoherence during transcription. This enzyme is a multifunctional biomolecular machine working like an artificially engineered device. Phosphorylation reactions catalyzed by protein kinases constitute the driving force. The coupling between n phosphorylation pulses and any particular SQUID qubit can be obtained selectively via frequency matching.

IT strategic planning is a critical driver of performance for small and medium-sized enterprises (SMEs), improving operational efficiency, decision-making, and innovation. This systematic review synthesizes findings from eighty studies, highlighting the varied impacts of IT strategic planning on SME performance. Quantitative analysis was used in 45% of studies, while thematic analysis was applied in 25%, showing the growing preference for data-driven insights. Cloud-based solutions, adopted in 11.25% of cases, and hybrid IT setups (17.5%) were identified as key enablers of flexibility and cost-efficiency. SMEs aligning IT strategies with business goals reported up to 20% reductions in operational costs and 15% improvements in customer satisfaction. However, 60% of studies identified resource constraints as a major challenge to IT adoption. Despite these barriers, 60% of studies demonstrated that effective IT planning enhances long-term sustainability and competitive advantage. This review provides actionable insights and strategic frameworks to guide SMEs in optimizing their IT investments for sustainable growth.

To date, the percentage composition of the lung microbiome in bronchopulmonary cancer has not been summarized. Existing studies on identifying the lung microbiome in bronchopulmonary cancer through 16S rRNA sequencing have shown variable results regarding the abundance of bacterial taxa. Objectives: To identify the predominant bacteria in the lung microbiome of bronchopulmonary cancer from samples collected through bronchoalveolar lavage and to assess their potential role in the diagnosis of bronchopulmonary cancers. Data Sources: A systematic review of English articles using MEDLINE, Embase, and Web of Science. Search terms included lung microbiome, lung cancer, bronchoalveolar lavage. Study Selection: Randomized clinical trials that investigated the lung microbiome in bronchopulmonary cancer with samples collected via bronchoalveolar lavage. Data Extraction: Independent extraction of articles using predefined data fields, including study quality indicators. Data Synthesis: Nine studies met the inclusion criteria, focusing on those that utilized a percentage expression of the microbiome at the phylum or genus level. There was noted heterogeneity between studies, both in terms of phylum and genus, with a relatively constant percentage of the Firmicutes phylum and the genera Streptococcus and Veillonella being mentioned. Significant differences were also observed regarding the inclusion criteria for study participants, the method of sample collection, and data processing. Conclusions: To date, there is no consistent percentage pattern at the phylum or genus level in bronchopulmonary cancer, with the predominance of a phylum or genus varying across different patient cohorts, resulting in non-overlapping outcomes.

This study explores the comparative performance of FinBERT, GPT-4, and Logistic Regression for sentiment analysis and stock index prediction using the NGX All-Share Index dataset. By leveraging advanced language models like GPT-4 and FinBERT, alongside a traditional machine learning model, Logistic Regression, we aim to classify market sentiment, generate sentiment score, and predict market price movements. The models were assessed using metrics such as accuracy, precision, recall, F1 score, and ROC AUC. Results indicate that Logistic Regression outperformed both FinBERT and GPT-4, with an accuracy of 81.83% and a ROC AUC of 89.76%. GPT-4 predefined approach exhibited a lower accuracy of 54.19% but demonstrated strong potential in handling complex data. FinBERT, while offering more sophisticated analysis, was computationally demanding and yielded a moderate performance. Hyperparameter optimization using Optuna and cross-validation techniques ensured the robustness of the models. This study highlights the strengths and limitations of these approaches in stock market prediction and presents Logistic Regression as the most efficient model for this task, with FinBERT and GPT-4 showing promise for future exploration.

Abstract: This article advances the discourse on sustainable and energy-efficient software by examining the performance and energy efficiency of intelligent algorithms within the framework of green and sustainable computing. Building on previous research, it explores the theoretical implications of Bremermann's Limit on efforts to enhance computer performance through more extensive methods. The study presents an empirical investigation into heuristic methods for search and optimisation, demonstrating the energy efficiency of various algorithms in both simple and complex tasks. It also identifies key factors influencing the energy consumption of algorithms and their potential impact on computational processes. Furthermore, the article discusses cognitive concepts and their interplay with computational intelligence, highlighting the role of cognition in the evolution of intelligent algorithms. The conclusion offers insights into the future directions of research in this area, emphasising the need for continued exploration of energy-efficient computing methodologies.

Worldwide cervical cancer ranks the fourth most common cancer in women. In Tanzania, cervical cancer is the leading female malignancy, and a major cause of cancer related mortality and morbidity in women. More than 80% of women with cervical cancer, are diagnosed with late stage, in which curative treatment options are not available. To explore the knowledge, perceptions and behaviors among women with late-stage cervical cancer who attended Bugando Medical Centre (BMC) from November to December 2021. Explorative qualitative study was conducted from November to December 2021 at BMC, involving 11 participants who had late-stage cervical cancer. Purposive sampling was used to recruit information-rich participants. Data collection was done by using open ended In-depth interview guide. The guide contained information on social demographic characteristics, knowledge on cervical disease, perceptions towards cervical cancer symptoms, health care seeking behaviors. Data was transcribed and analysed manually by using thematic approach. The coded texts generated three themes and nine subthemes. The results showed that Participants had poor knowledge about cervical cancer disease, causes, risk factors, prevention measures and treatment options which had impact on the perceptions and health care seeking behaviors. Participants misinterpreted the symptoms with other urogenital conditions like fungus and urinary tract infection. Misconception about the symptoms and disease, long distance to health facilities and financial challenges hindered involvement in seeking biomedical intervention and witch doctors were given a priority. Findings from this study suggest a strong need of intensive health promotion efforts and education through mass media and outreaches to improve adequate and correct knowledge and awareness about cervical cancer disease, causes, symptoms, risk factors, prevention and treatment options. Health care providers and policy makers should include cervical cancer disease as a topic of everyday health education in the health facilities.

Background: Mitral regurgitation (MR) is a common valvular disorder linked to high morbidity and mortality. For patients unsuitable for surgery, transcatheter mitral edge-to-edge repair (TEER) with the MitraClip® G4 system offers an alternative. This study aims to evaluate procedural, echocardiographic, functional, and quality of life (QoL) outcomes in patients who underwent TEER with the MitraClip® G4 system, along with possible predictors of New York Heart Associa-tion (NYHA) class I at 30 days and 1 year. Methods: Patients with moderate-to-severe (3+) or se-vere (4+) degenerative MR (DMR) or functional MR (FMR), classified as NYHA class III or IV, who underwent TEER with the MitraClip® G4 system at our center between January 2021 and De-cember 2023 were included. Results: A total of 83 patients [71% FMR, 66% male, median (IQR) age 70 (11) years] underwent TEER, with 100% procedural success. MR ≤2+ was achieved in 100% and 98% of patients at 30 days and 1 year, respectively. NYHA class I or II was achieved in 100% and 96.8% of patients at 30 days and 1 year, respectively. The Kansas City Cardiomyopathy Ques-tionnaire (KCCQ) score improved from 51±20 at baseline to 69±15 at 30 days (p< 0.001) and 70.5±15 at 1 year (p< 0.001). Lower baseline N-terminal pro-brain natriuretic peptide (NT-proBNP) pre-dicted achieving NYHA class I at 30 days (HR: 0.63, 95% CI: 0.41–0.95, p = 0.030), while lower Eu-ropean System for Cardiac Operative Risk Evaluation II (EuroSCORE II) and NT-proBNP pre-dicted it at 1 year [(HR: 0.50, 95% CI: 0.28–0.89, p = 0.019), (HR: 0.67, 95% CI: 0.44–0.99, p = 0.049), respectively]. Conclusions: The MitraClip® G4 system provides significant improvements in MR severity, functional class, and QoL. Lower NT-proBNP and EuroSCORE II were strong predictors of achieving optimal functional status (NYHA class I).

Large language models (LLMs) are essential to the field of natural language processing, and as their applications expand, security risks have become increasingly prominent. This paper introduces a novel benchmark for evaluating LLMs security, termed CS-Eval, designed to effectively assess the models' ability to address vulnerabilities. CS-Eval targets seven key security risks: ethical dilemmas, marginal topics, error detection, detailed event handling, cognitive bias, logical reasoning, and privacy identification, and establishes a Multi-Security Hazard Dataset (MSHD). The evaluated models include GPT-4o, Llama-3-70B, Claude-3-Opus, ERNIE-4.0, Abab-6.5, Qwen1.5-110B, Gemini-1.5-Pro, Doubao-Pro, SenseChat-V5, and GLM-4. We analyzed each model's performance in relation to these security risks and provided recommendations for improvement. Experimental results demonstrate varying levels of effectiveness across models, with GPT-4o exhibiting the best overall performance. Moreover, the relationship between security enhancement and model capa-bility is nonlinear, indicating that improving safety requires a multifaceted approach, considering various factors in both development and application.

GRP78/BiP, a stress-induced protein and autoantigen in rheumatoid arthritis (RA), exhibits different expressions in various biological fluids and tissues, namely serum, synovial fluid (SF), and synovium, all of which are pertinent to the disease activity and progression of RA; however, there is a scarcity of data linking both intracellular and extracellular GRP78/Bip to the disease activity and progression of RA within the realm of rheumatology. This study was undertaken to investigate the differential expression of intracellular and extracellular GRP78/Bip in serum, SF, and synovium, and to determine their association with disease activity and progression in patients with RA. Patients with RA, osteoarthritis (OA), and traumatic meniscal injury (TMI) without radiographic OA were consecutively recruited for the study at our hospital. Among the patients with RA, six different subgroups were established based on their disease activity and progression. Disease activity was measured using the DAS28 criterion, while disease progression was evaluated using the Steinbrocker classification grade. This study conducted a comprehensive analysis of the differential expressions of GRP78/Bip in multiple sample types, including serum, SF, and synovium, with the specific emphasis on their associations with disease activity and progression in RA. The expression levels of GRP78/Bip, TNF-α, and IL-10 were significantly elevated in serum, SF, and synovium of patients with RA when compared to both the CON and iCON groups(P

AYOLO differentiates itself with a new fusion architecture proposal that leverages the strengths of unsupervised learning and integrates the Vision Transformer approach, taking the YOLO series models as a reference. This innovation enables the model to effectively use rich, unlabeled data, providing a new example in pre-training methodology for YOLO architectures. On the benchmark COCO val2017 dataset, AYOLO demonstrates its superiority by achieving an impressive 38.7 % AP while maintaining an outstanding rendering speed of 239 FPS (Frame Per Second) on the Tesla K80 GPU. This performance outperforms the previous state-of-the-art YOLO v6-3.0 by a significant margin of +2.2 % AP while maintaining comparable FPS. AYOLO is presented as a demonstration of the potential of integrating complex information fusion techniques with supervised pretraining in improving the precision and speed of object detection models.

Thermomechanical, biocompatibility, durability, workability and economic characteristics are the main criteria for development of dental alloys. In an attempt to provide a noble metal dental alloy without the known adverse metallic elements, a novel alloy was produced with the addition of Germanium (Ge), as a more cost-effective alternative to other precious alloying metals. The objective of this investigation was to analyse the thermomechanical and biocompatibility properties of a high noble Au-Pt-Ge porcelain-fused-to-metal (PFM) alloy. These properties were investigated with DSC analysis, hardness, density and coefficient of thermal expansion (CTE) measurements, as well as with metallographic and SEM/EDX microstructure investigations, with the production of a test PFM dental bridge, static immersion testing for metal ion release, and an in vitro primary biocompatibility testing on human gingival cells. The results of the thermomechanical testing showed alloy properties suitable for dental restorations and clinical use, with somewhat lower hardness, making the alloy not suitable for extensive multiunit fixed restorations. The microstructure investigations showed segregations of Ge in a homogeneous alloy matrix, which reduce the alloy’s mechanical properties. The produced PFM dental bridge shows excellent workability of the alloy in a dental laboratory setting, as well as a high standard of the final dental restoration. The ion release was negligible, well below any harmful quantities, while the cell viability examination showed significantly higher viability ratings on polished alloy samples as compared to as-cast samples. The results showed that a dental substructure in direct contact with oral tissue and fluids should be highly polished.

Background: Although insect-based foods (IBF) have been recently proposed as a way to face climate crisis and starvation, they encounter the aversion from Western countries which perceive fear, disgust, and high risk. The contribution of psychology research to food choices highlighted how decisions are taken not only through reasoned attitudes and goal-directed behavior, but also through more automatic associations (dual-systems models). Methods: In this paper we investigated people’s disposition towards IBF by combining (a) explicit attitudes (as assessed via self-report scales), (b) automatic associations (as measured via indirect measures), (c) intention to taste, and comparing different profiles based on (d) psychological factors, including decision-making style, food neophobia, and trust in science and scientist. A pilot sample of 175 Italian University students participated in the study. Results: The analyses on the general sample highlighted rather negative attitudes. The cluster analysis identified 4 decision-making profiles: “the gut-feeling”, “the suspicious”, “the vicarious”, and “the mind”. It revealed more favorable opinions in “the mind” profile, characterized by a rational decision-making style and high trust in science, and very aversive reactions from “the suspicious” profile, characterized by high food neophobia and low trust in science. Conclusions: The results underline the importance of psychological factors in interpreting people's reactions to IBF and changes in dietary habits based on the decision-making process. They suggest possible strategies to promote eco-friendly diets

Arabidopsis and other model plants can help improve crops by providing a reference framework for assembling omics data from plants. Arabidopsis is extensively studied and has a well-curated and annotated genome, making it an excellent reference organism for comparative analysis. When there is no reference genome of the organism being investigated, using the genome of Arabidopsis may be an option thanks to the extensive knowledge we have about it. Crop improvement can help to increase crop yields, improve crop quality, and enhance resistance to pests, diseases, and environmental stresses. It can also help to reduce the use of pesticides and fertilizers, which can have negative impacts on the environment and human health. Therefore, crop improvement is essential to ensure food security and sustainability in the face of global challenges such as climate change, land degradation, and water scarcity. The assembly process of genomic, transcriptomic, and metabolomic data from plants using carefully selected information on Arabidopsis can lead to a deeper understanding of organisms and their potential for genetic improvement for crops. However, the successful integration of data requires expertise and experience to design more effective and precise crop improvement strategies.

Abstract: Background: The acute hemodynamic response to isometric exercise in hypertensive patients ischemic heart disease (IHD) has been poorly investigated. The aim of this study was to assess acute changes on left ventricular myocardial work (MW) during isometric bilateral knee extension in patients with IHD. Methods. Twenty stable hypertensive patients with IHD and ten healthy, age-matched controls (HC) were enrolled. All subjects performed an isometric knee extension exercise, at 30% of their maximal voluntary contraction. The effort was maintained for three minutes or until exhaustion. At baseline, at peak exercise and after 10 minutes of recovery, echocardiography evaluation was perforned, blood pressure (BP) and heart rate (HR) were measurement. Results. The exercise was well tolerated by all subjects. At peak exercise: systolic BP in the IHD was significantly higher than HC (37.6± 7.2 vs 8.4±2.3 mmHg; p = 0.002). HC group had a greater increase in HR than IHD (19.7±6.2 vs 8.4±2.2 bpm; p = 0.009). E/E’ ratio increased in IHD and was unchanged in control. Global work index increased significantly in IHD compared to HC (+15% vs +3%; p 0.026). Global constructive work increased significantly in IHD compared to HC (+29.8% vs +7.4 respectively, p 0.031). Global waste work increased by 92.3% in IHD and was unchanged in HC. Global work efficiency decreased in IHD (-18%) while was unchanged in HC (between-groups p 0.019). Stroke volume decreased in IHD. and was unchanged in HC. Cardiac output was unchanged in IHD while it increased in HC. Conclusion: In patients with hypertension and underlying IHD, an acute isometric load caused a great increase in systolic BP and LV filling pressure. It follows a mostly ineffective increase of MW that fails to maintain stroke volume.

The expansion of the universe yields two consistent differing values of a Hubble constant depending on the methods of measurement. The aim of this work was to explain the Hubble tension by properties of space corresponding to a radially inhomogeneous metrics. A cosmological model described by this perturbed FLRW metric has zero pressure and exponentially expanding space. But the observed space appears to be compressed in the radial direction. We consider the dependence of the change rate of photon energy on the direction of its motion. It is found applying the principle of the photon's energy integral extremum and the relationship between it and the energy of a material particle obtained using Lagrange mechanics. The change rate of a photon's energy varies depending on whether it moves in a radial direction or has an angular component. The metric coefficients are determined by the difference in measurements of the Hubble constant using gravitational lensing and a distance ladder.

Background: anti-calcitonin gene-related peptide monoclonal antibodies (CGRP mAbs) are clinically effective in preventing migraine attacks, photophobia, and migraine aura associated with headaches. However, no study has yet investigated the effectiveness of CGRP mAbs in preventing migraine aura without headache. Case report: A female patient of 49 years old has a long history (since age 10) of photosensitivity and typical migraine aura without headache. The symptoms slightly responded to oral medication but did not completely resolve. Just one day after the administration of galcanezumab, her photo-hypersensitivity and migraine aura had completely resolved. Consequently, the administration of the oral migraine preventive medication was discontinued. Monthly galcanezumab at the dose of 120 mg has been continuously given and she did not experience any aura or headache so far. Conclusions: CGRP mAbs is considered an effective treatment method in preventing migraine aura without headache. Although CGRP mAbs do not penetrate the blood-brain barrier, trigeminal nerve stabilization with CGRP mAb can prevent cortical spreading depression.

Background: Rod fracture (RF) is the most common cause of revision in adult spinal deformity (ASD) surgery, and various treatment strategies for preventing RF are reported in literature. This retrospective study involving 139 ASD patients (aged ≥65 years and a minimum 2-year follow-up) who underwent long-segment fixation from T10 to sacrum with pedicle subtraction osteotomy (PSO), analyzed long-term results, including radiographical parameters and the incidence of recurrent RF (re-RF), to determine the most effective revision method for preventing RF. Methods: Patients were classified into three groups according to the revision method performed for RF; simple rod replacement (SR group, n=17), lateral lumbar interbody fusion around PSO site (LLIF group, n=8), and accessory rod integration (AR group, n=22). Baseline characteristics, radiographical and clinical parameters were analyzed. Results: RF occurred in 47 patients (34%) at an average of 28 months following primary deformity correction. Re-RF occurred in six patients (13%) at an average of 37 months. Re-RF occurred most commonly in SR group (p=.048). Every re-RF in SR group occurred at the PSO site, none occurred in LLIF group, and one in AR group occurred near L4–5. After both primary deformity correction and revision surgery, spinopelvic parameters had shown favorable results and clinical outcomes had improved in all three groups, without significant intergroup differences. Conclusions: Accessory rod integration or additional LLIF around the PSO site seems to provide greater strength and stability to the previously fused segments than simple rod replacement, which demonstrates the need for additional support in revision surgery for RF after PSO.

(1) Background: Efficient detection and rectification of defects or post-processing manufacturing conditions such as sharp edges and burrs on metal components are crucial for quality control in precision manufacturing industries. (2) Methods: This paper describes a lab-scale integrated system for real-time and automated metal edge image detection using a customized YOLOv5 ma-chine vision algorithm. The YOLOv5 algorithm and model were developed and embedded in the NVIDIA Jetson Nano microprocessor. The YOLOv5 model can detect three different image types for classification i.e., normal, sharp, and burrs edge on aluminum workpiece blocks. An integrated system connects the NVIDIA Jetson Nano with an embedded YOLOv5 model to a Mitsubishi Electric Melfa RV-2F-1D1-S15 manipulator to perform selective automated chamfer-ing and grinding when certain condition defects are detected. (3) Results: The model demonstrates durable performance, achieving a mean average precision of 0.886 across defect classes with minimal misclassifications. The Mitsubishi Electric Melfa RV-2F-1D1-S15 manipulator received input from the machine vision system and performed an automated chamfering and grinding process accordingly; (4) Conclusions: By integrating camera, embedded deep learning in the microprocessor and manipulator, automated chamfering and grinding process in metal component shapes can be efficiently rectified. This tailored solution promises to improve productivity and consistency in metal manufacturing and remanufacturing.

Heavy rains result in significant threats to human health and life. Floods and other natural disasters, which have a global impact annually, can be attributed to extended periods of intense precipitation. Accurate rainfall prediction is crucial in nations such as Bangladesh, where agriculture is the predominant field of occupation. The efficiency of machine learning methods is enhanced by the nonlinearity of rainfall, surpassing the effectiveness of other approaches. This study proposes the novel combination of rainfall occurrence prediction, rainfall amount prediction, and daily average temperature prediction. This research implements machine learning techniques and an ensemble-based classifier to predict rainfall occurrence, as well as machine learning regressor models and an ensemble-based regressor to predict the rainfall amount and daily average temperature, using the Bangladesh Weather Dataset. The ensemble classifier demonstrated an accuracy of 83.41% and a recall of 78.17%, exhibiting the best performance in predicting when it will rain, but its precision was the lowest, at 51.16%. The ensemble regression model outperformed the base models, including linear regression, random forest, and support vector regression in rainfall amount prediction, with the lowest mean absolute error of 0.36 and root mean squared error of 0.90. Additionally, this model provided the most precise daily average temperature prediction results with the lowest mean absolute error of 0.42 and root mean squared error of 0.54, highlighting its superiority over the other regression models in forecasting temperature. Ensemble approaches consistently exhibit superior task performance metrics.

In recent years, consensus algorithms have gained significant importance in the context of blockchain networks. These algorithms play a crucial role in allowing network participants to reach agreements on the state of the Blockchain without needing a central authority. The present study focuses on carrying out a systematic mapping of these consensus algorithms to explore in detail their use, benefits, and challenges in the context of blockchain networks. Understanding consensus algorithms is essential to appreciating how blockchain networks achieve the reliability and integrity of their distributed ledgers. These algorithms allow network nodes to reach agreement on the validity of transactions and the creation of new blocks on the Blockchain. In this sense, consensus algorithms are the engine that drives trust in these decentralized networks. Numerous authors have contributed to the development and understanding of consensus algorithms in the context of blockchain networks. For example, Satoshi Nakamoto's (2008) original paper on Bitcoin introduced proof-of-work (PoW) as a method of achieving consensus on the network. This revolutionary concept paved the way for numerous cryptocurrencies and blockchain systems. Despite advances in this field, significant challenges remain: centralization, fair token distribution, scalability, and sustainability. The energy consumption of blockchain networks, particularly those using algorithms such as Proof of Work, Proof of Stake, Delegated Proof of Stake, Proof of Authority, and hybrid algorithms (Proof of Work/Proof of Stake), has raised concerns about their environmental impact, motivating the scientific and technological community to investigate more sustainable alternatives that promise to reduce energy consumption and contribute to climate change mitigation. Furthermore, interoperability between different blockchains and security in specific environments, such as IoT, are areas that still require significant research attention. This systematic mapping not only seeks to shed light on the current state of consensus algorithms in blockchain, but also their impact on sustainability, identifying those algorithms that, in addition to guaranteeing integrity and security, minimize the environmental footprint, promoting a more efficient use of energy resources, being a relevant approach in a context in which the adoption of sustainable technologies has become a global priority. Understanding and improving these algorithms are critical to unlocking the full potential of blockchain technology in a variety of applications and industry sectors.

Vitamin D deficiencies are very common in pregnant women, raising concerns about adverse health outcomes in children. This issue has hardly been studied in multiple pregnancies, the prevalence of which has been steadily increasing. Therefore, our study investigated the relationship between newborns’ anthropometric parameters and the concentration of 25(OH)D in maternal blood of women with twin pregnancies and umbilical cord blood. The study included 50 women who gave birth after the 36th week of twin gestation. 25(OH)D was determined in maternal blood collected during the antenatal period and in the umbilical cord blood of 100 newborns. Anthropometric parameters of the newborns (birth weight, length, head and chest circumference) were obtained from hospital records. Data on nutrition and lifestyle during pregnancy were collected from the patients during an interview conducted by a dietitian. No relationship between maternal and neonatal cord blood vitamin D concentrations and any of the anthropometric parameters of the newborns was found. However, only 6% of the mothers and 13% of the newborns had vitamin D deficiency (≤20 ng/mL). The type of pregnancy and maternal height were the main factors associated with neonatal size. Newborns from dichorionic pregnancies were on average 202 g heavier (p

Plastics are very versatile materials that have contributed to the development of society since the 19th century, however, their mismanagement has led to an accumulation of waste in almost every ecosystem affecting the fauna of the planet. In this way recently, studies have shown that some insects might be able to adapt, consuming a wide range of hydrocarbon base polymers. In this work it was studied the adaptive capacity of Zophobas morio larvae when feeding on different synthetic polymers derived from petroleum. Four 30-day larval feeding treatments were carried out with synthetic polymers as expanded polystyrene (PS), low density polyethylene (PBD), polyisoprene (PI) and butyl rubber (BT), in addition a positive control of organic diet was included. Intestinal bacteria were isolated from the treatments and identified by Sanger sequencing. To analyze the chemical composition and physical form of the frass produced, fourier transform infrared spectroscopy (FITR) and images of the feces surface were taken with scanning electron microscopy (SEM) respectively. Zophobas morio larvae were able to consume 54% of PS in 30 days, equivalent to 3.2 mg/d/larva. Nine bacterial strains associated with the decomposition of synthetic polymers were identified in the intestine of the larvae. As for the physicochemical analysis of the feces, FTIR spectra showed the scission of bands corresponding to functional groups of the synthetic polymers in the comparison of the plastic diet treatments versus the feces of antibiotic-treated and plastic-fed larvae, while the comparison of spectra of the plastic and control treatments also identified differences in the absorption peaks. SEM imaging demonstrated that superworm feces differed in dependence on the substrate consumed. The findings demonstrate that Zophobas morio larvae possess a gut biological complex that allows them to feed and survive by consuming various petroleum-derived polymers.

The study aimed to optimize protocols for the joint extraction of deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) from 0.025 x 106 CFU of Candida albicans, aiming to overcome challenges in the extraction of these genetic materials. From this, treated Silicon Carbide (SiC) granules were added to fungal samples from methods 1, 2 and 3 obtained from aliquots of BHI or Sabouraud medium to cause cell lysis and enable the isolation of these macromolecules by phenol and chloroform. The concentration and integrity of nucleic acids were obtained, respectively, by spectrophotometry using A260/A280 ratios and 1% agarose gel electrophoresis. Therefore, method 3 is the one that most comprises samples considered pure of both DNA and RNA, simultaneously. Furthermore, the presence of intact RNAs corresponding to the base pair size, such as 5.8 S rRNA and tRNA, was verified during electrophoresis, taking into account the particularities of RNA, which makes it very unstable and easily degraded. Therefore, it results in a faster and simpler method in addition to obtaining promising results using minimal amounts of biological sample, thus offering a valuable alternative for small laboratories working with molecular biology.

Aspergillus flavus is a medically relevant fungus, particularly in tropical regions. Thermotolerance is a key virulence factor as an opportunistic pathogen, as well as its ability to form biofilms, which can impact therapeutic outcomes. To know how temperature affects the growth and biofilm formation of an A. flavus isolate, we assessed its growth on solid media and described the morphological changes during conidial germination. We also examined formation and composition of in vitro biofilm incubated at different temperatures and compared the susceptibility of planktonic and biofilm cells to antifungal agents. Our results showed that temperature promotes conidiation on solid media. The radial growth was higher at 28 °C, but total conidia was higher at 37 °C, however, conidial density was stronger at 42 °C. Moreover, incubation at 37 °C accelerates conidial germination and is the optimal temperature for biofilm formation. We described four distinct phases in A. flavus biofilm development: initiation (0-12h), consolidation (12-48h), maturation (48-72h), and dispersion (&gt;72h), where the presence of aspergillar heads was notable at 42 °C. Carbohydrates and proteins are the main components of the extracellular matrix. We observed lipid droplets within the biofilm hyphae, and mature biofilms are resistant to amphotericin B and itraconazole, with MICs exceeding 16 g/mL for both antifungals, regardless of the incubation temperature.

Lung and colon cancers are among the leading causes of cancer-related mortality worldwide. Early and accurate detection of these cancers is crucial for effective treatment and improved patient outcomes. False or incorrect detection is more harmful. Accurately detecting cancer in a patient's tissue is crucial to their effective treatment. While analyzing tissue samples is complicated and time-consuming, deep learning techniques have made it possible to complete this process more efficiently and accurately. As a result, researchers can study more patients in a shorter amount of time and at a lower cost. Much research has been conducted to investigate deep learning models that require great computational ability and resources. However, None of these have had a 100% accurate detection rate for these life-threatening malignancies. Misclassified or falsely detecting cancer can have more harmful consequences. This research proposes a new lightweight, parameter-efficient, and mobile-embedded deep learning model based on a 1D convolutional neural network with Squeeze-and-Excitation layers for efficient lung and colon cancer detection. This proposed model diagnoses and classifies lung squamous cell carcinomas and adenocarcinoma of the lung and colon from digital pathology images. Extensive experiment demonstrates that our proposed model achieves 100% accuracy for detecting lung, colon, and lung and colon cancers from the histopathological (LC25000) lung and colon datasets, which is considered the best accuracy for around 0.35 million trainable parameters and around 6.4 million flops. Compared with the existing results, our proposed architecture shows state-of-the-art performance in lung, colon, and lung and colon cancer detection.

In today's interconnected global business landscape, companies face numerous risks, particularly from their supply chains. Supplier disruptions can cause financial damages, inefficiencies, and reduced competitive edge, highlighting the need for robust supplier risk management. This paper scrutinizes recent studies on supplier risk management spanning 2020 to 2023. This review examines supplier risk management, focusing on supplier evaluation, payment terms, contract duration, and buyer-supplier collaboration as critical factors influencing risk mitigation and control in the procurement of goods and services. Through a systematic literature review, the study identifies the multifaceted nature of supplier risks, including production delays, quality issues, and financial challenges. It underscores the importance of proactive risk identification, comprehensive supplier evaluation, strategic payment terms, appropriate contract durations, and robust buyer-supplier collaboration. Despite the benefits, challenges remain, such as understanding regulatory impacts and ensuring methodological consistency. This review contributes to the discourse on supplier risk management, offering insights for future research and practical applications to enhance resilience and operational efficiency in global supply chains. Based on the findings, future studies should concentrate on formulating holistic supplier risks management approaches that nurture buyer-supplier collaboration and harness innovative technologies to enhance risk management practices.

There are various types of charged particles in the space environment, which can cause different types of radiation damage to materials and devices, leading to on-orbit failures and even accidents of spacecraft. Developing lightweight and efficient radiation shielding materials is an effective approach to improve the inherent protection of spacecraft. The protective performance of different materials against proton and electron spectra in the Earth’s radiation belts is evaluated by Geant4 simulation. Based on the simulation results, suitable hardening components are selected to design composite materials, and B4C/PEEK composites with different B4C content were successfully prepared. Experimental results demonstrate that the simulated and experimental results for the electron, proton and neutron shielding performance of B4C/PEEK composites are consistent. These composites exhibit excellent radiation shielding capabilities against electrons, protons and neutrons, and the radiation protection performance improves with increasing B4C content in the B4C/PEEK composite materials.

Health and social needs exist along a dynamic continuum. Recognizing that health status is inextricably impacted by social determinants of health, community paramedicine has opportunities and a responsibility to reduce inequities. The objective of this scoping review was to investigate peer-reviewed and grey literature to explore how community paramedicine supports community needs along a health and social continuum. We conducted a scoping review of English language literature using the JBI Scoping Review methodology. We searched CINAHL, EMBASE, MEDLINE, Google Scholar, and organisational websites. 30 peer-reviewed and 13 grey literature articles met inclusion criteria. The findings describe the ways community paramedicine models evolved from minimising system pressures on emergency health services to addressing health and social needs. A key recommendation across the literature was the need to meaningfully engage communities early in program development to understand how best to implement and co-design integrated service models addressing specific community needs, though there was a lack of evidence to guide this approach. There is a notable lack of evidence pertaining to optimising technologies in program design and implementation. Results highlight opportunities to determine best practices for conducting holistic community needs assessments that include equitable stakeholder engagement and enhancing education to prepare paramedics for expanded roles. Community paramedicine provides opportunities to better meet the needs of structurally marginalised communities. There is a social responsibility and opportunity to engage communities to co-design service delivery, advance paramedic education, and enhance interprofessional collaboration to better support community needs and generate upstream solutions for individuals and communities

The resurgence of mpox, particularly in regions where the virus was previously non-endemic, has reignited interest in the cross-protective potential of smallpox antibodies. Given the genetic similarity between the mpox virus and variola virus (the causative agent of smallpox), antibodies generated against smallpox may offer a degree of protection against mpox. This study investigates the presence of smallpox and mpox antibodies in human immunoglobulin preparations containing IgG against both mpox virus and vaccinia virus using ELISA techniques. The implications of these findings for post-exposure prophylaxis (PEP) are discussed in light of the recent mpox outbreaks in Africa, as reported by the World Health Organization (WHO). Current data suggest that smallpox vaccination, even decades after eradication, may confer substantial protection against mpox, thereby serving as a critical tool in managing emerging outbreaks. Further the presence of mpox antibodies in immunoglobulin preparations was also significant. These findings strongly suggest the potential use of intravenous immunoglobulins preparation as possible therapeutic strategy. Particularly it also underscores the need for further research into the development of targeted post-exposure prophylaxis strategies that leverage the cross-reactivity of orthopoxvirus antibodies.

Background Hepatic resection offers promising outcomes for patients with hepatocellular carcinoma (HCC) but can be constrained by factors like patient suitability. Continuous advancements in laparoscopic and robotic technologies have made minimally invasive hepatectomies (MIH) viable alternatives to open hepatectomies with benefits in recovery and complications. Materials and Methods We completed a retrospective review on 138 HCC patients at the Hume-Lee Transplant Center that underwent OH or MIH between 2010 and 2020. Univariate and multivariate analyses were completed on demographic, clinical, and tumor-specific data were collected to assess the impact of these variables on overall and disease-free survival at 1-, 3-, and 5-years. Preoperative metrics like length of hospital stay (LOS) and operation duration were also evaluated. Results Of the 109 OH and 29 MIH patients, MIH patients demonstrated shorter LOS and operative times. However, overall survival (OS) and disease-free survival (DFS) were similar between groups, with no significant variations in 1-, 3-, and 5-year survival rates. Ages > 60 years and lack of preoperative transcatheter arterial chemoembolization (TACE) were significant predictors of inferior OS and DFS in multivariate analyses. Conclusions MIH is an efficient substitute to OH with comparable survival even in older patients. The reduced LOS and operation time enhances its feasibility, and older patients previously denied for curative resection may qualify for MIH. Preoperative TACE also enhances survival outcomes, emphasizing its general role in managing resectable HCCs. Both robotic and laparoscopic hepatectomies offer acceptable short- and long-term clinical outcomes, highlighting MIH as the standard choice for HCC patients.

Almost fifty years after the introduction of antibiotics as an antimicrobial agent, we are facing an increasing number of multi-resistant microbials. The search for new antimicrobial compounds emerged as a promising alternative to treatments for diseases caused by microorganisms such as fungi and bacteria. Thus, endophytic fungi are gaining recognition for their varied metabolites that may possess antimicrobial properties. In our research, we isolated, from Calea pinnatifida, six endophytic fungi identified as Colletotrichum siamense, Colletotrichum karstii, Hypomontagnella barbarensis, Neopestalotiopsis clavispora, Nigrospora sacchari-officinarum and Annulohypoxylon moriforme. Analyses of microbial secondary metabolites by utilizing UHPLC-HR-ESI-MS/MS in conjunction with molecular networking, allowed the annotation of 9 compounds, 5 of them from GNPS spectral library match, and 4 from prediction-based on the molecular network, and allowed the isolation of two pairs of Acyl-tetronic acid derivatives distereoisomeric identified by NMR spectrum. In this sense, the present study underlines the value of endophytic fungi as a reservoir of potentially new antimicrobial agents.

Aims: To investigate the road safety concerns associated with pet dogs in New Zealand. Methods: An online survey was developed and offered to New Zealanders via SurveyMonkey© from 8 January 2019 to 31 March 2019) using New Zealand residents aged ≥18 years. Questions about the demographics of respondents and their number of pet dogs along with those about dog transportation and dog roaming were asked. Results: Of 2744 respondents who completed the online survey, 1494 (65%) owned a dog. Of these, 1511 completed the question about how their dog or dogs travel in vehicles. Of these, 2 % (n = 29) let their dog sit on the driver's knee, and 12 % (n = 179) allowed their dog to sit on the passenger's knee. Only 7 % (n = 105) allowed the dog to roam free throughout the vehicle, while 44% (n = 663) allowed their dogs to roam free in the backseat area only and 19% (n = 280) put their dog in the boot/hatchback area. A minority of respondents (2%, n = 22) indicated they let their dog travel unrestrained on the deck of a ute or truck. Approximately half, 51% (n = 767) of the respondents, indicated that they restrained or crated their dogs in or on the vehicle. Respondents also expressed concerns about roaming dogs being a road safety hazard with the themes of responsible ownership, physical and psychological harm, and the dangers of rescue altruism emerging. Conclusions: Increased awareness about proper pet restraints during transportation and preventing dogs from roaming, especially near and on roads, is crucial to ensure traffic safety for humans and dogs.

Covariant scalar field quantization, nicknamed $(φ^r)_n$, where $r$ denotes the power of the interaction term and $n=s+1$ where $s$ is the spatial dimension and $1$ adds time. Models such that $r<2n/(n-2)$ can be treated by canonical quantization, while models such that $r>2n/(n-2)$ are trivial, or, if treated as a unit, emerge as `free theories'. Models such as $r=2n/(n-2)$, e.g., $r=n=4$, again using canonical quantization also become `free theories', which must be considered quantum failures. However, there exists a different approach called affine quantization that promotes a different set of classical variables to become the basic quantum operators and it offers different results. It is well-known that the canonical quantization of $\varphi^4_4$ fails. Here is how to fix it along with many other problems.

Train fault detection primarily relies on the comparative analysis of collected images with standard reference images, making the accurate identification of the collected image types crucial for effective fault detection. Most current systems achieve indirect classification of carriage images by using Automatic Equipment Identification (AEI) devices to recognize carriage identification number information while capturing the carriage images. However, damaged RF tags or blurred characters can hinder these devices, obstructing further train fault analyses. The unique features of carriage linear array images, such as high resolution, extreme aspect ratios, and local non-linear distortions, pose significant challenges to recognition algorithms. This paper proposes a method specifically designed to recognize these types of images. First, we implement an object detection algorithm to locate the positions of key components in carriage linear array images. By leveraging the spatial layout mapping between these key components and the carriage type classification, we simplify the complex image recognition problem into a sparse point set alignment task. To ensure effective registration despite local distortions in the linear array images, we propose a weighted radial basis function. To address the issues of unknown matching relationships and quantity mismatches during point set alignment, the proposed objective function is designed to maximize the similarity between Gaussian mixtures of point sets, thereby determining the RBF weights. Extensive experiments have demonstrated that our algorithm achieves 100% recognition accuracy under local nonlinear scale distortions of less than 15%. The algorithm remains effective even in the presence of some false positives or missed detections in key component object detection results. It can accurately identify the target category from 79 image categories within 24 milliseconds on an i7 CPU without GPU support. It is of great significance to reduce system cost and promote automatic exterior fault detection for train rolling stock.

This paper presents a comprehensive exploration of Data Quality terminology, revealing a significant lack of standardisation in the field. We propose a novel approach to aggregating disparate Data Quality terms used to describe the multiple facets of Data Quality, under common umbrella terms, with a focus on the ISO 25012 standard. Our aim is to design a Data Quality Data Model that serves as a universally applicable framework for Data Quality assessment. We introduce four additional Data Quality dimensions: Governance, Usefulness, Quantity, and Semantics, enhancing specificity, complementing the framework established by the ISO 25012 standard, and understanding of Data Quality aspects. The ISO 25012 standard, while tailored for software development, offers a foundation for the development of our proposed Data Quality Data Model. This is due to the prevalent nature of software development across a multitude of domains. In contrast, frameworks like ALCOA+ that are specific to certain domains lack the ability to be generalised. The model we propose can be seen as a "Rosetta Stone" for Data Quality terminology, facilitating a seamless communication of Data Quality between different domains when collaboration is required to tackle cross-domain projects or challenges.

COPD is a "heterogeneous lung condition characterized by chronic respiratory symptoms. It is a leading cause of death and illness and is associated with a substantial economic burden worldwide. Furthermore, it is marked by frequent exacerbations, which significantly contribute to morbidity and mortality, as well as the overall burden. These exacerbations are frequently triggered by bacterial infections.So far, the most commonly implicated Bacteria are Pseudomonas aeruginosa, Klebsiella pneumoniae, Haemophilus influenzae, Moraxella catarrhalis, and Streptococcus pneumoniae.The advent of culture-independent techniques, such as next-generation sequencing techniques (NGS), allows us to characterise the lung microbiome in different disease states.

Purpose: This study investigates the influence of human brands on the formation of Generation Z's digital identity. Given the increasing importance of the digital space in this generation's life, understanding how their identity is shaped by human brands in this environment is crucial. Method: Using a netnographic approach, qualitative data was collected and analyzed from 500 posts and interactions of Gen Z users on Instagram and TikTok platforms. Thematic content analysis was conducted using NVivo 12 software, with inter-coder reliability confirmed (κ = 0.85). Findings: Results indicate that human brands influence Gen Z's digital identity through three main mechanisms: 1) lifestyle emulation (β = 0.42, p < 0.001), 2) value alignment (β = 0.38, p < 0.001), and 3) self-presentation modeling (β = 0.35, p < 0.01). Multiple regression analysis showed these three factors explain 67\% of the variance in digital identity formation (R² = 0.67, F(3,496) = 335.42, p < 0.001). Conclusion: This study demonstrates that human brands play a significant role in shaping Generation Z's digital identity. Understanding this influence can help marketers and policymakers design more effective strategies for engaging with this generation.

The advancement of agricultural technology is intrinsically linked to the evolution of the agricultural industry, with the latter being contingent upon technological progress for its sustained enhancement. In the context of China, a nation of significant agricultural stature, the globalization of agricultural science and technology (Agri-Sci-Tech) has emerged as a pivotal narrative within the country's agricultural growth trajectory. The strategic alignment of China's Agri-Sci-Tech industry with its global counterparts is driven by the ambition to assimilate sophisticated agricultural production techniques and managerial acumen from its Western counterparts, thereby catalyzing the international expansion of the nation's Agri-Sci-Tech endeavors. This scholarly discourse delves into the imperative of interdisciplinary convergence, underscoring the paradigm and intrinsic attributes associated with the cultivation of agricultural expertise. It scrutinizes the prevailing state of foreign language instruction within the academic curriculum, positing strategic interventions and recommendations for the development of a robust agricultural foreign language talent cultivation framework. The treatise accentuates the criticality of cultivating a multifaceted agricultural talent pool proficient in foreign languages, advocating for a "professional+foreign language" model that facilitates international dialog within China's Agri-Sci-Tech industry and bolsters the creation of globally recognized brands. Moreover, the cultivation of a "foreign language+professional" composite talent model is posited as a transformative solution to the existing impasse in foreign language education in China, offering a novel pathway for educational innovation and global competitiveness within the agricultural domain.

Contextualising the narrative on energy efficiency as the first fuel in the clean energy transition, the energy efficiency first principle (EE1) was introduced in EU energy and climate policy with Energy Union communication in 2015 and made nonbinding with the EU Governance Regulation in 2018. In 2023, the EU legislators made EE1 legally binding to apply in policy, planning and investment decisions exceeding 100 million euros each and 175 million euros in the transport sector across all sectors that affect the energy system. By applying the multiple streams framework, this article analyses the role of policy entrepreneurs in the policy process and politics of making EE1 binding through qualitative text analysis of policy documents, position papers and reports from EU negotiations. The analysis shows how nonprofit and nongovernmental organisations such as the Regulatory Assistance Project, the European Climate Foundation and the Energy Efficiency Financial Institutions Group, as well as the European Commission and the rotating Council Presidency, were critical policy entrepreneurs for coupling the problem, policy and politics streams. The coupling was performed once a policy window was opened by the Paris Agreement on climate change, the entering office of the Ursula von der Leyen European Commission in late 2019, immediately presenting the European Grean Deal as the EU’s climate action plan and green growth strategy, and the energy crisis in Europe following Russia’s second war on Ukraine, leading the Commission to present the REPowerEU Plan and the EU Save Energy Strategy. This paper provides better knowledge of the policy processes and politics related to EE1, contextualising and concretising the concept of energy efficiency as the first fuel into binding law.

As a pioneering transdisciplinary field, innovationology transcends the traditional boundaries of academic disciplines and development practices to address persistent global challenges. This article provides a comprehensive exploration of innovationology, delving deep into its philosophical foundations, ontological commitments, and ethical imperatives. By unpacking the core tenets of this innovative approach, the paper positions it as a robust and holistic science capable of catalyzing transformative innovation and sustainable development worldwide. Innovationology represents a paradigm shift that moves beyond conventional innovation and development theories, embracing a systems-oriented, community-centric, and decolonial praxis. Drawing on a synthesis of theoretical perspectives and empirical insights, the article examines innovationology's ontological commitment to marginalized communities as active agents of change, its epistemological grounding in pluralistic knowledge systems, and its ethical imperatives of social justice, environmental sustainability, and inclusive innovation. This in-depth analysis strengthens the conceptual and theoretical foundations of innovationology, providing critical guidance for its practical implementation and continued refinement as a cutting-edge transdisciplinary science for global transformation.

High mountain rainforests are vital in the global energy and carbon cycle. Understanding the exchange of energy and carbon plays an important role to reflect responses to climate change. In this study, an eddy-covariance (EC) measurement system installed in the high Andean Mountains of South Ecuador was used. As EC measurements are affected by heterogeneous topography and vegetation height, the main objective was to estimate the effect of the sloped terrain and the forest on the turbulent energy and carbon fluxes considering the energy balance closure (EBC) and the heat storage. The results showed that the performance of the EBC was generally good and estimated to be 79.5%. This could be improved when the heat storage effect was considered. Based on the variability of the residuals in the diel modifications in the imbalances were highlighted. Particularly, during daytime residuals were largest (56.9 W/m² on average) with a clear overestimation. At nighttime mean imbalances were rather weak (6.5 W/m²) and mostly positive, while strongest underestimations developed in the transition period to morning hours (down to -100 W/m²). With respect to the Monin-Obukhov stability parameter ((z-d)/L) and the friction velocity (u*) it was revealed that largest overestimations evolved in weak unstable and very stable conditions associated with large u* values. In contrast, underestimation was related to very unstable conditions. The estimated carbon fluxes were independently modelled with a non-linear regression using a light-response relationship and reached a good performance (R²=0.52). All fluxes were additionally examined in the annual course to estimate whether both, energy and carbon fluxes, resembled the microclimatological conditions of the study site. This unique study demonstrated that EC measurements provide valuable insights into land surface -atmosphere interactions and contribute to our understanding of energy and carbon exchanges. Moreover, the flux data provide an important basis to validate coupled atmosphere ecosystem models.

This review highlights recent innovations in food packaging, emphasizing the shift from conventional petroleum-based materials to bio-based alternatives and smart packaging systems. Bio-based materials, such as starch, cellulose, and polyhydroxyalkanoates (PHA), offer sustainable solutions due to their biodegradability and reduced environmental impact. These materials are positioned as eco-friendly alternatives to traditional plastics, but face challenges related to production costs and scalability. Additionally, advancements in smart packaging technologies, including sensor and indicator systems, provide real-time food quality monitoring, enhancing food safety and reducing waste. Active packaging technologies, incorporating natural antioxidants and moisture control, extend product shelf life and improve food preservation. Also, these biopolymers usually present lower CO2 footprint, energetic cost, and water consumption during their production, in comparison to traditionally used synthetic plastics. The review identifies critical challenges, such as regulatory barriers and technological limitations, but also outlines significant opportunities for future research and innovation in the food packaging sector, aiming for more efficient, safer, and environmentally sustainable packaging solutions.

Objectives: The aim of this study was to determine why and how adults with attention deficit hyperactivity disorder (ADHD) experience variable impairment and identify the processes and strategies adults with ADHD use to develop positive self-regulation skills. Methods: A mixed cohort of 11 participants (6 female and 5 male) from a university, an adult ADHD clinic and an ADHD support group in the UK were interviewed online between September 2021 and February 2022. Data were collected and analysed simultaneously, inspired by constructivist grounded theory methodology. Results: Participants described a “polar awareness of difference” from others in terms of engagement and ADHD characteristics, and a “polar awareness of consciousness” experienced as the states of chaotic attention and hyperfocus, both of which impact core perceptions of self. Using an infinity paradigm, the results demonstrate unskilled attempts to self-regulate within and between these states using self-absorption or self-transcendence strategies including their inherent challenges and energetic cost. Our results further indicate that at the centre of this dynamic paradigm creative awareness strategies exist, which exemplify polarity awareness and the regulation of that awareness supported by an authentic inner compass (AIC). Conclusions: This paper presents the empirical foundation for the ADHD Creative Awareness Theory (CAT) – a new theory for understanding the experience of ADHD consciousness and environmental engagement. Practice implications are explored, and recommendations include use of the CAT as a framework for understanding and development of inherent self-regulation skills for adults with ADHD.

No consensus exists on whether acute aerobic exercise alters the glomerular filtration rate in older adults. Objective: To assess the immediate effects of three aerobic exercise intensities on the estimated glomerular filtration rate (eGFR) in healthy, sedentary older adults. Methods: Eighteen healthy, sedentary older adults (12 men and eight women) voluntarily participated in the study. The participants underwent three standardized aerobic exercise tests (100%, 80%, and 60% of the maximal heart rate) on a bicycle ergometer. Blood samples were collected to determine cholesterol, triacylglycerol, glucose, serum creatinine (Cr), Cystatin C (CysC) concentrations, and eGFR. Results: eGFR and serum concentrations of Cr and CysC were not modified at any exercise intensity. There was a negative correlation between blood total cholesterol vs. eGFR (R= -0.512, R= -0.582, R= -0.531; p &lt;0.05) at rest, 60%, and 100% of the maximal heart rate, respectively. Besides, a negative correlation existed between age vs. eGFR at 60% of the maximal heart rate (R= -0.516; p &lt;0.05). Conclusion: Short-time aerobic exercise of low, moderate, and vigorous intensity did not significantly affect eGFR and is considered safe for kidney function in healthy, sedentary older adults.

Nitrogen is essential for plant growth, yield and crop quality, and its deficiency limits food production worldwide. In addition, excessive fertilization and inefficient use of N can increase production costs and cause environmental problems. A possible solution to this problem is the application of biofertilizers, which improve N assimilation and enhance biomass and yield. Therefore, the objective of this research was to evaluate the impact of the application of the combination of green and red algal (Ulva lactuca, Solieria spp.), Rhizobium sp., Trichoderma asperel-lum and the combination of the above three biofertilizers on N assimilation. A completely ran-domized design was carried out with 10 plants per treatment and five treatments: T1=control; T2=algal extracts; T3=Rhizobium sp.; T4=T. asperellum; T5=T2+T3+T4. Analyses showed that the Rhi-zobium sp. treatment resulted in higher nitrate reductase enzyme activity quantified in maize leaves, which possibly favored more efficient phosphosynthetic activity, reflecting higher bio-mass accumulation and increased yield. The use of Rhizobium sp. showed increases in biomass (13.4%), yield (11.82%), SPAD values (12%), total chlorophyll (18.4%), carotenoids (13.6%), num-ber of leaves (11.4%) and plant height (11.27%) compared to the control. This results in nitrogen assimilation promises to be a key mechanism for sustainable agricultural practices in the future.

The COVID-19 pandemic has highlighted the need for a robust medical infrastructure and crisis management strategy as part of smart-city applications, with technology playing a crucial role. The Internet of Things (IoT) has emerged as a promising solution, leveraging sensor arrays, wireless communication networks, and artificial intelligence (AI)-driven decision-making. Advancements in Edge Computing (EC), Deep Learning (DL), and Deep Transfer Learning (DTL) have made IoT more effective in healthcare and pandemic-resilient infrastructure. When combined with medically oriented IoT setups, DL-architectures are suitable for integrating into pandemic-compliant medical infrastructure. The development of intelligent pandemic-compliant infrastructure requires combining IoT, edge and cloud computing, image processing, and AI tools to monitor adherence to social distancing norms, mask-wearing protocols, and contact tracing. The proliferation of 5G wireless communication has enabled ultra-wide broadband wireless communication, with high reliability and low latency, thereby enabling seamless medical support as part of smart-city applications. Such set-ups are designed to be ever-ready to deal with virus-triggered pandemic-like medical emergencies. This study presents the design of a pandemic-compliant mechanism leveraging IoT optimized for healthcare applications, edge- and cloud-computing frameworks, and a suite of DL-tools. The framework uses a composite attention-driven framework incorporating various DL-pre-trained models (DPTM) for protocol adherence and contact tracing. When connected to public networks, it can detect specific cyber-attacks. The results confirm the effectiveness of the proposed methodologies.

When we grow old, we experience loss of muscle strength and power, a condition commonly referred to as sarcopenia (ICD-10-CM code (M62.84)). Sarcopenia leads to disability in 5-10% of the population. In this review we emphasise that it is not a sudden event, but a deterioration that occurs over time but is only recognised as a disease when it becomes clinically manifest in the 6th-7th decade of life. Evidence from animal studies, elite athletes and longitudinal population studies all confers that once sarcopenia is manifested, the underlying process has been ongoing for decades. We present various hypotheses about the underlying cause(s) of this process and their supporting evidence, i.e. does this disease have a neurogenic, a myogenic or a scaffold-related origin? We briefly review various proposals to alleviate sarcopenia, including stem cell rejuvenation, combating scaffold degeneration, reducing senescent cell burden, skeletal muscle secretomes and muscle innervation. We conclude that although there are potential target candidates and ongoing preclinical and clinical trials with drug treatments, the only evidence-based intervention today is exercise. We present different execrcise programmes and discuss to what extent the inter-individual susceptibility to developing sarcopenia is due to our genetic predisposition or lifestyle factors.

This paper introduces a systematic approach to the design of Direct Current-to-Digital Converter (DCDC) specifically engineered to overcome the limitations of traditional current measurement methodologies in System-on-Chip (SoC) designs. The proposed DCDC addresses critical challenges such as high power consumption, large area requirements, and the need for intermediate analog signals. By incorporating a current mirror in a cascode topology and managing the current across multiple binary-sized branches with the Successive Approximation Register (SAR) logic, the design achieves precise current measurement. A simple comparator, coupled with an isolation circuit, ensures accurate and reliable sensing. Fabricated using the TSMC 180 nm process, the DCDC achieves 8-bit precision without the need for nonlinearity calibration, showcasing remarkable energy efficiency with an energy per conversion of 1.52 pJ, power consumption of 117 µW, and a compact area of 0.016 mm². This innovative approach not only reduces power consumption and area, but also provides a scalable and efficient solution for next-generation semiconductor technologies. The ability to conduct online measurements during both standard operations and in-field conditions significantly enhances the performance and reliability of SoCs, making this DCDC a promising advancement in the field.

Extreme Ultraviolet (EUV) photolithography process, is a cornerstone of semiconductor manufacturing and operates under demanding precision standards realized via nanometer-level overlay (OVL) error modeling. This procedure allows the machine to anticipate and correct OVL errors before impacting the wafer, thereby facilitating near-optimal image exposure while simultaneously minimizing the overall OVL error. Such models are usually high dimensional and exhibit rigorous statistical phenomena such as collinearities that play a crucial role in the process of tuning their parameters. Ordinary Least Squares (OLS) is the most widely used method for parameters tuning of Overlay models but in most cases it fails to compensate for such phenomena. In this paper we propose the usage of Ridge Regression, a widely known Machine Learning (ML) algorithm especially suitable for datasets that exhibit high multicollinearity. The proposed method has been applied in perturbed data from a 300 mm wafer fab and the results show reduced residuals when ridge regression is applied instead of OLS.

This paper intoduces a non-standard research technique to clarify how complex phenomena, such as those that are abundantly present in human physiology, can be faithfully described using fractal dynamical models with and without stochastic forces. This method for conducting research involves tracing the his- torical evolution of understaning an empirical medical process faciltated by the fractal calculus perspective. Herein we trace the analysis of the time series for heart rate varability (HRV) developed for diagnosing the cardiovascular health of a patient. This is done herein by introducing four (one empirical which en- tails three theoretical fractal models) distinct but related fractal models each one introduced to solve a particular problem arising from a fundamental defect in the previous model but in generalizing a model at one stage to resolve the problem associated with the defect another is invariably introduced by the re- placement model. It is through the utilization of the fractional calculus that the necessity for rethinking how to systematically incorporate additonal layers of complexity are revealed ultimately resulting in a 'complete' description of its empirical dynamics in fractal terms.