Iron supplementation is associated with side effects and thus reduces the intake compliance. Because it has fewer side effects, a recent trial recommended low-iron micronutrient powder (MNP) for the prevention of anemia in Bangladeshi children exposed to a high concentration of iron from drinking groundwater. In the present study, we hypothesize that the low-iron MNP is a potential intervention to prevent childhood anemia in the low groundwater-iron settings in Bangladesh. A cross-sectional study was conducted in Bangladesh with children aged 2-5 years who drank groundwater containing a low level of iron (0-<2 mg/L).The combined intake of iron was calculated from the key sources--diet, groundwater and MNPs. The intakes of iron were compared against the standard reference intake. The children’s hemoglobin was measured using a photometer. Combined intake of iron from dietary, groundwater and low-iron MNP in children was 5.8±2.0 and 6.9±2.5 mg/day comprising 193% and 169% of the Estimated Average Requirement in the 2-3 year-old and 4-5 year-old subgroups, respectively. Combined intake of bioavailable iron from dietary and groundwater sources was 0.42±0.023 and 0.22±0.019 mg/day in children exposed to groundwater concentration 0.8-<2.0 mg/L and 0.0-<0.8 mg/L respectively (p<0.001). The mean concentration of hemoglobin in the respective groups was 12.17±0.94 mg/dl and 11.91±0.91 mg/dl (p=0.30). The combined intake of iron from dietary and groundwater sources was associated with maintenance of hemoglobin concentration at the non-anemic level in most of the children. The finding highlights the potential of low-iron MNP in controlling childhood anemia in this setting.

Background: Virtual Reality (VR) is an emerging tool in the management of anxiety, one of the most disturbing symptoms of Alcohol Use Disorder (AUD), which often leads to relapse. Still, patients with Alcohol Use Disorder frequently have a history of Acute Symptomatic Seizures, and they are subsequently excluded from studies using VR because of the hypothesized fear of inducing photosensitive seizures. The first part of this narrative review explores the pathophysiological mechanisms of seizures associated with AUD, specifically acute symptomatic seizures. In contrast, the second part explores virtual reality’s safe use in seizure-prone individuals, highlighting that the current state-of-the-art is based on a biased per-ception and old epidemiological data from studies conducted on photosensitive seizures in the late 90s. VR exposure has yet to be reported as triggering seizures within known pharmacovigilance databases. Significance: The implications weigh heavily on VR research currently associated with this significant adverse event. Further studies are necessary to address the risk of seizures triggered by VR in patients predisposed to developing seizures, like alcohol use disorder patients with a history of acute symptomatic seizures. Such research is essential to ensure that the potential benefits of Virtual Reality as a tool for managing anxiety in individuals with alcohol use disorder and acute symptomatic seizures are not unduly restricted. Furthermore, AUD can also serve as an intervention model feasible to de-bias attitudes toward the current lack of evidence base concerning the significant adverse events of VR-induced seizures, therefore facilitating the clinical implementation of this unharnessed digital treatment interface.

(1) Background: Alzheimer’s Disease (AD) accounts for 70% of dementia cases and with no effective pharmacological treatments, new rehabilitation methods are needed. Motor activities and transcranial direct current stimulation (tDCS) have shown promise in stabilizing and enhancing cognitive functions. Objective: We want to investigate the effects of combined motor or cognitive activity with tDCS on cognitive functions in AD patients. (2) Methods: Patients with mild or moderate AD were randomized into four groups: motor activity or cognitive activity associated with anodic or sham tDCS (MotA, MotS, CogA, CogS). They received two weeks of treatment (45 minutes, 5 days/week), with the first 15 minutes using 2mA tDCS on the dorsolateral prefrontal cortex. Cognitive assessments were conducted pre-treatment (T0), post-treatment (T1), and one week after (T2). (3) Results: Twenty patients were included. Mann-Whitney test showed significant improvements in anodic groups (MotA+CogA) in the global cognitive status (p=.042), in selective attention (p=.012), and sustained attention (p=.012) at T1, but not at T2. In addition, MotA improved in the Picture Recognition Test (p=.027) and in sustained attention (p=.047) at T2. (4) Conclusion: Combined motor or cognitive activity with anodic tDCS could slow cognitive decline in AD patients, with notable improvements in global cognitive state and attention at T1.

This review details the extraction, characterization and utilization of seaweed-derived biopolymers for future packaging applications. The review is contextualized within the broader scope of the challenge of plastic pollution and the current urgent need for more sustainable packaging materials. Macroalgae (or seaweed) has been highlighted as a promising source of biopolymers, most commonly sodium alginate, agar and carrageenan, for reasons such as a rapid growth rate and decreased environmental impact when compared to terrestrial plant-life. Extraction methods detailed include the traditional solvent-based extraction to more sustainable developments such as ultrasound assisted extraction, microwave assisted extraction and bead milling. The review additionally presents the characterization techniques most pertinent in determining the applicability of these biopolymers in packaging applications. Properties of key importance to the development of sustainable packaging materials such as thermal properties, mechanical strength, barrier properties and biodegradability are highlighted in comparison to conventional petroleum-based plastics. This review concludes by realistically identifying the challenges faced by implementing seaweed-based biopolymers into packaging structures, such as cost-effectiveness, scalability, performance while suggesting future directions to mitigate these issues and improve the commercial viability of these materials for the packaging industry.

Gigantism in theropods represents one of the most intriguing phenomena in dinosaur evolution, showcasing extreme adaptations in body size and ecological roles. This literature review focuses on Giganotosaurus carolinii, one of the largest known theropods to have ever existed, to explore its unique aspects of gigantism and the subsequent ecological impacts. By analyzing features such as its skeletal morphology, comparative anatomy, and paleoenvironmental data, we assess the evolutionary pressures and biological constraints that shaped the gigantism observed in Giganotosaurus. We discover that the species exhibited a combination of physiological and ecological adaptations that allowed it to thrive as an apex predator in its Cretaceous environment. We will also further examine the implications of its size on prey dynamics, competition with other large theropods, and the overall ecosystem structure. Through a thorough, multidisciplinary approach, this research enhances our understanding of how gigantism influenced theropod behavior, interspecies interactions, and the broader paleoecological landscape.

The current study aimed to examine the relationship between sociodemographic variables (i.e., sex, age, marital status, educational level, socioeconomic status, and working mode) and physical activity levels declared by Chilean adults. The sample comprised 483 Chilean adults, 159 men (32.9%), and 324 women (67.1%) aged from 18 to 69 years old (36.5±12.0). The participants completed an ad-hoc sociodemographic online survey that included questions about characteristics of participants such as sex, age, educational level, household income, marital status, and working mode. Vigorous, moderate, and walking activities were measured using the International Physical Activity Questionnaire-Short Form (IPAQ-SF), a self-administered questionnaire. Results: Men declared significantly higher vigorous and moderated physical activity than women. People aged 18 to 25, single or unmarried, and with the lowest household income, showed significantly higher scores in vigorous physical activity than those aged 26 to 45, cohabiting with a partner or married, and middle household income, respectively. Regarding working mode, people working on their job site said walking more than people not working, working on hybrid, and online mode. Our findings suggest that promoting strategies that increase physical activity during the pandemic is necessary to avoid health problems.

Nowadays and in the web era, there is a huge amount of information available due to automatically and easily generated websites. This information has many shared opinions from people toward different entities. Over the last decade there was a significant huge interest to determine the sentiment of any type of text especially on twitter. Twitter provides unstructured text that has many misspelling words, slang words and shortened forms. More complexity and challenges have come to the sentiment analysis researchers in the context of twitter. In this paper we conducted a literature review of the recent research of Twitter sentiment analysis on different languages. After that, we investigated challenges related to twitter sentiment analysis of Arabic language. Then we found a new solution for Sentiment analysis of Arabic twitter. So, we designed the main component of the proposed system and presented its algorithm. Finally, we performed many experiments under two kinds of datasets. The system achieves highest accuracy and shows many important results..

The Axillary Seabream Pagellus acarne is of economic importance for recreational and commercial fisheries in the Atlantic and the Mediterranean, but a decline in total landings has been observed and raised some concern. Although biological information on the species has been provided for the Atlantic and Mediterranean, in the Portuguese continental slope data is restricted to the southern part of the country, which points to the relevance of the present work. A total of 520 individuals, between 7.8 cm and 39.9 cm TL, were monthly collected from September 2020 to August 2022 from commercial landings of fishing vessels operating off mainland Portugal, and used to study age and growth, the reproductive strategy and the annual sexual cycle. Age varied between 0 years (7.8 cm TL) and 15 years (34.2 cm TL) and the von Bertalanffy growth parameters were estimated from whole-otolith readings (L∞ = 35.2 cm, k = 0.133 yr–1 and t 0 = –2.78 yr). Pagellus acarne proved to be a protandric hermaphrodite species with males prevailing until 28.0 cm TL and females between 28.0 cm and 34.0 cm TL with a brief sex change phase. Females presented asynchronous oocyte development with a batch spawning pattern. The spawning season occurred from April to October with a spawning peak between June and August aas shown by histological analysis of the gonads and high levels of GSI during the period.

In the periodic table of the elements, ruthenium is ideally placed, right in the middle, just above iron. And like iron, it possesses several oxidation states, with +2 and +3 being the most common. Accordingly, ruthenium chemistry is extremely rich and well developed, ruthenium complexes show excellent catalytic aptitude, tremendous redox capacity, and intriguing biological activity. However, in the design of sensors, the use of ruthenium complexes remains scarce, despite valuable electro- and photochemical properties. Therefore, there is an opportunity here, and ruthenium-based complexes might become one day, key players in sensing technology. Starting ourselves a new research project with ruthenium-based sensors, writing this review was essential to see the current state of research in the field, to identify opportunities, and to have an overview of state-of-the-art examples.

Generative adversarial networks (GANs) have become a recent and rapidly developing research topic in Machine Learning. Since their inception in 2014, a significant number of variants have been proposed to address various topics across many fields, and has particularly excelled not only in image and language processing, but also in the medical and data science domains. In this paper, we aim to highlight the significance and advance that these GAN models can introduce in the field of Business Economics, where they have yet to be fully developed. To this end, a review of the literature of GANs is presented in general together with a more specific review in the field of Business Economics wherein only a few papers can be found. Furthermore, the most relevant papers are analysed in order to provide an approach the opportunity to research into GANs in the field of Business Economics.

Proteomics is the study of cellular proteome by investigating how different proteins interact with each other and the roles they play in the organism. For mRNA expression levels do not always correlate well with protein expression levels, and the study of mRNA does not consider protein posttranslational modifications (PTMs), complex formation and localization; all of which are key to protein function. Quantitative proteomics uses mass spectrometry to identify and quantify proteins so as to find differentially expressed proteins under different condition. This allows us to gain biological insights under stress or other pathological conditions. posttranslational modifications, such as lysine acetylation, succinylation and lactylation, occur after protein biosynthesis and play vital roles in protein activity and cell physiology regulation. Here we present the mass spectrometry analysis in detail to unveil protein acylase and deacylase’ roles in metabolic regulation based on PTMomics lysine acetylome and suincylome.

Abstract. Background: Injury of the spinal cord causes motor and sensory dysfunction, and pathological reflexes, leading to paraplegia or tetraplegia. The sequelae of traumatic spinal cord injury (SCI) is a significant burden and impact on healthcare systems. Despite constant progress in medicine, traumatic SCI still remains irreversible. Till now, no satisfying treatment that can enable neuronal regeneration and recovery of function at the damaged level has been found. Hundreds of experiments have been conducted on various possibilities of influencing spinal regeneration, some of them have yielded promising results, but unfortunately, the successes obtained in experimental animals have not translated into humans. Methods: This narrative review article presents the application of Extracorporeal Shock Wave Therapy (eSWT) in Spinal Cord Injury. The article has been divided into parts: 1. Use of extracorporeal Shock Wave Therapy for regeneration of the spinal cord after traumatic Spinal Cord Injury. 2. Application of extracorporeal Shock Wave Therapy in spasticity after Spinal Cord Injury. In both cases, the hypotheses of possible mechanisms of action will be described. Results and conclusions: A small number of clinical trials have demonstrated the potential of eSWT to influence the regeneration of the spine, as an innovative, safe, and cost-effective treatment option for patients with SCI. Some reports have shown that eSWT can improve spasticity, walking ability, urological function, quality of life, and independence in daily life.

Quality of life (QoL) focuses on a person’s ability to live a fulfilling life. In the elderly population, it helps in determining successful aging. Because of a scarcity of information regarding predictors of QoL in the elderly population in developing countries, this study was undertaken to assess the overall QoL and its predictors in the elderly population in an urban setting of Bangladesh. In this cross-sectional study, 275 participants were enrolled by systematic sampling at the outpatient department of a tertiary care hospital in Dhaka, Bangladesh. Data were collected by using a pretested semi-structured standard questionnaire for QoL (OPQOL-35). Pearson’s correlation was used to assess the association between QoL and sociodemographic factors. The Chi-square test was done for assessing the statistical significance between categorical variables. Multivariate linear regression was conducted to identify predictors of QoL, after controlling for potential confounders. The median age of the participants (n = 275) was 65 years (range, 60 to 85; 25th and 75th percentile, 60 and 68, respectively). The majority (78%) of them were married and 20% were widowed or divorced. The median score of QoL was 113 (25th and 75th percentile, 101 and 124, respectively). Most of the participants had a very poor (bad as can be) or poor (bad) QoL. Only 7.3% were observed to have “good” QoL (scores 140 or more). Females had significantly poorer QoL scores compared to males (p = 0.01). Educational status, marital status, body mass index and sleep duration significantly predicted QoL in a multiple regression analysis. In conclusion, the QoL of the elderly individuals in Bangladesh was very poor or poor. Future research should focus on service-oriented interventions, especially targeting women. Counseling of elderly people for enhancing their quality of life may include improving sleep patterns, healthy diets, regular exercise, and caring for their mental health.

This study focuses on the creation and implementation of a platform to improve personal financial management for scholarship students of the National Program of Scholarships and Educational Credit (Pronabec) in Peru. Using a mixed methodology that combines qualitative and quantitative techniques, the effectiveness and user satisfaction with the platform were evaluated. The results indicated that 50\% of users found the tool useful, 55\% considered it easy to use, 70\% reported a positive impact on their personal budget planning, and 85\% expressed high overall satisfaction. In addition to providing tools for financial recording and analysis, the platform promotes financial education among its users, significantly contributing to improving their financial management skills.

The integration of generative AI (GAI) into the financial sector has brought about significant advancements, offering new solutions for various financial tasks. This review paper provides a comprehensive examination of recent trends and developments at the intersection of GAI and finance. By utilizing an advanced topic modeling method, BERTopic, we systematically categorize and analyze existing research to uncover predominant themes and emerging areas of interest. Our findings reveal the transformative impact of finance-specific large language models (LLMs), the innovative use of generative adversarial networks (GANs) in synthetic financial data generation, and the pressing necessity of a new regulatory framework to govern the use of GAI in the finance sector. This paper aims to provide researchers and practitioners with a structured overview of the current landscape of GAI in finance, offering insights into both the opportunities and challenges presented by these advanced technologies.

The advent of 5G technology represents a transformative leap in the realms of retail marketing and supply chain operations, offering unprecedented enhancements and posing unique challenges. This study explores the multifaceted impacts of 5G, focusing on its influence on customer experiences, operational efficiency, and supply chain management. The integration of 5G enables advanced technologies such as augmented reality (AR) and virtual reality (VR), significantly enhancing the interactivity and personalization of the shopping experience. Real-time data collection and analysis, facilitated by 5G, allow for more precise inventory management and optimized store operations, leading to improved customer satisfaction and reduced operational costs. In the supply chain domain, 5G technology offers enhanced visibility and control through real-time tracking and environmental monitoring, which improves logistics efficiency and product quality management. Despite these advancements, the adoption of 5G technology involves considerable challenges, including high infrastructure costs, data security concerns, and global disparities in technology deployment. Businesses must navigate these obstacles while leveraging the benefits of 5G to achieve competitive advantage. The study also highlights the role of 5G in integrating with other advanced technologies, such as artificial intelligence (AI) and blockchain, to drive further innovation. Additionally, 5G supports sustainability efforts by optimizing energy use and reducing waste. Overall, the successful implementation of 5G requires strategic investment and adaptability, but it holds the potential to revolutionize retail and supply chain operations, enhancing efficiency, customer engagement, and sustainability in a rapidly evolving digital landscape.

This paper proposes a theoretical silicon dioxide (SiO2) solar cell based dielectric metamaterial unit surface consisting of zinc oxide (ZnO) periodic nanorods of bandgap energy of 2.2-2.4eV. It evaluates the absorptive efficiency of the film using S-parameter and Z-parameter testing of magnitude, return/insertion loss, and impedance matching with respect to a frequency band of 0.0014-10.5THz. Terahertz applications of the result are considered due to potential scope for infrared (IR) and microwave (MW) spectral selectivity with ZnO. Calculations of input n-impedance and loss tangents are also presented with emphasis on the exhibition of antireflection to gauge the performance of the thin-film meta-surface in tandem solar cells. The data for the reflectance parameters of this structure, primarily the S11 loss and impedance matching (dB/dB10) are compared to those of prior investigations with different metamaterial coatings on SiO2 solar photovoltaic cells, as well as ZnO nanorods used in photovoltaic applications, to ascertain the cylindrical meta-surface structure with the preset thicknesses of the study being an effective anti-reflector. Application can be extended to other broadband domains requiring plasmonic graded refractive surfaces, in further work.

Aedes albopictus is considered one of the major invasive species in the world and can transmit viruses such as dengue, Zika, or chikungunya. The Sterile Insect Technique (SIT) can be used to suppress the native populations of Ae. albopictus. To support the development of the release strategy during the SIT application, Mark-Release-Recapture (MRR) studies are crucial. Mean-while, weather conditions can affect the MRR trial’s results and it is critical to understand the influence of climatic factors on the results. In October 2022 in Faro, Southern Portugal, 84,000 ir-radiated sterile males were released for three consecutive weeks and mosquitoes were recapture by Human Landing Collections (HLC) one, two, four and six days after. Generalised linear models with a negative binomial family and log function were used to estimate the factors asso-ciated with the number of recaptured mosquitoes, prevalence ratios, and the 95% confidence in-tervals (CI). A total of 84,000 sterile male mosquitoes were released, with 528 recaptured (0.8%) by HLC. The prevalence of recaptured mosquitoes was 23% lower when the wind intensity was moderate. Sterile-marked males had an average median distance travelled of 88.7 m. Median probability of daily survival and the average life expectancy were 61.6% and 2.1, respectively. Wild male population estimates was 443.33 males/ ha. Despite no statistically significant asso-ciation was found with humidity, temperature and precipitation, it is crucial to consider weather conditions during MRR trials analyses to obtain the best determinants estimation and a more ef-ficient application of SIT in an integrated vector management program.

It is meaningful to monitor and identify the concrete cracks as they will seriously reduce the fatigue life of pavement. In this paper a new method based on distributed long-gauge optic fiber sensing is proposed to implement the crack identification. Firstly, the damage indicator, namely strain curve envelope area ratio (SCEAR), is proposed and calculated with the measured strain in time series when the plane goes across the pavement where the sensors are installed. Then the indicators with different sensors are normalization by the value of the indicator from one referenced sensor where damage normally will not happen. If the crack happens within the sensing gauge of the sensor, the value of damage indicator SCEAR from the sensor will increase. Therefore, the crack will be identified with the change of the damage indicator. The effect of the proposed method is first verified by some simulations of prefabricated pavement as the cracks can be accurately identified by the proposed method. Moreover, the influence from the aircraft, such as taxiing position, aircraft load and aircraft type, is not obvious enough to mislead the identification results. Lastly the static loading tests were implemented with one small-scale model of prefabricated concrete pavement in the lab. From the experimental results the prefabricated cracks inside the pavement slab can be identified accurately with the proposes method as the parameter SCEAR is increased by about 100% at the crack position, while it is only changed by about 3% at the undamaged position.

Background: Approximately 10-20% of subjects vaccinated with HBsAg-based hepatitis B virus (HBV) vaccines are non-responders. BM32 is a recombinant grass pollen allergy vaccine containing the HBV-derived preS surface antigen as immunological carrier protein. PreS includes the binding site of HBV to its receptor on hepatocytes. We investigated if immunological non-responsiveness to HBV after repeated HBsAg-based vaccinations can be overcome by immunization with VVX001 (i.e., Alum-adsorbed BM325, a component of BM32). Methods: A subject failing to develop protective HBV-specific immunity after HBsAg-based vaccination received five monthly injections of 20 µg VVX001. PreS-specific antibody responses were measured by ELISA and micro-array technology. Serum reactivity to subviral particles of different HBV genotypes was determined by sandwich ELISA. PreS-specific T cell responses were monitored by CFSE staining and subsequent FACS analysis. HBV neutralization was assessed using cultured HBV-infected HepG2 cells. Results: Vaccination with VVX001 induced a strong and sustained preS-specific antibody response composed mainly of the IgG1 subclass. PreS-specific IgG antibodies were primarily directed to the N-terminal part of preS containing the NTCP attachment site. IgG reactivity to sub-viral particles as well as to the N-terminal preS-derived peptides was comparable for HBV genotypes A-H. A pronounced reactivity of CD3+CD4+ lymphocytes specific for preS after the complete injection course remaining up to one year after the last injection was found. Maximal HBV neutralization (98.4%) in vitro was achieved 1 month after the last injection which correlated with the maximal IgG reactivity to the N-terminal part of preS. Conclusion: Our data suggest that VVX001 may be used as a preventive vaccination against HBV even in non-responders to HBsAg-based HBV vaccines.

Equine laminitis is a debilitating condition characterized by the breakdown of the dermal–epidermal junction, resulting in severe lameness and pain, often requiring euthanasia. This study aimed to establish an in vitro model of laminitis using a continuous keratinocyte cell line exposed to anoxia-reoxygenation and activated neutrophil supernatant. A significant decrease in keratinocytes metabolism was noted during the reoxygenation period, indicative of cellular stress. Adding muscle-derived mesenchymal stem/stroma cells during reoxygenation demonstrated a protective effect, restoring keratinocytes metabolic activity. Moreover, incubation of keratinocytes with activated neutrophil supernatant or myeloperoxidase alone induced increased keratinocyte myeloperoxidase activity, which was modulated by stem cells. These findings underscore the potential of muscle-derived mesenchymal stem/stroma cells in mitigating inflammation and restoring keratinocyte metabolism, offering insights for future cell therapy research in laminitis treatment.

Light is dispersive in gravitational field. It results in that, under the condition of strong gravitational field, the observed numbers, size, distance and spectrogram of a celestial object is different from the real ones. Therefore, the observed jets of the Sgr A* and M87 need be understood further with the orbit of the stars around them. And, the observed orbits of the S-stars around the Sgr A* is different from the real ones. It implies that the observed optical images of the distant celestial body with strong gravitational field need be further understood with other observations and physics laws to accurately and precisely know the real physical configuration of it.

This qualitative study explores the role of Supplier Relationship Management (SRM) in optimizing inventory within the context of e-commerce. Through semi-structured interviews with e-commerce managers and supply chain experts, the study investigates the strategies, challenges, and outcomes associated with SRM practices. Key findings highlight the critical importance of strategic partnerships with suppliers, emphasizing trust, transparency, and goal alignment as foundational elements for effective SRM. Participants underscored the transformative impact of technological advancements, including advanced analytics and blockchain technology, in enhancing supply chain visibility, optimizing inventory levels, and improving operational agility. However, the study also identifies challenges in SRM implementation, such as cultural differences, regulatory compliance, and geographical distances, which require proactive management strategies. Innovation emerges as a key driver of competitive advantage in e-commerce, facilitated through collaborative R&D initiatives and co-innovation with suppliers. Strategic alignment with organizational goals is crucial for integrating SRM practices with broader business objectives, including operational efficiency and risk mitigation. Recommendations include adopting integrated SRM strategies that leverage technological innovations while maintaining a focus on relationship-building and sustainability. Enhanced supplier performance monitoring, proactive risk management, and the integration of sustainable practices are proposed to strengthen supplier relationships and enhance supply chain resilience. By addressing these insights, e-commerce firms can optimize inventory management, drive innovation, and achieve sustainable growth in the digital economy.

A comprehensive design framework is proposed for optimizing sparse MIMO (multiple-input, multiple-output) arrays to enhance multi-target detection. The framework emphasizes efficient utilization of antenna resources, including strategies for minimizing inter-element mutual coupling and exploring alternative grid-based sparse array (GSA) configurations by efficiently separating interacting elements. Alternative strategies are explored to enhance angular beamforming metrics, including beamwidth (BW), peak-to-sidelobe ratio (PSLR), and grating lobe limited field of view. Additionally, a set of performance metrics is introduced to evaluate virtual aperture effectiveness and beamwidth loss factors. The study employs the desirability function and explores optimization strategies for the partial sharing of antenna elements in multi-mode radar applications. A novel machine learning initialization approach is introduced for rapid convergence. Observations include the potential for peak-to-sidelobe ratio (PSLR) reduction in dense arrays and insights into GSA feasibility and performance compared to uniform arrays. The study validates the efficacy of the proposed framework through simulated and measured results. The study underscores the importance of effective sparse array processing in multi-target scenarios and highlights the advantages of the proposed design framework, offering clarity on sparse array efficiency and applicability in processing hardware providing clarity on its advantages over uniform and nonuniform arrays.

Autoencoders are neural networks that have applications in denoising processes. Their use is widely reported in imaging (2D), though 1D series can also benefit from this function. Here, three canonical waveforms are used to train a neural network and achieve a signal-to-noise reduction with curves whose noise energy was above that of the signals. A real-world test is carried out with the same autoencoder subjected to a set of time series corrupted by noise generated by a zener diode, biased on the avalanche region. Results showed that, observed some guidelines, the autoencoder can indeed denoise 1D waveforms usually observed in electronics, particularly square waves found in digital circuits.

This study presents a comprehensive approach to calculating the free energy of hydration of methane using Molecular Dynamics simulations. We employed the Free Energy Perturbation (FEP) method to determine the Gibbs free energy associated with transferring a methane molecule into water. Simulations were conducted in a cubic simulation box containing one methane molecule and 1000 TIP4P water molecules. The forward and backward routes were both analyzed, providing consistent free energy differences, which validates the robustness of our methodology. The Gibbs free energy profiles demonstrated characteristic energy barriers and stabilization points, indicative of the interaction dynamics in the solvation process. The results demonstrate the robustness of the FEP method implemented in LAMMPS, yielding free energy in close agreement with experimental data and previous computational studies. The calculated free energy differences for methane hydration exhibited deviations of 13% and 8% for the forward and backward routes, respectively, compared to experimental values. These findings underscore the accuracy and efficiency of using LAMMPS for free energy calculations, contributing to the broader understanding of solvation thermodynamics. Additionally, radial distribution functions were analyzed to provide insights into the structural arrangement of water molecules around methane, revealing strong interaction between methane's carbon atom and water molecules.

Sugarcane Streak Mosaic Virus (SCSMV) poses a significant threat to sugarcane productivity. PRG sugarcane is a product of genetic engineering aimed at enhancing resistance to various environmental stresses, pests, and diseases. This study aimed to test and compare the resistance of two PRG sugarcane SPS events to SCSMV infection. The study was conducted using a single-factor Completely Randomized Design (CRD), comprising four treatments: Bululawang sugarcane variety as a control, PS 882 as a susceptible variety, PRG Sugarcane SPS 1, and PRG Sugarcane SPS 3, with 15 replications of 60 sugarcane mules each. PCR results indicated that mosaic infection in the sugarcane plants was most likely caused by SCSMV. Based on the morphological identification of the SCSMV virus and RT-PCR testing, the sugarcane plants positively infected with SCSMV were SPS1 U1 R1, SPS3 U2 R4, SPS3 U3 R3, and PS882 U3 R4. The highest incidence rate of the disease was observed in the SPS3 variety, while the lowest incidence rate was in the Bululawang variety. The PS882 variety exhibited the slowest SCSMV incubation period, showing symptoms of infection, whereas the SPS1 variety showed the fastest incubation period. The highest disease severity was recorded in the SPS3 variety, and the lowest in the PS882 variety. The sugarcane varieties with the highest klorofil content were SPS3, while the SPS1 variety had the lowest klorofil content.

The emergence of antimicrobial resistance (AMR) Candida auris presents a formidable global health challenge, causing severe healthcare-associated infections with high mortality rates. Its ability to colonize surfaces and resist standard disinfectants undermines traditional hygiene practices, prompting an urgent need for novel strategies. Ultraviolet C (UVC) light offers a promising approach due to its rapid and broad-spectrum germicidal efficacy. This review comprehensively examines the current knowledge of UVC LED technology in combating C. auris, highlighting its effectiveness, limitations, and potential applications in healthcare hygiene. UVC light has potent activity against C. auris, including multidrug-resistant (MDR) strains. UVC can reduce C. auris on contaminated surfaces, aiding in transmission prevention. This review explores implementation strategies, including mobile UVC systems for targeted disinfection of high-risk areas and equipment, integration into air handling units (AHUs) to continuously disinfect recirculating air, and incorporation into water treatment systems. Current limitations in our understanding of UVC safety and effectiveness necessitate further research to optimize application protocols and ensure treatment safety while maintaining efficacy against C. auris. Integrating UVC disinfection technology into infection control programs holds promise for strengthening hygiene practices that will curb the global spread of C. auris and improve patient outcomes.

Generative Adversarial Networks (GANs) are powerful models for generative tasks but face significant challenges such as instability, mode collapse, and inefficient convergence. This paper introduces a theoretical framework leveraging Fermi-Dirac statistics, Ruppeiner geometry, phase transition dynamics, and graph theory to address these issues. The proposed theorems provide a robust mathematical foundation for enhancing GAN stability, diversity, and convergence. Experimental validation on the MNIST dataset demonstrates that the application of these theorems results in improved Inception Scores, reduced Fr´echet Inception Distances, and stable training dynamics, as indicated by Ruppeiner curvature analysis. The findings suggest that these theoretical insights offer a comprehensive solution to the persistent challenges in GAN training, paving the way for more reliable and effective generative models

(1) Objective: To develop a clinically useful nomogram that may provide a more individualized and accurate estimation of cancer-specific survival (CSS) for patients with clear-cell (CC) metastatic renal cell carcinoma (mRCC) treated with nephrectomy and tyrosine kinase Inhibitor (TKI) based sequential therapy; (2) Methods: A prospectively maintained database of 145 patients with mRCC treated between 2008-2018 was analyzed to predict CSS of patients receiving sunitinib and 2nd and 3rd line therapies according to current standards of practice. A nomogram taking into account four independent clinical predictors (ECOG status, IMDC score, MASS and RECIST response criteria) was calculated. The corresponding 1- to 10-year CSS probabilities were then determined from the nomogram; (3) Results: The median age was 60 years (95% CI 57.9-61.4). Disease was metastatic at diagnosis in 59 (40.7%) and 86 (59.3%) developed metachronous metastasis after nephrectomy. Patients were followed for a median 48 (IQR 72; 95% CI 56-75.7) months after first-line TKI initiation. Concordance probability estimator for the nomogram is 0.778 ± 0.02 (mean ± SE); (4) Conclusions: A nomogram to predict CSS in patients with CC mRCC that incorporates patient status, clinical risk classification and response criteria to first-line TKI at 3 months is presented. This new tool may be useful to clinicians assessing risk and prognosis of patients with mRCC.

This Paper delves into a thorough analysis of the DC-DC buck converter tailored for renewable energy applications. Given the prevalent utilization of renewable resources producing DC voltages, this converter plays a pivotal role in stepping down high-level DC voltages to more manageable levels. The research focuses on varying the duty cycle to assess its impact on the converter's output voltage performance. Simulations conducted using PSIM are meticulously compared with hardware implementation results, demonstrating the converter's efficacy in generating significantly reduced output voltages. The findings underscore the practical applicability of this converter in renewable energy scenarios, offering a succinct yet insightful perspective on its performance and adaptability.

A recent publication regarding a March 2023 Google Scholar search found Japan unique in healthcare providers’ reaction to COVID-19 vaccines. According to one article, negative sentiment by healthcare providers toward vaccines defines the research in this area, with concerns about side effects outweighing worries regarding infection. This current study investigates the anomaly through a scoping review of “COVID-19, Japan, vaccine hesitancy” in six relevant databases: Cochrane COVID-19 Study Register, OVID, ProQuest, PubMed, Scopus, and Web of Science. By following PRISMA guidelines, the intent is a more thorough examination of this unusual evalua-tion of COVID-19 vaccines by Japanese healthcare providers than offered by the March 2023 search. The finding is that of the 997 returns, only four were relevant for assessment inclusion. Of these four, three, published in Vaccines, support vaccine hesitancy in Japanese healthcare providers and their becoming more so regarding a subsequent dose of the vaccine. One article published in BMJ Open did not find this. Yet, the design of none of the studies was specific to investigating vaccine hesitancy in Japanese healthcare providers, making the conclusion questionable. Suggest-ed future research directions include investigating the primacy of those databases searched and the need for timeliness in examining COVID-19 anomalies

Background: Oxytocin is recommended as the first-line choice for labor induction in term pregnant women with premature rupture of membranes (PROM); however, it can occasionally lead to failed induction and delivery complications. The primary objective of this study was to compare delivery information and outcomes following the administration of oxytocin infusion or oral low-dose misoprostol solution for labor induction in term PROM women with an unfavorable cervical condition. Methods: A prospective nested case-control study was conducted at a tertiary general hospital in China from March 2020 to February 2024, comparing oxytocin infusion and oral administration of 25ug misoprostol every 2 hours for labor induction in term PROM women with a Bishop score

With the rapid development of Intelligent Transportation Systems (ITS), vehicle type classification, as a key link in Automatic Toll Collection systems (ATC), is of great significance in improving traffic efficiency and reducing economic losses. This study proposes an intelligent vehicle classification system based on deep learning and multi-sensor data fusion to address the accuracy issues existing in vehicle classification methods based on optical sensors (OS) and human observers. The system significantly improves the accuracy and robustness of vehicle classification by combining deep Convolutional Neural Networks (CNN), LiDAR sensors, and machine learning algorithms. We first constructed a large-scale annotated dataset containing multiple vehicle types and complex traffic scenes to improve our model's capability to identify different vehicle characteristics. Next, CNN models based on different architectures were designed to extract global and local features of the vehicle, respectively. In addition, the LiDAR sensor was used to achieve the spatial structure architectures of the vehicle and combined with the output of the CNN model to improve classification performance under occlusion and complex scenes.

Cassava brown streak disease (CBSD) threatens cassava production in sub-Saharan Africa despite the availability of resistant varieties since extreme environmental factors weakens plant defenses hence reduce CBSD resistance. This study examined the inheritance of CBSD in cassava populations, assessed genetic variability and identified superior sources of resistance through F1, S1 and half-sib offspring of populations derived from resistant sources. The offsprings underwent field evaluation at two distinct sites from 2019 to 2021, and the symptoms free genotypes were analyzed using reverse transcription quantitative polymerase chain reaction (RT-qPCR). Resistance to CBSD was categorized as most resistant, resistant, most tolerant and tolerant based on symptoms and virus titer. The findings of the study indicate that the resistance to Cassava Brown Streak Disease (CBSD) is highly influenced by genotypes, F1/S1 types, and environmental conditions. The analysis of combining abilities revealed significant general combining abilities (GCA) for CBSD, cassava mosaic disease (CMD), and traits associated with yield. The heritability estimates for resistance to CBSD varied between 43.4% to 63.2% for foliar symptoms and 14.6% to 57.9% for root necrosis across locations. The inheritance pattern involved a combination of additive and recessive genes and selfed (S1) populations displayed stronger and effective resistance to the disease. There was a high prevalence of cassava brown streak virus (CBSV) and a low prevalence of Ugandan cassava brown streak virus (UCBSV). Four genotypes were identified to be highly resistant to Cassava Brown Streak Disease (CBSD) that could be used as key sources of resistance to CBSD.

The young adolescents’ sexual behaviors are under-investigated. In this quantitative descriptive study, we examined the 2019 Middle School Youth Risk Behavior Survey data of 66,366 young adolescents ages 10-14 from 10 states to determine the prevalence of early sexual debut among young adolescents. Complex sampling design was used to produce descriptive statistics and cross-tabulations to describe young adolescents' sexual behaviors. Early sexual debut was reported at nearly 8%, and 4% of those reporting the age of first sex had their first sexual intercourse before 13 years. Compared to the Whites, Black/African Americans (13.2% vs 5.1%, OR=4.51, 95% CI: 2.78-7.31) and Hispanic young adolescents (8.3 % vs 5.1 %, OR=1.44, 95% CI=1.13-1,84) were more likely to report having early sexual intercourse. Males were more likely to report having multiple sexual partners than females (48.1% vs 36.5%, OR = 1.68, 95% CI = 1.22-2.29) and were more likely to report using condoms during the last sex compared to their female counterparts (59.7% vs 50.8%, OR = 1.43, 95% CI = 1.06-1.94). This study suggests the need for more research on young adolescents’ sexual behaviors and develop sexual health interventions that target preadolescence.

Ever since the discovery of dendritic cells by Ralph Steinman and Zanvil Cohn in 1973, it is increasingly evident that dendritic cells are integral for adaptive immune responses, and there is an undeniable focus on them for vaccines development. Fungal infections, often thought in-nocuous, are becoming significant threats due to increased immunocompromised or im-mune-suppressed population and climate change. Further, the recent COVID-19 pandemic unraveled the wrath of fungal infections and devastating outcomes. Invasive fungal infections cause significant case fatality rates ranging from 20% to 90%. Regrettably, no licensed fungal vaccines exist, and there is an urgent need for preventive and therapeutic purposes. In this re-view, we discuss the ontogeny, subsets, tissue distribution, and functions of lung dendritic cells. In the latter part, we summarize and discuss the studies on the DC-based vaccines against pulmonary fungal infections. Finally, we highlight some emerging potential avenues that can be incorporated for DC-based vaccines against fungal infections.

A Physical Unclonable Framework (PUF) is a detached hardware component or, the structural arrangement of hardware that provides natural variations in the physical parts of semiconductor devices to generate unique and unpredictable identifiers based on challenging-to-replicate. In this case, PUFs are giving us a tool to utilise these variations to create an unclonable identifier for your device. PUFs generate a response or a unique pattern based on the physical characteristics of the device, and this response is difficult to predict or duplicate. This study aims to introduce a PUK device to provide security research on the Internet of Things technology. The elliptic curve cryptography algorithm was modified with an advanced equation to calculate a private key and K value. In this study, a PUF device will be employed with a modified elliptic curve cryptography algorithm on a novel security system to be used in current and future communication technology.

This study investigates the implementation of equipment to obtain pyroligneous acid as an alternative to control aphids in mandarin cultivation on the El Arrayanal farm, Monte Olivo parish, Carchi province, Ecuador. Derived from mandarin pruning biomass using a dry distillation pilot oven, pyroligneous acid is an ecological option compared to synthetic phytosanitary products, with less impact on the environment and human health. Using a DBCA design, different concentrations of pyroligneous acid (250 ml, 500 ml, 750 ml, and 1000 ml) applied to immature shoots affected by aphids were analyzed. The 1000 ml concentration achieved 98% control of the pest. Analysis by Gas Chromatography and Mass Spectrometry (GC-MSD) and Total Polyphenols confirmed the effectiveness of pyroligneous acid. The results of the study, carried out in the research and linkage laboratories of the State University of Bolívar, Guaranda, Ecuador, demonstrated significant differences between the doses applied (p-value

Newton's kinetic energy equation delineates the laws of motion for objects travelling at speeds much less than the speed of light, an equation extensively validated at these lower velocities. Any new, more complete formulation of kinetic energy must approximate Newton's kinetic energy equation under these conditions. Einstein's special relativity, utilizing the Lorentz factor, introduces a new perspective on spacetime, adhering to the requirement that the Newtonian kinetic energy equation serves as an approximation at low velocities. In this paper, we identify all factor expressions satisfying the condition that their approximate solutions at low velocities align with Newton's kinetic energy equation. We introduce the Yuyunrui factor, incorporating two real number hyperparameters, $\alpha$ and $\beta$, encompassing all potentially correct factors, with the Lorentz factor as a specific instance where $(\alpha=0$, $\beta=1)$. We classify all potentially correct $(\alpha, \beta)$ combinations into six categories based on their distinct scaling effects and present the corresponding scaling effects. The combinations of $(\alpha, \beta)$ values applicable in the real world are necessarily included within these candidates, with the precise values requiring rigorous experimental validation. This work aims to refine our understanding of special relativity beyond the Lorentz factor.

Magnetic resonance imaging (MRI) is crucial for its superior soft tissue contrast and high spatial resolution. Integrating deep learning algorithms into MRI reconstruction enhances image quality and efficiency, but a comprehensive review of optimization-based deep learning models for MRI reconstruction has been missing. This study fills that gap by examining the latest optimization-based algorithms in deep learning for MRI reconstruction, including gradient descent algorithms, proximal gradient descent algorithms, ADMM, PDHG, and diffusion models combined with gradient descent. Learnable optimization algorithms (LOA) are highlighted for their ability to map optimization processes to structured neural networks, improving model interpretability and performance. The study demonstrates significant advancements in MRI reconstruction through deep learning, with successful clinical applications. These findings provide valuable insights and resources for researchers aiming to advance medical imaging using innovative deep learning techniques.

Background: This study provides a comprehensive analysis of Acinetobacter baumannii in aquatic environments and fish microbiota by integrating culture-dependent methods, 16S metagenomics, and antibiotic resistance profiling. Methods: A total of 83 A. baumannii isolates were recovered using culture-dependent methods from intra-hospital infections (IHI), wastewater (WW) and surface water (SW) samples from two southern Romanian cities in august 2022. The antibiotic susceptibility was screened using disc diffusion, microdilution, PCR and Whole Genome Sequencing assays. Results: The highest microbial load in the analyzed samples was found in Glina, Bucharest for both WW and SW samples across all investigated phenotypes. For Bucharest isolates, the resistance levels corresponded to fluoroquinolones > aminoglycosides > β-lactam antibiotics. In contrast, A. baumannii from upstream SW samples in Targoviste showed the highest resistance to aminoglycosides. The blaOXA-23 gene was frequently detected in IHI, WW, and SW isolates in Bucharest but was absent in Targoviste. Molecular phylogeny revealed presence of ST10 in Târgoviște isolates and ST2 in Bucharest isolates, while other minor STs were not specifically correlated with a sampling point. Using 16S rRNA sequencing, significant differences in microbial populations between the two locations was identified. The low abundance of Alphaproteobacteria and Actinobacteria in both locations suggests environmental pressures or contamination events. Conclusions: These findings indicate significant fecal contamination and potential public health risks, emphasizing the need for improved water quality monitoring and management.

The extensive utilization of drones has led to numerous scenarios that encompass both advantageous and perilous outcomes. By using deep learning techniques, it is aimed to reduce the dangerous effects of drone use through early detection of drones.The state of the art of the study is evaluation of deep learning approaches such as pre trained YOLOv8 drone detection for security issues. In this study, dataset collected by Mehdi Özel for a UAV competition are sourced from GitHub. These images are labeled using Roboflow, and the model is trained on Google Colab. To enhance model performance, dataset augmentation techniques including rotation and blurring are implemented. Following these steps, a significant precision value of 0.946, a notable recall value of 0.9605 and a considerable precision-recall curve value of 0.978 are achieved, surpassing many popular models such as Mask CNN, CNN, and YOLOv5.

The work and life routine of long-haul truck drivers involves the use of truck stops and rest areas to meet their basic human needs. These extensions of their workspaces while on the road, do not always offer adequate physical structures and services that drivers need for optimal health. Aim: To evaluate long-haul truck drivers’ perceptions about the services of food, safety, physical activity, rest and personal hygiene offered at truck stops and rest areas in Brazil. Method: Quantitative study conducted with long-haul truck drivers from the Southern region of Brazil. For data collection, a sociodemographic questionnaire and a Likert scale on food, rest, personal hygiene, safety and physical activity services were used, offered in general at the truck stops and rest areas along Brazilian roads, from March to August 2023. The data were analyzed with simple frequency descriptive statistics. Results: 175 long-haul truck drivers participated, 70.29% declared that the services of the truck stop and rest areas were charged; more than half (53.59%) of the professionals evaluated the rest service as “good” or “excellent”; the food services, was “good” or “excellent” for 42.24% of the drivers. The spaces for physical activities were the worst evaluated as “bad” or “terrible”, by 41.61%, followed by bathroom service (28.42%) and safety (34.24%), evaluated as “bad” or “terrible”. Conclusions: In the perception of long-haul truck drivers, the rest and feeding services had better evaluations, on the other hand, the services of bathroom, safety and physical activity presented worse evaluations. The perception of drivers demonstrates the lack of investments to improve basic services such as personal hygiene, safe environment and physical exercises, fundamental to the health of the worker, aiming to reduce vulnerability, sedentary lifestyle, and meeting the basic human needs of long-haul truck drivers.

This paper is devoted to Petri net (PN) based models of Automated Manufacturing Systems (AMS) with resources in order to prevent deadlocks in them. The sustainability can be seen as the process of deadlock-freeness leading to correct and fluent production, because AMS with deadlocks work neither correctly nor fluently, need reconstruction and cause downtime in production. A class S3PR (Systems of Simple Sequential Processes with Resources) of such PN models is well known as to the deadlock prevention point of view. Here, Extended S3PR (ES3PR) will be explored as to modelling and deadlock prevention. While in case of S3PR Ordinary Petri Nets (OPN) were used for these aims, here, for ES3PR Generalized Petri Nets (GPN) are used. The reason for such a procedure is a possible presence of multiplex directed arcs in the structure of PN models of AMS. The significant alternation is that while in former case the elementary and dependent siphons were sufficient for the supervisor synthesis, here, in later case, the GPN and their siphons have to satisfy the max-cs property.

The evaluation of crowdfunding campaigns varies from person to person; some investors are more interested in the creativity of the project, and others are more concerned with the profiles of entrepreneurs. The study investigated how entrepreneurs’ face disclosure influenced the success of crowdfunding. Secondary data was collected from multiple crowdfunding platforms for projects in Africa, that is, cross-country data from 54 African countries, to overcome data limitations from a single country. Econometrics analysis revealed that the facial disclosure of entrepreneurs increases the probability of crowdfunding success. Images, videos, and backers had a positive influence on the success of crowdfunding. On the contrary, the duration of the crowdfunding campaign was negatively associated with its success. To reduce the knowledge asymmetry between creators and backers, those prepared to start a crowdfunding project must provide as much information as possible to show their abilities. This study contributes to understanding the role of disclosing an entrepreneur’s profile on economic exchanges in the success of online crowdfunding. The signalling and agency theories support the finding. The findings were limited to crowdfunding projects on the African continent and are not necessarily applicable to some developed countries.

Background: In susceptible hosts, SARS-CoV2 induced hyperinflammation accounts for an increased mortality. The search of adjuvant immunomodulatory therapies has been ongoing ever since the pandemic outbreak. Aim: Our purpose was to evaluate the efficacy of cyclosporin A (CsA) as add-on therapy to standard of care in patients with severe COVID-19 pneumonia. Methods: We conducted a randomized clinical trial in patients admitted to 8 Spanish Tertiary Hospitals. Patients were stratified into two severity categories and randomized 1:1 to receive a high dose corticosteroid-based standard therapy with or without CsA. The primary endpoint was FiO2 recovery by day 12 without relapses. Results: 109 patients were included and 98 randomized: 51 of them were assigned to CsA+SoC group and 47 to Standard of Care (SoC). 96/98 of them were included in the Intention to Treat population. 35 (68.6%) patients from CsA+SoC group and 32 (71,1%) patients from SoC group reached the primary endpoint in the ITT analysis. No differences were found after stratification into age groups, severity level at admission or the combination of both. Overall, time to FiO2 normalization was 7.4 days vs 7.9 days in the experimental and control groups, respectively. Global mortality was 8.2%. Severe adverse events were uncommon and equally distributed between arms. Conclusion: Addition of CsA did not show differences over a high-dose corticosteroids-based treatment in the clinical course of included patients. A better identification of candidates who will benefit from receiving immunomodulatory drugs is necessary in future studies.

One of the relevant goals in forging technologies is simplifying the existing process and developing new processes that can retain the original or very close to the original dimensions and billet shape after the end of the deformation cycle. This study attempts to develop a forging equipment design that provides alternating cyclic loading; the alternate cyclic forging (ACF) method is proposed. To preliminary evaluate the ACF and study the plastic deformation behavior, numerical simulation via finite element modeling (FEM) was conducted using DEFORM-3D. The main attention was paid to the metal flow characteristics, effective strain and stress distribution, and the forging load. The ACF results in non-uniform metal flow with the formation of diagonal and vortex flows were demonstrated. Maximum effective strains and stresses zones are localized at two points of the billet contact with the surface of the tooling container and at two points of the interface between the punches. It was also assumed that reducing the contact area between the punch and the billet using separate semi-cylindrical punches can reduce the forging load. Preliminary finite element analysis (FEA) results of the proposed process after four successive steps of the deformation cycle confirmed the feasibility of its general implementation.

The management of ghost gear - abandoned, lost, or discarded fishing gear- is a critical issue of marine conservation in Tunisia. This code of conduct proposed outlines a comprehensive framework aimed at mitigating the negative impacts of ghost gear on marine ecosystems and fisheries. The guiding principles of this code encompass sustainability, responsibility, collaboration, transparency, and innovation. Key guidelines outlined in the code include promoting responsible fishing practices, marking and tracking gear, incentivizing the retrieval and proper disposal of gear, and raising awareness through education. The code emphasizes regulatory compliance while encouraging active community involvement and cooperation among all stakeholders. The code supports the allocation of financial and technical resources to develop innovative solutions. Its implementation involves the involvement of stakeholders, execution of pilot projects, capacity building, and continuous monitoring and evaluation. The adoption of such code of conduct would contribute significantly to reduce the prevalence of ghost gear, protect marine life, and foster sustainable fishing practices. This collaborative and proactive approach is essential for the long-term health and sustainability of Tunisia’s marine environments.

The aim of this study was to evaluate the quality of legends associated with diagnostic images in the published oral and maxillofacial radiology literature using a novel rating scale. Images and their corresponding legends were randomly selected from published manuscripts over the last ten years in the Oral Radiology journals, namely Dentomaxillofacial Radiology, Imaging Science in Dentistry, Oral Radiology, and Oral Surgery Oral Medicine Oral Pathology Oral Radiology. An Image Quality Rating Scale (IQRS) was introduced to assess the quality of the legends associated with images. A program was developed for the rating scale form using Google Apps Script API to gather and analyze the data. The rating scale ranged from 1 to 5, with 5 as the highest rating. The highest average IQRS rating for one journal was 3.04. The overall IQRS rating average across all four journals was 2.87, which is 2.13 rating scores lower than the ideal score of 5. There is room for improvement in the legends that accompany diagnostic images and figures in the oral and maxillofacial radiology literature. A proper legend provides an excellent diagnostic teaching opportunity for the reader and enhances the quality of a publication.

Background: Recent studies suggest an association between long-term air pollution, diet, and health outcomes, but there are limited studies on how short-term air pollution affects dyslipidemia in particular. We aimed to investigate this association with modified fruit and vegetable consumption. Methods: The Cardiovascular Disease Association Study (CAVAS) included 8,779 participants between 2007 and 2016. Particulate matter (PM2.5) and nitrogen dioxide (NO2) levels were estimated using community multiscale air quality models. Short-term air pollution exposure was defined as a moving average over a lag time from “lag 1 days” to “lag 14 days.” Fruit and vegetable intake was categorized into 'total vegetables (TV),’ 'non-salted vegetables (NSV),' 'salted vegetables (SV),' 'fruits (F),' 'non-salted vegetables + fruits (NSVF),' and 'total vegetables + fruits (TVF).' Dyslipidemia was defined by total cholesterol ≥240 mg/dl, LDL cholesterol ≥160 mg/dl, HDL cholesterol

Exosomes are produced by all cell types, their biogenesis is of endosomal origin, originating as a result of cell membrane invagination. Furthermore, physiochemical characteristics are important prerequisites for their commercial application. The representative size range of exosomes is 50-200 nm. Whereas, comparative to animal cell, exosomes from plant cell sources have a relatively larger size range, moreover, exhibit broader therapeutic potential. In this review study, we have briefly discussed the research potential of plant-derived exosomes. Including their earlier studies on biogenesis, characterization, isolation methods, and possible therapeutic applications. Additionally, we have quoted targeted drug delivery for targeted therapies and have provided insights of methods for customizing exosomes produced from plants with various drugs. Most-importantly, here we have embodied exosomes from plant sources are natural nano-inducers possessing the ability for inter-kingdom communication, resulting in cross-kingdom regulations.

The task of human action recognition (HAR) based on skeleton data is a challenging yet crucial technique owing to its wide-ranging applications in numerous domains, including patient monitoring, security surveillance, and observation of human-machine interactions. While numerous algorithms have been proposed in an attempt to distinguish between a myriad of activities, most practical applications necessitate highly accurate detection of specific activity types. This study proposes a novel and highly accurate spatiotemporal graph autoencoder network for HAR based on skeleton data. Furthermore, an extensive investigation was conducted employing diverse modalities. To this end, a spatiotemporal graph autoencoder was constructed to automatically learn both spatial and temporal patterns from human skeleton datasets. The powerful graph convolutional network, designated as GA-GCN, developed in this study, notably outperforms the majority of existing state-of-the-art methods when evaluated on two common datasets, namely NTU RGB+D and NTU RGB+D 120. On the first dataset, the proposed approach achieved accuracies of 92.3\% and 96.8\% for the cross-subject and cross-view evaluations, respectively. On the more challenging NTU RGB+D 120 dataset, GA-GCN attained accuracies of 88.8\% and 90.4\% for the cross-subject and cross-set evaluations, respectively.

Pancreatic cancer is a refractory cancer with limited treatment options. Various cancer types are resistant to the tumor necrosis factor-related apoptosis-inducing ligand (TRAIL). Eugenol, the main component of clove oil, exhibits anticancer, anti-inflammatory, and antioxidant effects. However, no studies have reported that eugenol increases TRAIL sensitivity by upregulating death receptor (DR) expression. Here, we aimed to investigate eugenol as a potent TRAIL sensitizer. Increased apoptosis and inhibition of cell proliferation was observed in pancreatic cancer cells treated with eugenol and TRAIL compared with those treated with eugenol alone. Eugenol upregulated the expression of DR5, inhibited FLICE-inhibitory protein (FLIP), an anti-apoptotic protein, and increased p53, a tumor suppressor protein. In addition, eugenol induced the generation of reactive oxygen species (ROS) and caused endoplasmic reticulum (ER) stress. C/EBP-homologous protein (CHOP) knockdown using siRNA decreased the expression of DR5 and reduced the combined effects of eugenol and TRAIL. These results demonstrate that eugenol enhances TRAIL-induced apoptosis by upregulating DR5 through the ROS-mediated ER stress-CHOP pathway, enhancing ER stress by inducing p53 and downregulating FLIP expression. This suggests that eugenol has the potential to treat pancreatic cancer by increasing cell sensitivity to TRAIL.

Recently, flares have been considered as a major source of air pollution from the petroleum refining industry. The United Nations have instigated an international effort related to the management of flare emissions to reduce the global warming impact related to flaring. Eliminating or removing the need for gas flares is difficult because these devices are generally used as safety devices to allow combustion of flammable gases in a controlled fashion which supports safe operation. However, reducing flaring is generally possible using well designed efficiently operated flare equipment. In general, flare performance can be enhanced following the API-521 methodology and using assist- media including air and steam to achieve smokeless operation. This present work will discuss flare emissions in petroleum refining industry, and a method to manage flare emissions. Moreover, this work will discuss flare Combustion Efficiency (CE) and Distraction and Removal Efficiency (DRE) in terms of efficient flare operation. This work uses actual operating flare data, published previously, will be used in this work together with the CFD Code C3d. This code, developed at the USDOE Sandia National Laboratory is based on a standard LES methodology to conduct transient flare analysis is used to simulate flare operation to estimate flame shape and emissions produced. In this work, a new air-assisted flare tip design which uses the Coanda effect to improve flare operation was analysed. This new flare design reduces the emission rate that demonstrates the design effectiveness. The analysis considers a flare 1m high and (6") diameter in the centre of a 4m*4m*4m domain. Boundary conditions considering no cross wind and an ambient temperature of 300K. The initial condition is a hydrostatic pressure profile across the computational domain. In the air assist simulation, stoichiometric ratio will be a variable and therefore more than one case was considered.

Cardiovascular disease is a major global health concern, with early detection being critical. This study assesses the effectiveness of the portable ECG device, based on Internet of Medical Things (IoMT) technology, for remote cardiovascular monitoring during daily activities. We conducted a clinical trial involving 2,000 participants who wore the HiCardi device while engaging in hiking activities. The device monitored their ECG, heart rate, respiration, and body temperature in real-time. If an abnormal signal was detected while a physician was remotely monitoring the ECG at the IoMT monitoring center, he notified the clinical research coordinator (CRC) at the empirical research site, and the CRC advised the participant to visit a hospital. Follow-up calls were made to determine compliance and outcomes. Of the 2,000 participants, 318 showed abnormal signals, and 182 were advised to visit a hospital. Follow-up revealed that 139 (76.37%) responded, and 30 (21.58% of those who responded) sought further medical examination. Most visits (80.00%) occurred within one month. Diagnostic approaches included ECG (56.67%), ECG and ultrasound (20.00%), ultrasound alone (16.67%), ECG and X-ray (3.33%), and general treatment (3.33%). Seven participants (23.33% of those who visited) were diagnosed with cardiovascular disease, including conditions such as arrhythmia, atrial fibrillation, and stent requirements. The portable ECG device using the patch-type electrocardiograph detected abnormal cardiovascular signals, leading to timely diagnoses and interventions, demonstrating its potential for broad application in preventative health care.

(1) Background: With the global population aging, the demand for effective rehabilitation products for the elderly is increasing. Traditional products often fail to meet the comprehensive needs of elderly users, who require solutions addressing physical, psychological, and social aspects; (2) Methods: Using the Analytic Hierarchy Process, Quality Function Deployment, Axiomatic Design, and the System Usability Scale to identify and prioritize user needs, market research and user interviews were conducted to understand the preferences of the elderly and obtain design specifications. The focus was on multimodal sensory feedback, which was validated through the study; (3) Results: Elderly users have a strong preference for rehabilitation products that are durable, easy to use and have comprehensive sensory feedback. Intelligent rehabilitation bed as the carrier, strengthened structure, high strength material, ergonomic adjustment, integrated health monitoring system. Using digital technology for real-time health monitoring and personalized feedback to provide a comprehensive recovery experience, high user satisfaction was reported in the evaluation; (4) Conclusion: It is important to emphasize user-centered design and integrate multi-modal sensory feedback to meet the comprehensive rehabilitation needs of elderly users. Future studies should expand user samples and expert opinions to refine and validate the design and improve its effectiveness and usability.

Warsaw is among European cities with the worst atmospheric air quality, mainly due to very high pollution emitted by the residential sector and road traffic. This results in high concentrations of particulate matter and nitrogen oxides, often exceeding WHO standards. The paper discusses the current and expected effects of actions taken by the Warsaw authorities, to significantly improve air quality in the city. The Clean Air Program currently implemented mainly involves the reduction, and ultimately the total elimination, of coal combustion in all heat sources of the municipal sector, which is identified in numerous publications as the main source of air quality degradation in cities. The program's goal, which timeframe covers the years 2018-2029, is a definite reduction in particulate pollution. The second part of the remedial activities, covering the years 2024-2034, mainly concerns the reduction of nitrogen oxide pollution, the main source of which is urban road traffic. In the latter case, the expected effects of introducing Low-Emission Zone (launched July 2024) in the city center are considered. According to the project, the zone's implementation will consist of five two-year stages, in which the car emission limits will gradually tighten. The paper compares the effectiveness of two being considered versions, differing in size zones.

Despite the demonstrated benefits of data monetization initiatives in achieving competitive advantages, many of these efforts struggle to realize their potential. Companies often find it challenging to sustain even initially successful data monetization initiatives due to significant data quality issues. This reflects a disconnect between advancements in data monetization research—spanning digitization, digitalization, and digital transformation—and practical implementation within companies. Consequently, misguided approaches to data monetization are relatively common. A critical prerequisite for successful data monetization is the establishment and maintenance of clean, high-quality data. This study underscores the importance of data quality by conducting an in-depth analysis of Medical Inc., a company engaged in preparing pristine customer master data for advanced customer analytics. The investigation aims to elucidate Medical Inc.’s approach to addressing data cleanliness challenges and to distill a general framework for the customer master data cleansing process. This framework illuminates a relatively unexplored aspect of data monetization supplementing existing literature on digitization, digitalization, and digital transformation.

This literature review investigates the morphological and behavioral significance of the horns of Carnotaurus sastrei, a theropod dinosaur from the Late Cretaceous period. Through a comprehensive analysis of fossil specimens, comparative anatomy, and biomechanical modeling, this review explores the evolutionary adaptations and potential functions of these distinctive cranial structures. The horns of Carnotaurus are hypothesized to serve multiple roles, including species recognition, thermoregulation, and intraspecific combat. Our findings suggest that its cranial horns likely served a primary role in species recognition, intimidating rivals and signaling mating readiness. It also could have been used occasionally in head-butting contests, potentially as a display of dominance, but likely did not play a role in thermoregulation. Additionally, we examine the ecological context in which Carnotaurus lived, assessing how environmental factors could have influenced the development of such features. The implications of this study extend to broader discussions on the functional morphology and adaptive strategies of theropod dinosaurs, highlighting the complex relationship between form and function in the evolutionary history of predatory dinosaurs like Carnotaurus sastrei.

The sub-economic dynamics studies the relationship between people's economic impulses and market behavior. Using Bourbaki structuralism as a philosophical consideration and quantum chromodynamics as a conceptual and modeling method, sub-economic dynamics theory is constructed. In this theory, we discuss the deep structure of the free market and analyze the errors in Mises' anthropological arguments. Sub-economic dynamics is the psychological basis of market dynamics. Guided by the preface, this paper is divided into six sections: (1) The Free Market and Impulse Flavor Charge, including topics such as the fate of the free market, the food chain and psychology, and the flavor charge of economic impulses; (2) The Nature of Economic Impulses, including topics such as the flavor and generation of impulses and counter-impulses, and the bound state of impulses and fractional market charges; (3) Freud's Color Charge Space and Spin, including topics such as the Pauli exclusion rule and color charge, and Freud's personality theory; (4) Consciousness and Gluons; (5) Gauge Transformations and SU(3) Symmetry Group; and (6) General Discussion.

Ecological city is currently a hot topic in urban planning research. How to balance urban development and ecological environment protection has become a focus and difficulty of research. Traditional urban planning is mainly based on experience, but its disadvantage is that it is easy to overlook key geographical factors such as terrain and vegetation, and cannot fully reflect the actual spatial characteristics of the city. Therefore, this article applied the combination of urban and rural planning and environmental remote sensing technology to urban planning and design, obtaining a massive amount of data. Through the actual analysis of Langfang, this paper monitored the vegetation, water, land and landscape data of Langfang through environmental remote sensing technology. By using these data to identify existing problems, this article proposed reasonable planning for the problems, in order to achieve the effective goal of ecological city planning and design. It provided a more accurate analysis of urban structure, including urban planning and urban ecological landscape planning. Among them, ecological landscape planning includes renovating ecological embankments, protecting ecological green belts around the city, and creating characteristic landscape areas, thereby providing more scientific basis for urban planning and design, making urban planning and design more reasonable and scientific. This paper investigated the planned Langfang City, and the average errors of vegetation, soil, water quality, and landscape data obtained from traditional field surveys were 0.35, 0.33, 0.34, and 0.31, respectively. The average errors of remote sensing technology in obtaining vegetation, soil, water quality, and landscape data were 0.16, 0.15, 0.17, and 0.15, respectively. The total suspended solids content of waterscape 1-10 before planning was 5.51mg/L and 7.41mg/L, respectively. After planning, the total suspended solids content of waterscape 1-10 was 3.60mg/L and 4.47mg/L, respectively. From this, it can be seen that the error in obtaining data after planning has decreased, and the total suspended solids content has also decreased.

Ensuring banking stability is essential for economic resilience and the prevention of financial crises. Financial Stability Reports (FSRs) provide critical data on the financial system. This study advocates for the use of text mining tools to enhance the analysis of FSRs. Text mining offers a systematic method for extracting insights from unstructured data, allowing analysts to uncover detailed information on capital adequacy, risk management, and regulatory compliance. Additionally, this approach identifies emerging trends and vulnerabilities that may not be apparent through traditional analysis methods. The paper examines the application of text mining techniques to FSR analysis within the Republic of Armenia.

Background: The study demonstrates the sustainable synthesis of translucent zinc oxide (ZnO) nanowires using Moringa seed extract, highlighting eco-friendly and sustainable approaches. Methods: Characterization techniques such as X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), energy dispersive spectroscopy (EDS), transmission electron microscopy (TEM), and X-ray photoelectron spectroscopy (XPS) confirmed the formation of ZnO nanostructures with diverse morphologies, including nanowires and spherical clusters. Results: The synthesized ZnO nanowires exhibited remarkable photocatalytic activity, achieving over 89% degradation efficiency of methylene blue (MB) under UV light within 80 minutes, demonstrating their potential for wastewater treatment applications. Conclusions: The research findings support Sustainable Development Goals (SDGs) related to clean water, climate action, and life below water and on land, highlighting the role of eco-friendly nanostructures in environmental remediation. The dual advantage of Moringa seed extract, providing both a sustainable synthesis route and health benefits due to its rich content of vitamins and phytochemicals, underscores the potential of integrating natural resources in nanotechnology for environmental and health applications.

Luminal breast cancer has a high incidence worldwide and is a severe health threat. Estrogen receptor alpha (ER-α) is activated by 17β-estradiol (E2), and its overexpression promotes cancerous characteristics. Luminal breast cancer is an epithelial type; however, the cytokine IL-6 secreted by cells from the tumor microenvironment stimulates the epithelial to mesenchymal transition (EMT) and promotes metastasis. Also, IL-6 decreases ER-α levels, favoring the tamoxifen (TMX) resistance development. However, genes under E2 regulation continue expressing even though this receptor is absent. GPR30 is an alternative E2 receptor present in both luminal and aggressive triple-negative breast cancer and is related to TMX resistance and cancer progression. The roles of GPR30 and IL-6 on metastasis have been individually established; however, their interplay remains unexplored. This study aims to elucidate the role of GPR30 in IL-6-induced metastatic properties in MCF-7 luminal breast cancer cells. Results showed that GPR30 contributes to the E2-induced MCF-7 proliferation because its inhibition with the antagonist G15 and the Pertussis toxin (PTX) diminished it. Besides, GPR30 upregulated vimentin and downregulated E-cadherin levels in MCF-7 and in TMX-resistant (R-TMX) cells and is also involved in the IL-6-induced migration, invasion, and TMX resistance in MCF-7 cells. In addition, in MDA-MB-231 triple-negative cells, both basal and IL-6-induced metastatic properties were related to GPR30 activity. These results indicate that the GPR30 receptor regulates the EMT induced by IL-6 in breast cancer cells.

Submergence stress challenges direct seeding in rice cultivation. In this study, we identified a heat shock protein, NAL11, with a DnaJ domain, which can regulate the length of rice coleoptiles under flooded conditions. Through bioinformatics analyses, we identified cis-regulatory elements in its promoter, making it responsive to abiotic stresses, such as hypoxia or anoxia. Expression of NAL11 was higher in the basal regions of shoots and coleoptiles during flooding. NAL11 knockout triggered the rapid accumulation of abscisic acid (ABA) and reduction of Gibberellin (GA), stimulating rice coleoptile elongation and contributes to flooding stress management. In addition, NAL11 mutants were found to be more sensitive to ABA treatments. Such knockout lines exhibited enhanced cell elongation for coleoptile extension. Quantitative RT-PCR analysis revealed that NAL11 mediated the gluconeogenic pathway, essential for the energy needed in cell expansion. Furthermore, NAL11 mutants reduced the accumulation of reactive oxygen species (ROS) and malondialdehyde under submerged stress, attributed to an improved antioxidant enzyme system compared to the wild-type. In conclusion, our findings underscore the pivotal role of NAL11 knockout in enhancing the tolerance of rice to submergence stress by elucidating its mechanisms. This insight offers a new strategy for improving resilience against flooding in rice cultivation.

It has been discovered that representing odd integers as modified binary expressions of the form $\sum \limits_{n > m}2^n + 2^m - 1$ for $m \geq 1$ helps in understanding the dynamics of Collatz-type sequences. Starting with the original Collatz sequence $3x + 1$, it is found that when the odd step is applied to an odd integer $\sum \limits_{n > m}2^n + 2^m - 1$, an even integer $3\left(\sum \limits_{n > m}2^{n}\right) + 2^{m + 1} + 2^m - 2$ is obtained, which is exactly once divisible by 2, unless the lowest index reduces to zero. This implies that the sequence alternates between odd and even steps $m$ times. This governs the dynamics of the Collatz-type sequences because the value of $m$ determines the number of times the integer can be divided by 2 in each even step. A shortcut method is developed based on this dynamics that states that the even integer after $m$ odd-even steps are completed is $\left(\left(\frac{3}{2}\right)^m \sum \limits_{n > m}2^n\right) + 3^m - 1$. A shortcut method of this magnitude has never been utilized anywhere. The shortcut method for the modified Collatz sequence $5x + 1$ is also presented.

Over the past decade, the production of biofuels from lignocellulosic biomass has steadily increased to offset the use of fuels from petroleum. To make biofuels cost-competitive, however, it is necessary to add value to the “ligno-” components (up to 30% by mass) of the biomass. The properties of lignin, in terms of molecular weight (MW), chemical functionality, and mineral impurities often vary from biomass source and biorefinery process, resulting in a challenging precursor for product development. Activated carbon (AC) is a feasible target for the lignin-rich byproduct streams because it can be made from nearly any biomass and it has a market capacity large enough to use much of the lignin generated from the biorefineries. However, it is not known how the variability in the lignin affects the key properties of AC, because until now, they could not be well controlled. In this work, various fractions of ultraclean (< 0.6 % ash) lignin are created with refined MW distributions using Aqueous Lignin Purification using Hot Agents (ALPHA) and used as precursors for AC. AC is synthesized via zinc chloride activation and characterized for pore structure and adsorption capacity. We show that AC surface area and the adsorption capacity increase when using lignin with increasing MW, and furthermore, that reducing the mineral content of lignin can significantly enhance the AC properties. The surface area of the AC from the highest MW lignin can reach ~1830 m2/g (absorption capacity). Furthermore, the single step activation carbonization using zinc chloride allows for minimal carbon burnoff (< 30%) capturing most of the lignin carbon compared to traditional burn off methods in biorefineries for heat generation.

Background: Experiential learning (EL) experiences are an important component of a university education, positively impacting career-related attitudes, knowledge, and skills. Students also require EL opportunities to gain experiences required for admission to competitive graduate and professional programs. Students with disabilities face barriers accessing and benefiting from such opportunities. Purpose: This study examined the degree to which demographic factors, type and severity of disability, dispositional factors, and overall adjustment and wellbeing are predictive of the quality of EL experiences among university students with disabilities. Methodology/approach: A survey was distributed to undergraduate students with disabilities who have participated in EL courses. Results were analyzed using multiple linear regression. Findings/conclusions: Dispositional and adjustment and wellbeing variables, including the environmental mastery dimension of their psychological wellbeing rather than demographic factors, such as gender or type and severity of disability, are significant predictors of the quality of EL experiences among students with disabilities. Implications: For students with disabilities to have academically and professionally successful EL experiences, post-secondary institutions must continue to provide appropriate accommodations and educate instructors about the diverse and complex needs of this student group. This must include an understanding of the uniqueness of each individual student’s needs.

Explosion monitoring is performed by infrasound and seismoacoustic sensor networks that are distributed globally, regionally, and locally. However, these networks are unevenly and sparsely distributed, especially in the local scale as maintaining and deploying networks is costly. With increasing interest in smaller yield explosions, the need for more dense networks has increased. To address this issue, we propose using smartphone sensors for explosion detection as they are cost-effective and easy to deploy. Although there are studies using smartphone sensors for explosion detection, the field is still in its infancy and new technologies need to be developed. We applied a machine learning model for explosion detection using smartphone microphones. The data used were from the Smartphone High-explosion Audio Recordings Dataset (SHAReD), a collection of 326 waveforms from 70 high-explosive (HE) events recorded on smartphones, and the ESC-50 dataset, a benchmarking dataset commonly used for environmental sound classification. Two machine learning models were trained and combined into an ensemble model for explosion detection. The resulting ensemble model classified audio signals as either “explosion,” “ambient,” or “other” with true positive rates (recall) greater than 96% for all three categories.

Stage IV grade C localized periodontitis (pre-puberal localized aggressive periodontitis/LPP), an extremely rare form of periodontal disease, occurs in otherwise healthy individuals (no signs of dental plaque/calculus) due a hyper-aggressive auto-immune response to high periodontopathic bacteria levels. Methods: A 4-year-old Caucasian girl with unusual high mobility of temporary lower left canine and localized gingival inflammation was misrecognized by multiple clinicians (initially diagnosed with hypophosphatasia, genetic and metabolic disorders, all turning negative), over a period of 4-6 month, despite initial radiographs showing clear pathognomonic signs. LPP diagnostic was made by the last clinician, but by now the tooth was lost. Similar inflammation signs appeared around the lower temporary right canine. X-ray examination showed similar bone and periodontal loss as previously seen, while periodontopathic bacteria tested highly positive. The patient received both mechanical cleaning and ten days systemic antibiotic treatment (Augmentin and Metronidazole). Results: Two months after, inflammation signs disappeared, with periodontal regeneration radiologically present, and only small periodontopathic bacteria precursors concentrations. Conclusions: Despite initial periodontal loss, an adequate treatment can keep under control an LPP disease. Moreover, bone and periodontal regeneration appears if periodontopathic bacteria scores are kept lower, showing the importance of fast adequate diagnostic and treatment.

The effects of shade netting on the morphological and chemical composition (organic acids, sugars, total polyphenols, total and certain anthocyanins, proanthocyanidins) and in vitro antioxidant activity of raspberries and blackberries was conducted in the southeastern region of the Republic of North Macedonia. HPLC analysis confirmed a significant effect of shading nets on the amount of the most dominant anthocyanin in blackberries and raspberries. The amount of cyanidin-3-O-glucoside in blackberries covered with shading nets was significantly higher in comparison to the amount of the same anthocyanins in uncovered blackberries. The same tendency was observed for cyanidin-3-O-sophoroside for covered and uncovered raspberries from 2023. However, an opposite relationship was noticed for total anthocyanin content in favor of uncovered blackberry and raspberry from 2023. From all examined organic acids, the amount of acetic acid was the most affected in covered raspberries. The shade netting had a significant influence on polyphenolic content (such as high molecular proanthocyanidins, total and particular anthocyanins), the amounts of sugars (glucose and fructose), and organic acids as well as the intensity of color in blackberry and raspberry. Although net shading significantly influenced particular phenolic compounds presented in examined berry fruits, antioxidant activity was weakly correlated to this environmental change.

The central aim of this paper is to present the Green Web Meter software which is designed to support the measurement of digital sustainability performance of websites and web apps, as well as providing sound based analytic tools for improving their overall quality. We analyzed scientific literature and specifically selected three quality assessment models (E-S Qual, Sitequal, Webqual 4.0) which we used as a starting point for identifying the most crucial aspects for the monitoring activities. A consistent set of Key Performance Indicators (KPIs) suitable for automated tracking has been provided. We finally developed model-based software able to deliver solutions for real-time website performance analysis and reports. We structured the model to offer accurate references for digital sustainability reporting by aggregating the KPIs into an Environmental, Social, and Governance (ESG) framework and computing a comprehensive score for its pillars.

Background/Objectives: It is known that balance disorders involve occupational hazards. However, the inverse relationship (between certain occupations and an increased incidence of vertigo or dizziness) has been scarcely studied. The objective of this work was to analyze the occupation of a group of patients with vertigo, compared to economically active general population, and to evaluate the prevalence of occupational noise and/or vibration-exposure in both groups. Methods: A cross-sectional, observational, case-control study was carried out including 461 patients (220: Meniere’s disease; 73: vestibular migraine; 28: vestibular neuritis; 140: BPPV) (310 women and 151 men). These patients were compared to a control group from the general population, obtained from 6th EWCS-Spain (2015). Possible differences regarding sex, age, occupation, exposure to noise, and exposure to mechanical vibrations were analyzed. Results: Differences in distribution of occupations between patients with vertigo and general population were observed (Chi-square, p=6.142 e-30). Patients with vertigo were significantly more exposed to noise (p=8.19 e-12; OR=2,127, CI95% (1.720;2.630)) and vibrations (p=1.11 e-12; OR=2.288, CI95% (1.835;2.853)) than the control group. These differences were observed both between men and women. Conclusions: A relationship between occupational exposure to noise and/or vibrations and the presence of vertigo was observed. Protective and preventive measures could help prevent the occurrence of some diseases involving vertigo.

Strawberry fruit is highly appreciated worldwide for its organoleptic and healthy properties. However, this plant is attacked by many pathogenic fungi, which significantly affect fruit production and quality at pre- and post-harvest stages, making chemical applications the most effective but undesirable strategy to control diseases, so far. Alternatively, genetic manipulation employing plant key genes involved in defense, such as members of the NPR-like gene family, has been successful in many crops to improve resistance. The identification and use of the endogenous counterpart genes in the plant of interest (as it is the case of strawberry) is desirable as it would increase the favorable outcome and requires prior knowledge of their defense related function. Using RNAi technology in strawberry, we show that transient silencing of Fragaria ananassa NPR3 members in fruit significantly reduces tissue damage after Colletotrichum acutatum infection whereas the ectopic expression of either FaNPR3.1 or FaNPR3.2 does not have an apparent effect. Furthermore, the ectopic expression of FaNPR3.2 in Arabidopsis thaliana double mutant npr3npr4 reverts the disease resistance phenotype to Pseudomonas syringe to wild type levels. Therefore, our results reveal that members of the strawberry FaNPR3 clade negatively regulate the defense response to pathogens, as do their Arabidopsis AtNPR3/AtNPR4 orthologs. Also, we provide evidence that FaNPR3 members act in strawberry (F. ananassa) as positive regulators of WRKY genes, FaWRKY19 and FaWRKY24, and that in Arabidopsis, FaNPR3.2 negatively regulates its orthologous genes AtNPR3/AtNPR4. We report for the first time the functional characterization of FaNPR3 members in F. ananassa which provides a relevant molecular basis for the improvement of resistance in this species through new breeding technologies.

3.9 million individuals rely on kidney replacement therapy worldwide. They experience height-ened susceptibility to cardiovascular diseases and mortality, alongside an increased risk of in-fections and malignancies, with inflammation being key to explaining this intensified risk. This study utilized semi-targeted metabolomics to explore novel metabolic pathways related to in-flammation in this populations. We collected pre and post-session blood samples of patients who had already undergone one year of chronic hemodialysis. And used liquid chromatography and high resolution mass spectrometry to perform a metabolomic analysis. Afterwards, we employed both univariate (Mann-Whitney test) and multivariate (logistic regression with LASSO regulari-zation) to identify metabolites associated with inflammation. In the univariate analysis, in-dole-3-acetaldehyde, 2-ketobutyric acid, and urocanic acid showed statistically significant de-creases in median concentrations in the presence of inflammation. In the multivariate analysis, metabolites positively associated with inflammation include allantoin, taurodeoxycholic acid, norepinephrine, pyroglutamic acid, and L-hydroorotic acid. Conversely, metabolites showing negative associations with inflammation include benzoic acid, indole-3-acetaldehyde, methio-nine, citrulline, alphaketoglutarate, n-acetyl-ornithine and 3-4-dihydroxibenzeneacetic acid. Non inflamed patients exhibit preserved autophagy and reduced mitochondrial dysfunction. Under-standing inflammation in this group hinges on the metabolism of arginine and the urea cycle. Additionally, the microbiota, particularly uricase-producing bacteria and those metabolizing tryptophan, play critical roles.

This research explores the pivotal role of big data analytics in enhancing retail marketing and supply chain optimization. As the retail industry undergoes rapid digital transformation, big data analytics has emerged as a critical tool for gaining competitive advantage and improving operational efficiency. The study delves into the various applications of big data analytics, including customer segmentation, personalized marketing, demand forecasting, inventory management, dynamic pricing, and supply chain optimization. It highlights how retailers leverage data from diverse sources to gain insights into consumer behavior, market trends, and operational performance, leading to more informed decision-making. The findings underscore the significant benefits of big data analytics in enhancing customer experiences through targeted engagement and personalized service. Additionally, the research addresses the ethical considerations and challenges associated with data privacy, security, and potential biases in analytics models. It emphasizes the importance of maintaining high standards of data quality and ethical practices to ensure the responsible use of data. The conclusion reiterates the transformative impact of big data analytics in the retail sector, suggesting that while it offers substantial advantages, careful management of its challenges is crucial. The study provides valuable insights into how retailers can harness the power of big data analytics to drive growth, innovation, and long-term success in an increasingly competitive and data-driven market environment.

The nuclear magnetic resonance (NMR) metabolomic analysis was applied to investigate the differences within nineteen Sicilian Nocellara del Belice mono varietal Extra Virgin Olive Oils (EVOO), grown in two zones different for altitude and soil composition. Several classes of endogenous olive oil metabolites were quantified through a nuclear magnetic resonance (NMR) three-experiment protocol coupled with a yet developed data-processing called MARA-NMR. This method, taking around one-hour experimental time per sample, faces the possible quantification of different class of compounds at different concentration’s range which would require at least three alternative traditional methods. NMR results were compared with the data of traditional analytical methods to quantify free fatty acidity (FFA), fatty acid methyl esters (FAMEs), and total phenol content. The presented NMR methodology is compared with traditional analytical practices and its consistency is also tested through slightly different data treatment. Despite the rich literature about the NMR of EVOOs, the paper points out that there are still several advances potentially improving this general analysis and overcoming the other cumbersome and multi-devices analytical strategies. Monovarietal EVOO’s composition is mainly affected by pedoclimatic conditions in turn relaying upon the nutritional properties, quality, and authenticity. Data collection, analysis and statistical processing are discussed touching the important issues related to the climate changes in Sicily and to the specific influence of pedoclimatic conditions.

This paper presents a mathematically rigorous investigation into the incompleteness of Einstein-de Sitter spacetime. The incompleteness of the spacetime is proved using results regarding isometries between connected Semi-Riemannian manifolds following the computation of the Einstein tensor of the Einstein-de Sitter spacetime. Various properties of the Einstein-de Sitter spacetime apart from its incompleteness is also discussed based on its incompleteness with the help of various results of Semi-Riemannian manifolds. Also, few properties of spacetime which cannot be concluded from the incompleteness of the spacetime is also discussed.

(1) Background: Impaired colonic motility is common in patients with ulcerative colitis. The patients often complain the symptoms related to colonic motility dysfunction such as loose stools or diarrhea and these symptoms make therapy more challenging; (2) Methods: This study aimed to examine the movement of the colon in mice with ulcerative colitis and determine if the glial-derived neurotrophic factor (GDNF) may enhance aberrant colonic motility through Connexin 43 (Cx43).A model of ulcerative colitis was created using 32 male C57BL/6 mice. Colonic transit time was measured in all mice, and colon tissues were examined using hematoxylin-eosin staining (HE) and transmission electron microscopy (TEM). The levels of GDNF and Cx43 were determined via immunohistochemistry, immunofluorescence, and RT-PCR techniques. The levels of C-reactive protein (CRP),TNF-α, IL-1β, and IL-6 were quantified using the enzyme-linked immunosorbent assay (ELISA); (3) Results: 1) The UC models were effectively established by administering dextran sodium sulfate (DSS) water. Mice with ulcerative colitis exhibited a delay in colonic transit time. 2) GDNF can improve the delayed movement of the colon in mice with UC by activating the Cx43 protein; (4) Conclusions: GDNF can ameliorate impaired colonic motility in mice with ulcerative colitis, and its mechanism is related to the activity of Cx43. GDNF could potentially be utilized as a therapeutic option for addressing aberrant colonic motility in patients.

Accurate forecasting of available energy portion that corresponds to the reservoir inflow of the month(s) ahead provides important decision support for the hydropower plants in energy pro-duction planning for revenue maximization, as well as for the environmental impact prevention, and flood control at the upstream and downstream of a basin. Therefore, a reliable forecasting tool or model is deemed necessary and crucial. Considering the fluctuation and nonlinearity of data which significantly influence the forecasting results, this study develops an effective hybrid model by integrating Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) called “PSO-ANN” model based on the hydrological and meteorological data pre-processed by cross-correlation function (CCF), autocorrelation function (AFC), and normalization techniques for predicting the available energy portion corresponding to the reservoir inflow mentioned above for a case study hydropower plant in Laos namely, Theun-Hinboun hydropower plant (THHP). The model was evaluated by using correlation coefficient (r), relative error (RE), root mean square error (RMSE), and Taylor diagram plots in comparison with the popular single algorithm approaches such as ANN, and NARX models. Results demonstrated the superiority of the proposed PSO-ANN approach over the other two models.

This paper presents a methodology based on deep learning models and metaheuristic algorithms for the optimization of airfoils for the design of aircraft wings with large endurance. The use of AZTLI-NN (a neural network with an architecture composed of a multilayer perceptron and a variational autoencoder) is implemented for the prediction of graphs of the aerodynamic coefficients of the airfoil as a function of the angle of attack. This neural network presents good predictions of the aerodynamic coefficients, similar to the coefficients obtained with computational fluid dynamics simulations. AZTLI-NN in combination of metaheuristic algorithms and the CST profile parameterization method show excellent performance in single-objective and multi-objective profile optimization tasks.

Photoplethysmography (PPG) signals, which measure blood volume changes through light absorption, are increasingly used for non-invasive Cardiovascular Disease (CVD) detection. Analyzing PPG signals can help identify irregular heart patterns and other indicators of CVD. This research involves a thorough analysis of CVD classification using the Capnobase dataset, which includes data from 20 CVD subjects and 21 normal subjects. In the initial stage, heuristic optimization algorithms, such as ABC-PSO, the Cuckoo Search algorithm (CSA), and the Dragonfly algorithm (DFA), were applied to reduce the dimension of the PPG data. Next, these Dimensionally Reduced (DR) PPG data are then fed into various classifiers such as Linear Regression(LR), Linear Regression with Bayesian Linear Discriminant Classifier (LR-BLDC), K-Nearest Neighbors (KNN), PCA-Firefly, Linear Discriminant Analysis (LDA), Kernel LDA (KLDA), Probabilistic LDA (ProbLDA), SVM-Linear, SVM-Polynomial, SVM-RBF, to identify CVD. Classifier performance is evaluated using Accuracy, Kappa, MCC, F1 Score, Good Detection Rate(GDR), Error rate, and Jaccard Index(JI). The SVM-RBF classifier for ABC PSO dimensionality reduced values outperforms other classifiers, achieving an accuracy of 95.12% with a MCC of 0.90.

Introduction. The Fat Mass and Obesity-Associated (FTO) gene encodes a demethylase that modulates RNA N6-methyladenosine (m6A) playing a regulatory role in adipocyte differentiation and pathogenesis of human obesity. Methods. To understand the potential role of FTO in osteoporosis (OP) we investigated five single nucleotide variations (SNVs) in intron 1 (rs8057044, rs8050136, rs9939609, rs62033406, rs9930506) in a cohort of postmenopausal women (n = 188) from Central Europe. Genotyping was performed by allele discrimination assay while haplotypes were reconstructed in population by PHASE 2.1. Results. The rs9930506 was strongly associated with OP (P &lt; 0.0035), supported by Bonferroni correction (P &lt; 0.0175) and all SNVs were stronger associated with severe OP with fragility fractures. Among 17 haplotypes, two were frequent (h1 and h9) and distributed in three main haplotypes pairs (h1/h1, h1/h9, h9/h9). Pathogenic pair h1/h9 was associated with leaner phenotype, fractures and lower bone mineral density (BMD) and carried heterozygous GA of rs9930506, while protective pair h9/h9 was associated with obesity and carried AA alleles of rs9939609. Conclusions. Concordant associations with OP, fractures risk and lower BMD at all skeletal sites indicate FTO as a promising candidate for OP explaining complex relationship with obesity and offer new perspectives for the study of epigenetic regulation of bone metabolism.

Technological equipment in quarries that extract and deliver aggregates for different uses operates in a predetermined flow depending on the type of rocks exploited and the dimensional characteristics imposed on the final products. In this context, the interruptions in operation required to replace high-wear parts (such as the teeth of excavators and bucket loaders) must be limited as much as possible through technological solutions to increase their service life. The evolution of the wear of the teeth of the quarry equipment that come into direct contact with the rocks was concretely established in the production process, in parallel with the wear values obtained by simulating the wear phenomenon in laboratory conditions, in order to validate the data collected during the operation of the machines. Preventive-repetitive maintenance, within the activities of reconditioning the worn surfaces of the teeth, through the charging process by manual electric welding with covered electrodes, was applied directly to the machine, which led to the shortening of the interruptions in operation necessary to replace these spare parts.

The fracture strength was compared in a scratch test of coatings based on the ZrN system with the introduction of Ti, Nb and Hf, which were deposited on a titanium alloy substrate. The coatings were deposited using Controlled Accelerated Arc (CAA-PVD) technology. The comparison was carried out according to two parameters: adhesion strength to the substrate and overall coating strength. The (Zr,Hf)N coating showed better resistance to destruction, but had worse adhesion to the substrate. As a result, although the coating is retained directly in the scribing groove, a large area of delamination and destruction is formed around the groove. The (Ti,Zr,Nb)N coating, with its somewhat lower strength, has high adhesion to the substrate; no noticeable delamination is observed along the groove boundary.

Microplastics (MPs), typically defined as plastic fragments smaller than 5mm, are pervasive in terrestrial and marine ecosystems. There is a need for rapid, portable, low-cost detection systems to assess health and environmental risks. Fluorescent tagging with Nile Red (NR) has emerged as a popular detection method, but variations in fluorescent emissions based on NR solvent, plastic polymer, excitation wavelength, and additives complicate standardization. In this study, seven plastic samples stained with acetone-based NR were analyzed using a fluorescent spectrometer to identify optimal emission peaks across UV-Vis excitation wavelengths. These findings aid in selecting appropriate excitation wavelengths and optical filters for future detection systems. Additionally, a straightforward polymer identification scheme was validated against field-collected plastic samples, whose material composition was confirmed via Fourier Transform Infrared Spectroscopy. This work contributes towards developing accessible microplastic detection technologies by characterizing the fluorescent properties of NR-stained plastics and enhancing the capability for effective environmental monitoring. Future research will expand the dataset to include diverse plastics with varying additives and weathering, and incorporate computer-vision tools for automated data processing and polymer identification.

Differences in metabolic regulation among obesity phenotypes, specifically metabolically healthy obese (MHO) and metabolically unhealthy obese (MUO) women, may lead to varied responses to interventions, which could be elucidated through metabolomics. Therefore, this study aims to investigate the differences in metabolite profiles between MHO and MUO women and the changes following a lifestyle intervention. Serum from 36 MHO and 34 MUO women, who participated in a lifestyle intervention for weight loss were analyzed using an untargeted proton nuclear magnetic resonance spectroscopy (1H NMR) at baseline and 6 months post-intervention. Anthropometric, clinical and dietary intake parameters were assessed at both time points. Both groups showed differential metabolites profile at baseline and sixth month. Seven metabolites including trimethylamine-N-oxide (TMAO), arginine, ribose, aspartate, carnitine, choline and tyrosine significantly changed between group post-intervention, in which all showed a decreasing pattern in MHO. Significant reduction in body weight and BMI in the MUO correlated with changes in carnitine and tyrosine levels. In conclusion, metabolite profiles differed significantly between MHO and MUO women before and after a lifestyle intervention. Changes in carnitine and tyrosine levels in MUO correlated with weight loss, suggesting potential targets for therapeutic intervention.

The economic development of the Yangtze River Delta region is in a stage of rapid growth. As a unique intersection of urban and rural functions, contradictions are prominent. The production, life, and ecological functions of suburban rural landscapes are forming a new trade-off and synergistic relationship. The planning policy of the Yangtze River Delta from 2021 to 2035 aims to achieve the coordinated development of ecology, economy, and people's lives in the suburban areas of the Yangtze River Delta. To evaluate the current status of the “production-living-ecological” functions of suburban rural areas in the Yangtze River Delta region and provide data support for future planning decisions in the Yangtze River Delta region, this study selected the suburban rural landscape of Jiashan as the research object and used temporal rank correlation analysis and spatial bivariate autocorrelation analysis to explore the evolution characteristics of the suburban rural landscape of Jiashan from 2000 to 2020. The results show that (1) the linear increase of the production function and life function of Jiashan rural areas has led to a decline in ecological functions, especially a decline in life functions. (2) There is a trade-off and synergistic relationship between the different functions of the suburban rural landscape of Jiashan, showing a temporal change pattern dominated by trade-offs. (3) The multifunctional trade-off and synergistic relationship of the suburban rural landscape in Jiashan County exhibited obvious spatial pattern variability, which was manifested as the evolution from street-centered areas to compatibility-trade-trade-off and from town-centered areas to compatibility-synergy-trade-off. The relevant policies for the construction of ecological green comprehensive demonstration areas affect the economic, social, and ecological development of the Yangtze River Delta region, resulting in substantial differences in the types and trends of the spatiotemporal evolution of the functional trade-off and synergistic relationship of suburban rural landscape. Targeted measures should be taken according to local conditions to guide the coordinated development of landscape functions.

The different sociocultural context and lifestyle may influence oral health. To evaluate the interference of these factors, we conducted a quantitative-qualitative cross-sectional study. The research was carried out in a private dental office, in an urban area, and in the Family Health Unit (USF), in a rural area. Fifty patients of each location participated on the research. A semi-structured questionnaire covering sociocultural profile, oral hygiene habits, drug consumption, use of medicinal plants, and antibiotics usage was applied. Also, laboratory tests to evaluate the tendency to have dental decay and Streptococcus sp colony counting was performed. There was no significant difference between patients in the two groups regarding gender, race, marital status, type of residence, number of residents in the same household, and type of employment. Private clinic participants were more prone to consuming alcohol and/or cigarettes and reported to brush the teeth more often. There was no association between salivary acidity and higher biofilm bacterial density. The usage of medicinal plants was more evident in the public service. Data presented here revealed that social inequality between private and public dental assistance are greater from the point of view of investment in public health for the population with difficult access and not from cultural or educational level diversity.

The goal of the study is to assess the potential correlation between maternal Sars-cov-2 infection , detected three months before the second trimester anomaly scan, and fetal iperecogenicity in order to determine whether or not this could be considered a predictive factor for maternal-fetal complications . The study included 48 pregnant women who did not have any clear maternal or fetal problems. These individuals had an anomaly scan in the second trimester, as well as amniocentesis and fetal cariotyping, and then RT-PCR testing on their placental tissue after birth to rule out transplacental transmission. There have been no reports of fetal abnormalities via amniocentesis or fetal cariotyping. The patients experienced a high rate of spontaneous births (83%). Fetal development problems (2%) and premature delivery (4%) were uncommon, although placental swabs revealed a possibility of viral transmission across the placenta itself. To the best of our knowledge, this is the first observational study to show that detecting isolated intestinal hyperechogenicity during an abnormality scan in fetuses of women infected with Sars-Cov-2 in the previous three months does not appear to be a predictor of poor maternal-fetal outcomes, but may be linked to transplacental virus transmission.

Taking due account of users' perspectives is crucial for improving the quality of healthcare services. This study aimed to analyse the representations and evaluation criteria that users of a public health agency express towards care and treatment services and to explore whether and how the content and meaning of their communications varied according to pre-pandemic, pandemic or post-pandemic periods. 501 communications (73.4% complaints, 16% appreciations, 10.6% enquiries) sent to the public relations office of an Italian health agency were collected. An automatic content analysis procedure (ACASM) was applied to the textual corpus. Four main thematic cores were identified concerning the request for care and respect, the value of the doctor-patient relationship and the difficulties in contacting services and accessing care. Two main latent dimensions of meaning were identified, which capture the dialectic between the demand for relationships and the demand for access to care, and between attention to the relational competence of health workers and attention to the needs and rights of users. Communications collected during the pre-pandemic and post-pandemic periods mainly concern the difficulty of access to care; those collected during the pandemic period mainly concern the doctor-patient relationship. Interpersonal aspects and timely access to care appear to be crucial in users' assessment of the quality of care.

Lithium-metal batteries offer high energy density, but are challenged by dendrite growth, which can lead to short circuits and battery failure. Various models with varying degrees of accuracy and computational cost have been developed to understand and predict dendrite growth. In this study, a simple simulation model to predict macroscale dendrite growth on lithium metal electrodes is presented. The model uses a 3D single-particle diffusion-limited aggregation (DLA) algorithm, biased by the electric field, to simulate dendrite growth. The model was validated using 3D MRI of dendrite growth in a lithium symmetric cell. It was used to simulate deposition between consecutive measured dendrite structures. The model also introduces an important parameter, namely the biasing factor c, which determines the balance between diffusion and electric field effects. This parameter was calibrated using the overlap and displacement of deposition sites metrics, achieving optimal results at c = 0.7. The model predictions were in good agreement with experimental data confirmed by fractal dimension analysis. This model provides a useful tool for predicting and studying dendrite formation in lithium and other metal batteries.

The purpose of this study was to establish the association between total and segmental body composition (BC) variables and anaerobic performance and to create optimal models that best predict such performance in CrossFit® (CF) athletes. Fifty athletes, 25 males and 25 females (age: 33.26 ± 6.81 years; body mass: 72.57 ± 12.17 kg; height: 169.55 ± 8.71 cm; BMI: 25.06 ± 2.31 kg·m−2) were recruited to participate and underwent BC analysis using dual-energy X-ray absorptiometry (DXA) and an all-out laboratory test on a cycle ergometer (Wingate) to determine their anaerobic performance. The results show a significant correlation between BC values and performance, ranging from moderate (r = -0.34, p = 0.015) to near-perfect (r = 0.96, p &lt; 0.01). Furthermore, the created performance prediction models exhibited predictive capacities ranging from 19% (p = 0.017) to 93% (p &lt; 0.001). All prediction models were created using total or segmental lean mass variables, excluding others. BC and performance variables found significant differences between males and females. The findings demonstrate that BC variables are crucial indicators of anaerobic performance in CF athletes. Professionals responsible for athlete performance should use this information to monitor athletes and design training programs.

Single-cell RNA sequencing (scRNA-seq) technique has enabled detailed analysis of gene expression at the single cell level, enhancing the understanding of subtle mechanisms that underly pathologies and drug resistance. To derive such biological meaning from sequencing data in oncology, some critical processing must be performed, including identification of the tumor cells by markers and algorithms that infer copy number variations (CNVs). We compared the performance of sciCNV, InferCNV, CopyKAT and SCEVAN tools that identify tumour cells by inferring CNVs from scRNA-seq data. Sequencing data from Pancreatic Ductal Adenocarcinoma (PDAC) patients, adjacent and healthy tissues were analysed, and the predicted tumor cells were compared to those identified by well-assessed PDAC markers. Results from InferCNV, CopyKAT and SCEVAN overlapped by less than 30% with InferCNV showing the highest sensitivity (0.72) and SCEVAN the highest specificity (0.75). We show that the predictions are highly dependent on the sample and the software used, and that they return so many false positives hence are of little use in verifying or filtering predictions made via tumor biomarkers. We highlight how critical this processing can be, warn against the blind use of these software and point out the great need for more reliable algorithms.

Building Information Modeling (BIM) is undeniably the most important trend in the digitization of the construction sector in recent years. BIM models currently being built are extremely geometrically rich, that is, they are modeled at a high level of detail in terms of geometry. Thanks to object-oriented programming paradigms, BIM models include high-level relationships to ensure interactions between objects, rapid view generation and documentation. However, these models are not always equally rich in non-graphical data. This is true for parameters at the library object level, with which building object models are saturated, but also at the project, site, building or floor level according to the structure of the interoperable Industry Foundation Classes (IFC) format. For this reason, experimental work was undertaken on semantic enrichment in non-graphical data of a public building (Public Kindergarten, Secemin, Poland), which has its BIM model at a high level of geometric detail but is poor in non-graphical data. As a result of the research and development work, all levels of the IFC structure were saturated with non-graphical data, validated, and the possibilities of their use were shown from the perspective of the facility manager. The article contributes to the discussion on semantic enrichment from CAD3D to BIM by presenting a detailed process for entering non-graphical data into a BIM model.

The primary purpose of this article is to study the asymptotic and numerical estimates in detail for higher degree polynomials in $\pi(x)$ having a general expression of the form, \begin{align*} P(\pi(x)) - \frac{e x}{\log x} Q(\pi(x/e)) + R(x) \end{align*} $P$, $Q$ and $R$ are arbitrarily chosen polynomials and $\pi(x)$ denotes the \textit{Prime Counting Function}. The proofs require specific order estimates involving $\pi(x)$ and the \textit{Second Chebyshev Function} $\psi(x)$, as well as the famous \textit{Prime Number Theorem} in addition to certain meromorphic properties of the \textit{Riemann Zeta Function} $\zeta(s)$ and results regarding its non-trivial zeros. A few generalizations of these concepts have also been discussed in detail towards the later stages of the paper, along with citing some important applications.

This study aims to investigate the microphysical properties of Arctic, tropospheric aerosol in the transition from the Arctic Haze in spring towards the summer season in 2021. A special focus lies on the hygroscopicity of the aerosol. Therefore, a one-parameter growth curve model is applied to lidar data from the Koldewey Aerosol Raman Lidar (AWIPEV in Ny-Ålesund, Svalbard) and simultaneous radiosonde measurements. Hygroscopic growth depends on different factors like aerosol diameter and chemical composition. To detangle this dependency, three trends in hygroscopicity are additionally investigated by classifying the aerosol first by its color ratio, and then by its season and altitude. Furthermore, two special days are discussed using Mie-theory. They show on the one side the complexity of analyzing hygroscopic growth by means of lidar data, but on the other side demonstrate that it is in fact measurable with this approach. For these two case studies we calculated, that the aerosol effective radius increased from 0.16 μm (dry) to 0.18 μm (wet), and from 0.28 μm to 0.32 μm for the second case. Generally, we found two different modes of stronger or weaker hygroscopic particles and a complex altitude dependence with the least hygroscopic particles in the middle free troposphere.

The implementation and sustainability of evidence-based practice (EBP) is a complex process. Best practice guidelines (BPGs) can facilitate the translation of knowledge from theory into practice, but they require working groups to adapt recommendations and encourage adherence to them. The aim of this study was to identify facilitators and barriers of BPGs in the Registered Nurses’ Association of Ontario®, within the framework of Best Practice Spotlight Organisations® (BPSOs®), in a hospital setting. The study was conducted using the modified Delphi method (two rounds) with a cohort of BPSO® Best Practice Champions. The facilitators and barriers included in the first round were identified through a bibliographic search. The degree of concordance was determined in the second round using the highest mean resulting values. A total of 44 nurses responded, with a mean age of 42.57±8.563. Facilitators included: Work Environment; Working in a hospital that encourages research; and Dedicating time to EBP (p&lt;0.001). Barriers: Excessive workload; Professional mobility (p&lt;0.001); and Lack of time (p=0.002). As such, it is necessary to approach human resource management by implementing new policies that guarantee systemic sustainability. The BPSO® programme is an excellent framework for promoting the translation of theoretical knowledge into practice and effecting institutional change.

Background: Inflammatory demyelination and impaired recovery processes result in permanent neurodegeneration and neurological disability in patients with multiple sclerosis (MS). In terms of smoldering MS, chronic neuroinflammation develops in the early period of the disease and leads to confirmed disability accumulation. There is a great need to identify biomarkers of neuro-degeneration and disease progression. Methods: We evaluated the concentrations of the param-eters of brain injury (NF-H, GPAF, S100B, UCHL1) in the cerebrospinal fluid (CSF) and the se-lected interleukins (ILs) in serum of 123 relapsing-remitting MS (RRMS) and 88 progressive MS (PMS) patients. Results: The levels of GFAP, S100B and UCHL were higher in the PMS group compared to RRMS as opposed to the levels of NF-H. We observed a positive correlation be-tween the selected pro-inflammatory cytokines and the parameters of brain injury. The Ex-panded Disability Status Scale score (EDSS) increased with GFA and NF-H levels and correlated with the selected ILs. The concentrations of S100B, UCHL1 and NF-H reflected the duration of MS symptoms. Conclusions: The levels of brain injury parameters in the CSF and the selected serum ILs in MS patients seem to be promising biomarkers to determine neurodegeneration and neuroinflammation in smoldering MS. Further studies are warranted in this respect.