In January 2019, the French Agency for Food, Environmental and Occupational Health and Safety (ANSES) published an opinion on risks related to the presence of hazardous chemicals in infant diapers. ANSES found that health reference values were largely exceeded for polycyclic aromatic hydrocarbons (PAHs), dioxins (PCCD/Fs) and dioxin-like polychlorobiphenyls (DL-PCBs). The levels of formaldehyde and of some fragrances were also considered potentially unsafe. Therefore, ANSES concluded that actions have to be taken to restrict levels of these contaminants in diapers. Under the exposure scenario deemed the most reliable by ANSES, estimates of cancer risks of the most potent PAHs detected in diapers exceeded 10-3 and hazard quotients for neurobehavioral effects attained values up to 66. Regarding dioxins and DL-PCBs, ANSES derived a hazard quotient of 12 for the risk of decreased sperm count at adult age. The aim of this critical review was to examine whether the exposure and risk assessment conducted by ANSES contained potential flaws that could explain such a high exceedance of health reference values. The review will also put into perspective the exposure from diapers with that from breast milk whose benefits for child’s health are undisputable despite contamination by PAHs, dioxins and DL-PCBS.

Background: DNA-RNA compounds have shown promising protection against cell oxidative stress. This study aimed to assess the cytotoxicity, protective, or preventive effect of different experimental formulations on oral epithelia’s oxidative stress in vitro. Methods: Reconstituted human oral epithelia (RHOE) were grown air-lifted in a continuous-flow bioreactor. Mouthwash and gels containing DNA-RNA compounds and other bioactive molecules were tested on a model of oxidative stress generated by hydrogen peroxide treatment. Epithelia viability was evaluated using a biochemical MTT-based assay and confocal microscopy; structural and ultrastructural morphology was evaluated by light microscopy and TEM. Results: DNA-RNA showed non-cytotoxic activity and effectively protected against oxidative stress, but not in its prevention. Gel formulation did not express adequate activity compared to the mouthwash. Excipients played a fundamental role in enhancing or even decreasing the bioactive molecules’ effect. Conclusion: A mouthwash formulation with hydrolyzed DNA-RNA effectively protected against oxidative stress without additional enhancement by other bioactive molecules. Active compounds such as hyaluronic acid, β-Glucan, allantoin, bisabolol, ruscogenin, and essential oils showed a protective effect against oxidative stress, which was not synergistic with the one of DNA-RNA. Surfactant agents showed harmful activity against oral epithelia.

Electric mobility begins to enter East-African markets. This paper aims to investigate what policy level solutions and stakeholder constellations are established in the context of e-mobility in Dar es Salaam, Kigali, Kisumu and Nairobi and in which ways they attempt to tackle implementation of electric mobility solutions. The study employs two key methods including content analysis of policy and programmatic documents as well as interviews based on purposive sampling ap-proach with stakeholders involved in mobility transitions. The study findings point out that transport operators and their representative associations are less recognized as major players in the transition, far behind new e-mobility players (start-ups) and public authorities. The study further indicates that a set of financial and technical barriers persist such as high upfront invest-ment costs in vehicles and infrastructure, or anxieties regarding competitiveness with fossil fuel vehicles, that constrain the uptake of such private e-mobility initiatives. This study concludes by identifying current gaps that need to be tackled by policy makers and stakeholders in order to implement inclusive electric mobility in East-African cities, considering modalities that include transport providers and address their financial constraints.

Background: The progression of periodontal diseases at national Portuguese level and its public awareness are of great interest, mainly due to the high burden of periodontitis. Objectives: To evaluate the prevalence progression of periodontal diseases in Portugal and correspondent public awareness, between 2004 and 2017, by using data from the Global Burden of Disease (GBD), Directorate-General of Health (DGH) and Google® Trends (GT). Methods: For the period 2004-2017, Portuguese national data of periodontal diseases prevalence were searched in the Institute for Health Metrics and Evaluation of GBD and DGH and for public awareness, GT comparison tool between Portuguese words for “Periodontitis”, “Gingivitis”, “Gums” and “Periodontal disease” trends was used. Results: For the period 2004-2017, the overall prevalence of periodontitis slightly increased from 11.3% to 11.7%. During that period the GT search term “Gums” (“Gengivas”) was the most relevant. It increased steadily over time while the search term “Periodontal disease” (“Doença periodontal”) decreased, being these search trends significantly correlated (

Background and Objectives: To evaluate the diagnostic accuracy of widely available biomarkers longitudinally measured throughout pregnancy to predict all and term (delivery at ≥ 37 weeks) preeclampsia (PE). Materials and Methods: This is a retrospective study performed at Hospital Universitario de Torrejón (Madrid, Spain) and Shterev Hospital (Sofia, Bulgaria) between August 2017 and December 2022. All pregnant women with singleton pregnancies and non-malformed live fetuses attending their routine ultrasound examination and first-trimester screening for preterm PE at 11+0 to 13+6 weeks' gestation at the participating centers were invited to participate in a larger study for the prediction of pregnancy complications. The dataset was divided into two subsets to develop and validate a joint model of time-to-event outcome and longitudinal data, and to evaluate how the area under the receiving operating characteristic curve (AUROC) evolved with time. Results: 4056 pregnancies were included in the training set (59 all PE, 40 term PE) and 944 in the validation set (23 all PE, 20 term PE). For the joint model and all PE, the AUROC was 0.84 (95% CI 0.73 to 0.94) and the detection rate (DR) for a 10% screening positive rate (SPR) was 56.5 (95% CI 34.5 to 76.8). For term PE, AUROC was 0.80 (95% CI 0.69 to 0.91), and DR for a 10% SPR was 55.0 (95% CI 31.5 to 76.9). The AUROC using only information from the first trimester was 0.50 (95% CI 0.37 to 0.64) and it increased up to 0.84 (0.73 to 0.94) when using all information available. Conclusions: Routinely measuring MAP and UtA-PI throughout pregnancy may improve the predictive prediction power for all and term-PE.

Presented study demonstrates the application of actual technologies and tools for the development of a large scale axial flux electricity generator. (1) Background: The specifics of its application – run-of-river site – predefine design requirements, close to wind turbine generator. An extensive study of available solutions is used as a start point for further concept development; (2) Methods: A step based methodology is defined for concept parameters assessment and feasibility study. It demonstrates the advantages of virtual prototyping when assessing various design parameters; (3) Results: Several simulations over built different virtual prototypes provide sufficient information to elaborate an improved design concept. Major result is a ready for detailed design concept, with defined major parameters and studied work behavior; (4) Conclusions: The major outcome is the presentation of the application of virtual prototyping in assessment of large structures, where physical prototyping is expensive and time consuming approach. Application of virtual prototyping at very early product development stage is an effective way to undertake efficient solutions towards the concept of the product.

Background: Cardiovascular diseases increasingly impact youth, with early development of risk factors such as obesity, hypertension, and inadequate nutrient intake. Proper nutrient intake and physical fitness are vital for reducing these risks, especially in pediatric populations. This study explores the connection between physical fitness, metabolic risk, and nutrient status among 1656 Chilean schoolchildren from diverse so-cio-economic backgrounds. Methods: Anthropometric measures included weight, height, skinfold thickness, waist circumference, and blood pressure. Physical fitness was assessed via handgrip strength, standing long jump, and a six-minute walk test. Nutrient intake was also evaluated, and a composite metabolic risk score was calculated based on waist circumference, skinfolds, and blood pressure. Results: Children from public schools exhibited higher obesity rates and waist circumferences than those from private schools. Physical fitness performance was strongly linked to cardiovascular health indicators: better fitness correlated with smaller waist circumferences, lower skinfold measurements, and healthier blood pressure levels. Children from private schools had better fitness test outcomes, indicating socio-economic disparities in access to physical activity and nutrient resources. Students with lower fitness and nutrient intake had higher metabolic risk scores, pointing to increased cardiovascular risk. Conclusions: Nutrient intake and physical fitness are essential to reducing cardiovascular risk in children. Significant differences in nutrient intake and fitness levels across socio-economic lines suggest that equal access to nutrition and physical activity programs in schools could benefit public health outcomes. Further research should investigate the lasting effects of nutri-ent-focused interventions on childhood metabolic health.

Objectives: Cerebral edema, a prevalent consequence of brain injury, is associated with significant mortality and disability. Timely diagnosis and monitoring are crucial for patient prognosis. There is a pressing clinical demand for a real-time, non-invasive cerebral edema monitoring method. Ultrasound methods are prime candidates for such investigations due to their non-invasiveness nature; Methods: Acute cerebral edema by permanently occluding the left middle cerebral artery (MCA) was introduced in rats. Ultrasonic echo signals were collected at nine time points over a 24-hour period to extract features from both the time and frequency domains. Concurrently, histomorphological changes were examined. We utilized support vector machine (SVM), logistic regression (LR), decision tree (DT), and random forest (RF) algorithms for classifying cerebral edema types, and SVM, RF, and feedforward neural network (FNN) for predicting the cerebral infarction volume ratio; Results: The integration of 16 ultrasonic features associated with cerebral edema development with the RF model enabled effective classification of cerebral edema types with a high accuracy rate of 97.9%. Additionally, it provided an accurate prediction of the cerebral infarction volume ratio, with an R2 value of 0.8814; Conclusions: Our proposed strategy classifies cerebral edema and predicts the cerebral infarction volume ratio with satisfactory precision. The fusion of ultrasound echo features with machine learning presents a promising non-invasive approach for the monitoring of cerebral edema.

The aim of this study was to evaluate the growth and flowering characteristics of three Oncidesa cultivars under four distinct canopy treatments in a greenhouse environment. The control canopy treatment (canopy D) involved covering the greenhouse with black shade nets to reduce natural light penetration. Canopy B included black shade nets in combination with a transparent plastic screen on the sides to further modify light conditions. Additionally, two other treatments were applied: black shade nets with (canopy A) or without (canopy C) plastic sheeting, supplemented with daily lighting from high-pressure sodium lamps. Ten horticultural traits were measured to assess the impact of these canopy treatments on different genotypes at various growth stages. The results showed that canopy treatments significantly affected all measured traits of the Oncidesa cultivars, except for leaf width. Apollo plants treated under canopy A exhibited longer and thicker pseudobulbs, wider leaves, higher chlorophyll content, and a greater number of branches and florets compared to Honey Angel and Golden Star. Plants under canopy C showed superior growth traits across all cultivars, while those under canopies A and D exhibited better flowering quality. These findings suggest that different canopy treatments had varying effects on the growth and flowering traits of Oncidesa cultivars, with each cultivar showing distinct adaptability to specific canopy conditions.

Background: Conventional echocardiography used to assess volumes of the left ventricle (LV) and left atrium (LA) along with mitral regurgitation grade is routine studies before and after transcatheter edge-to-edge mitral valve repair (Mitral TEER). Previous studies focus on LV parameter changes and comparison of the functions before and a few months following Mitral TEER implantation, as well as LA reverse remodeling, by assessing LV volumes. However, less is known regarding LA strain changes in the early phase after the procedure. The objective of the study was to assess the effect of Mitral TEER on LA strain early after TEER procedure. Methods: The retrospective study included 44 patients who underwent Mitral TEER. LA strain and volumes were evaluated by speckle tracking echocardiography at baseline and 24-48 hours following the procedure. Demographic, echocardiographic, and clinical characteristics were obtained and statistically analyzed. Results: LA global longitudinal strain (GLS) reservoir improved significantly (from 12.2±7 to 14.7±6.4, p=0.0079) after Mitral TEER. Significant improvements were also seen in LA volumes (LA maximal and minimal volume), which reduced by 17% and 22.5% respectively. LV GLS was significantly changed (from -9.8% to -12.8%, p

Contrary to a prevailing dogma in twentieth-century neuroscience, emerging evidence suggests that the generation of new neurons continues throughout life, contributing significantly to crucial life functions owing to their robust ability to enhance neuroplasticity. Hippocampal neurogenesis occurs in the subgranular layer of the dentate gyrus, a region characterized by a specific microenvironment conducive to the proliferation of neuronal cells. A substantial body of evidence supports the belief that this phenomenon impacts various functions, including learning, emotional responses, and the formation of new memories. Diseases affecting these areas can significantly disrupt cognition and general behavior. For instance, in mesial temporal lobe epilepsy, aberrant neurogenesis has been observed, leading to increased cell proliferation, altered patterns of cellular integration, errors in information processing, and the formation of abnormal synaptic connections and transmissions. These abnormalities have the potential to generate epileptic seizures, underscoring the profound impact of these diseases on cognitive functions. Conversely, the chronic recurrence of seizures, can result in chronic recurrence of seizures, which can lead to a persistent reduction of neurogenesis in the epileptic brain. This reduction directly impacts various learning processes and the formation of hippocampus-dependent declarative memory. The mechanisms behind these processes remain fully elucidated and involve the delicate balance between neuronal excitation and inhibition, directly influencing brain performance and potentially leading to a decline in cognitive functions. In mesial temporal lobe epilepsy, this condition stands out as one of the most severe disorders affecting the well-being of epileptic patients. While memory is highlighted as one of the most affected functions, it is not the sole cognitive function compromised by this comorbidity. The alterations can extend from language to executive and spatial functions.

Brazil hosts one of the world's most diverse reptilian faunas, with over 800 species distributed across its various biomes. This paper examines the historical development of herpetological research in Brazil, particularly focusing on two pioneering institutions: the Vital Brazil Institute and the Butantan Institute. These centers have been instrumental in both the scientific study of reptiles and the development of antivenoms since the early 20th century. We explore the remarkable morphological adaptations of Brazilian reptiles across different biomes, from the Amazon rainforest to the Caatinga desert, highlighting how these adaptations reflect the country's diverse ecological pressures. This review synthesizes current knowledge while identifying gaps in our understanding of Brazilian herpetofauna.

Cardiovascular diseases (CVDs), which comprise coronary heart disease, hypertension and stroke, collectively represent the number one cause of death globally. Atherosclerosis is the dominant cause of CVDs; and its risk factors are elevated levels of low-density lipoprotein-cholesterol and triglycerides, hypertension, cigarette smoking, obesity and diabetes mellitus. In addition, diverse evidence highlighted the role played by inflammation and clonal haematopoiesis, eventually leading to the immunity involvement. The human microbiota project and subsequent studies using next-generation sequencing technology have highlighted that thousands of different microbial species are present in the human gut. The disturbances in the gut microbiota (GM) composition, i.e., gut dysbiosis, have been associated with diseases ranging from localized gastrointestinal disorders to metabolic and cardiovascular illnesses. Of note, experimental studies suggested that GM, host immune cells and marine-derived ingredients work together to ensure the intestinal wall integrity. This review discusses current evidence concerning the links among GM, marine-derived ingredients and human inflammatory disease. In detail, we summarize the impact of fish-derived proteins and algae components, on CVDs risk factors and gut microbiome. Furthermore, we describe the interplay among these dietary components, probiotics/prebiotics and CVDs.

Floating offshore wind turbine (FOWT) systems are advancing quickly, but they still need to become economically viable for large-scale commercial adoption. Design optimization is essential for reaching this objective by reducing costs while ensuring the best possible system performance and structural integrity. The present study aims to carry a comparative multi objective optimization (MOO) of a 10MW FOWT semisubmersible using three different metaheuristic optimizations technics and sophisticated approach for optimizing a floating concept through the utilization of global limit states. The optimization is performed in Python, integrating PyMAPDL and PyMOO for intricate modeling and simulation tasks. The ZJUS10 floating offshore wind turbine (FOWT) platform, developed by the state key laboratory of wind power at Zhejiang University, is employed as the basis for this study. Key criteria, such as platform stability, overall structural mass, and the stress, are pivotal in formulating the objective functions. Based on a preliminary study the three Metaheuristic Optimization Algorithms chosen for optimization are Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Ant Colony Optimization (ACO). Then, the solutions are evaluated based on Pareto dominance, leading to a Pareto front, a curve that represents the best possible trade-offs among the objectives. Each algorithm's convergence is meticulously evaluated, leading to the selection of the optimal design solution. The results evaluated in simulations elucidate the strengths and limitations of each optimization method, providing valuable insights into their efficacy for complex engineering design challenges. In the post-processing phase, the performance of the optimized FOWT platforms is thoroughly compared both among themselves and with the original model, resulting in validation and endorsement.

In slope engineering, the foundation grooves of slope protection gird beams are mostly excavated manually. However, manual excavation presents problems, such as low construction efficiency, uneven groove bottoms, inconsistent cross-section groove widths, and curved groove axis, which reduces the quality of cast-in-place grid beams or complicates the installation of prefabricated grid beams. To address these issues, a mechanized grooving device for slope protection grid beams has been developed and successfully applied in the project. The grooving device is composed of interlocking dual spiral cutter heads, a mechanism for the groove width and depth adjustment, a cutter head working platform, a platform azimuth adjuster, a cutter head sliding mechanism, a hydraulic motor, a hydraulic power system and a platform anchoring mechanism. It can be used to groove on soil and strongly weathered rock slopes at different slope angles, and the foundation grooves of slope protection gird beams of different depths and widths can be carved according to the design requirements. Moreover, the grooved trenches feature flat bottoms, consistent widths, and straight axis, making it easy to install prefabricated grid beams and greatly improving the quality of cast-in-place grid beams. According to the construction statistics of a slope project in Guangdong, the grooving speeds of the grooving device on the slope of soil and strongly weathered rock are about 1.5m/min and 0.5m/min respectively, which greatly improves the construction efficiency and quality.

The emergence of vertebrates represents one of the most significant evolutionary transitions in animal history. The earliest definitive vertebrate fossils appear in the Lower Cambrian Period (approximately 525-520 million years ago), with Myllokunmingia fengjiaoa and Haikouichthys ercaicunensis from the Chengjiang biota of China being among the oldest known specimens. These early vertebrates evolved from chordate ancestors, likely similar to modern lancelets (amphioxus). The key transition occurred through the development of neural crest cells, a mineralized skeleton, and a more complex brain. The foundation for vertebrate origins was laid in the Pre-Cambrian with early chordates like Pikaia gracilens, which exhibited basic chordate characteristics. The early Cambrian period witnessed a rapid diversification of vertebrate forms, leading to the establishment of major vertebrate lineages, including both jawless (agnathan) and later jawed (gnathostome) groups.

Bayesian networks have emerged as a powerful tool for modelling complex probabilistic relationships in uncertain environments. The process of inducing Bayesian networks involves learning both the structure of the network and the parameters of the associated probability distributions. This paper provides a detailed examination of the two main approaches to Bayesian network induction: structure learning and parameter estimation, highlighting the use of both constraint-based and score-based techniques. Furthermore, the role of distribution-based methods in the estimation and inference of probabilistic models is explored. These methods, including Maximum Likelihood Estimation, Bayesian Estimation, and Monte Carlo techniques, offer robust ways to handle parameter uncertainty and facilitate efficient inference in large, complex models. This comprehensive view aims to bridge the gap between the theoretical foundations and practical applications of these methods, providing insights for researchers and practitioners in the field of machine learning and probabilistic reasoning.

Porcine recombinant NK-lysin (prNK-lysin) has been shown to inhibit the proliferation and metastasis of hepatocellular carcinoma (HCC) cells in vitro. However, its effects on the proliferation and metastasis of HCC cells in vivo remain unclear. In this study, allograft murine model using the murine HCC cell line Hepa1-6 was employed to investigate the anticancer effects of prNK-lysin. Initially, the in vitro anticancer efficacy of prNK-lysin was evaluated on Hepa1-6 cells, demonstrating that prNK-lysin effectively inhibited both proliferation and metastasis through the induction of oncosis. Subsequently, the in vivo anticancer efficacy of prNK-lysin was assessed using a mouse liver orthotopic implantation model and a lung metastasis model of Hepa1-6 cells in BALB/cA-nu mice. Administration of 13 mg/kg prNK-lysin could inhibited tumor growth in the liver and metastasis to the lungs. Our results demonstrate that prNK-lysin possesses strong anti-HCC effects both in vitro and in vivo, with the induction of oncosis as potential molecular mechanisms for its anticancer activity.

The gene sets of the two cells produced by each division of the fertilized egg are different, and these differences gradually accumulate. When the similarity between a certain cell and a certain type of cell is below a threshold, the differentiation ability in that direction is lost. When all cells can only have one developmental direction, the expression of zygotic genes is turned on. When a stem cell differentiates, it first divides into two cells. One of two cells, similar to the next level cell, becomes the next level cell, while the other, similar to the mother cell, continues to become the other stem cell. When a cell undergoes dedifferentiation, it expresses a group of genes. When the gene set is similar to the upper level stem cell, the cell undergoes dedifferentiation and becomes the upper level stem cell.

This article introduces a new paradigm in nutritional science for long-term weight management, based on the integration of bioneurofeedback and bioimpedance analysis. Bioneurofeedback, a technique that combines the monitoring of physiological parameters with the modulation of brain activity, is employed to improve the self-regulation of eating behaviors by adjusting brainwave activity and reducing stress. Bioimpedance analysis provides a precise evaluation of body com-position, enabling the personalization of interventions according to individual physiological and metabolic characteristics. This integrated approach utilizes the principles of cybernetics and complex systems theory to better understand the interaction between mind and body, enhancing weight regulation through a dual top-down and bottom-up strategy. Additionally, the article discusses how an excess or deficiency in specific brainwaves can affect disordered eating behaviors, proposing biofeedback integration as a method to mitigate the yo-yo effect and promote sus-tainable lifestyle changes.

This paper explores the influence of various camera settings on the quality of 3D reconstructions, particularly in indoor crime scene investigations. Utilizing Neural Radiance Fields (NeRF) and Gaussian Splatting for 3D reconstruction, we analysed the impact of ISO, shutter speed, and aperture settings on the quality of the resulting 3D reconstructions. By conducting controlled experiments in a meeting room setup, we identified optimal settings that minimize noise and artifacts while maximizing detail and brightness. Our findings indicate that an ISO of 200, a shutter speed of 1/60 seconds, and an aperture of f/3.5 provide the best balance for high-quality 3D reconstructions. These settings are especially useful for forensic applications, architectural visualization, and cultural heritage preservation, offering practical guidelines for professionals in these fields. The study also highlights the potential for future research to expand on these findings by exploring other camera parameters and real-time adjustment techniques.

The evolution of jaws represents one of the most transformative innovations in vertebrate history, fundamentally reshaping both animal anatomy and ecological dynamics. At the core of this innovation lies the neural crest, a unique vertebrate cell population whose molecular and developmental properties enabled the formation of articulated jaws. This review explores how neural crest cells, as multipotent stem cells, contributed to jaw development through their extraordinary capacity to form cartilage, bone, and associated tissues. Beyond the developmental perspective, we examine how the emergence of jawed vertebrates dramatically altered marine ecosystems, leading to new predator-prey relationships and driving an unprecedented diversification of feeding strategies. This innovation triggered an evolutionary arms race that shaped marine biodiversity and established new trophic relationships that persist in modern oceans. Understanding this ancient innovation provides crucial insights into both developmental mechanisms and ecological dynamics that defined vertebrate evolution.

Self-organization in complex systems is a process in which internal entropy is reduced and emergent structures appear that allow the system to function in a more competitive way with other states of the system or with other systems. It occurs only in the presence of energy gradients, facilitating energy transmission through the system and entropy production. Being a dynamic process, self-organization requires a dynamic measure and dynamic principles. The principles of decreasing unit action and increasing total action and the principle of decreasing unit entropy and increasing total entropy are dynamic variational principles that are viable to utilize in a self-organizing system. Based on this, average action efficiency can serve as a quantitative measure of the degree of self-organization. Positive feedback loops connect this measure with all other characteristics of a complex system, providing all of them with a mechanism for exponential growth, and indicating power law relationships between each of them as confirmed by data and simulations. In this study, we apply those principles and the model to agent-based simulations of ants traveling between two locations on a 2D grid. We find that those principles explain self-organization well and that the results confirm the model. We derive a compact model of ant-behavior based on the action of their trajectories, and then estimate a variety of metrics from the simulated behavior. By measuring action efficiency we can have a new answer to the question: "What is complexity and how complex is a system?". This work shows the explanatory and predictive power of those models, which can help understand and design better complex systems.

(1) Background: This study aimed to analyze the temporal dynamics of blood lactate concentration and oxy-gen consumption following supra-maximal efforts, providing insights into recovery kinetics and physiologi-cal responses. (2) Methods: Ten highly trained 100-meter sprinters participated. Each athlete completed a graded exercise test (GXT) and a 30-second Wingate Anaerobic Test. Blood lactate concentrations were meas-ured before, immediately after, and at intervals up to 20 minutes post-exercise. Oxygen consumption (VO2) was continuously monitored during and after the GXT. (3) Results: Peak blood lactate concentration was reached around 3 minutes post-exercise, averaging 14.9 ± 3.5 mmol/L. Lactate clearance followed a bi-exponential decay, with a rapid initial phase (τ1 = 60 seconds) and a slower secondary phase (τ2 = 300 seconds). Peak VO2 values were high (45.4 ± 4.1 ml/kg/min), reflecting significant aerobic capacity. VO2 re-covery also showed a bi-exponential pattern, suggesting efficient recovery mechanisms. (4) Conclusion: The findings support previous research by demonstrating a high anaerobic glycolytic contribution during sprint-ing and rapid lactate clearance due to effective recovery. The bi-exponential models for both lactate and VO2 kinetics provide deeper insight into metabolic responses and recovery profiles, which are crucial for optimiz-ing training in competitive sports. This study offers valuable data for improving recovery strategies and per-formance in sprint athletes.

This treatise represents a significant contribution to the field by integrating quality constraint functions and predicates into Possibility Computation Tree Temporal Logic (PoCTL), thus giving rise to Fuzzy Computation Tree Temporal Logic with Quality Constraints (QFCTL). We provide a comprehensive definition of QFCTL's syntax, conduct an in-depth analysis of its logical characteristics, outline a precise model checking algorithm for QFCTL, and perform a meticulous complexity assessment of said algorithm. Moreover, by enriching QFCTL with a quantitative characteristic predicate operator, we innovate, culminating in the development of an enhanced Fuzzy Computation Tree Temporal Logic with Quality Constraints (QFCTL*). The treatise explores the logical facets of QFCTL*, formulates a bespoke model checking algorithm for QFCTL*, and conducts a rigorous analysis of its algorithmic complexity. To attest to the practical utility and robust expressive power of QFCTL and QFCTL*, we present a model checking example that serves as empirical evidence of the efficacy of the proposed model checking algorithms.

Background/Objectives: Cancer is caused by disruptions in the homeostatic state of normal cells, which results in dysregulation of the cell cycle, and uncontrolled growth and proliferation in affected cells to form tumors. Successful development of tumorous cells proceeds through the activation of pathways promoting cell development and functionality, as well as the suppression of immune signaling pathways; thereby providing these cells with proliferative advantages, which subsequently metastasize into surrounding tissues. These effects are primarily caused by the upregulation of oncogenes, of which SPP1 is one of the most well-known. Methods: In this study, we conducted a further examination of the transcriptomic expression profile of SPP1 (Osteopontin) during the progression of cancer in four human tissues, in order to understand the circumstances conducive to its activation and dysregulation, the biological pathways and other mechanisms involved, as well as differences in its splicing patterns influencing its expression and functionality. Results: A significant overexpression of SPP1 as well as a set of other highly correlated genes, was seen in a majority of tissues, indicating their extensive implication in cancer. Increased expression was observed with higher tumor stages, while applying therapeutic modalities targeting these genes dampened this effect. Pathway analyses showed gene signatures related to cell growth and development enriched in tumorigenic conditions and earlier cancer stages, while later stages of cancer showed pathways associated with weakened immune response. Moreover, the utilization of therapeutic methods showed the activation of immunogenic pathways, thereby confirming their viability. Further analyses of differential transcript expression levels in these oncogenes showed their exonic regions to be selectively overexpressed similarly in tumorigenic samples, while also displaying significant differences in exon selectivity between constituent transcripts, providing a basis for their high degree of multifunctionality in cancer. Conclusions: Overall, this study corroborates the entrenched role of SPP1 in cancer, while also highlighting its promising role in therapeutic development.

Since Carl Linnaeus introduced the binomial nomenclature system in the 18th century, zoological classification has experienced significant transformations. While Linnaeus’ taxonomy provided a foundational structure based on morphological traits, advancements in evolutionary theory, genetics, and molecular biology have revolutionized the way organisms are classified. This article traces the evolution of classification methods, from Linnaeus’ hierarchy to Darwin’s theory of evolution, and highlights modern phylogenetic techniques based on genetic data. However, the discussion also introduces the need for a reformulation of zoological classification to incorporate species interactions and ecological relationships, as proposed by Johann Friedrich Humboldt over 50 years before Darwin. Humboldt’s holistic view of nature, where species interact and co-evolve within ecosystems, offers valuable insights for understanding evolution as a collective process, shaping life on Earth as a unified organism. Embracing this perspective will help the next generation of scientists approach biodiversity and conservation with a deeper appreciation of species interdependence.

The aim of this study was to investigate the effect of these drugs on reproductive function in Rattus norvegicuss and to show the role of oxidative stress in possible reproductive dysfunction. Rattus norvegicuss were selected as experimental animals and were divided into healthy (control group), Clonidine (CL), Rilmenidine (RLD), Methyldopa (MTL), Amlodipine (ALD) and Ramipril (RML) groups. Using blood obtained from rattus norvegicuss, serum malondialdehyde (MDA) levels; The high-temperature thiobarbituric acid. Was based on the spectrophotometric measurement of the adsorption of the pink colored complex formed by MDA at a wavelength of 532 nm. Total glutathione (tGSH) levels were determined by spectrophotometer.According to the study, the increase in MDA levels was not statistically significant in CL and RLD groups when compared with the control group. MDA was significantly increased in methyldopa, amlodipine and Ramipril groups. While total glutathione levels were only close to the control group in the CL group; RLD, MTL, ALD and RML groups showed statistically significant decrease. While CL and RLD did not cause infertility, infertility was observed in the groups treated with MTL-ALD-RML. Thus, it was found that MTL, ALD and RML, antihypertensive drugs, which were the aim of the study, had different effects on infertility and it was found that the use of such drugs decreased fertility due to increasing oxidative stress and decreasing antioxidant levels.

This paper presents a comprehensive review of the You Only Look Once (YOLO) framework, a transformative one-stage object detection algorithm renowned for its remarkable balance between speed and accuracy. Since its inception, YOLO has evolved significantly, with versions spanning from YOLOv1 to the most recent YOLOv11, each introducing pivotal innovations in feature extraction, bounding box prediction, and optimization techniques. These advancements, particularly in the backbone, neck, and head components, have positioned YOLO as a leading solution for real-time object detection across a variety of domains. In this review, we explore YOLO's diverse applications, including its critical role in medical imaging for COVID-19 detection, breast cancer identification, and tumor localization, where it has significantly enhanced diagnostic efficiency. YOLO's robust performance in autonomous vehicles is also highlighted, as it excels in challenging conditions like fog, rain, and low-light environments, thereby contributing to improved road safety and autonomous driving systems. In the agricultural sector, YOLO has transformed precision farming by enabling early detection of pests, diseases, and crop health issues, promoting more sustainable farming practices. Additionally, we provide an in-depth performance analysis of YOLO models—such as YOLOv9, YOLO-NAS, YOLOv10, and YOLOv11—across multiple benchmark datasets. This analysis compares their suitability for a range of applications, from lightweight embedded systems to high-resolution, complex object detection tasks. The paper also addresses YOLO's challenges, such as occlusion, small object detection, and dataset biases, while discussing recent advancements that aim to mitigate these limitations. Moreover, we examine the ethical implications of YOLO's deployment, particularly in surveillance and monitoring applications, raising concerns about privacy, algorithmic biases, and the potential to perpetuate societal inequities. These ethical considerations are critical in domains like law enforcement, where biased object detection models can have serious repercussions. Through this detailed review of YOLO's technical advancements, applications, performance, and ethical challenges, this paper serves as a valuable resource for researchers, developers, and policymakers looking to understand YOLO’s current capabilities and future directions in the evolving field of object detection.

(1) Background: The androgen receptor (AR) is critical for mediating the effects of androgens. The polymorphic CAGn locus in exon 1 of the AR gene is associated with several diseases, including spinal and bulbar muscular atrophy (SBMA), prostate cancer, and male infertility. This study evaluated the CAGn locus in 9,000 infertile Russian men and 286 fertile men (control group); (2) Methods: The CAGn locus was analyzed using the polymorphism of lengths of amplified fragments method; (3) Results: In the infertile cohort, CAG repeats ranged from 6 to 46, with a unimodal distribution. The number of CAG repeats in infertile and fertile men was 22.15 ± 0.93 and 22.02 ± 1.36, respectively. In infertile men, variants with 16 to 29 repeats were present in 97% of alleles. A complete mutation (≥42 CAG repeats) was found in three patients, while three others had 39-41 repeats. The incidence of SBMA was 1: 3,000 infertile men. Significant differences (p<0.05) were observed between infertile and fertile men in alleles with 21, 24 and 25 repeats; (4) Conclusions: This study revealed certain differences in the CAGn polymorphic locus of the AR gene in Russian infertile and fertile men, and determined the frequency of SBMA in infertile patients.

The Balanced Scorecard (BSC) is a widely recognized strategic performance metric that has been extensively studied in large corporations but remains underexplored in the context of Small and Medium-sized Enterprises (SMEs). Understanding the effectiveness of BSC in aligning performance metrics with strategic goals and enhancing performance management in SMEs is critical for fostering growth and competitive advantage. This systematic review aims to evaluate the effectiveness of the Balanced Scorecard in measuring performance and strategy execution within SMEs, particularly focusing on financial and non-financial outcomes such as employee motivation, customer satisfaction, and business sustainability. A systematic review was conducted following the PRISMA framework, incorporating 136 studies sourced from Google Scholar, Web of Science, and SCOPUS. Eligibility criteria focused on publications from the last ten years that address the implementation of BSC in SMEs. Quantitative, qualitative, and mixed-method studies were included, with 49.63% of studies using quantitative methods, 26.67% qualitative, and 23.70% mixed-methods. Risk of bias was assessed using the Chrohane tool, revealing high risks in 90 studies due to incomplete outcome data and selective reporting. The review found that 74% of studies adopted a holistic approach by focusing on all four BSC perspectives (Financial, Customer, Internal Processes, and Learning and Growth). ROI emerged as the most frequently discussed key performance indicator, addressed in 36.84% of publications, while customer satisfaction and employee engagement were covered in 26.32% each. Geographic disparities were observed, with most of the research concentrated in Asia and Europe (40%) and only 8% in Africa. Despite positive outcomes such as improved business sustainability and competitive advantage (38.52% of studies), challenges persisted, including poor strategy execution and limited resources, necessitating careful customization of BSC to SME-specific contexts. The Balanced Scorecard demonstrates significant potential in enhancing performance management and strategic execution within SMEs, particularly when tailored to address unique constraints such as resource limitations. This review highlights the need for further research to standardize key performance indicators and explore sector-specific applications. By addressing these gaps, the BSC can be more effectively utilized to drive growth, agility, and long-term competitive advantage in SMEs.

The integration of Customer Relationship Management (CRM) systems within Small and Medium Enterprises (SMEs) is increasingly recognized for its potential to enhance business performance. While CRM adoption is on the rise, the specific impacts on operational efficiency, customer satisfaction, and financial outcomes in SMEs require further investigation. This systematic review evaluates the impact of CRM systems on SME performance, with a focus on operational improvements, sales growth, and customer retention. The study aims to identify key determinants of successful CRM implementation and assess the long-term effects on SMEs' competitive advantage. The review encompasses studies published between 2014 and 2024, sourced from Google Scholar, Web of Science, and SCOPUS. A total of 46 studies were included after screening 18,306 records. The mixed-methods approach involves quantitative analysis of survey data from SME owners and managers, along with qualitative synthesis of case studies. Metrics evaluated include customer acquisition, retention rates, revenue growth, and organizational efficiency. The analysis indicates that CRM adoption leads to a 25-40% improvement in customer retention and a 15-30% increase in sales across SMEs. Operational efficiency gains range from 20-35%, primarily driven by process automation and enhanced data management capabilities. Successful CRM implementation is strongly linked to managerial support, system customization, and user training. However, barriers such as limited financial resources and technical expertise hinder the full realization of CRM benefits. CRM systems significantly contribute to SME growth by optimizing customer relationships and enabling data-driven decision-making. The review underscores the importance of strategic CRM implementation tailored to the specific needs of SMEs to maximize its impact. Future research should focus on overcoming adoption barriers and refining CRM strategies for sustained performance improvements.

Federated Learning (FL), as a distributed machine learning method, is particularly suitable for training models that require large amounts of data while meeting increasingly strict data privacy and security requirements. Although FL effectively protects the privacy of participants by avoiding the sharing of raw data, balancing the risks of privacy leakage with model performance remains a significant challenge. To address this, this paper proposes a new algorithm—FL-APB (Federated Learning with Adversarial Privacy-Performance Balancing). This algorithm combines adversarial training with privacy protection mechanisms to dynamically adjust privacy and performance budgets, optimizing the balance between the two while enhancing and ensuring performance. Experimental results demonstrate that the FL-APB algorithm significantly improves model performance across various adversarial training scenarios, while effectively protecting the privacy of participants through adversarial training of privacy data.

Phishing emails pose a significant threat to cybersecurity worldwide. There are already tools that mitigate the impact of these emails by filtering them, but these tools are only as reliable as their ability to detect new formats and techniques for creating phishing emails. In this paper we investigated how traditional models and transformer models work on the classification task of identifying if an email is phishing or not. We realized that transformer models, in particular DistilBERT, BERT, and RoBERTa had a significantly higher performance compared to traditional models like Logistic Regression, Random Forest, Support Vector Machine, and Naive Bayes. The process consisted in using a large and robust dataset of emails and applying preprocessing and optimization techniques to maximize the best result possible. roBERTa showed its outstanding capacity to identify phishing emails by achieving the maximum accuracy of 0.9943. Even though they were free successful, traditional models performed marginally worse; SVM performed the best, with an accuracy of 0.9854. The results emphasize the value of sophisticated text processing methods and the possibility of transformer models to improve email security by thwarting phishing attempts.

Objective: In recent years, the field of Essential Thrombocythemia (ET) research has seen a significant accumulation of scientific findings, but comprehensive bibliometric analyses remain lacking. This study aims to fill that gap by performing a thorough bibliometric analysis of ET research, identifying key contributors and collaboration networks, and mapping the development trends to provide insights for future research directions. Methods: A bibliometric analysis of ET-related publications from 2001 to 2024 was conducted using CiteSpace, VOSviewer, and R packages. Data were retrieved from the Web of Science Core Collection, with a focus on publication volume, citation analysis, co-authorship networks, co-citation relationships, and citation bursts. Results: A total of 4,297 studies published in 778 journals were included in the analysis. ET research has seen rapid growth, with researcher clusters in the United States and Europe driving progress through extensive regional and international collaborations. Leading researchers, such as Ayalew Tefferi from the Mayo Clinic and Alessandro M. Vannucchi from the University of Florence, have made significant advances in ET classification, molecular mechanisms, targeted therapies, and disease management. The discovery of driver mutations, such as JAK2, has revolutionized the diagnostic and therapeutic approaches to ET. Research focus has shifted from clinical morphological diagnosis to molecular diagnostics, with the field now entering the era of targeted therapies. However, the inherent heterogeneity of ET continues to present challenges for the widespread implementation of personalized precision treatment. Conclusion: Bibliometric analysis demonstrates significant advances in ET research, particularly in molecular pathology and targeted therapies. However, the heterogeneity of ET remains a major obstacle to personalized treatment. Future research should focus on further elucidating the pathogenesis of ET and improving stratified management approaches to achieve individualized precision therapy.

Spontaneous preterm birth (sPTB), defined as live birth before 37 weeks of gestational age, is associated with immune dysregulation and pro-inflammatory conditions that profoundly impact newborn health. The question of immune integrity at the maternal-fetal interface is a focus of recent studies centring not only sPTB but the conditions often affiliated with this outcome. Regulatory T cells (Tregs) play a critical anti-inflammatory role in pregnancy, promoting fetal tolerance and placentation. Due to this gestational role, it is hypothesised that decreased or dysfunctional Tregs may be implicated in cases of sPTB. This review examines studies comparing Treg presence in healthy term pregnancies and those with sPTB-associated conditions, revealing conflicting findings across different conditions and within sPTB itself. Treg deficiencies may contribute to health issues in preterm newborns. Current sPTB treatments are limited, underscoring the potential of in utero therapies targeting inflammation, including T cell interventions. Future research aims to establish consensus on the role of Tregs in sPTB and associated conditions and advancing understanding of mechanisms leading to Treg deficiencies in adverse pregnancy outcomes.

This study examines the relationship between financial investment, shareholder preference, corporate social responsibility (CSR) performance and life satisfaction for BP and Shell within the UK, together with their alignment with specific Sustainable Development Goals (SDGs). about, using regression analysis, in both projects Hypotheses were tested to assess the significance of these predictors in the performance of CSR developed. The results showed that financial investment, shareholder ownership, and social satisfaction did not significantly predict CSR performance for BP and Shell. Furthermore, there were no significant differences in the impact of these predictors between the two companies. Further research revealed Shell’s strong commitment to SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action), with both initiatives drawing on Shell’s significant investments in renewable energy and climate recognition in the management system established him as a leader in sustainable development efforts. BP’s goal of achieving net zero emissions by 2050, especially focused on SDG 7 and SDG 12 (Responsible Consumption and Production), this proposal was adopted. Comparing the two sectors, Shell showed significant advantages, which can be attributed to its diversified capabilities and its strong alignment with the sustainability objectives of BP, while growing largely, it faces significant financial challenges in its transition to clean energy. The study concludes that although both companies are committed to sustainability, Shell now leads in terms of effective CSR and financial performance

The article discusses the use of satellite data and drones for land monitoring in Kazakhstan, emphasising their importance in the context of population growth and increasing agricultural demand. Satellite technologies, such as Sentinel-2 and Landsat, provide essential data for analysing land resources, including soil erosion and the condition of agricultural lands. Unmanned aerial vehicles (UAVs) complement satellite data by offering detailed low-altitude imagery, enabling the detection of local issues such as irrigation system failures and soil compaction.The study demonstrates that integrating drone and satellite data allows for more efficient land resource management. For example, 35% of agricultural land in southern Kazakhstan is affected by soil degradation, while overgrazing issues were identified in the northern regions. These innovative monitoring methods enable early detection of threats, allowing timely measures to be taken to improve land management practices.The conclusion highlights that the use of these technologies requires not only infrastructure but also skilled professionals. The integration of satellites and drones creates opportunities for more precise and timely monitoring, which is essential for sustainable land resource management in Kazakhstan.

In a large part of ecological niches, bacterial species are united in communities and develop together in the form of heterogeneous structures called biofilms, which are formed on surfaces of different nature and nature with the participation and as a result of the interaction of two or more bacterial species. The main objective of the present work is to study the effects of pH-value in the culture medium on the biofilm development and architecture of biofilms of the bacterium (B. subtilis) during their interactions with Escherichia coli K-12 1655 strain. Effects of pH-value in the culture medium on bacterial biofilm formation and the mechanisms were analyzed by the crystal violet staining method combined with cultivated microbial analysis, and confocal laser scanning microscopy. In the presence of phosphate at pH-value of pH 6,0 , the bacterial growth rates of single-species biofilms of Bacillus subtilis strains and biofilms as result of their interaction with Escherichia coli strains were similar and determines the mutualism between two strains in the structure of biofilms, formed by the participation of B.subtilis 170 and E.coli K-12 1655, B.subtilis 168 and E.coli K-12 1655 strains. Considering that static growth in a biofilm is a highly relevant growing state for many soil bacteria, more research on phosphate acquisition and pH homeostasis by model soil bacteria in static cultures is required to draw a more comprehensive picture of the role of biofilm production in phosphate use efficiency and pH homeostasis.

Background: Gait analysis systems are crucial for rehabilitation but are often time-consuming and less accessible in low and middle-income countries like Brazil. GAITWell^® was developed to address these challenges with its modular and portable design for automated gait data extraction and analysis. This study evaluates its methodological properties. Methods: GAITWell^® uses discrete binary sensors on interconnected plates to collect gait data, analyzed via the DBSCAN algorithm. Each plate measures 44 cm x 37 cm and features an 11 x 7 discrete sensor array with 4 cm spacing. Test-retest reliability was assessed using the intraclass correlation coefficient (ICC), standard error of the mean (SEM), and Bland-Altman plots. Concurrent validity was evaluated by comparing the GAITWell^® measurements with those obtained from the Qualisys Pro-Reflex through correlations. Results: Participated 38 healthy young adults, including 18 females (47.4%). The sample had an average age of 33.2 years (SD 13.0) and an average BMI of 26.6 kg/m² (SD 5.4). Correlations between GAITWell^® and Qualisys ranged from moderate to very high for most gait variables, except stride length (r = .360, p < .05). ICC_2,1 showed moderate to good agreement for most variables, except for double support times, cadence, and base of support. Preliminary analysis suggests that increasing the sensor resolution to 1.2 cm x 1.2 cm could potentially decrease the measurement error from 2 cm to 0.6 cm, representing a theoretical reduction of 70%. Conclusions: While low resolution affected GAITWell^® reliability, it remains a promising tool. Future research should focus on enhancing sensor accuracy to improve reliability.

This study deals with the turning of AISI O2 steel, which has high machinability and dimensional stability in heat treatment and is used in the manufacture of many machine elements, with cryogenically treated and untreated and coated and uncoated cutting tools. In the first phase of the study, along with the determined cutting parameters, Fc and Ra output parameters were also optimized using the RSM method. In addition, numerical significance was obtained with ANOVA analysis, and estimated values were calculated by preparing regression equations. In the second phase, the wear performance of cutting tool tips with and without deep cryogenic treatment was explained with the support of SEM images, EDX, XRD, and microhardness analyses. Feed (F) was found to be the most influential parameter on Fc at 78.89%. The lowest optimized Fc value found with the RSM method was 339.99 N. In order to obtain this value, Vc 252.525 m/min and F 0.16 mm/rev should be selected. In addition, CVD-coated and cryogenic heat-treated cutting tools should be preferred for ideal Fc. It was shown that the most effective parameter on Ra was F, with 69.38%. For both heat treatment and coating conditions, Ra increased significantly as the feed rate increased. In the case of selecting Vc as 243.434 m/min and F as 0.16 mm/rev, the smallest Ra value optimized with the RSM method is 1.04 μm. In addition, a cutting tool without coating and cryogenic heat treatment is also needed for ideal Ra. According to the results of the analysis confirming the effect of deep cryogenic heat treatment on the wear performance of cutting tools in the second stage, coated and uncoated cutting tools had at least 16% longer life compared to the others.

In Alzheimer’s disease (AD), tau dissociates from microtubules (MTs) due to hyperphosphorylation and misfolded. It is degraded by various mechanisms, including the 20S proteasome, chaperone-mediated autophagy (CMA), 26S proteasome, macroautophagy, and aggrephagy. Neurofibrillary tangles (NFTs) form upon impairment of aggrephagy, and eventually, the ubiquitin chaperone valosin-containing protein (VCP) and heat shock 70 kDa protein (HSP70) are recruited to the sites of NFTs for extraction of tau for the ubiquitin-proteasome system (UPS)-mediated degradation. However, impairment of tau degradation in neurons allows tau to be secreted into the extracellular space. Secreted tau can be monomers, oligomers, and paired helical filaments (PHFs), which are seeding competent pathological tau that can be endocytosed/phagocytosed by healthy neurons, microglia, astrocytes, oligodendrocyte progenitor cells (OPCs) and oligodendrocytes, often causing proteotoxic stress, and eventually triggers senescence. Senescent cells secrete various senescence-associated secretory phenotype (SASP) factors, which trigger cellular atrophy, causing decreased brain volume in human AD. However, the molecular mechanisms of proteotoxic stress and cellular senescence are not entirely understood and are an emerging area of research. Therefore, this comprehensive review summarizes pertinent studies that provided evidence for the sequential tau degradation, failure, and the mechanistic link between tau-driven proteotoxic stress and cellular senescence in AD.

Metered Dose Inhalers (MDIs) play a crucial role in managing respiratory diseases, but their effectiveness depends on whether the intended dose is delivered to the target, which can be influenced by various factors. Accurate assessment of MDI performance is crucial for optimizing MDI delivery and ensuring drug efficacy. This study numerically examined the role of evaporation dynamics and dosimetry methods in assessing MDI delivery efficiency to different regions in a mouth-lung model extending to G11. Experimentally determined spray exit speed, applied dose, and droplet size distribution were implemented as the initial/boundary conditions. Large eddy simulations (LES) were used to resolve the transient inhalation flows, and a chemical species model was applied to simulate vapor and temperature variations in the airflow. A multi-component model was used to consider the heat and mass transfer between the droplets and airflow. The model was validated against literature data and applied to evaluate the impact of evaporation on pulmonary drug delivery using MDI, in comparison to inert particles. Three methods were used to quantify deposition: based on droplet count, droplet mass, and the drug carried by the droplets. Results demonstrate that evaporation notably alters the spray droplet size distribution and subsequent deposition patterns. Compared to inert particles, evaporation led to significantly more droplets ranging from 1–5 µm entering the pulmonary region. For a given region, large discrepancies were observed in deposition fraction (DF) using different dosimetry methods. In the lower lung, the count-based DF (33.9%) and mass-based DF (2.4%) differed by more than one order of magnitude, while the drug-based DF fell between them (20.5%). This large difference highlights the need to include evaporation in predictive dosimetry, as well as to use the appropriate method to quantify the delivery efficiency of evaporating droplets.

This article discourses on how the inheritance and innovation in paper-cutting art have taken place through its integration with modern design language. Xuzhou paper-cut is regarded as representative of China's traditional folk art, carrying a rich cultural heritage, but it faces both challenges and opportunities that come with modern society. In the course of reviewing the development history of Xuzhou paper-cutting, the value of Xuzhou paper-cut as intangible cultural heritage was discussed. This paper will seek to find out the possibility of application for Xuzhou paper-cutting in modern design with both qualitative and quantitative methods, including questionnaires and interviews. Successful examples are used in this research to look for evidence that effectively proves traditional paper-cutting combines well with the principles of modern design in developing concepts, artistic expressions, and cultural symbolism. It shows that Xuzhou paper-cutting has a great potential to apply in modern design, and education, exhibitions, and community activities will be also necessary to revive it. On the other hand, traditional craft could adapt to current social needs only when the design language has been innovated. The research brought in new input with regard to integrating traditional culture with modern design from a theoretical and practical point of view, thus serving as an informant for the direction of further research in heritage and design integration.

The modern economic landscape, now more competitive than ever, compels companies to create increasingly innovative, cutting-edge, and cost-effective products. In this regard, design and development phases play a crucial role, since they closely determine the final satisfaction of the customer. As a consequence, the need for a structured approach is evident, since it allows the companies to identify product key characteristics. With the purpose of creating a new product with an extended horizon to various markets, this work is based on the use of Design for Six Sigma (DFSS) methodology for the identification and optimization of Critical-To-Quality (CTQ) features, aiming at making the project as responsive as possible to customer needs. Two main themes are addressed. The first one is the analysis of the integrated product development process with the Six Sigma methodology for achieving design excellence; the second one is the application of the Identify phase of the IDOV (Identify, Design, Optimise, Validate) approach through a DFSS case study.

The intricate orchestration of molecular events that constitutes life, as we know it, is oftentimes ascribed to an intelligent design motive. It is portrayed as if a paranormal watch maker has engineered the assemblage of parts and events that eventually manifests as the clockwork precision with which ontogeny and evolution operate within the boundary conditions imposed by what we know as life. The debate between those who advocate evolution versus intelligent design as the shaping force for modern life forms persists. However, that is not the motive for this essay. Though there has been several studies and scholarly articles that aspire to decipher and present the complexity of the molecular events that constitutes life in an insular fashion, there is a paucity of resources for undergraduates that summarizes the complexity cogently, as parts of a whole, for appreciating the enormity of the multidimensional nature of biological information and the inherent contradictions that are embedded therein. The fluidity inherent in the way information flows through signalling networks, the way biomolecular architectures maintain homeostasis in the ying-yang of life and the way multicellular complexity evolves through precise cell-cycle control is inherent in the evolution, maintenance and reproduction of biological systems. The matrix of events that encode information at the sequential and structural level across different macromolecules is a fascinating jungle of information. Combining these with how small-molecule metabolites interact with macromolecules in a spatially and temporally coordinated manner to evolve signal over random noise generates a staggering picture of informational organization and relays that, most often, defies human comprehension. This perspective is an attempt to array the various sources of information and put them together in context to appreciate the nuances of how information is stored and relayed within the biological systems. Further, it speculates on how these multitiered organization of biological information could be adapted to guide synthetic biology approaches and the future of information storage and organization.

This study examines Cambodian higher education teachers' readiness for online teaching and learning (OTL) using a structural equation modeling (SEM) approach. The framework centers around three key dimensions: teachers' self-efficacy in technological, pedagogical, and content knowledge (TPACK), their perceived online teaching presence, and the institutional support they receive. A quantitative survey was administered to 140 teachers at the university level. Teachers' online teaching experience positively influenced their TPACK self-efficacy. Online teaching experience also had a significant positive impact on teachers perceived online teaching presences. Furthermore, teachers' perceptions of institutional support were positively associated with their online teaching experience. These results highlight the critical role of teachers' online teaching experience in shaping their readiness for OTL. The findings suggest that targeted professional development programs and institutional support mechanisms can effectively enhance teachers' self-efficacy, online teaching presence, and perceptions of institutional support in Cambodian higher education.

BAT-based regulations necessitate defining Key Environmental Issues (KEIs) to focus data collection and discussions on the main topics when establishing Best Available Techniques (BATs) at the sector level. However, Article 14 of the Industrial Emissions Directive (IED) suggests that some significant environmental issues may not be covered by BAT conclusions but still require the implementation of BATs at the local level, even in the absence of defined sectoral BAT references. The IED, along with the associated Commission Implementing Decisions and guides, does not offer guidelines for selecting KEIs at the local level, whether by the competent authority or the operator. To ensure full compliance with the IED by installations under its scope, this paper proposes a methodology for determining KEIs locally. Based on the environmental aspects of the installation, the sensitivity of the environment, and the levels of emissions or consumption, this methodology has been tested on a case study at the plant level to demonstrate its effectiveness. The paper then discusses the contributions and limitations of the methodology and suggests areas for future research.

Thanks to the Chinese government's strong monetary stimulus initiative, the Chinese stock market has seen a recent surge. A similar scenario in history reminds us that such a surge carries significant potential risks and undermines economic sustainability. This study examines the economic stimulus by the Chinese government during 2008 and 2015 utilizing monetary and real estate policy to stimulate stock market expansion, and identifies a pattern of path dependency in the government's approach to economic challenges. In the context of the fiscal pressures of 2024, the government has again employed an extraordinary level of intervention to catalyze a significant uptick in the stock market. While this has led to a temporary increase in stock market indices, it poses substantial risks. The potential for a stock market bubble, detached from fundamental values, could precipitate a subsequent market collapse. Additionally, the government's market stimulus may be aimed at providing financial support to underperforming state-owned enterprises, which in the long-run will damage China's economic sustainability. There is also a concern that the measures to stimulate the stock market could interlink the bond market and banking sector with the equity, potentially inciting broader systemic risks. Furthermore, the housing market crisis and investors' skepticism towards the extent of China's marketization reforms will also intensify. Overall, we believe that the current round of stock market stimulus is not sustainable and leaves the door open for future risks.

This study discusses the application of reinforcement learning (RL) in financial asset risk assessment, especially how to improve the ability of risk prediction and asset portfolio optimization through deep reinforcement learning (DRL) model. Traditional financial risk assessment methods are often unable to effectively cope with the dynamic changes and complexities of the financial market, but RL technology provides innovative solutions for modern financial risk management by means of real-time adjustment and adaptation mechanism. This paper experimentally verifies the advantages of DRL models in improving forecasting accuracy and risk management efficiency and discusses the potential impact of AI technology in the financial sector. The findings show that despite challenges such as data quality and model interpretation, the application of AI technology provides financial institutions with more precise and flexible risk management tools, driving further development of fintech.

The study focuses of analysis the impact of corporate social responsibility (CSR) on economic growth and reducing inequality, highlighting the importance of CSR in achieving sustainable development and social justice. The main aim is to analyze how different CSR initiatives contribute to economic development, social prosperity and the reduction of inequality, reviewing the methods used to assess their impact. The research methodology includes a detailed literature review, bibliometric analysis and scientific mapping, surveys of various business organizations and a Gap Analysis regarding the identification of gaps between the current state of CSR activities and the expected outcomes. The research shows that companies perceive CSR as a key tool for improving corporate image, responding to stakeholder expectations and investing in social justice. Despite positive intentions, challenges include the lack of clearly defined methodologies for measuring impact on economic inequality, as well as difficulties in assessing the long-term effects of CSR initiatives. Key conclusions highlight the need for more structured approaches to assessing the social and economic effects of CSR, recommending that companies improve their transparency and accountability, and implement clear indicators of success to achieve sustainable economic and social outcomes.

Trueperella pyogenes is a widely distributed opportunistic pathogenic bacterium which can infect livestock, wildlife, community animals, and humans, resulting in suppurative infection of tissue and organ mucosa, including pneumonia, liver abscessation, mastitis, metritis, endocarditis, and osteoarthritis. TP1804 and TP1808 were isolated from the uterine lavage fluid of cows with endometritis. Several virulence factors are significant contributors to the pathogenicity expressed by T. pyogenes, including purulent pyolysin (PLO), fimbriae (Fim), neuraminidase (NanH and NanP), and collagen-binding protein (CbpA). These virulence factors are contributed to promote adhesion, including fimbriae, extracellular matrix-binding proteins, and neuraminidases. This study analyzed the prokaryotic transcriptomics and proteomics of two strains of T. pyogenes with similar growth curves but different cytotoxicity. Studying the metabolic mechanisms of these differentially expressed genes and proteins can greatly promote the discovery of new biomarkers and improve the accuracy of biomarker identification, which is of great value for molecular mechanisms, biomarkers, early diagnosis of diseases, molecular typing, prognosis, and clinical diagnosis.

Programming languages drive most if not all of modern problem- solving using computational methods and power. Research into new programming languages and methods is essential to the furthering or improvement of computational problem-solving by making the design, implementation, and application of automation to general or particu- lar problem-solving ever easier, more accessible, and more performant. General-purpose Programming Languages (GPLs) typically are designed to be purely domain agnostic—meaning they can be applied in any field, for any kind of problem. However, this typically also makes them hard and difficult to apply in problems where non-programmers or even ex- perts with little or no GPL programming skills are required to leverage programmatic problem solving capabilities, which is why Domain Specific Languages (DSLs) come into play; they are generally more fine-tuned to- wards improving human productivity and performance than that of the machine, while making solving particular, domain-oriented problems sim- pler. In this paper, we review the literature concerning how to design and then fully implement a new DSL, with special focus on a DSL for generic problem-solving leveraging Text Processing methods—essentially, a Text Processing Language (TPL). We consider leveraging the design research paradigm and philosophy as a systematic framework for guiding research into the development of new TPLs. This work presents for the first time, new unifying theory concerning general, but also TPL-specific language engineering theory and guiding frameworks—UPLT, PLEF & PLEf. We consider quantitative but also qualitative evaluation of programming lan- guages. The SOE framework is re-introduced for this purpose too. Finally, we set the pace for future theoretical and practical research into the field of programming language engineering especially with focus on TPLs.

This paper presents a comprehensive review of the transformative impact of 3D printing technology on smart cities. As cities face rapid urbanization, resource shortages, and environmental degradation, innovative solutions such as additive manufacturing (AM) offer potential pathways for sustainable urban development. By synthesizing 66 publications from 2015 to 2024, the study examines how 3D printing improves urban infrastructure, enhances sustainability, and fosters community engagement in city planning. Key benefits of 3D printing include reducing construction time and material waste, lowering costs, and enabling the creation of scalable, affordable housing solutions. The paper also addresses emerging areas such as the integration of 3D printing with digital twins (DT), machine learning (ML), and AI to optimize urban infrastructure and predictive maintenance. It highlights the use of smart materials and soft robotics for structural health monitoring (SHM), and repairs. Despite the promising advancements, challenges remain in terms of cost, scalability, and the need for interdisciplinary collaboration among engineers, urban planners, and policymakers. The findings suggest a roadmap for future research and practical applications of 3D printing in smart cities, contributing to the ongoing discourse on sustainable and technologically advanced urban development.

This study applied the principal-agent model to interpret the impact of routine biased technological change (RBTC) on the structure of employment relations. RBTC increases the proportion of routine tasks in work activities, due to their fixed procedural nature, routine tasks reduce uncertainties in the principal-agent problem. Employers, being able to predict work contingencies and to verify employee efforts, find the benefit of offering standard labor contracts is reduced, thereby increasing the ratio of non-standard employment. This study applied logistic regression based on the Program for the International Assessment of Adult Competencies (PIAAC) survey data to verify this framework. The results indicated that with each standard deviation increased in routine task intensity, the odds ratio of non-standard employment increased by 13.6%. This effect was heterogeneous in various institutional contexts. This study expends the toolkit for analyzing employment relations problems, and extends the scope of RBTC to employment relations.

Peptic ulcers, characterized by mucosal erosion in the stomach or duodenum, continue to pose significant health challenges worldwide. This comprehensive review explores the pathophysiology of peptic ulcers, including the roles of Helicobacter pylori, acid secretion, and mucosal defense mechanisms. We examine key risk factors such as NSAID use, smoking, alcohol consumption, and genetic predisposition. Advances in diagnostic techniques, including endoscopy, biopsy, and non-invasive tests like urea breath tests and stool antigen tests, are discussed, alongside recent improvements in molecular diagnostics and imaging. The review also covers current management strategies, focusing on pharmacological treatments like proton pump inhibitors (PPIs) and H2 receptor antagonists, as well as surgical interventions for complicated cases. Emerging treatment modalities, such as novel drugs, probiotics, and minimally invasive surgical techniques, are highlighted. Additionally, the paper identifies future research directions, including the exploration of new therapeutic targets, enhanced diagnostic tools, and the impact of gut microbiota on ulcer formation. By integrating recent advancements and identifying gaps in current knowledge, this review aims to provide a comprehensive understanding of peptic ulcers and inform future research and clinical practice.

Emerging research into the human microbiome, an intricate ecosystem of microorganisms residing in and on our bodies, reveals that it plays a pivotal role in maintaining our health, highlighting the potential for microbiome-based interventions to prevent, diagnose, treat, and manage a myriad of diseases. The objective of this review is to highlight the importance of microbiome studies in enhancing our understanding of rare genetic epilepsy and related neurological disorders. Studies suggest that the gut microbiome, acting through the gut-brain axis, impacts the development and severity of epileptic conditions in children. Disruptions in microbial composition can affect neurotransmitter systems, inflammatory responses, and immune regulation, which are all critical factors in the pathogenesis of epilepsy. This growing body of evidence points to the potential of microbiome-targeted therapies, such as probiotics or dietary modifications, as innovative approaches to managing epilepsy. By harnessing the power of the microbiome, we stand to develop more effective and personalized treatment strategies for children affected by this and other rare neurological diseases.

Here I examine the overexploitation of Artemisia granatensis, a narrow endemic medicinal plant species from Sierra Nevada, Spain, and the consequences for its conservation. With over 50,000 flowering plant species used for medicinal purposes worldwide, many species face sustainability issues due to overharvesting and habitat loss. Historical documentation of A. granatensis use dates back to the 13th century, highlighting its significance in traditional medicine. However, this species has suffered extensive overexploitation, particularly in the 19th and 20th centuries, leading to a significant decline in populations. Conservation concerns were first raised in 1909, and despite the species being legally protected since 1982, illegal collection and environmental pressures persist. Today, A. granatensis is critically endangered, with fewer than 2,000 individuals remaining in fragmented populations. The study synthesizes historical and recent literature to understand the long-standing pressures on this species and the limited conservation efforts made. Cultivation of A. granatensis is proposed as a crucial strategy to reduce pressure on wild populations and ensure the survival of this flagship important plant species.

The importance of continuously emerging new distribution is a mandate to understand the world and environment surrounding us. In this paper, the author will discuss a new distribution defined on the interval (0,1) as regards the methodology of deducing its PDF, some of its properties and related functions. A simulation and real data analysis will be highlighted.

Background and Objectives: The rising incidence of modifiable lifestyle risk factors and cardiovascular diseases, driven by poor diet, inactivity, excessive alcohol use, and smoking, may influence the development and rupture of intracranial aneurysms (IA). This study aimed to examine the impact of lifestyle-related and cardiovascular risk factors on IA rupture and patient outcomes. Materials and Methods: We developed the "MARVIN" (Metabolic and Adverse Risk Factors and Vices Influencing Intracranial Aneurysms) model and conducted a retrospective analysis of 303 patients with 517 IAs, treated between 2007 and 2020. Of these, 225 patients were analyzed for rupture status and 221 for clinical outcomes. The analysis focused on hypertension, diabetes, hypercholesterolemia, vascular diseases, nicotine and alcohol abuse, obesity, aneurysm rupture status, and clinical outcomes. Logistic regression was used to evaluate the impact of these risk factors. Results: Among those with risk factors, 24.9% (56/225) and 25.3% (56/221) had one, 32.0% (72/225) and 30.8% (68/221) had two, 20.0% (45/225) and 20.4% (45/221) had three, 12.0% (27/225) and 12.2% (27/221) had four, 4.0% (9/225) and 4.1% (9/221) had five, 0.9% (2/225) had six in both groups, and 0.4% (1/225) and 0.5% (1/221) had seven risk factors, respectively. Strong relationships were found between lifestyle-related vascular risk factors, indicating multiple comorbidities in patients with unhealthy habits. Smokers with ruptured aneurysms had higher WFNS (World Federation of Neurosurgical Societies) scores, but nicotine abuse did not affect long-term outcomes. The most significant predictors for poor outcomes were WFNS score and age, while age and a history of vascular diseases were protective against rupture. Despite the high prevalence of modifiable risk factors, they did not significantly influence rupture risk. Conclusions: The findings suggest a need for multifactorial risk assessment strategies in managing IA patients. Future studies with larger cohorts are required to confirm these results and better understand IA progression.

Malnutrition, particularly micronutrient deficiencies, remains a significant public health challenge in Nepal, affecting vulnerable populations such as children under five, pregnant women, and lactating mothers. This thesis examines the complex landscape of malnutrition in Nepal, focusing on the causes, health impacts, and socio-economic consequences of nutrient deficiencies. The research highlights the severe effects of malnutrition, including stunting, anemia, and developmental delays, which contribute to broader social and economic disparities. A detailed analysis of the most affected populations and regions, based on national surveys and studies, underscores the critical need for targeted interventions. This study also evaluates existing nutrition programs in Nepal, assessing their effectiveness and identifying gaps in coverage, particularly in remote and rural areas. The findings emphasize the importance of a multi-sectoral approach that integrates nutrition-specific interventions with broader efforts to improve food security, healthcare access, and economic opportunities.

This paper explores the application of Blockchain technology in food traceability, focusing specifically on olive oil. It addresses the significant challenges in the food industry, such as verifying authenticity and detecting harmful substances. The research highlights the potential of Blockchain to enhance traceability by leveraging its decentralized and secure architecture, which en-sures data integrity and facilitates rapid monitoring throughout the supply chain. The paper also examines the synergy between Blockchain and the Internet of Things (IoT), which is critical for real-time tracking in product production. The research conducted has led to a practical demonstration through a web application connected to the Ethereum test network, designed for the quality control of olive oil via smart contracts. Overall, this study provides a comprehensive analysis of Blockchain technology and its transformative potential in ensuring food traceability, particularly for high-value commodities like olive oil.

This article examines the pressing challenges facing Industrial Organization (IO) in the contemporary economic landscape, with a particular focus on merger analysis. The rapid pace of technological advancement and emergence of novel business models have strained conventional analytical frameworks. We explore how the rise of digital platforms, data as a competitive asset, and multi-sided markets have exposed gaps in our understanding of market power and consumer welfare. Through an examination of empirical methodologies, including structural models and difference-in-differences approaches, we highlight the limitations of current techniques in accurately predicting merger outcomes. The paper argues for a disciplinary reinvention of IO, drawing upon insights from behavioural economics, network theory, and data science. We posit that an interdisciplinary approach is essential for developing more robust analytical tools capable of navigating the complexities of modern markets and informing effective antitrust policy in an increasingly interconnected global economy.

This paper explores the application of machine learning techniques and statistical analysis to identify patterns of victimization and the risk of gender-based violence in San Andrés de Tumaco, Nariño, Colombia. Models were developed to classify women according to their vulnerability and risk of suffering various forms of violence, which were integrated into a decision-making tool for local authorities. The algorithms employed include K-means for clustering, artificial neural networks, random forest, decision trees, and multiclass classification algorithms combined with fuzzy classification techniques to handle incomplete data. Implemented in Python and R, the models were statistically validated to ensure their reliability. Analyses based on health data revealed key patterns of victimization and risks associated with gender-based violence in the region. This study presents a data science model that uses a social determinants approach to assess the characteristics and patterns of violence against women in the Pacific region of Nariño. The research was conducted within the framework of the Orquídeas Program of the Colombian Ministry of Science, Technology, and Innovation.

Background/Objectives: Photoimmunotherapy (PIT) is an innovative approach for the targeted treatment of cancer. In PIT, photosensitizer dyes are conjugated to tumor-specific antibodies for targeted delivery into cancer cells. Upon irradiation with visible light, the photosensitizer dye is activated and induces cancer specific cell death. In the present study, we describe the PIT of prostate cancer as novel therapeutic option for the targeted treatment of localized prostate cancer. Methods: We conjugated the new silicon phthalocyanine dye, WB692-CB2, to recombinant cysteine-modified anti-CD44 and EpCAM antibodies via a maleimide linker and tested the antibody dye conjugates for PIT on prostate cancer (PC) and prostate cancer stem cell (PCSC)-like cells. Results: The anti-CD44 and anti-EpCAM antibody dye conjugates showed specific binding and high cytotoxicity against PC and PCSC-like cells following irradiation with red light. Combinatorial treatment with both conjugates led to enhanced cytotoxic effects. Conclusions: WB692-CB2-based PIT can serve as an effective focal therapy, preserving the prostate gland, and minimizing side effects. It can be employed during radical prostatectomy (RP) to treat residual tumor cells or lymph node metastases in areas where further surgical intervention is not feasible. Utilizing multiple conjugates against antigens expressed on differentiated PC and PCSC-like cells, such as CD44 and EpCAM, could be an effective method to eradicate residual cancer cells in heterogeneous tumors. This approach could lead to the prevention of local recurrence after RP and thus to the complete cure of PC patients.

Gastric pH seems to be a key factor for the development of Equine Gastric Ulcer Syndrome (EGUS). It is influenced by the amount and duration of roughage uptake, as chewing increases the production of alkaline saliva. The protone-pump-inhibitor omeprazole is currently recommended by the American College of Veterinary Internal Medicine/European College of Equine Internal Medicine (ACVIM/ECEIM) consensus statement for therapy of both Equine Squamous Gastric Disease (ESGD) and Equine Glandular Gastric Disease (EGGD). We hypothesized that dental disorders decrease saliva production due to altered chewing and in consequence gastric pH, therefore predisposing horses to EGUS. In 54 horses, a gastroscopy with measurement of the intragastric pH after 12 hours of fasting and an examination of the oral cavity were carried out. Omeprazole therapy was recommended (n=32), dental disorders were corrected (n=22) and gastroscopy was repeated 4 weeks later (n=9). No influence of moderate-severe dental disorders on ESGD (grade ≥2/4) (p = 0.394) and EGGD (grade ≥2/3) (p = 0.857) could be determined. Nevertheless, there was a trend that moderate- severe dental disorders were associated with a low gastric pH (1-4) (p = 0.100). In conclusion, we found no evidence that gastroscopy should be recommend in horses with dental disorders and vice versa.

Clostridioides difficile, the causative agent of C. difficile infections (CDI), can be naturally infected by bacterial viruses known as bacteriophages. All characterized bacteriophages of this bacterium are temperate, meaning that upon infection their genetic material integrates and replicates with host’s genome. Such lysogenic strains can exhibit altered physiology and virulence, which in turn can be an important factor for epidemiology of CDI. In this study we characterized the phiCDKH02 bacteriophage infecting clinical isolates of C. difficile belonging to hypervirulent ribotypes 027 and 176. The bacteriophage was found to be identical to phi027. To get some insight into the role of this bacteriophage in physiology of its host and interaction with human colon cells, we made use of CRISPR-Cpf1 technology to cure the lysogenic C. difficile of the prophage. The prophage-free strain exhibited altered sporulation efficiency, lowered adhesion and decreased cytopathic effects to-wards human colon cells. These results emphasize importance of prophages in shaping virulence of C. difficile.

The digital divide, particularly within the context of Artificial Intelligence (AI) sport podcasts, presents significant behavioral and psychosocial challenges for student engagement. This study examines the disparities in access to and proficiency with Information Communication Technologies (ICT) across different demographic groups, focusing on gender, age, and religious level. The advent of the commercial web has heightened the significance of these divides, as first-level digital divide concerns access to the internet, while the second-level digital divide pertains to the ability to use technology proficiently. Existing literature has consistently highlighted persistent inequalities in these areas, which significantly impact the extent to which students from various backgrounds can engage with AI sport podcasts effectively. Understanding these dynamics is crucial for developing strategies to bridge the gap and ensure equitable access to digital learning resources.

This work investigates mixed convection flow over a vertical thin needle with variable surface heat, mass, and microbial flux, incorporating the influence of gyrotactic microorganisms. The governing partial differential equations are transformed into ordinary differential equations using appropriate similarity transformations. These transformed equations are then numerically solved employing MATLAB's Bvp4c solver. The study's primary focus lies in examining the influence of various dimensionless parameters, including the mixed convection parameter, power-law index, buoyancy parameters, bioconvection parameters, and needle size parameter, on the velocity, temperature, concentration, and microbe profiles. The results demonstrate that these parameters significantly impact surface (wall) temperature, surface fluid concentration, surface motile microbe concentration, and the corresponding velocity, temperature, concentration, and microorganism profiles. The study's findings offer valuable insights into the intricate dynamics of mixed convection flow with bioconvection and have potential applications in diverse fields such as biomedicine and engineering.

A new family of inequality indices based on the deviation between the expected maximum and the expected minimum of random samples, called the nth Gini index is presented. These indices generalize the Gini index. At the same time, this family of indices and the S-Gini index are generalised by proposing the uv−Gini index, which turns out to be a convex combination of the S-Gini index and the Lorenz family of inequality measures. This family of Gini indices provides a methodology for achieving perfect equality in a given distribution of incomes. This is achieved through a series of successive and equal increases in the incomes of each individual.

Recommending products effectively based on historical transaction data and metadata is crucial for improving user satisfaction and increasing sales. This study presents a recommendation system evaluated using Mean Average Precision (MAP@12), leveraging a diverse dataset that includes apparel types, customer demographics, product descriptions, and images. Our approach integrates Mult-Recall methods and LightGBM Ranker, utilizing various recall strategies and advanced feature engineering techniques to enhance recommendation accuracy. By combining these advanced machine learning models and ensemble techniques, our proposed solution outperforms existing methods, providing a robust and efficient recommendation system tailored to diverse customer needs and product characteristics. This integrated approach not only addresses the challenges of data sparsity and cold start but also adapts to the dynamic nature of user preferences and market trends. The results demonstrate significant improvements in both recommendation precision and user satisfaction, highlighting the potential of our approach for real-world e-commerce applications.

Obviously, the discussion of different factors that could have contributed to the origin of life and evolution represents a clear speculation, since there is no way to check the validity of the most of the related hypotheses on practice, as the corresponding events are not only already happened, but took place in a very distant past. However, there are a few undisputable facts obviously present at the moment, such as the existence of a broad variety of living forms and the abundant presence of intrinsically disordered proteins (IDPs) or hybrid proteins containing ordered domains and intrinsically disordered regions (IDRs) in all those living forms. Since it seems that the currently existing living forms have originated from a common ancestor, their variety represents a result of evolution. Therefore, one could ask a logical question of what role(s) those structure-less and highly dynamic but vastly abundant and multifunctional IDPs/IDRs might have in evolution. This study represents an attempt to consider various ideas pertaining to the potential roles of protein intrinsic disorder in origin of life and evolution.

Terpenoids are crucial secondary metabolites found in plants, essential for numerous physiological functions such as defense strategies, signal communication, and adaptation to environmental stresses. The enzymes referred to as Terpene Synthases (TPS) play crucial roles in the production of these compounds, fulfilling an essential role throughout the entire biosynthetic pathway.Despite their importance, systematic studies on the TPS genome in Physcomitrium patens (P. patens) remain inadequate. This research offers an extensive bioinformatics evaluation of the TPS genes in P. patens, aiming to clarify their possible functions in plant development, growth, and responses to stress. Preliminary examinations were conducted on the physicochemical characteristics, secondary protein structures, subcellular distribution, and genetic configurations of the TPS genes in P. patens. Initial analyses were performed on the physicochemical properties, protein secondary structures, subcellular localization, and gene structures of the TPS genes in P. patens. The findings demonstrate that TPS family members are dispersed across six chromosomes, with ten TPS family members identified. Analysis of conserved motifs revealed that the PpTPS members exhibit high sequence conservation, suggesting that these enzymes maintain stable structural characteristics during terpene synthesis. Further examination of evolutionary pressures indicated that TPS family members are predominantly subjected to purifying selection, which helps preserve the structural stability of TPS enzymes. Furthermore, an analysis of cis-acting elements indicated that TPS genes might respond to various factors, including light, plant hormones, tissue development, and environmental stressors. This suggests that TPS genes could be vital in managing how plants adapt to changes in their environment. Tissue expression profiling revealed that TPS family members have significant functions across various developmental stages and tissues. This is demonstrated by qRT-PCR analyses, indicating that the expression of the PpTPS gene is influenced by abscisic acid (ABA), methyl jasmonate (MeJA), sodium chloride (NaCl), and polyethylene glycol (PEG). The findings suggest that the PpTPS genes could play an essential role in the functional development of plant leaves and in their responses to abiotic stress.In summary, this research presents a comprehensive examination of the TPS family members in P. patens, yielding important perspectives on the functions of the PpTPS7 and PpTPS8 genes in the development of leaves and responses to abiotic stress.The findings contribute to a deeper understanding of the functions and regulatory mechanisms of TPS genes and present new perspectives and strategies for the enhancement and application of P. patens.

To explore cast HEAs reinforcement by MC carbides three equimolar CoNiFeCrC alloys without or with Carbon and either hafnium or tantalum were investigated. Their microstructures were specified and tests at 1100°C in oxidation by air and flexural creep tests were carried out. The HEAbase was single–phased. The HEAhfc and HEAtac contained eutectic script HfC and TaC carbides respectively, in the interdendritic spaces. After oxidation for 50h at 1100°C all alloys were covered by a (Cr,Mn)2O3 scale with various proportions in Cr and Mn. HfO2 or CrTaO4 also formed. The subsurface was depleted in Cr and especially in Mn. The HEAbase alloy deformed quickly. The MC carbides greatly improved the creep resistance, and divided deformation by five to ten times.

Interactive conversational agents, also known as chatbots, have the potential to increase the success rate of digital technology interventions to promote healthy behaviors. However, due to their newness and limited use, little is known about their integration, usefulness, and effectiveness in promoting smoking and vaping cessation. The aim of this mixed methods systematic review was to assess the effectiveness and characteristics of current interactive conversational agents in promoting and supporting smoking and vaping cessation. A mixed methods systematic review was conducted to identify studies published in the last 20 years in five relevant databases. Eight studies, including seven on smoking cessation and one on smoking and vaping cessation, were included. The results showed that, compared to other smoking cessation methods, chatbots can lead to better engagement in treatment, resulting in higher rates of sustained abstinence and improved quality of life. In addition, chatbots can be perceived as empathetic and establish a decent therapeutic alliance thanks to their communication skills. This knowledge could be useful for the development of interactive conversational agents to support smoking and/or vaping cessation. Alternative intervention tools targeting younger generations, such as chatbots, may offer an additional way for public health professionals to reach them.

Background/Objectives: Energy availability (EA) is essential for maintaining physiological func-tions, significantly influencing athletes' health and performance. Nutritional behaviours, how-ever, vary across sports. This study aims to assess EA levels in athletes from different disciplines, focusing on the relationship between EA and body composition in endurance athletes compared to rugby players. Methods: The study involved 18 endurance athletes (15 men, 3 women) and 36 rugby players (all men). Data were gathered through interviews, questionnaires, and bioim-pedance analysis. Energy intake (EI) was measured with a 24-hour dietary recall, and exercise energy expenditure (EEE) was calculated using the IPAQ questionnaire. EA was calculated as EA = (EI - EEE) / fat-free mass (FFM), with results categorized into clinical, subclinical, and optimal ranges. Results: The endurance group had a lower average FFM (57.81 kg) compared to the rugby players (67.61 kg). EA was also significantly lower in endurance athletes (11.72 kcal/kg FFM) than in rugby players (35.44 kcal/kg FFM). Endurance athletes showed more restrictive nutritional behaviour with lower EI and higher EEE, but both groups maintained body composition within normal ranges. Conclusions: Endurance athletes exhibit greater nutritional restrictions compared to rugby players, though their body composition remains healthy. Further research is required to investigate the long-term effects of low EA on performance, injury risk, and potential impairment when EA falls below the optimal threshold of 45 kcal/kg FFM/day.

In recent years, the demand for solid, thin, and flexible energy storage devices has surged in modern consumer electronics, which require autonomy and long duration. In this context, hybrid supercapacitors have become strategic, leading to significant efforts to develop cells with an energy density close to that of batteries, while maintaining the power density of conventional supercapacitors. Motivated by these requirements, we report the development of a new high-performance dual-redox-mediator supercapacitor. In this study, cells were constructed using fully moldable buckypapers (BPs), composed of carbon nanotubes and cellulose nanofibers, as electrodes. We evaluated the compatibility of BPs with hydrogel polymer electrolytes, based on 1 mol L-1 H2SO4 and polyvinyl alcohol (PVA), supplemented with different redox species: methylene blue, indigo carmine, and hydroquinone. Solid cells were constructed containing two active redox species to maximize the specific capacity of each electrode. Considering the main results, the dual-redox-mediator supercapacitor exhibits a high energy density of 32.0 Wh kg-1 (at 0.8 kW kg-1) and is capable of delivering 25.9 Wh kg-1 at high power demands (4.0 kW kg-1). Stability studies conducted over 10,000 galvanostatic cycles revealed that the PVA polymer matrix benefits the system by inhibiting the crossover of redox species within the cell.

The relationship between Portugal and England spans centuries, encompassing political alliances, economic cooperation, cultural exchanges, and significant periods of tension and exploitation. This article provides a comprehensive and critical overview of the historical relations between Portugal and England from medieval times to the present, highlighting both the cooperation and the conflicts that have shaped this complex alliance. It explores key events, treaties, and figures that have influenced the trajectory of the relationship, including the heavy indebtedness Portugal accumulated with England and how this debt was transferred to Brazil in exchange for its independence—a factor that perpetuated social distortions such as slavery and inequality in Brazil. The article also discusses the contemporary context of Portugal-England relations, emphasizing the continued importance and challenges of this partnership in the modern world.

The current developments in the theory of quantum static triplet correlations and their associated structures (real r-space and Fourier k-space) in monatomic fluids are reviewed. The main framework utilized is Feynman’s path integral formalism (PI) and the issues addressed cover quantum diffraction effects and zero-spin bosonic exchange. The structures are associated with the external weak fields that reveal their nature, and due attention is paid to the underlying pair level structures. Without the pair level one cannot fully grasp the triplet extensions in the hierarchical ladder of structures, as both the pair and the triplet structures are essential ingredients in the triplet response functions. Three general classes of PI structures do arise: centroid, total continuous linear response, and instantaneous. Use of functional differentiation techniques is widely made and, as a bonus, this leads to identify an exact extension of the “classical isomorphism” when the centroid structures are considered. In this connection, the direct correlation functions, as borrowed from classical statistical mechanics, play a key role (either exact or approximate) in the corresponding quantum applications. Additionally, as an auxiliary framework, the traditional closure schemes for triplets are also discussed, owing to their potential usefulness for rationalizing PI triplet results. To illustrate some basic concepts, new numerical calculations (path integral Monte Carlo PIMC and closures) are reported. They are focused on the purely diffraction regime and deal with supercritical helium-3 and the quantum hard-sphere fluid.

Per- and polyfluoroalkyl substances (PFAS) are arguably the most common water contaminants in the world today. While several research experiments have been done to understand and remove PFAS from the environment, there is still a lot of unknown. Little has been known about the use of Machine learning (ML) to understand PFAS. This work hence reviews some leading ML approaches and applications in PFAS studies in the distribution, transport, removal, and occurrence predictions of PFAS. Several evaluation matrices were examined and used to perform this function. There are still a lot of areas whereby ML can be used to improve our PFAS knowledge base, some of these were briefly stated in this review.

The prediction of stock prices is a challenging task, particularly for retail investors who may lack the resources and expertise to perform sophisticated quantitative trading. This study focuses on enhancing stock price prediction for retail investors by employing advanced machine learning techniques on data from the stock exchange market. We utilize a comprehensive methodology that includes data preprocessing to handle missing values and outliers, feature engineering, cross-validation, and parameter tuning. The techniques applied include Keras Deep Neural Networks (DNN), LightGBM, LSTM, GRU, and linear regression (LR). Our proposed ensemble model, which combines time series and deep learning models, demonstrates superior performance compared to individual models. This integration of methods leads to significant improvements in prediction accuracy, providing a robust solution for retail investors.

This study presents an application of fractal mathematics to financial markets through the utilization of the Multifractal Model of Asset Returns (MMAR). The model is employed to analyse a 30-year dataset encompassing three distinct financial markets: the USD/NOK currency pair, the OMXS30 stock index, and the 12-month LIBOR rate. The MMAR model successfully captures essential stylized facts about financial markets, including high kurtosis, non-independent price movements, and clustered volatile days. Simulations generated by the model demonstrate realistic behaviour and multifractal characteristics. However, the model lacks a statistical method for assessing its accuracy and may underestimate kurtosis in high-kurtosis markets. Notably, the model is capable of producing simulations across a wide range of kurtosis values and can be instrumental in stress-testing portfolios. Despite its potential, the model is subject to limitations, such as the need for human judgment, ambiguous predictions, and interpretability issues. Furthermore, the model may become unreliable following a high-kurtosis event.

UML (Unified Modeling Language) diagrams are graphical representations used in software engineering which play a vital role in the design and development of software systems and various engineering processes. Large, good-quality datasets containing UML diagrams are essential for different areas in the industry, research and teaching purposes, however few exist in the literature and it is common to find duplicate elements in the existing datasets. This might affect the evaluation of the models obtained when using these datasets. This paper addresses the challenge of creating a ground truth dastaset of UML diagrams, including semi-automated inspection to remove duplicates and ensure the correct labeling of all UML diagrams contained in the dataset. In particular, a dataset of six UML diagram classes has been assembled, comprising a total of 2,626 images (426 activity diagrams, 636 class diagrams, 352 component diagrams, 357 deployment diagrams, 435 sequence diagrams, and 420 use case diagrams). Importantly, unlike other existing datasets, ours contains no duplicate elements and all diagrams are correctly labeled. Our curated dataset is a valuable and unique resource for the research community because it serves as a foundation for training and evaluating various models. In this paper, we demonstrate this by training and testing several deep learning models using our dataset, achieving highly satisfactory results compared to those presented in other works in the literature. Additionally, our experimental results highlight the potential of Visual Transformers for UML diagram classification, setting our approach apart from others that predominantly used Convolutional Neural Networks for similar tasks.

Indoor air quality (IAQ) is a critical determinant of human health, with fungal contamination posing significant risks. In 2023, a total of 160 samples were collected from 40 selected rooms. These samples were cultured on Sabouraud Dextrose Agar (SDA) medium using a portable air sampler and incubated at 28°C for 7 days. Initial identification of fungal isolates was performed based on their morphology. Suspected isolates underwent further analysis using internal transcribed spacer (ITS) rDNA analysis. The results revealed 11 species across 8 genera in various rooms. The most prevalent fungi were Aspergillus niger (183 CFU) , Penicillium chrysogenum (139 CFU) and Cladosporium halotolerans (135 CFU). On the other hand, Curvularia hawaiiensis and A. ustus were rarely isolated (10 and 19 respectively). Effective treatment involves a combination of preventive measures, such as controlling humidity and ensuring proper ventilation, alongside targeted interventions like antifungal treatments and thorough cleaning. Continuous monitoring and management of indoor environments are essential to reduce fungal contamination, enhance air quality, and protect human health from the harmful effects of airborne fungi. This abstract underscore the importance of addressing indoor fungal contamination to promote safer, healthier living and working spaces.

Music has been proposed for a long time as a treatment for mental disorders. However, some special types of music or stimulating sounds might induce negative emotions. Herein, 8-week-old male mice were exposed to two specific genres of music, hip-hop (HH) and heavy metal (HM), and construction noise (CN), and a series of behavioral experiments were performed to evaluate the impact of these music or sounds on the emotion of mice. The experiment lasted 1 week in which 8-week-old male mice were used as animal models. We found that long term exposure HM, HH, and CN induced depressive symptoms with a decrease in dopamine receptor 1 and BDNF in the mixture of hippocampus and frontal cortex of mice. Our results have highly addressed that certain special types of sound or music might induce negative emotions and loss of dopaminergic neurons in mice, and it is necessary to intervene in adolescences who are exposed to these music or sounds for a long time.

Recent advancements have been observed in the global legal framework, particularly in the recognition of Indigenous peoples' rights, among which the right to self-determination stands out. This right encompasses the ability to define their political status and to pursue economic, social, and cultural development autonomously. The Inter-American Commission on Human Rights (IACHR) has identified six criteria to evaluate adherence to these rights. This study aims to assess the progress made by Chile, Colombia, and Mexico in meeting these criteria. Employing a non-experimental, cross-sectional, and exploratory research design, the study utilized a questionnaire based on a Likert scale to gather data from residents of Indigenous communities within the three countries under consideration. Findings suggest that the criterion most adequately met is that of cultural identity and non-discrimination, whereas the criterion pertaining to the right to prior, free, and informed consultation and consent is the least fulfilled. Through principal component analysis, it was noted that Colombia exhibits a higher level of compliance with the right to self-determination, followed by Mexico, with Chile demonstrating a divergence and lagging in progress. The study concludes that a significant challenge for these countries lies in continuing efforts to effectively meet these standards and ensure the right to self-determination for Indigenous and tribal peoples.

The effects of reaction temperature, reaction time, heating rate, nitrogen flow rate and catalyst quality on the oil yield and oxygen content in the oil were investigated by using the single factor principle. Too high and too low reaction temperature, nitrogen flow rate, heating rate and reaction time have improved the yield of bio-oil, but the oxygen content in bio-oil is higher. The reduction of catalytic dose is beneficial to the increase of oil yield but not conducive to the increase of aromatic compound content. The optimal reaction conditions were observed as reaction temperature of 500℃, nitrogen flow rate of 110 mL/min, heating rate of 20℃/min, reaction time of 15min, mass ratio of catalyst to coconut coat powder was 1, and the oil yield was 4.97%, aquatic yield was 35.80%, and solid yield was 30.78%. The gas yield was 28.45%, the oxygen content was 10.3%, and the aromatic compound content was 89.7%.The analysis of catalytic cracking mechanism shows that most of the oxygen-containing compounds in the bio-oil are generated into water and aromatic compounds by catalytic cracking reaction at high temperature, thus removing oxygen.

This paper explores a novel approach to modeling the positional dynamics of stars using discrete dynamical systems. We define star evolution through discrete-time update rules based on right ascension, declination, and distance, incorporating chaotic behavior via nonlinear functions and external perturbations. By applying Ricci flow and Riemannian metrics, we provide new insights into the positional dynamics of stars. Theoretical computations of Perelman entropy are used to assess system complexity, with high-precision Runge-Kutta methods ensuring accurate solutions for our chaotic model. We quantify chaos using Lyapunov exponents and perform bifurcation analysis to study how parameter variations affect the dynamics. Comparing our model to the Lorenz attractor reveals both similarities and unique characteristics in stellar dynamics. Our results show that entropy increases exponentially, indicating that predicting star positions with precision becomes increasingly challenging over time. This study advances the understanding of chaos in celestial systems and contributes to dynamical systems theory by integrating chaos theory with astronomical modeling.

The Surface Water and Ocean Topography (SWOT) mission was launched on December 16, 2022 to measure water levels over both open ocean and inland waters. To achieve these objectives, the SWOT Payload contains an innovative Ka-band radar interferometer, called KaRIn, completed with a conventional nadir altimeter called POSEIDON-3C that has been switched on a month after launch and a few days before KaRIn. POSEIDON-3C measurements provide a link between large scale phenomena and high resolution. POSEIDON-3C design is based on POSEIDON-3B, its predecessor on-board JASON-3. It is also a dual frequency radar altimeter operating in C and Ku bands, but with some improvements to enhance its performance. Even though it is a Low Resolution Mode altimeter, its performances over open ocean, inland waters and coastal zones are indeed excellent.This paper first describes the POSEIDON-3C design, and its modes with a focus on its new features and its Digital Elevation Model that drives its open-loop tracking mode. Then we assess the in-flight performances of the altimeter from an instrumental point of view. For that purpose, special and routine calibrations have been realized. They show the good performances and stability of the radar. In-flight assessments give thus confidence to ensure excellent altimeter measurement stability throughout the mission duration.

Ribavirin and its analogues exhibit an in vitro antiproliferative effect in cancer cells. In this work we studied the biological activities of a number of oxymethyl derivatives of ribavirin’s aglycon – 1,2,4-triazole-3-carboxamide. Oxymethyl derivatives of 1,2,4-triazole-3-carboxamide with substitu-tions at the fifth or first position of the triazole ring, were synthesized and their antiproliferative and antimicrobial effects were assessed. For both series the presence of an antiproliferative effect was inves-tigated and in the case of 1-oxymethyl derivatives was shown an antimicrobial potential against a Gram-positive bacteria Micrococcus luteus and Gram-negative bacterium Pseudomonas aeruginosa. The obtained results showed that the n-decyloxymethyl derivatives induced leukemia cell death at low mi-cromolar concentrations. We confirmed that n-decyloxymethyl derivatives of ribavirin inhibited cell cy-cle progression and induced accumulation of leukemia cells in subG1-phase. The molecular docking results suggest that oxymethyl derivatives may act by inhibiting translation initiation due to interfering with eIF4E assembly. The outcome results relived that active derivatives (1- or 5-n-decyloxymethyl-1,2,4-triazole-3-carboxamides) can be considered as a lead compound for anti-cancer treatments.

Purpose: This systematic review investigates the implications of Industry 4.0 technologies on supply chain visibility and operational efficiency. The primary aim is to discern the impact of technological integration on contemporary supply chain dynamics. Design/Methodology/Approach: A comprehensive search strategy identified 67 pertinent studies published between 2015 and 2023. The review adheres to systematic methodologies, employing the Critical Appraisal Skills Programme framework for quality assessment. Data synthesis incorporates qualitative and quantitative analyses to distill key themes and patterns. Findings: The review unveils the pivotal role of information visibility in fortifying supply chain outcomes, emphasizing the need for a dual investment strategy encompassing technological solutions and a collaborative organizational culture. Regional variations in supply chain practices, insights from humanitarian supply chains, and the influence of environmental factors on agility broaden the understanding of Industry 4.0 implications. Research Limitations/Implications: The study acknowledges limitations in the geographical focus of the included studies and the evolving nature of Industry 4.0 technologies. These considerations prompt a call for ongoing research to capture emerging trends and global nuances. Practical Implications: Organizations are urged to adopt a context-specific, adaptive approach, recognizing the significance of intangible assets and tailoring strategies to local contexts for optimal supply chain performance. Originality/Value: This systematic review contributes a nuanced understanding of Industry 4.0's transformative potential in supply chain management, emphasizing the interplay between technology, organizational culture, and regional contexts.

To obtain high quality grain refiner, a new Al-5Ti-1B-1RE master alloy grain refiner was synthesised by melt-matching method. Thermodynamic calculations and kinetic analyses, which were seriously missing from the study of key issues during the preparation process, were investigated. The microstructure and properties, comparison of the refining effect of different grain refiners on industrial pure aluminium and its refining mechanism were analysed with the help of X-ray diffraction (XRD), optical microscopy (OM), scanning electron microscopy (SEM) and energy spectroscopy, transmission electron microscopy (TEM) and an electronic universal testing machine (CSS-44100). The experimental results show that the second-phase particles in the Al-5Ti-1B-1RE master alloy matrix synthesised by the melt-matching method are mainly TiB2、 A13Ti、Ti2A120RE, etc. The magnitude of the free energy ΔG of the synthesis reaction is calculated to be ΔGTiB2<ΔGAl3Ti<ΔGTi2A120RE. When the temperature is low (undercooling ΔT is large), the nucleation rate N mainly depends on the kinetic atomic diffusion activation energy Q, while the thermodynamic nucleation work becomes a secondary factor, and vice versa. The industrial pure aluminum refined by the new grain refiner has almost transformed from coarse columnar crystals to fine equiaxed crystals, with an average grain size of 70.2 μm, which was 36.18% and 20.66% smaller than that refined by domestic and imported Al-Ti-B wire master alloy grain refiner, and its mechanical properties of tensile strength σb was increased by 11.94%, 8.29%, and elongation at break δ was improved by 31.79%, 17.41%, respectively. The main mechanism for refining pure aluminum in Al-5Ti-1B-1RE master alloy is the formation of TiA13 on TiB2 particles, the release of RE atoms from Ti2A120RE phase surrounding TiAl3, and the partial transformation into TiA13 phase, which promotes the dual nucleation refinement mechanism and the pinning effect of rare earth elements on grain boundaries.

Childhood is an obvious period for motor learning since children’s musculoskeletal and nervous systems are still in development. Adults adapt movements based on reward feedback about success and failure but it is less established whether school-age children also exhibit such reward-based motor learning. We designed a new ‘circle-drawing’ task suitable for assessing reward-based motor learning in both children and adults. Participants drew circles with their unseen hand on a tablet. They received binary reward feedback after each attempt based on the proximity of the average radius of their drawing to a target radius set as double the radius of their baseline drawings. We rewarded about 50% of the trials based on a performance-dependent reward criterion. Both children (7-17 years old) and adults increased the radius of their drawings in the direction of the target radius. We observed no difference in learning between children and adults. Moreover, both groups changed the radius less following reward than following reward absence, which is a sign of reward-based motor learning. We conclude that school-age children, like adults, exhibit reward-based motor learning.

Objective: The main purpose of this meta-analysis was to assess the efficacy and safety of Oncolytic Viruses (OVs) in glioma therapy. Methods: We searched the literature in PubMed, EMBASE, Web of Science, and the Cochrane Library. The primary outcomes assessed were overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and adverse events (AEs). The risk of bias was evaluated by sensitivity analysis and publication bias. Results: We identified 21 studies with 440 patients in this meta-analysis. In the single-arm analysis, the results showed that the 1-year OS rate was 47% (95% CI: 34%-61%, I2 = 75%) and the 2-year OS rate was 14% (95% CI: 10%-20%, I2 = 0%). The median OS was 12.48 months (95% CI: 10.71-14.25, I2 = 96%). The 1-year PFS rate was 13% (95% CI: 5%-24%, I2 = 0%), with the median PFS being 4.01 months (95% CI: 2.99-45.03, I2 = 96%). The pooled estimate of ORR was 7% (95% CI: 3%-12%, I2 = 23%). Funnel plots for median PFS were asymmetric with Egger’s test P < 0.01 indicating publication exists. The incidence of OVs-related AEs was 49% (95% CI: 20%-79%, I2 = 95%), and AEs > grade 3 was 8% (95% CI: 3%-16%, I2 = 62%). Conclusion: This meta-analysis indicated that OVs therapy was relatively safe but did not significantly extend survival in patients with gliomas.

This study provides a detailed examination of Natural Language Processing (NLP) for the complexscript of Nepali. We analyze available data resources today and discuss relevant points includingcharacter encoding, advanced tokenization methods and morphological complexity. This allows us tocontrast Nepal’s unique language and technology ecosystem with Hindi and Thai in terms of NLPadvancements. Quantitative assessment of existing tools and resources is offered by this investigationwhich highlights immediate weaknesses as well as areas that need more effort. Various ethicalconcerns are addressed here while potential topics for future research as e-governance, healthcare oreducation with a focus on new domain-specific applications, and cross-lingual transfer learning aresuggested. The objective of this attempt is to enhance understanding of natural language processing(NLP) in Nepali language and beyond aimed at developing NLP technologies that are more efficient,inclusive, culturally appropriate across diverse linguistic communities.

This essay examines the complex interplay between human rights developments in Saudi Arabia and the country's Vision 2030 initiative, which aims to modernize its social, economic, and political landscape. The research question posed is: How are human rights being addressed within the framework of Vision 2030? The goal of the essay is to provide a qualitative analysis of the reforms and policies emerging under Vision 2030, highlighting both advancements and ongoing challenges. Key discussions include historical human rights perspectives, specific strategies aimed at enhancing women’s rights and labor rights, and the implications of legal reforms. The essay draws upon a range of scholarly sources to underscore the intricate relationship between human rights improvements and socio-economic objectives in Saudi Arabia. The main conclusions suggest that while significant strides have been made, particularly in women's empowerment and labor rights, substantial obstacles—rooted in cultural norms and institutional practices—persist. Ongoing international scrutiny and a robust domestic dialogue on human rights remain critical for the success of these reforms. The essay ultimately advocates for sustained efforts to uphold human rights as a cornerstone of the Vision 2030 agenda.

The SARS-CoV-2 virus (COVID-19) has led to an initiative to develop safe and effective vaccines for public health safety. In December 2020, the United States Food and Drug Administration (FDA) administered an Emergency Use Authorization (EUA) for the Pfizer-BioNTech COVID-19 vaccine. Other vaccines from Johnson & Johnson, Moderna, and AstraZeneca shortly followed. This paper aims to examine the EUA process for COVID-19 vaccine approvals, including the requirements for the safety and efficacy of data, along with the role of the FDA and EMA in the approval process. Further, public concerns about vaccine approvals, including issues on the approval process's safety, efficacy, and speed, were also discussed. Using qualitative analysis of the extracted studies, trends and insights into the factors influencing public acceptance of COVID-19 vaccines, such as vaccine hesitancy and misinformation, were discussed. Overall, a transparent and rigorous regulatory process for vaccine approval is critical for education efforts between policymakers, vaccine developers, and the public to address concerns for increased population vaccine uptake.

Photodynamic therapy (PDT) is a minimally invasive treatment that elicits tumor apoptosis using laser light exclusively applied to the tumor site. IR-783, an HMC dye, impedes the proliferation of breast cancer cells, even without light. However, the mechanism by which IR-783 exerts its antiproliferative effects remains unclear. We aimed to determine the efficacy of PDT using IR-783 liposomes. A heptamethine cyanine (HMC) dye excited by long-wavelength infrared light and with high tissue permeability was used for PDT after liposomization to enhance tumor tissue accumulation. PDT was performed using IR-783 in two patients with tongue and breast cancers. IR-783 liposomes similarly inhibited cell proliferation in lung cancer cells even when not excited by light. Tumor size was markedly reduced in both cases, with no significant adverse events. Furthermore, the patient with tongue cancer exhibited improved respiratory, swallowing, and speech functions, which were attributed not only to the shrinkage of the tumor, but also to the improvement of airway narrowing. In conclusion, PDT using IR-783 liposomes effectively reduces tumor size in tongue and breast cancers.

With the development of human-computer interaction, the importance of emotion recognition is increasing. Emotion recognition technology provides practical benefits in various industries such as improving user experience, education, and organizational productivity. In education, emotion recognition can enable real-time understanding of students' emotional states and provide tailored feedback, and in the workplace, by monitoring employees' emotional states, it can improve work performance and job satisfaction. Therefore, multimodal-based research that combines text, speech, and video data for emotion recognition is being conducted in various industries. In this study, we propose an emotion recognition method that combines text and speech data, reflecting the characteristics of the Korean language. For text, KoELECTRA is used to perform embedding, and for speech, MFCC and pitch analysis are used to extract features. Finally, a multimodal transformer model is proposed that combines these two data types to perform emotion recognition. The multimodal transformer model processes text and speech data separately, and then learns the interaction between the two modalities through a Cross-Modal Attention mechanism. Through this, the complementary information from text and speech is effectively combined, improving emotion recognition performance. Experimental results show that the proposed model outperforms single-modality models, achieving a high accuracy of 73.13% and an F1-Score of 0.7344 in emotion classification. This study contributes to the advancement of emotion recognition technology by combining various language and modality data, and higher performance can be expected through the integration of additional modalities in the future.

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