According to current knowledge, although the beneficial effects of regular exercise training are well recognized, there are conflicting data regarding its relation with the occurrence of atrial fibrillation (AF) in competitive athletes. The aim of this literature review was to demonstrate a comprehensive report of incidence, pathophysiology and management of AF in elite athletes. The frequency of AF to be 2 to 10 times greater in high intensity endurance athletes compared to sedentary individuals. A U-shaped relationship between male elite athletes and AF has been observed, while the type and the dose of physical activity seems to play a major role in AF development. Male gender, increasing age, endurance sports, and time of training are associated with AF, thereby confirming a likely association between athleticism and AF. Pathophysiology of AF still remains unclear but, there are suggested theories that include complex mechanisms such as alterations of autonomic tone, left atrial enlargement and fibrosis, electrical remodeling, and increased inflammation. Elite athletes with AF require a comprehensive clinical evaluation and risk factor optimization, similar to that for nonathletes. Although anticoagulation and rate or rhythm control are corner stones for AF management, however, there are still no specific guidelines for elite athletes.

Developmental tasks are formed from an analysis of the culture and social demands in which the individual lives and interacts. Developmental psychology defines Milestones as substantial progress in a developmental task. When a person is able to complete developmental tasks at his stage, that person will be happy and consider his development according to his stage. So far, the current developmental tasks are different from the situation and social demands in Indonesia. Thus, the overview of development tasks has yet to be depicted in the psychological development of Indonesians. The aim of the study was to formulate developmental tasks according to the social environment. This research method was carried out through a survey that was distributed online in all regions of Indonesia. The development task screening questionnaire was prepared according to Havighurst's developmental tasks, including a description of open input at each stage. Respondents involved in the study were 345 participants, with a ratio of 78 men and 287 women. The age range of the respondents was 17 years to 63 years. Respondents came from 19 provinces, which means 50 percent of the total number of new provinces in Indonesia. Based on the number of major islands in Indonesia, the respondents represented 4 out of 5 islands or 80%. Hence, the research results can be used to describe Indonesian people. The results show that each stage of development has task contradictions, loss of developmental tasks, addition of developmental tasks and mutual substitution at the previous and subsequent stages. The details at each stage are possible to be elaborated in further studies.

Kolmogorov–Arnold Network (KAN) is an emerging interpretable neural network compared to fully black-box MLPs. Recently, emerging works focus on comprehensive and fair comparisons between KAN and MLP in various tasks. However, these works didn't focus on the strongest advantage of KAN: generating symbolic outputs. The ability of KAN to provide scientific insights or even discover new science is under-examined. In this work, we propose several novel metrics to measure how well a KAN performs on symbolic function fitting: R^2-Mean, weighted R^2-complexity loss, and ranking metrics. We also propose a metric to determine mathematical complexity of a target function and evaluate KAN with several functions of different mathematical complexity. Additionally, we also tried inputs with different ranges to find the effect of normalization.

Background: Before the COVID-19 outbreak, food microbiology was crucial in identifying and preventing foodborne pathogens like Salmonella and E. coli. The pandemic underscored the importance of this discipline, highlighting the need for enhanced surveillance and microbiological control in the food chain to prevent future global public health threats. This research aimed to advance knowledge management in Bioeconomy, focusing on Eco-Intensification and biotechnology applications that directly affect food microbiology, especially in the context of the COVID-19 crisis. Methodology: The study involved a comprehensive literature review grounded in the epistemological principles of Bioeconomy, utilizing the Bibliometric Scopus database. This approach facilitated the exploration of the development of eco-intensification and biotechnology applications within food safety and nutrition microbiology. Results: The findings emphasize the importance of understanding the social and economic impacts of microbiological activities in food intake through a Bioeconomy lens. The research highlights the need for integrating microbiological insights into broader bioeconomic frameworks to address food safety and sustainability challenges effectively. Conclusion: Recommendations are provided for decision-makers to enhance public health agendas from a bioeconomic perspective. These recommendations aim to strengthen the focus on Bioeconomy and improve the management of interactions between food microbiology and the challenges exacerbated by the COVID-19 pandemic.

Background- Fertilizers are essential to enhancing food security by enhancing crop growth. The selection of chemical fertilizers over organic fertilizers brings environmental consequences. The government is trying to achieve food security and promote organic fertilizers over chemical fertilizers by implementing various programs. Aim- The study is carried out to understand the effect of government policies on farmers' chemical fertilizer consumption patterns. Design- The data of fifteen years of chemical fertilizer consumption was divided into three sub-groups. Statistical analysis was done to understand the trend of chemical fertilizer consumption of the farmers. Results- The analysis of data clearly shows the steadily increasing pattern of chemical fertilizer consumption. Conclusions- The study clearly shows there is no effect of sustainable agriculture policies framed by the government on the consumption of chemical fertilizers by farmers. Across the study period, farmers were inclined to use chemical fertilizers, related to a lack of awareness of the principles of appropriate fertilizer management that can limit environmental damage.

Veterinary science has benefitted from technological advancements in the past and will hopefully continue to do so, especially in the field of equine gait analysis. There is a need for rapid and easy to use technology that is accurate and portable, but at the same time is also readily interpretable. This manuscript presents two new approaches to equine gait analysis, both of which are commer-cial devices. The use of Acoustic MyoGraphy (AMG) has been proven in the field of muscle func-tion and performance as well as ligament health, but it can also provide details concerning gait when placed on the wall of the hoof of a horse. The TendibootTM system is a limb focused unit that can be easily attached, activated and used in a Veterinary clinic setting, rapidly providing gait details for equine diagnostic purposes. In conclusion this manuscript provides an insight into gait parameters from recent research trials that are novel and challenge current thinking.

Detecting small objects in images poses significant challenges due to their limited pixel representation and the difficulty in extracting sufficient features, often leading to missed or false detections. To address these challenges and enhance detection accuracy, this paper presents an improved small object detection algorithm, CRL-YOLOv5. The proposed approach integrates the CBAM attention mechanism into the C3 module of the backbone network, which enhances the localization accuracy of small objects. Additionally, the Receptive Field Block (RFB) module is introduced to expand the model's receptive field, thereby fully leveraging contextual information. Furthermore, the network architecture is restructured to include an additional detection layer specifically for small objects, allowing for deeper feature extraction from shallow layers. When tested on the VisDrone2019 small object dataset, CRL-YOLOv5 achieved an mAP50 of 39.2%, representing a 5.4% improvement over the original YOLOv5, effectively boosting the detection precision for small objects in images.

Background: Three-dimensional gait analysis plays a crucial role in the rehabilitation assessment of post-stroke hemiplegic patients. However, the data generated from such analysis is often complex and difficult to interpret in clinical practice, requiring extensive time and complicated procedures. The Gait Deviation Index (GDI) serves as a simplified metric for quantifying the severity of pathological gait. Although isokinetic dynamometry is widely employed in muscle function assessment and rehabilitation, its application in gait analysis remains underexplored. Objective: This study aims to investigate the use of isokinetic muscle strength data obtained from the Biodex system, combined with machine learning techniques, to predict the GDI in hemiplegic patients. Methods: A cohort of 150 post-stroke hemiplegic patients was evaluated. The peak torque, peak torque/body weight, maximum work of repeated actions, coefficient of variation, average power, total work, acceleration time, deceleration time, range of motion, and average peak torque for both flexor and extensor muscles on the affected side were measured at three angular velocities (60°/s, 90°/s, and 120°/s) using the Biodex System 4 Pro. GDI was calculated using data from a Vicon three-dimensional motion capture system. The study employed four machine learning models—Lasso regression, random forest (RF), support vector regression (SVR), and BP neural networks—to model and validate the data. Model performance was evaluated using mean squared error (MSE), the coefficient of determination (R²), and mean absolute error (MAE). SHapley Additive exPlanations (SHAP) analysis was used to enhance model interpretability. Results: The RF model outperformed others in predicting GDI, with an MSE of 16.18, an R² of 0.89, and an MAE of 2.99. In contrast, the Lasso regression model yielded an MSE of 22.29, an R² of 0.85, and an MAE of 3.71. The SVR model had an MSE of 31.58, an R² of 0.82, and an MAE of 7.68, while the BP neural network exhibited the poorest performance with an MSE of 50.38, an R² of 0.79, and an MAE of 9.59. SHAP analysis identified the maximum work of repeated actions of the extensor muscles at 60°/s and 120°/s as the most critical features for predicting GDI, underscoring the importance of muscle strength metrics at varying speeds in rehabilitation assessments. Conclusion: This study highlights the potential of machine learning techniques in analyzing complex clinical data. The developed GDI prediction model, based on isokinetic dynamometry data, offers a novel, streamlined, and effective tool for assessing rehabilitation progress in post-stroke hemiplegic patients, with promising implications for broader clinical application.

The aim of this work was to classify the business incubators in Spain according to the four phases of the startup’s incubation process. Considering that the graduation rate implies greater survival and business success of the incubated companies, they have been identified at each stage of the incubation (spread of entrepreneurship, pre-incubation, advanced incubation, and graduation). The activities that present higher impacts on the success of the incubated companies and the activities carried out by the business incubator that have a greater relevance on the graduation of the companies have concretely been considered. Principal component (PC) cluster analysis has been applied. All the incubation variables were used simultaneously, reducing their number, and grouping them into factors. Finally, the cases were grouped according to these latent variables. Principal components analysis reduced dimensionality to 8 factors with a 74 % of explained variance. Factor 1 was positively related to pre-incubation variables, factor 2 was linked to training and collaboration variables within the entrepreneurship diffusion phase. Factor 3, named activity monitoring and control, was related to phase 3 or basic incubation variables. Cluster analysis facilitates the grouping of business incubators into three clusters: Group 1 (16 % of the total), incubators with strong deficits in incubation phases 1, 2 and 3. They are small sized business incubators, often located in rural areas or cities and low graduation rate. Group 2 (30 %), business incubators with very high graduation rate, and strongly positive values in factors 1 and 2. Factor 3, although positive, it is susceptible to improvement. They are the largest group of business incubators and usually located in industrial and technological parks. Group 3 (54 %) is the majority, with values close to cluster 2 and 3.

The isotopic and nuclear techniques discussed, namely: Fallout Radionuclide, Fingerprint, and Isotope Hydrology, are linked to the evaluation of soil and water degradation processes. A traditional literature review was conducted to gain the most comprehensive understanding of both the current state of the art and the subject matter, with the aim of proposing a hypothesis that integrates the synergistic convergence of these techniques in order to offer results that are more reliable. A case study is presented as a synthesis of the preceding discussion.

Background: Over the past two decades, the Copenhagen Psychosocial Questionnaire (COPSOQ) has been established as a valid instrument to measure psychosocial stress at work. Currently, the COPSOQ international network is responsible for monitoring and improving COPSOQ. In 2019, a new questionnaire was published, and the Greek version is now being validated. Methods: The measurement qualities of the Greek COPSOQ III have been explored in accordance with the usual requirements of a validation study as defined by DIN EN ISO 10075-3. A sample of observations from 2,189 participants surveyed with the COPSOQ in Greece was used to validate the current version with appropriate statistical analyses. Exploratory factor analysis was used to assess the statistical relationships for many scales. Results: With its 108 items and 40 scales, the Greek COPSOQ III includes all psychosocial work factors that have been internationally validated and remain compatible with approximately 72% of the COPSOQ III German version content. In addition to the primary results, congruence with widely used theoretical approaches such as the demand-control (−support) model (DCM) or the job demands-resources model (JDR) is generally satisfactory. In summary, our validation study for the Greek COPSOQ III version showed adequate reliability and validity, which is in line with the findings of the COPSOQ III questionnaire from other European countries, and it is also compatible with the validation of the German COPSOQ III (Lincke, 2021). Our regression analysis revealed that 34 psychosocial work factors (34 “context” scales) could adequately predict the scores of the satisfactory and health scales (6 “outcome” scales). The analysis also revealed the top five (5) predictors (context variables) for each of the six (6) “effect” scales (outcome variables). Conclusions: With the launch of COPSOQ III in Greece, current and new workplace psychosocial aspects could be explored. COPSOQ III (GR) is a beneficial instrument for enterprise research and risk assessment.

Ti-Al based alloys, particularly TiAl and Ti3Al two-phase alloys, have garnered significant attention as potential replacements for various high-temperature structural materials due to their exceptional properties, including low density, oxidation resistance, and high strength at elevated temperatures. Despite these advantages, experimental studies on the microstructure evolution of Ti-Al based alloys under complex conditions remain challenging to observe and characterize. This review article examines the current research on molecular dynamics (MD) simulations of Ti-Al based alloys, focusing on two-phase Ti-Al alloys, Ti-Al amorphous alloys, Ti-Al composite materials, and the welding and multi-layer/film applications of Ti-Al alloys. The review highlights the unique capabilities of MD simulations in predicting the behavior of Ti-Al based alloys and addresses existing scientific challenges. Furthermore, the article discusses future research directions and development prospects in this field.

Microorganisms have colonized all barrier tissues and are present on the skin and all mucous membranes since birth. Bacteria have many ways of influencing the host organism: activation of innate immunity receptors by PAMPs; synthesis of various chemical compounds, such as vitamins, short-chain fatty acids, bacteriocins, toxins. Bacteria, using extracellular vesicles, can introduce high-molecular compounds, such as proteins and nucleic acids, into the cell, regulating the metabolic pathways of the host cells. Epithelial cells and immune cells recognize bacterial bioregulators and, depending on the microenvironment and context, determine the direction and intensity of the immune response. A large number of factors influence the maintenance of symbiotic microflora, the diversity of which protects host against pathogen colonization. Reduced bacterial diversity is associated with pathogen dominance and allergic diseases of the skin, gastrointestinal tract, upper and lower respiratory tract, as seen in atopic dermatitis, allergic rhinitis, chronic rhinosinusitis, food allergies, and asthma. Understanding the multifactorial influence of microflora on maintaining health and disease determines the effectiveness of therapy and disease prevention, changes our food preferences and lifestyle to maintain health and active longevity.

In 2018, Iran witnessed approximately 21,000 fatalities resulting from road accidents, ranking it as the fifth leading cause of death across all age groups. These incidents carry substantial economic and social burden for the nation, claiming more than 5% of its GDP. Human behavior emerges as a significant contributing factor to high-risk driving offenses. It has been proposed that by influencing general deterrence, a rigorous defense can be established against such violations. Various theories posit that by reinforcing or diminishing influential factors, general deterrence perception can be molded, presenting a potential avenue for an effective deterrent against traffic infractions. This study examines deterrence perceptions prevailing among the public in Iran regarding the enforcement of traffic laws. Employing Structural Equation Modeling (SEM), we delve into the intricate relationships between variables, allowing for a comprehensive exploration of the factors influencing general deterrence. The study draws on the data derived from a survey comprising 548 questionnaires for modeling purposes. An innovative concept, private sector enforcement, is introduced and incorporated into the questioning and modeling process. The findings reveal that socioeconomic status (p-value<0.01), police enforcement (p-value<0.05), private sector enforcement (p-value<0.05), specific deterrence (p-value<0.01), and the use of technology (p-value<0.01) have direct and indirect impacts on the general deterrence perception of drivers.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive impairment and memory loss. Oxidative stress (OS) has emerged as a central element in the pathophysiology of AD, linking various pathological features including amyloid-β (Aβ) plaque formation, tau hyperphosphorylation, and synaptic dysfunction. This review comprehensively examines the role of OS in AD, focusing on the mechanisms of reactive oxygen species (ROS) production, mitochondrial dysfunction, and their impact on neuronal integrity. Additionally, the review highlights recent advances in antioxidant therapy. Elevated ROS levels in the aging brain exacerbate oxidative damage to lipids, proteins, and DNA, contributing to neuronal atrophy and synaptic loss. Mitochondrial dysfunction further amplifies OS, disrupting cellular energy metabolism and promoting neurodegeneration. Despite the strong association between OS and AD, antioxidant therapies have shown inconsistent clinical outcomes. Emerging strategies targeting offer promising avenues for therapeutic intervention. This review highlights the need for a multifaceted approach in understanding and mitigating OS-related damage in AD, aiming to pave the way for more effective treatments and improved patient outcomes.

The bulk LiNa5Mo9O30 (LNM) crystals were successfully grown in the [010] and [001] directions without internal inclusions and cracks by the Czochralskii method with low temperature gradient. The crystal grown in the [010] direction showed a tendency to twinning. The crystal grown in the [001] direction demonstrated high structural perfection (FWHM=13'') for the (001) plane and high optical quality Δn≈210-5. The laser induced damage threshold was measured along a, b and c axes and was 15.7, 27.0 and 27.5 J/cm2 respectively. The thermo-optical coefficient dn/dT was measured for the main crystallographic axes, which was -5.7510-6, -2.0210-5 and 3.6510-6 K-1 along the a, b and c axes, respectively. The second harmonic generation (SHG) is conducted in crystalline LNM sample. The maximum efficiency value of 3.5% at pump power of 12 W had been achieved.

This study employs a bibliometric analysis of the emerging trends in the area of Money Laundering for Ter-rorism Financing to provide direction for future research in the area. The authors used VOSviewer and an-alysed 2577 published documents generated from the SCOPUS search using the PRISMA methodology. The findings reveal a growing research interest in this area, with a focus on understanding the complex inter-play between money laundering and terrorism financing. Emerging trends such as the use of cryptocurren-cies, trade-based money laundering, and the role of non-profit organizations in facilitating these activities are highlighted. The research contributes to the broader understanding of money laundering for terrorist financing by providing insights into the evolving research landscape and identifying areas for future re-search.

The current globally dominant SARS-CoV-2 variants are showing immune escape and reduced susceptibility to antiviral drugs. Therefore, agencies responsible for drug evaluation and regulation such as the FDA and EMA are revising their emergency authorization use of several COVID-19 neutralizing antibodies. These MAbs proved to be unlikely effective against new variants especially Omicron descendants and several pharmaceutical companies are pursuing the development of more potent neutralizing antibodies. To address this issue, we used In Silico method we previously developed to assess 10 anti-SARS-CoV-2 antibodies propensity to neutralize the new Omicron’s subvariants EG.5, BA.2.86 and JN.1, based on comparative binding affinity of 3D generated models and previous experimental and clinical observations. Nine of these MAbs were once granted emergency use authorization, and one is currently under clinical investigation. The results showed that one antibody showed a marked increase of the binding energy for EG.5 compared to two antibodies that showed a significant increase with Pirola (BA.2.86) and JN.1. This data indicates that the new SARS-CoV-2 variant escapes neutralization of most of the available therapeutic NAbs. Furthermore, the data showed new potential therapeutic MAbs combination that could be effective for the treatment countermeasure of the new Omicron’s descendants or potential novel variants.

IntroductionGlycogen synthase kinase 3 (GSK-3) has been associated with a lot of diseases, such as Alzheimer’s Disease and diabetes. Its role in depression is connected to stress and neuroinflammation. Lithium is an essential inhibitor of GSK-3. Some studies have suggested that the use of this psychiatric medication may lower suicide rates. Here, we outline the current understanding and discoveries regarding the link between GSK-3 and suicide. MethodWe used the PRISMA statement and defined the PECOS strategy, such as Population (P) = individuals; exposure (E) = GSK-3 alteration function; comparison (C) = without GSK-3 alteration function; outcome (O) = suicide; and study design(S) = all kinds of studies with an association between GSK-3 and suicide.ResultsTwelve studies were included in this review. GSK-3 appears to be related to suicidal behavior by activating inflammatory pathways, activating genes, altering neurotransmission, and increasing impulsivity. Clinical studies do not present robust results that allow defining an epidemiological profile of greater risk for suicidal behavior in association with changes in the functioning of GSK-3.ConclusionThe interference of GSK-3 functioning in suicidal behavior seems inevitable, with solid evidence from laboratory animal studies. However, there is a need for further clinical studies on this crucial topic to answer better some questions about the association between GSK-3 and suicide.

In this letter, we show that the effects usually attributed to the presence of dark matter in the Universe can equally be seen as the ”external” effect on our Universe due to extra dimensions. For this, we consider a braneworld scenario where our universe, which is a 3-brane, is immersed in a 6D Minkowski bulk. We derive the 1/r force on the 3-brane at large distances as a response of cosmic fluid to external force due to extra dimensions. This scaling reproduces the effect of dark matter, such as the flat rotation curves of galaxies and a higher deflection angle around some gravitational lenses. It also explains the large-scale structure formation of the universe. This approach thus offers a novel perspective on dark matter, suggesting that higher-dimensional physics can account for these cosmic phenomena.

The medicinal herb Centaurium erythraea is well-known for its natural ability to treat digestive issues like dyspepsia and liver insufficiency by promoting the secretions of the liver and esophagus. This study aimed to analyze the effectiveness of Centaurium erythraea extracts in treating diabetes. Decoction and Soxhlet procedures were used to examine the extracts, with water serving as the solvent. The extracts were tested for antioxidant capacity, antimicrobial effects, and inhibitory effects on alpha-amylase activity. In comparison to the aqueous extract, the decocted extract had higher levels of polyphenols, total tannins, and anthocyanins, as well as more effective antioxidant capacities. Due to its reduction of alpha-amylase activity and enteric absorption of glucose, Centaurium erythraea has also shown considerable anti-hyperglycemic action.

The measurement of the grounding resistance of protection grids in large installations as well as grounding electrodes in urban areas is addressed in this article. The resistance value is obtained using a three-pin array by measuring the fall-of-potential on the ground surface. The resistance measured by this method is corrected using the so-called correction factor that adjusts the measured resistance value to its real value. The proposed measurement method obtains correct values of the grounding resistance even when the auxiliary and potential electrodes of the tree-pin array are close to the electrode to be measured. Thus, it can be applied to large electrodes as well as electrodes in urban areas. Several simulated examples are used to illustrate the method, and some real cases with field measurements are presented for a final validation of the method.

The rapid urbanization process in recent decades has altered the carbon cycle and exacerbated the impact of climate change, prompting many cities to develop tree planting and green area preservation as mitigation and adaptation measures. While numerous studies have estimated the carbon stocks of urban trees in temperate and subtropical cities, data from tropical regions, including tropical botanic gardens, are scarce. This study aimed to quantify the aboveground biomass and carbon (AGB and AGC, respectively) stocks in trees at the Rio de Janeiro Botanical Garden arboretum, Rio de Janeiro, Brazil. Our survey included 6793 stems with a diameter at breast height (DBH) ≥ 10 cm. The total AGB was 8,047.24 Mg, representing 4,023.62 Mg of AGC. The AGB density was 207.4 Mg.ha-1 (AGC = 103.7 Mg.ha-1), which is slightly lower than the density stored in Brazil's main forest complexes: the Atlantic and Amazon forests, but much higher than in many cities worldwide. Our results suggest that, in addition to their global importance for plant conservation, tropical botanic gardens could function as significant carbon sinks within the urban matrix.

Background: The relationship between the intake of artificial sweetener (AS) and adverse pregnancy outcomes is under-researched, and existing studies yield inconsistent conclusions. Methods/Objectives: A mendelian randomization (MR) approach was employed to investigate the causal relationship between the intake of AS and adverse pregnancy outcomes. Instrumental variables related to the exposure phenotype were selected for analysis. The analysis was conducted using genome-wide association studies summary data from public datasets. Inverse variance weighted, MR-Egger, weighted median, simple mode, and weighted mode were used to evaluate the causal relationship between exposure and outcomes. Sensitivity analysis and multivariable mendelian randomization enrolling body mass index, type 2 diabetes mellitus and fasting glucose was employed to further validate the consistency and robustness of results. Results: The intake of AS added to tea was associated with an increased risk of ectopic pregnancy [OR=1.821 (1.118 - 2.967), p=0.016] in univariable MR. The intake of AS added to cereal was linked to a reduced risk of ectopic pregnancy [OR=0.361 (0.145 - 0.895), p=0.028] and premature rupture of membranes [OR=0.116 (0.019 - 0.704), p=0.019], while the intake of artificial sweetener added to coffee was associated with an increased risk of placenta previa [OR=1.617 (1.042 - 2.510), p=0.032] adjusting for body mass index and type 2 diabetes mellitus in multivariable MR. No causal relationship was identified between the intake of artificial sweetener and the other adverse pregnancy outcomes. Conclusions: The consumption of artificial sweeteners during pregnancy warrants careful consideration.

Let's define $\delta(x) = (\sum_{{q\leq x}}{\frac{1}{q}}-\log \log x-B)$, where $B \approx 0.2614972128$ is the Meissel-Mertens constant. The Robin theorem states that $\delta(x)$ changes sign infinitely often. Let's also define $S(x) = \theta(x) - x$, where $\theta(x)$ is the Chebyshev function. It is known that $S(x)$ changes sign infinitely often. We define the another function $\varpi(x) = \left(\sum_{{q\leq x}}{\frac{1}{q}}-\log \log \theta(x)-B \right)$. We prove that when the inequality $\varpi(x) \leq 0$ is satisfied for some number $x \geq 3$, then the Riemann hypothesis should be false. The Riemann hypothesis is also false when the inequalities $\delta(x) \leq 0$ and $S(x)\geq 0$ are satisfied for some number $x \geq 3$ or when $\frac{3 \times \log x + 5}{8 \times \pi \times \sqrt{x} + 1.2 \times \log x + 2} + \frac{\log x}{\log \theta(x)} \leq 1$ is satisfied for some number $x \geq 13.1$.

In some clinical applications pulsating currents are delivered into a body region for therapeutic purposes. In this paper we analyze the generated thermal field with the aim of determining the amplitude, period and duration of those stimuli, guaranteeing that the temperature of the interested tissue stays below the necrosis threshold.

Imbalanced classes can cause machine learning models to classify positive class instances poorly, and the models require hyperparameter sets to optimal values. This study aims to develop random forest (RF) and support vector machine (SVM) models that use the optimal hyperparameters obtained through 5-fold cross-validation data. Both models were trained using optimal hyperparameter pairs that fit two data scenarios, the original and oversampling training sets, to produce the benchmark and best models. The model's performance was evaluated using six metrics, including training and testing data. The acquired optimal RF hyperparameter pair was (500, 10) for the minimum number of instances and tree depth level. In contrast, the acquired optimal SVM hyperparameter pair was (0.001, 500) for the gamma and constant values. The benchmark model performed approximately 98% on Accuracy, Precision, Recall, and F1 score metrics but failed to deliver any performance on Mathew's Correlation Coefficient (MC) and Area under Curve (AUC) metrics. The best RF and SVM perform less than both benchmark models in four famous metrics. Both best models have performance improvements of approximately 6% and 11% for the MCC and AUC metrics, respectively. The best RF performance was slightly better than the best SVM performance.

Objective: This analysis investigates patient experiences regarding pain management during gynecological procedures through a comprehensive survey, aiming to address the lack of clear guidelines in the clinical landscape and emphasize the importance of effective pain management strategies in enhancing patient care and satisfaction. Design: A cross-sectional survey with 258 respondents assessed pain management experiences during gynecological procedures. The survey was distributed via Qualtrics, and 258 responses were obtained. Participants/Materials, Setting, Methods: Participants provided demographic information and responded to questions regarding contraceptive methods, procedures undergone, pain management discussions, anxiety levels, and willingness to accept pain management interventions. Confidentiality and informed consent protocols were strictly observed throughout the study. Results: Most female respondents reported diverse contraceptive methods and procedures. Pain management discussions and provisions varied, with a significant portion reporting never being offered pain management. Many experienced anxiety and pain during procedures, with varying intensities, that were not addressed or discussed by their treating provider. Most expressed willingness to accept pain management interventions. Limitations: This study relies on self-reported data, potentially introducing recall bias, and has limited generalizability due to its cross-sectional design. Conclusions: Findings reveal a significant gap in pain management provision and discussion during gynecological procedures. Improved guidelines and practices are necessary to address patient discomfort effectively. Standardized pain management protocols are crucial to enhance patient experiences and outcomes, with further research needed to tailor strategies to individual patient needs.

Sodium valproate is a established antiepileptic drug of first choice, primarily used to treat epilepsy, but also bipolar disorder and prevent migraine headaches. Valproate increases concentrations of gamma-aminobutyric acid in the brain, due to inhibition of enzymes responsible for the catabolism of gamma-aminobutyric acid. Main goal of this research consisted in the optimization and development for a new sensitive spectrophotometric quantitative analysis method of sodium valproate from tablets in the Visible field. Following the quantitative color reaction of sodium valproate with diazonium salt of alpha naphthylamine in alkaline medium, a light yellow azo dye was formed and spectrophotometrically analyzed at the wavelength of 386 nanometers. The amount of pure sodium valproate found on pharmaceutical extended-release tablet was 492.578 miligrams compared to the official declared pure amount of 500 miligrams on prolonged-release tablet. Amount found was assigned to a percentage content of 98.5156 percent pure sodium valproate in a pharmaceutical tablet. Mean average percentage error (deviation) from the officially stated value was about 1.4844 percent, which was included within normal limits, below five percent maximum limit allowed by European and Romanian Pharmacopoeias rules. Spectrophotometric method was subjected to the statistical validation procedure and was successfully validated.

This study presents a mixed review of research on the competencies of built environment professionals in infrastructure delivery within emerging economies. By integrating bibliometric analysis with a systematic review, the research offers a comprehensive understanding of key themes, trends, and debates in the field. The review process culminated in the selection of 59 publications, which were analyzed to identify critical topics including embedding emerging skills in built environment competencies, competency-based curriculum, technology infrastructure and assessment, municipal competency training, and the role of the Fourth Industrial Revolution (4IR). The findings underscore the importance of evolving educational frameworks to meet the demands of globalization, integrating advanced technologies, and fostering industry-academic collaborations to enhance the skills of professionals in the built environment sector. This approach ensures a robust examination of the competencies necessary for effective infrastructure delivery in emerging economies.

Traditional Chinese medicine requires decoction and consumption, which is time-consuming and labor-intensive. Scientific Chinese medicine (SCM), on the other hand, uses concentrated granules that can be taken with hot water, making it widely welcomed. With the widespread adoption of SCM, the packaging technology has started to receive attention. Whether it is manpower allocation, operation time, or the uniformity and residue of the medicine powder, these factors can become bottlenecks in the process. A traditional SCM pharmacy machine is purely mechanically hardwired. This paper proposes SCMtalk, an Internet of Things (IoT)-based SCM mechanism designed to enhance the efficiency and accuracy of SCM medicine packets generation. We show how SCMtalk develops IoT hardware and software modules for medicine selection, mixing, dispensing, packaging, and cleaning. We also explain how the mechanical IoT hardware is integrated with the corresponding software module to become an individual IoT device. These IoT devices can be transparently replaced, allowing us to easily upgrade the individual components of the SCM packet generation through the IoT concept. Finally, we address quality control issues by leveraging IoT. One contribution of this paper is to introduce an interesting SCM application to IoT experts and to demonstrate how we collaborated with pharmacists and mechanical experts to use a powerful IoT platform to create a smart pharmacy.

The increasing rearrangement is a rewarding instrument in financial risk management. In practice, risk must be managed in different perspectives. A common example is portfolio risk which often can be seen from at least two perspectives: market value and book value. Different perspectives that is different distributions can be coupled by increasing rearrangement. One distribution is regarded as underlying, and the other distribution can be expressed as increasing rearrangement of the underlying distribution. Then, the risk measure for the latter can be expressed in terms of the underlying distribution. Our first objective is to introduce the increasing rearrangement for application in financial risk management and apply the increasing rearrangement to the class of distortion risk measures. We derive formulas to compute risk measures in terms of the underlying distribution. Afterwards, we apply our results to a series of special distortion risk measures, namely Value at Risk, Expected Shortfall, Range Value at Risk, Conditional Value at Risk, Wang's risk measure. Finally, we present the connection of increasing rearrangement to inverse transform sampling, Monte Carlo simulation, and cost-efficient strategies. Butterfly options serve as illustrative example for the method.

Rift Valley fever (RVF) is a mosquito-borne zoonotic viral disease that infects both human and animals. This is a descriptive study summarizes investigation and response to RVF outbreak, using surveillance data. A RVF outbreak occurred in Berber locality in northern Sudan. We identified 246 cases of RVF including seven deaths among humans, 119 deaths and 82 abortions among Livestock. Our entomological surveillance confirmed the presence of Anopheles arabiensis, An. stephensi, Aedes aegypti, Ae. vexans, Culex pipiens, Cx. Quinquefasciatus, and Cx. theileri in the area during the outbreak. In a comparison with another outbreak of Rift Valley fever in nearby area while ago, a multisectoral One Health response strategy has resulted in strengthening the health system through re-innovating health facilities and centres and training about 200 healthcare and emergency responders. Additionally, it reduced cases and fatalities among humans by 78% and 63%, respectively, and loss in livestock due to deaths and/or abortions by 82%. The added-value of implementing One Health strategy was saving more lives and resources, while strengthening the health system. We recommend the systematic institutionalization of One Health strategy in the country as a core part of the national prevention and control strategy for zoonotic diseases epidemics and pandemics.

Recently, the destructive impact of wildfires has proliferated; for instance, the August Complex wildfire in 2020 burned around 4% of California's landmass. This has caused increased economic damage and risk to human life. Additionally, climate change is anticipated to increase the severity of wildfires, making it imperative for accurate and efficient detection of wildfires. Machine Learning approaches allow for the automatic detection of wildfires, simultaneously prioritizing accuracy and efficiency, with minimal human intervention, thus decreasing the likelihood of increased economic damage and increasing firefighting responses. Convolutional Neural Networks (CNNs), while showing promise, are often limited by their inability to learn and capture deep spatial dependencies in satellite imagery tasks. In this paper, we propose FlameViT, a novel wildfire detection architecture based on Vision Transformers (ViT). Satellite images are more efficient to obtain and can cover wide areas prone to wildfires. We obtain a dataset of 40K+ satellite images from Canada's Open Government Portal, allowing FlameViT to be optimized to detect wildfires in satellite imagery. FlameViT uses Patch Extractor and Patch Embedding layers, followed by multiple Transformer Encoder layers with Multi-Head Self-Attention and feed-forward neural networks. FlameViT is hyperparameter-tuned and achieves a validation accuracy of 95%, outperforming various baselines in the wildfire detection task. FlameViT shows the power of Vision Transformers for wildfire detection tasks, and in conjunction with the use of satellite imagery, can provide an efficient and accurate way of detecting wildfires.

Venous and arterial thromboembolism (VTE/ATE) often coexist with onco-hematologic diagnosis. The study aimed to assess the time relationship between the diagnosis of VTE/ATE and blood cancers. The second aim was to identify VTE/ATE risk factors related to the type of hematology disease and cardiac history. 1283 patients underwent cardio-oncology evaluation at the Institute of Hematology and Transfusion Medicine in Warsaw from March 2021 through March 2023 (2 years), and 101 (7.8%) cases were identified with VTE/ATE. ATE compared to VTE significantly more often occurred before the diagnosis and treatment of hematologic malignancy: 33/47 (70.2%) vs. 15/54 (27.8%), p<0.0001. The risk of a VTE episode is exceptionally high in the first months after the diagnosis of an onco-hematological disease. The higher frequency of VTE was associated with acute myeloid leukemia (17 cases/270 patients/6.30%/p = 0.055), acute lymphocytic leukemia (7 cases/76 patients/9.21%/p = 0.025) and chronic myeloproliferative disease (7 cases/48 patients/14.58%/p=0.0003). Only the risk of VTE was significantly increased before (OR=6.79; 95%CI: 1.85-24.95; p=0.004) and after diagnosis of myeloproliferative disease (OR=3.12; 95%CI: 1.06-9.16; p=0.04). ATEs occur more often than VTE before a diagnosis of blood cancer. The risk of VTE is exceptionally high before and after diagnosis of chronic myeloproliferative disease.

Euler's theorem about the relationship between the numbers of individual elements of planar graphs (vertices, edges, faces) is also satisfied when the graph has zero vertices. The problem of the existence or not of such graphs has so far been the subject of very few studies. Plus, research that didn't prove anything. The paper presents an interpretation of this kind of graphs as mathematical formations lying on piecewise smooth surfaces. The lines of intersection of two or more smooth surfaces (faces of a graph) form edges without a vertex of the graph. The elementary properties of such graphs are discussed, the method of counting non-isomorphic 0-vertex graphs and their importance in physics and chemistry are given.

There is an observed general low performance of Ethiopian Sport Governing Bodies (SGBs) in service delivery. Evidence shows that the trend can be corrected with proper understanding and insight into the process of organisational and management factors of these SGBs. This study aimed to investigate the contribution of organisational and management factors on effective sport service delivery in SGBs. For this study, a cross-sectional research design was employed. Purposive sampling was used to choose 350 samples. Pearson Product Moment Correlation Coefficient and multiple regression were used to analyse the data at the 0.05 significant level. The findings confirmed that organisational and management factor variables had a significant positive relationship with sport service delivery (organisational structure (r=0.515), organisational capabilities (r=0.701), communication (r=0.549), management practice (r=0.571), materials &amp; resources (r=0.527) and stakeholders satisfaction (r=0.742). In addition, Management factors were found to be significant contributors for predicting effective sport service delivery (F(3, 329)= 165.868, R2= 0.612). Furthermore, organisational factors had a joint significant contribution to effective sport service delivery (F(3, 329) = 148.743, R2= 0.576). In addition, organisational and management factor variables jointly best predicted effective sport service delivery in Ethiopian SGBs (F(6, 326) =130.81, R2= 0.707). Therefore, this study suggests organisational and management factor variables can be used to predict effective sport service delivery.

Facing global warming consequences is a major issue of present times. For what concerns climate, projections say that heavy rainfalls are going to increase with high probability together with temperature rise, thus also the hazard related to rainfall induced shallow landslides will likely increase in density over susceptible territories. Different modeling approaches exist and any of them is forced to make simplifications in order to reproduce landslide occurrences over space and time. Process based models can help in quantifying the consequences of heavy rainfall on the different involved processes. In this paper, a narrative review about physically-based deterministic distributed models (PBDDMs) is presented. Models were selected based on: the adoption of the infinite slope scheme (ISS), the use of a deterministic approach (i.e., input and output are treated as absolute values), the inclusion of new approaches in modeling slope stability through the ISS. Models are presented in chronological order with the aim of drawing a timeline of the evolution of PBDDMs and to give researchers and practitioners a basic knowledge on how much has been already done. Results indicate that including vegetation effects on slope stability has raised in importance over time but that there is still the need of finding an efficient way to include them, and that in the last years the literature production seems to be more focused on probabilistic approaches.

Background/Objectives: Survival prospects following SARS-CoV-2 infection may extend beyond the acute phase, influenced by various factors including age, health conditions, and infection se-verity. However, this topic has not been studied in detail. We therefore investigated mortality risk post-acute COVID-19 in the CRIT-COV-U cohort. Methods: We could retrieve survival data from 651 patients and also assess the association between urinary peptides and future death. Data spanning until December 2023 were collected from six countries, comparing mortality trends with age- and sex-matched non-infected controls. A death prediction classifier was developed and validated using pre-existing urinary peptidomics datasets. Results: Notably, 13.98% of post-COVID-19 patients succumbed during the follow-up, with mortality rates significantly higher than non-infected controls, particularly evident in younger individuals (&lt;65 years). These data for the first time demonstrate that SARS-CoV-2 infection highly significantly increases the risk of mortality not only during the acute phase of the disease, but also beyond, for a period of about one year. In our study we were further able to identify 201 Urinary peptides linked to mortality, and integrated these into a predictive classifier (DP201). Higher DP201 scores, alongside age and BMI, significantly predicted death. Conclusions: Our findings underscore the utility of urinary peptides in prognosticating post-acute COVID-19 mortality, offering insights for targeted interventions. Our data also suggest that mortality should be considered as one possible symptom or conse-quence of post-acute sequelae of SARS-CoV-2 infection, a fact that is currently overlooked.

This study aimed to characterize the physicochemical properties of broiler poultry litter (BPL) produced from intensively reared commercial broiler farming areas in tropical zone of Tamil Nadu, India. The BPL samples were collected from 110 commercial poultry farms at the end of the production cycle (6th week), further 20 samples were collected from the point of utility. The dry matter (DM), moisture, ash, organic matter (OM), and organic carbon (OC) from the manure samples were 83.04 ± 0.77, 16.96 ± 0.77, 27.08 ± 1.18, 72.92 ± 1.18 and 42.39 ± 0.69 per cent respectively. The pH, Electrical conductivity (EC) (ds m-1), and Kjeldahl Nitrogen (N) were 8.43 ± 0.06, 5.74 ± 0.13, and 24.2 ± 0.84 g kg-1 respectively. The BPL from the cement floor had higher levels of P and K (12.74 and 13.28 ) than the mud floor (10.88 and 10.96). According to the findings of this study, BPL as such at the end of the production cycle is rich in OM, nitrogen, macro and micro minerals at the same time at the point of utility there was a loss of OM, N, and concentration of minerals was noticed, which advocate proper storage and composting process.

Many definitions of cancer have been proposed over the years to explain cancer’s origins and pathophysiology, but the disease is too variable to allow any single description to completely encompass all of the myriad traits. A true, unifying theory of cancer must not only reveal how all known carcinogens contribute toward malignancy, but must also elucidate why cancers occur predominately in older patients, why they express so many similar behaviors, as well as account for each cancer’s individual variations. A conceptual reorganization of decades of cancer data may reveal the fundamental process that underlies the malignant transformation of cells, which not only provides a new understanding of the etiology of cancers, but also helps to interpret the subsequent events that occur after transformation, which is of significant clinical interest. The revised concepts of initiation, promotion, and progression suggest that cancer is a failed or incomplete attempt at regeneration that occurs when ordinary cellular events unintentionally coincide over time to force the use of a DNA repair pathway that is normally restricted for use in germ cells.

This paper proposes causal economic machine learning (CEML) as a research agenda that utilizes causal machine learning (CML) built on causal economics (CE) decision theory. Causal economics is better suited for use in machine learning optimization than expected utility theory (EUT) and behavioral economics (BE) based on its central feature of causal coupling (CC), which models decisions as requiring upfront costs, some certain and some uncertain, in anticipation of future, uncertain causally-linked benefits. This multi-period, causal-linked process incorporating certainty and uncertainty replaces the single period lottery outcomes augmented with intertemporal discounting used in EUT and BE, providing a more realistic framework for AI machine learning modelling and real world application. It is mathematically demonstrated that EUT and BE are constrained versions of CE. Causal coupling can also be applied at the macroeconomic level to gauge the effectiveness of policies that deliver various levels of cost and benefit coupling for individuals. With the growing interest in natural experiments in statistics and CML across many fields, such as healthcare, economics and business, there is a large potential opportunity to run AI models on CE foundations and compare results to models based on traditional decision making models that focus only on rationality, bounded to various degrees.

In this proposed work, five systems were developed to classify four motor functions—forward hand movement (FW), grasp (GP), release (RL), and reverse hand movement (RV)—from EEG signals. The dataset used is WAY-EEG-GAL, involving participants performing a sequence of hand movements. During preprocessing, bandpass filtering was applied to remove artifacts and focus on the mu and beta frequency bands. The first system, a preliminary study model, explored the overall framework of EEG signal processing and classification. It utilized time-domain features like variance and frequency-domain features like alpha and beta power, with classification performed using a KNN model. Insights from this system informed the development of a baseline system, which uses the same dataset but different feature extraction and classification paradigms. This baseline system combined the common spatial patterns (CSP) method with continuous wavelet transform (CWT) for feature extraction and employed a GoogLeNet classifier with transfer learning. Classification was performed on six unique pairs of events derived from the four motor functions, using both intrasubject and intersubject methods. The baseline system achieved the highest accuracy of 99.73% for the GP-RV pair and the lowest accuracy of 80.87% for the FW-GP pair in intersubject classification. Building on this, three additional systems were developed to perform 4-way classification. Among these, the final model, ML-CSP-OVR, achieved the highest intersubject classification accuracy of 78.08% using all combined data and 76.39% for leave-one-out intersubject classification. This proposed model, which features a novel combination of CSP-OVR, CWT, and GoogLeNet, demonstrates strong potential as a general system for motor imagery (MI) tasks without being subject-dependent.

Despite the lack of evidence that suggests hemodiafiltration (HDF) offers a better survival outcome than standard hemodialysis (HD), the number of patients initiating HDF in Japan continues to rise. This study examined the temporal change in the number of HDF incidents, evaluated factors associated with all-cause mortality, and compared the mortality risk and survival time of patient on HDF with patients receiving standard HD in three sets of 2-year cohorts. The primary analyses included the insurance claims data of 460 HDF patients and propensity score-matched 903 standard HD patients who initiated dialysis therapy between April 1, 2012 and March 31, 2018. Patient follow-up was censored at the time of death or the end of the 2-year study period. The influence of comorbidities on all-cause mortality was investigated, and the survival outcomes between HDF and standard HD patient groups throughout cohorts were compared. The number of HDF patients was increasing throughout cohorts, but the proportions of mortality cases across cohorts slowly decreased. Dementia status increases the mortality risk of patients regardless of dialysis modality. The mortality risk among HDF patients with dementia was more significant than HD patients. Adjusting for all covariates, we observed that HDF patients had a reduced mortality risk and longer survival time than patients on standard HD. This study supports the notion that HDF reduces all-cause mortality compared with standard HD in an incident dialysis population in Fukuoka Prefecture, Japan.

The aim of this study was to investigate the differentially expressed genes associated with intramuscular fat deposition in the longissimus dorsi muscle of Xinjiang Brown Bulls. The longissimus dorsi muscles of 42 Xinjiang Brown Bulls were selected under the same feeding conditions. The intramuscular fat content of muscle samples were determined by Soxhlet extraction method, from which 5 samples with high intramuscular fat content (HIMF group) and 5 samples with low intramuscular fat content (LIMF group) were selected. It was found that the intramuscular fat content of HIMF group was 46.054% higher than that of LIMF group. Muscle samples produced by paraffin section for morphological observation. It was found that the fat richness of the HIMF group was better than that of the LIMF group. Transcriptome sequencing technology was used to analyze the gene expression differences of longissimus dorsi muscle. Through in-depth analysis of the longissimus dorsi muscle by transcriptome sequencing technology, we screened a total of 165 differentially expressed genes. The results of Gene Ontology (GO) enrichment analysis showed that the differentially expressed genes in the two groups were mainly clustered in biological pathways related to carbohydrate metabolic processes, redox processes and oxidoreductase activities. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that the differentially expressed genes were significantly clustered in 15 metabolic pathways, which mainly covered fatty acid metabolism (related to lipid metabolism and glucose metabolism), pentose phosphate pathway, Peroxisome Proliferator-Activated Receptors (PPAR) signalling pathway and other important metabolic processes. The three genes that were predominantly enriched in the glycolipid metabolic pathway by analysis were SCD5, CPT1C, and FBP2, all of which directly or indirectly affect intramuscular fat deposition. In summary, the present study investigated the differences in gene expression between high and low intramuscular fat content in the longissimus dorsi muscle of Xinjiang Brown Bulls by transcriptome sequencing technology, and revealed the related signalling pathways. Therefore, we hypothesized that SCD5, CPT1C, and FBP2 were the key genes responsible for the significant differences in intramuscular fat content of the longissimus dorsi muscles in a population of Xinjiang Brown Bulls. We expect that these findings will provide fundamental support for subsequent studies exploring key genes affecting fat deposition characteristics in Xinjiang Brown Bulls.

Nanocomposites prepared with a terpolymer of poly(L-lactide) (PLLA)–poly(-caprolactone) (PCL)–poly(ethylene glycol) (PEG) and partially oxidized carbon nanotubes (CNTspo) were synthesized and characterized to evaluate their ability to act as an effective nanocarrier of the anticancer drug methotrexate. The homopolymers of PLLA and PCL were synthesized through ring-opening polymerization (ROP) and characterized through gel permeation chromatography (GPC). The PLLA–PCL–PEG terpolymers were synthesized through a four-step chemical route using oxalyl chloride as a linker agent and analyzed through 1H-NMR, 13C-NMR, and FT-IR spectroscopies. Also, the nanocomposites were characterized through FT-IR, 1H-NMR, and X-ray photoelectron spectroscopy (XPS) spectroscopies and, using the differential scanning calorimetry (DSC) technique. XPS analysis revealed that CNTspo is grafted to PLLA–PCL–PEG terpolymer. Besides, evaluation through FT-IR and DSC strongly suggests that the CNTspo are preferentially oriented to the PCL-rich domains. The release tests exhibited a “burst effect” profile, which was more evident in the terpolymers than in the nanocomposites. Five models were used to assess the methotrexate in vitro release. For the nanocomposites, the best fit to the experimental data was obtained using the first-order model, whereas the results obtained from the Korsmeyer–Peppas model indicated that a Fickian diffusion drives the methotrexate release.

Recently, oriented external electric fields (OEEFs) have earned much attention due to the possibility of tuning the properties of electronic systems. From a theoretical perspective, one can resort to electronic structure calculations to understand how the direction and strength of OEEFs affect the properties of electronic systems. However, for multi-reference (MR) systems, calculations employing the popular Kohn-Sham density functional theory with the traditional semilocal and hybrid exchange-correlation energy functionals can yield erroneous results. Owing to its decent compromise between accuracy and efficiency for MR systems at the nanoscale (i.e., MR nanosystems), in this study, thermally-assisted-occupation density functional theory (TAO-DFT) is adopted to explore the electronic properties of n-acenes (n = 2--10), containing n linearly fused benzene rings, in OEEFs, where the OEEFs of various electric field strengths are applied along the long axes of n-acenes. According to our TAO-DFT calculations, the ground states of n-acenes in OEEFs are singlets for all the cases examined. The effect of OEEFs is shown to be significant on the vertical ionization potentials and vertical electron affinities of ground-state n-acenes with odd-number fused benzene rings. Moreover, the MR character of ground-state n-acenes in OEEFs increases with the increase of the acene length and/or the electric field strength.

Cadmium (Cd) contaminated soil is a serious environmental problem due to its effect on crop production, soil microbial community, and risk to human health. The commercial amendments are widely used in the immobilization of heavy metal-contaminated soil because of their cost-effectiveness, eco-friendliness, and community acceptance. This study is conducted to assess the effect of sole commercial amendments including bentonite (B), talc (T), activated carbon (AC), cornstarch (CS), and their combination on exchangeable Cd content in soil, Cd uptake by the plant, and soil microbial community. The results indicated that CS and its composites (T+CS and B+CS) showed the highest effect on reducing Cd mobility in soil (up to 10% in CS), decreasing the Cd uptake by lettuce (up to 78% in T+CS), and increasing the microbial community in soil compared to the control. Furthermore, results from Biolog Ecoplate and realtime-Polymerase chain reaction indicated that the mixture amendments (B+CS and T+CS) showed the highest effect on the microbial community by increasing soil microbial population maximum of 3.81-fold difference (in B+CS) compared to the control. The results indicated that applying composite amendments provided an efficient option for immobilizing Cd in soil, reducing the Cd uptake by plants, and increasing microbial community.

This paper presents an interpretable support vector machine (SVM) and its application to rehabilitation assessment. We introduce the concept of nearest boundary point to standardize the one-class SVM decision function and determine the shortest path for data from abnormal cases to become those from normal cases. This analytical approach is computationally simple and provides a unique solution. The nearest boundary point of abnormal data can also be used to analyze the cause of abnormal classification and indicate countermeasures for normalization. These properties render the proposed interpretable SVM valuable for medical assessment applications and other problems that require careful consideration of classification results for treatment. Simulation and application results demonstrate the feasibility and effectiveness of the proposed method.

Deep convolutional neural networks (CNNs) have revolutionised computer vision technology by automatically learning hierarchical representations of image data, leading to state-of-the-art performance in visual recognition tasks. This article presents a comprehensive review of deep CNNs, from their evolution and architectures to the current state-of-the-art research. The review also provides the core concepts and building blocks of CNNs and their concise mathematical representations. Furthermore, it explores applications of deep CNNs in three popular domains, including medical diagnosis, remote sensing, and facial recognition. This review will benefit researchers and practitioners in computer vision and artificial intelligence, as well as industry professionals seeking to leverage the latest advancements in deep learning technologies.

In this letter, we construct an effective field theory of gravity at large distances. We obtain to the leading order for large distances: a cosmological constant term and a logarithmic term in addition to the usual General Relativistic terms. The novel logarithmic term has important implications in explaining phenomena such as flat galactic rotation curves and stronger gravitational lensing than predicted that are generally attributed to the presence of dark matter.

Lung cancer remains the most common cancer worldwide with a limited prognosis despite personalized treatment regimens. Low-dose computed tomography (CT) scanning as a means to early diagnosis has been disappointing due to the high false positive rate. Other non-invasive means of testing need to be developed that offer both timely diagnosis and predict prognosis. Methods: In the course of stool testing in a large-scale testing of 2,922 patients at increased risk of CRC we were able to ascertain 112 patients documented to have prospectively been diagnosed with lung cancer. Stool and colonic effluents were tested for p87 with anti-adenoma antibody (Adnab-9) reactivity by ELISA and western blot. Survival data were obtained where available. Results: Of 112 cancers, approximately 27.6% were squamous (SSC), 17.9% were adenocarcinoma, 8% small, 6.25% large cell and 3.57% were designated non-small cell cancer (NSCLC), 0.89% indeterminate, 0.89% lepidic spread (1), 3.57% had metastasis and in 31.25% data was unavailable. 49.1% of the lung cancer patients had fecal Adnab-9 testing. Overall, 60% had positive testing compared to 38%, which was significant (OR2.19[1/06-4.53];p=0.045). Cancers with higher lethality were less likely to test positive (approximately, 8.5% each for both small and large cell lung cancers), and higher, 56% with SCC and 25% for adenocarcinoma. 0% NSCLC). In the larger groups overall survival was worse in those testing positive: 474 testing positive versus 844 days in SCC and 54 testing positive versus 749 days in adenocarcinoma patients. Most importantly, the time from a positive test to the clinical diagnosis ranged from 2.72 years for small cell, 3.13 for adenocarcinoma, 5.07 for NSCLC, 6.07 for SSC, and 6.24 for large cell cancer. In excluded cases where cancer in the lung was believed to be metastatic,83.3% of cancers were positive. Conclusions: At a projected real-world sensitivity of 0.60 and specificity of 0.60 and the ability to predate diagnosis by up to 4.7 years overall, this test could help direct lung cancer screening. In addition, the Adnab-9 testing selectively detects worse tumor types (87.5%) and those with worse prognosis amongst the more common, favorable phenotypes thus making early diagnosis possible in those patients who stand to benefit most from this strategy. Metastatic lung cancer also detected by the test should be identified by the follow-up imaging studies and therefore would not be considered to be a major pitfall.

The study of supermassive black holes has significantly advanced our understanding of galaxy structures, yet fundamental questions remain, particularly concerning the structure and dynamics of massive black holes. This article addresses the core structure of massive black holes, defined as those originating from supernova events. Integrating the concept of "Dachus density," a state of maximum matter density, I propose that the core of all massive black holes exists at this density. The research investigates the size of these cores and their event horizons, using Cygnus X-1 and Sagittarius A* as examples. Calculations reveal that the core size is exceedingly small compared to the overall black hole, with the core's volume being up to ~155 orders of magnitude smaller than the event horizon volume in the case of Sagittarius A*. The article briefly explores the implications of a rotating Dachus core on the black hole's structure, including the "mantle" region between the core and the event horizon. The discussion extends to absolute zero temperature, asserting that for a non-spinning, static Massive black hole, the temperature of its core reaches 0[K] as in this state, particles do not move in relation to each other and do not have any kinetic and in particular rotational Energy.

Emergency Medical Response Systems (EMRS) play a vital role in delivering medical aid during disasters. In Serbia, these systems encounter several obstacles due to economic limitations, scarce resources, and ongoing healthcare reforms, all of which affect their performance. This study focuses on examining the risk factors and effectiveness of Serbia's EMRS, identifying systemic issues, and proposing enhancements to improve emergency preparedness for disasters. A survey was conducted across healthcare institutions in Serbia to gather comprehensive insights. The survey explored various aspects, including organizational structures, resource distribution, communication systems, and the speed of emergency responses. Statistical methods such as Pearson's correlation, multivariate regression analysis and chi-square tests were utilized to interpret data collected from 172 healthcare institutions. The multivariate regression analysis of EMS organization identified that factors such as the structuring of working hours and shift patterns are significant predictors, contributing to small but meaningful variations in various EMS outcomes. In contrast, other factors, such as financial resources and the availability of ambulance vehicles, showed more targeted significance depending on the specific EMS aspect being analyzed. Institutions with written mass casualty plans and procedures demonstrate superior effectiveness in EMS activities, excelling in resource allocation, operational continuity, and emergency preparedness, particularly through enhanced coordination and specialized systems for managing large-scale emergencies. The findings highlighted notable disparities in how EMRS are organized, with only 46% of institutions having specific emergency departments. Resource limitations were evident, as many facilities operated with minimal staff and outdated equipment. The communication systems revealed weaknesses, with just 54% having established protocols for handling emergency calls. Response times frequently surpassed international standards, which negatively affected patient outcomes. Additionally, the study found training deficiencies, with 68% of participants expressing a need for further education. Serbia’s EMRS are significantly impacted by systemic issues such as insufficient funding, disjointed service delivery, and inadequate training. Overcoming these challenges necessitates a comprehensive approach that includes boosting financial support, implementing standardized protocols, and fostering enhanced collaboration across different sectors.

Extreme weather events, such as heat waves and heavy precipitation, are becoming increasingly frequent due to climate change, necessitating the development of effective early warning systems (EWS) to mitigate their impacts. This study introduces an advanced EWS designed specifically for Italy, which integrates the FourCastNet global forecasting model with Elevation-integrated TEmperature and Precipitation SRGAN downscaling (E-TEPS) to enhance the spatial resolution and accuracy of climate predictions. Building on previous work that demonstrated E-TEPS's effectiveness in downscaling temperature and total precipitation variables, this research applies the integrated system to two severe weather events in Central Italy: the Emilia-Romagna floods of 2023 and the Marche floods of 2022. The system's performance was evaluated using key metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Pearson Correlation, by comparing downscaled outputs from FourCastNet and ERA5 datasets against high-resolution ground truth data from the Euro-Mediterranean Center on Climate Change (CMCC) dataset. The results indicate that the integrated system offers improved predictive accuracy, particularly in capturing critical climate variables, with the entire EWS delivering high-resolution forecasts and final outputs in under one minute. Although potential limitations were identified due to biases in the underlying datasets, which could affect forecast reliability in regions with complex topography or extreme weather conditions, this research highlights the potential of combining machine learning models with downscaling techniques to enhance EWS precision, providing valuable insights for future climate forecasting and disaster preparedness strategies.

Scholars in the field of environmental economics have extensively examined the relationship between foreign direct investment (FDI) and economic growth. However, they have often failed to consider the crucial roles played by technological innovation and financial development in determining environmental costs. The rapid economic growth and urbanization witnessed in BRICS countries have led to a substantial increase in energy demands, resulting in significant environmental degradation. This study aims to investigate the impact of financial development (including measures such as broad money, domestic credit to the private sector, FDI, and green technological innovation) on carbon emissions in BRICS countries using data from 2001 to 2023. The results of the study demonstrate a strong cross-sectional dependence among the countries in the panel. The Augmented Mean Group (AMG) estimator reveals a negative and statistically significant long-term relationship between broad money, FDI, green technological innovation, and CO2 emissions. On the other hand, domestic credit to the private sector shows a positive and significant association with carbon emissions. To determine the direction of causality, the study utilizes the Dumitrescu and Hurlin panel causality test. The findings indicate a non-directional long-term causality between financial development and CO2 emissions. However, a unidirectional causality is observed between green technological innovation and carbon emissions. Based on these findings, the study suggests that the development of industries, financial institutions, and technological innovation is crucial for attracting high-quality FDI in BRICS countries. However, it also highlights the significant contribution of these developments to environmental degradation, necessitating urgent policy responses.

The Colorado and Mississippi River Basins are crucial for water supply, agriculture, and ecological stability in the U.S., yet climate change, water management practices, and energy sector demands pose significant challenges to their sustainability. This paper highlights the potential of leveraging the Voluntary Carbon Market (VCM) to address these challenges by creating new revenue streams and incentivizing sustainable water management practices. The paper provides high-level estimates by extrapolating from existing literature. The results suggests that water projects in these basins could generate over 45 million carbon credits annually, potentially attracting around $4.5 billion in investments over the next decade. However, challenges such as high costs, complex regulations, and stakeholder coordination must be addressed. The paper also identifies opportunities for advancing water resiliency projects, including increasing public awareness, engaging corporations, and utilizing innovative financing mechanisms. Recommendations include promoting the VCM-water relationship, encouraging methodology innovation, developing pilot programs, investing in digital monitoring technologies, and conducting localized analysis to optimize carbon credit potential in water management. By integrating carbon markets with water management strategies, the paper suggests significant contributions to global climate goals and enhanced water resilience in these critical regions.

This ambitious research project seeks to revolutionize sustainable glucose production by harnessing solar energy for the conversion of atmospheric gases. The process involves water collection, electrolysis for hydrogen and oxygen production, PDMS membrane-based CO2 collection, CO2 splitting, formaldehyde generation, and the synthesis of glucose/sucrose. Key innovations include the strategic application of PDMS membranes with varying pore sizes and non-reactive chemical layers to facilitate selective permeability for specific gases. Our goal is to create a cost-effective, eco-friendly glucose production method, paving the way for renewable and sustainable energy solutions.

New data obtained from core samples of two boreholes in Alba Guyot in the Magellan Seamount Trail (MST), Western Pacific, including 40Ar/39Ar age of basanite, mineralogy of basanite, tuff, tuffite, mantle-derived inclusions in basanite and tuff (lherzolite xenolith and Ol, Cpx, Opx xenocrysts), and calcareous nannofossils biostratigraphy, have implications for the guyot history. Volcanics in the upper part of the Alba Guyot main edifice and its Oma Vlinder satellite, at sea depths between 3600 and 2200 m, were deposited during the Cretaceous 112 to 86 Ma interval. In the following ~60 myr, the Alba Guyot became partly submerged, and denuded with the formation of a flat summit platform while the respective fragment of the Pacific Plate was moving to the Northern Hemisphere. Volcanic activity in the northeastern part of the guyot summit platform rejuvenated in the Miocene (24–15 Ma) and produced onshore basanitic volcanoes and layers of tuff in subaerial and tuffite in shallow-water near-shore conditions. In Middle-Late Miocene (10–6 Мa), after the guyot had submerged, carbonates containing calcareous nannofossils were deposited on the porous surfaces of tuff and tuffite. Precipitation of the Fe-Mn crust (Unit III) recommenced during the Pliocene-Pleistocene (< 1.8 Mа) when the guyot summit reached favorable sea depths. The location of the MST guyots in the northwestern segment of the Pacific Plate near the Mariana Trench, along with the Miocene age and alkali-basaltic signatures of basanite, provide evidence for petit-spot volcanism on the Alba Guyot. This inference agrees with the geochemistry of Cenozoic petit-spot basaltic rocks from the Pacific and Miocene basanite on the Alba Guyot. The numerous volcanic cones reaching up to 750 m high and 5.1 km in base diameter, which were discovered on the Alba summit platform, provide the first evidence of voluminous Miocene petit-spot basanitic volcanism upon the Cretaceous guyots and seamounts of the Pacific.

Chronic stress is a pervasive issue that significantly impacts both physical and mental health. Unlike acute stress, which is short-lived and can be beneficial, chronic stress persists over extended periods, leading to a range of adverse health outcomes. This paper explores the myriad physical, mental, and behavioral consequences of chronic stress, emphasizing the underlying physiological mechanisms and potential long-term health implications. Through a comprehensive literature review and data analysis utilizing Python, we aim to provide an in-depth understanding of chronic stress and its effects. Key findings include the critical roles of cortisol and other stress-related chemicals in exacerbating health issues, and the potential for the normalization of high stress levels as an adaptive yet harmful response.

Sensory processing challenges are crucial yet often neglected aspects in the care of children with neurodevelopmental disorders and genetic conditions. They represent a key area of interest in neuroscience, as they significantly impact children's daily functioning and quality of life. This observational study examines the characteristics of these challenges in a group of 614 children, aged 3 to 14 years and 11 months, divided into three groups: 183 with neurodevelopmental disorders (Autism Spectrum Disorder, Attention Deficit Hyperactivity Disorder, developmental delays, learning disorders), 89 with genetic conditions (22q11.2 Deletion Syndrome, Williams Syndrome, Pseudohypoparathyroidism), and 342 controls. Sensory processing was assessed using the Sensory Profile 2 (SP2). Results indicated that children with neurodevelopmental disorders and genetic conditions exhibited significant sensory processing difficulties compared to controls. The SP2 identified sensory challenges across different sensory systems, with varying configurations depending on the diagnosis. Children with neurodevelopmental disorders and genetic conditions show marked sensory processing challenges impacting daily lives and well-being. These findings highlight the importance of early identification and tailored evidence-based interventions to address these specific sensory processing issues. Further research should explore the long-term impact of these interventions in these different populations and their integration into broader therapeutic programs.

Minimum Inhibitory Concentration (MIC) measures the lowest concentration of antibiotic that inhibits in-vitro growth of microorganisms. Canine pyometra is a frequently occurring reproductive disorder where reliable antibiotics along with surgical excision of uterus predicts better prognosis. Scarce literature is available to facilitate proper antibiotic selection to avoid septicemia in clinical cases. The present study was conducted to determine sensitivity and potency of selected antibacterials based on MIC values against bacterial isolates obtained from Canine Pyometra in Kathmandu district, Nepal by Agar Dilution method. Isolates obtained from 63 intra-uterine samples after ovariohysterectomy were Enterococcus spp (n=41), Klebsiella spp. (n=7), Staphylococcus spp. (n=11) and Streptococcus spp. (n=4). Enrofloxacin and Amikacin were found highly sensitive (0% resistance) with a null hypothesis of resistance to all bacterial isolates and were significantly more potent than other antibacterials tested (p<0.05; Kruskal-Wallis test) based on lower MIC ranks. Similarly, ceftriaxone was found to be least potent and was highly resistant to Enterococcus spp. (75.61%) and completely resistant to other bacterial species tested (100%). Comparative results on sensitivity and potency in the study revealed Enrofloxacin and Amikacin to be better antibacterial of choice in pyometra innervation in in-vitro setting. Further clinical trials considering other equally important variables along with MIC should be conducted to predict actual clinical efficacy of antibacterials during therapeutic management of canine pyometra in Nepal.

Sustainable water resource management in Bangladesh's Gumti River Basin faces challenges from climate change, urbanization, and population growth. This study aimed to calibrate and validate the HEC-HMS model for the Gumti River to enhance flood prediction and water resource planning. The model was set up using precipitation, temperature, and discharge data from 2019-2021, with SRTM elevation data for catchment delineation. Manual calibration was performed for 2019-2020, followed by validation for 2021, using parameters such as Muskingum routing and SCS Curve Number. Model performance was evaluated using R², Nash-Sutcliffe efficiency (NSE), RSR, and PBIAS metrics. Results showed satisfactory to good performance, with R² values of 0.64 and 0.68 for calibration and validation, respectively. NSE values were within the satisfactory range, while PBIAS indicated very good performance with slight underestimation. The study concludes that the calibrated HEC-HMS model provides a reliable tool for simulating the Gumti River's hydrology, though there's room for improvement. Integration with flood frequency analysis, water quality monitoring, and climate change projections is recommended for comprehensive water resource management, alongside the implementation of Sustainable Water Resource Management principles and community engagement strategies.

The immune system is central in controlling acute hepatitis B infection and in patients resolving chronic hepatitis B (CHB). Given that 221 million (75%) of CHB patients reside in low- and middle-income countries, developing a vaccine with therapeutic properties represents a rational and cost-effective approach more than a romantic endeavor. This review systematically analyses the critical variables related to the safety, efficacy, and effectiveness of CHB treatments. HeberNasvac's experience is revisited to address the challenges and potentialities of therapeutic vaccines, as well as the current roadblocks in research and development, registration, and large-scale implementation.

To explore the coupling mechanism between urban-rural transformation (URT) and the ecological environment in counties forms the basis for achieving high-quality and sustainable county development. Despite the richness of research on the relationship between urban and rural development and the ecological environment, the county level remains the basic unit of rural revitalization in China, and further research on the coupling mechanism between URT and the ecological environment at this level is needed. Therefore, the study developed an evaluation system for URT, incorporated NDVI data indicators, and employed the coupling coordination degree (CCD) model along with GIS tools to investigate the spatio-temporal pattern of URT and NDVI evolution, and the CCD in each administrative village within Lingbao City. Additionally, a geoDetector model was employed to analyze the influencing factors affecting the CCD of URT and NDVI. Finally, the study explores the coupling mechanism and optimization path. The result shows that: (1) During the study period, the URT level in administrative villages within the study area markedly improved, while NDVI exhibited an increasing-then-decreasing trend; (2) The CCD between URT and NDVI in the northern and central regions of the study area is higher than that in the southern region, with an overall low level; (3) The spatial heterogeneity of CCD is significant, largely influenced by socio-economy, natural environment, policy funds, and geographical location, where the elements comprising CCD mutually influence each other; (4) Drawing upon the coupling and coordination dynamics between URT and the ecological environment within Lingbao City, this research categorizes it into seven types with distinct optimization paths. This study provides a valuable reference for guiding the high-quality and sustainable development strategies for mountainous and hilly counties within the Yellow River basin.

Background The operator's ability in performing carotid stenting (CAS) has improved clinical outcomes. However, more than 3% of patients need to be treated again after CAS. Most of the cases requiring further intervention are affected by hyperplasia. The recommended procedure is the DEBalloon. On the other hand, the literature reports a small number of carotid neoatherosclerosis cases and is recommended to be treated using elective Micronet-covered stent. Discriminating between the two types of in-stent-restenosis ISR (hyperplasia or neoatherosclerosis) is critical for a positive outcome.Case summary We describe a case in which a patient treated with carotid stenting 8 years before, was diagnosed with ISR. Due to the development of neurological symptoms and progressive increases in Peak Systolic Velocity (PSV) eight years following carotid stenting, a DEBalloon was used in a carotid in-stent-restenosis (ISR) standard procedure. About ten minutes after the procedure, the patient developed hemiplegia consistent with the treated carotid territory. The implantation of a MicroNet-covered stent excluded the thrombus and reverted symptoms with a normal MR control at 24 h. Conclusion This case illustrates that when in-stent stenosis evolves years after the stent implantation, neoatherosclerosis should be assessed, and a MicroNet-covered stent should be considered.

Over the coming decades, global freshwater withdrawals are expected to grow, especially in low and middle-income countries. Unless there are significant improvements in the efficiency of water use by economies, freshwater stress, crises and scarcity will worsen. This paper explores further the economic relationship between water use efficiency and scarcity. Because growing scarcity of freshwater in many regions and countries is not adequately reflected in markets, there is often insufficient incentives for investment and innovation to improve the efficiency of water use. To explore further changes in water use efficiency across countries, we conduct a panel analysis of water productivity changes for 130 countries over 1995 to 2020. Countries with lower initial levels of water use efficiency tended to have higher water productivity growth, whereas more agriculturally dependent economies displayed lower improvements in water use efficiency. Better institutional quality and capacity for innovation may also increase water use efficiency. We discuss the implications of these results for improving water use productivity in economies, and in particular, the opportunities and challenges for improving water markets and trades to alleviate water scarcity. We conclude by identifying further areas of research.

Around $1637$, Pierre de Fermat famously scribbled, and claimed to have a proof for, his statement that equation $a^{n} + b^{n} = c^{n}$ has no positive integer solutions for exponents $n>2$. The theorem stood unproven for centuries until Andrew Wiles' groundbreaking work in $1994$, with a notable caveat: Wiles' proof, while successful, relied on modern tools far beyond Fermat's claimed approach in terms of complexity. The present work potentially offers a solution which is closer in spirit to Fermat's original idea.

Cementitious materials exhibit excellent binding properties and micropore structures that support the stabilization of TiO2 nanoparticles within the matrix. Therefore, these materials are ideal for the development of functional construction products with TiO2 nanoparticles. This study investigated the degree of hydration, microstructure, and mechanical properties of cement-containing TiO2 nanoparticles. The experimental results demonstrated that TiO2 nanoparticles accelerated the hydration rate of cement, reducing the setting times. Moreover, TiO2 nanoparticles changed matrix microstructure, decreasing the porosity and improving early-age strength. Although TiO2 nanoparticles function as nucleation sites in the cement hydration reaction, the tendency of the particles to clump implies that proper dispersion should be ensured when mixing in large quantities. When developing new building materials incorporating TiO2 nanoparticles, considering the effect of TiO2 nanoparticles on the hydration reaction of cementitious materials is crucial.

This article presents a novel resolution of the Collatz Conjecture, centered on the concept of Generative Completeness of the inverse Collatz function. We introduce and rigorously prove that for all N ∈ ℕ⁺, there exists a minimal generator m_N = 1 such that all positive integers up to N can be generated through successive applications of the inverse Collatz function G. This key property, which we term "Generative Completeness", forms the cornerstone of our proof. Building upon this foundation, we establish several crucial results: The boundedness of all Collatz sequences The existence and uniqueness of cycles in Collatz sequences The nature of the unique cycle as {1, 4, 2} We then present three distinct approaches to resolving the Collatz Conjecture, all fundamentally rooted in the Generative Completeness property. These diverse methods not only prove the conjecture but also provide deep insights into the structure of Collatz sequences.

This study aims to investigate the psychological cognitive mechanisms of rural residents amidst the renewal of suburban historical and cultural villages by examining the interplay between nostalgia, collective memory, subjective well-being and place identity in rural tourism destinations. Using Naobao Village as a case study—a suburban historical and cultural village in Hohhot, Inner Mongolia, this research employs a comprehensive approach integrating experimental methods and questionnaire surveys. The findings demonstrate that analyzing the interaction among the four emotions can elucidate the psychological cognitive mechanism of residents. Moreover, it uncovers that tourism effects significantly influence residents' perception of their living environment with positive effects on collective memory, subjective well-being and place identity. In the theoretical model for generating psychological cognition among residents in suburban historical and cultural villages, subjective well-being and collective memory play crucial mediating roles. This study provides a theoretical foundation and practical guidance for spatial reconstruction and cultural evolution of tourist landscapes in suburban rural tourist destinations.

Visual thinking leverages spatial mechanisms in animals for navigation and reasoning. Therefore, given the challenge of abstract mathematics and logic, spatial reasoning-based teaching strategies can be highly effective. Our previous research verified that innovative box and ball coloring activities help teach elementary school students complex notions like quantifiers, logical connectors, and dynamic systems. However, given the richness of the activities, correction is slow, error-prone, and demands high attention and cognitive load from the teacher. Moreover, feedback to the teacher should be immediate. Thus, we propose to provide the teacher with real-time help with LLMs. We explored various prompting techniques with and without context Zero shot, Few shot, Chain of Thought, Visualization of Thought, Self Consistency, logicLM and Emotional to test GPT-4o’s visual, logical, and correction capabilities. We obtained that Visualization of Thought and Self Consistency techniques enabled GPT-4o to correctly evaluate 90% of logical-spatial problems that we tested. Additionally, we propose a novel prompt combining some of these techniques that achieved 100% accuracy on a testing sample, excelling in spatial problems and enhancing logical reasoning.

Background/Objectives: Kinanthropometry is the science that deals with the measurement of variations of the physical dimensions and composition of the human body, according to the age and different degrees of nutrition of a person. This discipline postulates that all the measurements obtained in statics will have an impact on the dynamics. This study’s objective was to evaluate the kinanthropometric differences; Methods: The study was done in a population of 403 persons, aged between 18 and 42 years, who were categorized based on their biological sex and in what sport they usually practiced in a purely recreational way. Measurements and indices were obtained following the protocol of the International Society for the Advancement of Kinanthropometry (ISAK); Results: We observed significant differences in most of the values, for example, men use the presented higher values in relation to weight, height and size of some circumferences, in addition to Body Weight Index (BMI), Relative Index of the Lower Limbs (RILL), percentage of muscle weight (%M) and percentage of residual weight (%R). On the other hand, women presented higher values in most body folds, in addition to the Cormic Index (CI), Body Density Index (BDI), percentage of fat weight (%F) and percentage of bones weight (%B); Conclusions: This work could be useful for people to choose what kind of sport will be better for them according to their morphotype.

Toxicity mechanisms of per- and polyfluoroalkyl substances (PFAS), a chemical class present in diverse ecosystems, are not well characterized in aquatic species. Perfluorooctanesulfonamide (PFOSA, C8H2F17NO2S) is a common precursor of perfluorooctane sulfonic acid (PFOS), a long-chain PFAS. Here, we assessed sub-lethal endpoints related to development, oxidative stress, locomotor activity, and gene expression in zebrafish embryos and larvae following continuous exposure to PFOSA beginning at 6 hours post fertilization (hpf). PFOSA decreased survival in fish treated with 1 µg/L PFOSA, however the effect was modest relative to the controls (difference of 10%). Exposure up to 10 µg/L PFOSA did not affect hatch rate, nor did it induce reactive oxygen species in 7-day-old larvae fish. However, locomotor activity of larval fish treated with 100 µg/L PFOSA was reduced relative to the solvent control. Transcripts related to oxidative stress response and apoptosis were measured and BCL2 associated X, apoptosis regulator (bax), cytochrome c, somatic (cycs), catalase (cat), superoxide dismutase 2 (sod2) were induced with high concentrations of PFOSA. Genes related to neurotoxicity were also measured and transcript levels of acetylcholinesterase (ache), elav-like RNA binding protein 3 (elavl3), growth associated protein 43 (gap43), synapsin II (syn2a), and tubulin 3 (tubb3) levels were all increased in larval fish with higher PFOSA exposure. This study contributes to our knowledge regarding the relative toxicity of PFOSA to fish and contributes data to future risk assessment strategies of these concerning, persistent environmental pollutants.

Reduction of the density operator for quantum subsystems is derived without the use of the Born Rule.

In this paper, a distributed algorithm for reaching average consensus is proposed for multi-agent systems with tree communication graph, when the edge weight distribution is unbalanced. First, the problem is introduced as a key topic of core algorithms for several modern scenarios. Then, the relative solution is proposed as a finite-time algorithm, which can be included in any application as a preliminary setup routine, and it is well-suited to be integrated with other adaptive setup routines, thus making the proposed solution useful in several practical applications. A special focus is devoted to the integration of the proposed method with a recent Laplacian eigenvalue allocation algorithm, and the implementation of the overall approach in a wireless sensor network framework. Finally, a worked example is provided, showing the significance of this approach for reaching a more precise average consensus in uncertain scenarios.

This paper presents a dynamic homotopy technique that can be used to calculate a preliminary result for a power flow problem (PFP). This result can then be used as an initial estimate to efficiently solve the PFP using either the classical Newton-Raphson (NR) method or its fast decoupled version (FDXB) while still maintaining high accuracy. The preliminary stage for the dynamic homotopy problem is formulated and solved by employing integration techniques, where implicit and explicit schemes are studied. The dynamic problem assumes an initial condition that coincides with the initial estimate for a traditional iterative method such as NR. In this sense, the initial guess for the FPF is adequately set as a flat start, which is a starting for the case when this initialization is of difficult assignment for convergence. The static homotopy method requires a complete solution of a PFP per homotopy pathway point, while the dynamic homotopy is based on numerical integration methods. This approach can require only one LU factorization at each point of the pathway. Allocating these points properly helps avoid several PFP resolutions to build the pathway. The hybrid technique was evaluated for large-scale systems with poor conditioning, such as a 109272-bus model and other test systems under stressed conditions. A scheme based on the implicit backward Euler scheme demonstrated the best performance among other numerical solvers studied. It provided reliable partial results for the dynamic homotopy problem, which proved to be suitable for achieving fast and highly accurate solutions using both the NR and FDXB solvers.

This study examines the association of sociodemographic risk factors and the comorbidities of COVID-19. Most COVID-19-related studies were conducted in inpatient settings among patients with severe infection during the first and second waves of the pandemic. The third wave with less severe Omicron variant presented a unique opportunity to observe the adult patients treated in outpatient settings. We conducted a cross-sectional study (117 patients aged &gt; 40 years presented with symptomatic COVID-19). We employed the Charlson Comorbidity Index (CCI) to compute comorbidity burden, Medical Outcomes Survey Short form (SF-12) to measure general health, and Self-care Self- Efficacy Scale (SCSES) to assess self-care ability. Fisher’s exact test and analysis of variance (ANOVA) were used to compute the relationship between comorbidities and demographics. This study report focuses on the distribution of the comorbidities and its relationship to demographic variables. The top five comorbidities were hypertension (42%), diabetes (31%), pulmonary disease/asthma (19%), depression (14%), and cancer (11%). Depression was significantly higher among younger age groups (p&lt;0.01) and women (p&lt;0.05). Age (p&lt;0.0001) and income (p&lt;0.01) were the most significantly associated demographics with the comorbidity burden. Incidence of pulmonary disease showed a positive association to educational status (p=0.05). This was the first study to report a significant prevalence of depression among adults with symptomatic COVID-19.

A smart precipitation estimation method based on FY-4B meteorological satellite data (FY-4B_AI) is proposed in this paper. This method spatiotemporally matches 125 features derived from multi-temporal and multi-channels of FY-4B satellite data with the precipitation at stations. Then a precipitation model using the Light Gradient Boosting Machine algorithm is constructed. Comparative results between FY-4B_AI and GPM/IMERG-L products for over 450 million station cases throughout 2023 shows that: 1) FY-4B_AI is superior to GPM IMERG-L in the average absolute error, root mean square error, relative error, correlation coefficient, probability of detection and critical success index. While in the mean error and false alarm rate, FY-4B_AI is slightly inferior to GPM/IMERG-L. 2) Evaluation of strong weather event applications reveals that both FY-4B_AI and GPM/IMERG-L can accurately represent the spatial distribution characteristics of precipitation, no matter in the southeast humid region or the northwest dry region. Notably, FY-4B_AI, due to its higher spatiotemporal resolution, provides a more detailed distribution of precipitation.

The paper examines the possibility of increasing the wear and corrosion resistance of a CP-Ti surface by duplex plasma electrolytic treatment (borocarburizing and polishing). The structure and composition of diffusion layers, their microhardness, surface morphology and roughness, wear resistance during dry friction and corrosion resistance in Ringer's solution were studied. The formation of a surface hardened layer up to 200 m thick with a microhardness of up to 950 HV, including carbides and a solid solution of boron and carbon, is shown. Subsequent polishing makes it possible to reduce surface roughness and remove weak areas of the porous oxide layer, which is formed during high-temperature oxidation in aqueous electrolyte vapor during borocarburizing. Changing the morphology and structural-phase composition of the CP-Ti surface helps reduce weight wear by 3 times (the mode of frictional interaction changes from microcutting to oxidative wear) and corrosion current density by 4 times after borocarburizing in a solution of boric acid, glycerin and ammonium chloride at 950 °C for 5 minutes and subsequent polishing in an ammonium fluoride solution at a voltage of 250 V for 3 minutes.

Streptococcus agalactiae is a highly contagious Gram- positive bacterium that causes mastitis, has a high infectivity for mammary epithelial cells, and is challenging to treat. However, the molecular interactions between it and mammary epithelial cells remains poorly understood. This study analyzed differential gene expression in mammary epithelial cells with varying levels of S. agalactiae infection using UID- Dual Seq and bioinformatics tools. This study identified 211 differentially expressed mRNAs (DEmRNAs) and 452 differentially expressed lncRNAs (DElncRNAs) in host cells, primarily enriched in anti-inflammatory responses, immune responses, and cancer-related processes. Additionally, 854 pathogen differentially expressed mRNAs (pDEmRNAs) were identified, mainly enriched in protein metabolism, gene expression, and biosynthesis processes. Mammary epithelial cells activate pathways, such as the ERK1/2 pathway to produce, to produce reactive oxygen species (ROS) to eliminate bacteria. However, the bacteria disrupt the host's innate immune mechanisms by interfering with the alternative splicing processes of mammary epithelial cells. Specifically, the bacterial genes of tsf, prfB, and infC can interfere with lncRNAs targeting RUNX1 and BCL2L11 in mammary epithelial cells, affecting the alternative splicing of target genes and altering normal molecular regulation.

An explicit mass formula for charged leptons is proposed which employes the $(2 \ell +1)$ degeneracy of the $SU(2)$ Laplacian of quantum mechanics. Possible cosmological implications are briefly discussed.

Artificial intelligence (AI) is transforming many healthcare fields including pediatric urology. This in-depth review explores recent advances in AI tools for pediatric urology, emphasizing how state-of-the-art technologies can be used to enhance patient treatment. An initial overview of pediatric urology is given to provide context for this interdisciplinary field and the wide spectrum of problems this entails, before discussing potential uses of AI to improve diagnostic accuracy, treatment planning, and surgical results. AI-powered predictive models are already changing pediatric clinical practice by guiding interpretation of medical images and supporting decision making. In addition to outlining the aims, methods, and conclusions of previous studies on AI applications in pediatric urology, this review also highlights knowledge gaps and priority areas for future study. Particular focus is given to specialized surgeries such pyeloplasty, where AI-guided treatment may help overcome specific technical challenges of this complex procedure. Also highlighted are practical, ethical, and legal aspects of integrating AI into pediatric urology, which will be crucial for responsible innovation in patient-centered treatment. Overall, this article aims to enlighten and inspire clinicians, researchers, and other healthcare stakeholders by offering insight into the future of AI in pediatric urology.

This paper proposes a novel facial expression recognition network called the Lightweight Facial Network with Spatial Bias (LFNSB). The LFNSB model balances model complexity and recognition accuracy. It has two key components: a lightweight feature extraction network (LFN) and a Spatial Bias (SB) module for aggregating global information. The LFN introduces combined channel operations and depthwise convolution techniques, effectively reducing the number of parameters while enhancing feature representation capability. The Spatial Bias module enables the model to focus on local facial features while also capturing the dependencies between different facial regions. Additionally, a novel loss function called Cosine-Harmony Loss is designed. This function optimizes the relative positions of feature vectors in high-dimensional space, resulting in better feature separation and clustering. Experimental results on the AffectNet and RAF-DB datasets show that the proposed LFNSB model performs excellently in facial expression recognition tasks. It achieves high recognition accuracy while significantly reducing the number of parameters, thus substantially lowering model complexity.

Let $R$ be a commutative ring with identity. A proper ideal $I$ of a ring $R$ is called a square-difference factor absorbing primary ideal of $R$ if for $a,b\in R$, whenever $a^{2}-b^{2}\in I$, then $a+b\in\sqrt{I}$ or $a-b\in I$. Several characterizations and properties of this class of ideals are presented.Various examples are provided to illustrate the obtained results and demonstrate the applicability of our findings. Furthermore, the properties of this class of ideals are investigated in extensions of rings.

Diabetic peripheral neuropathy (DPN) is a prevalent complication of type 2 diabetes that affects a significant proportion of diabetic patients worldwide. Although the pathogenesis of DPN involves axonal atrophy and demyelination, the exact mechanisms remain elusive. Current research has predominantly focused on neuronal damage, overlooking the potential contributions of Schwann cells, which are the predominant glial cells in the peripheral nervous system. Schwann cells play a critical role in neurodevelopment, neurophysiology, and nerve regeneration. This review highlights the emerging understanding of the involvement of Schwann cells in DPN pathogenesis. This review explores the potential role of Schwann cell plasticity as an underlying cellular and molecular mechanism in the development of DPN. Understanding the interplay between Schwann cell plasticity and diabetes could reveal novel strategies for the treatment and management of DPN.

The elderly population is particularly susceptible to myocardial damage caused by heat stress (HS). With the continuous increase in global temperatures, this poses an undeniable challenge to human health. This study aimed to investigate the therapeutic effect of Shikimic Acid (SA) in treating HS-induced myocardial damage through network pharmacology, molecular docking, molecular dynamics (MD) simulations, and in vitro experiments. Network pharmacology analysis revealed that SA alleviates the inflammatory response in the heart muscle associated with HS by targeting 60 disease-related targets, such as TP53, TNF, and IL-6. The tumor proteoglycan, the PI3K-Akt signaling pathway, and hepatitis B were identified as the primary therapeutic pathways. Molecular docking and MD simulation results confirmed that SA can stably bind to both TNF (-6.642 kcal/mol) and IL-6 (-7.261 kcal/mol), and the complexes of Shikimic Acid-IL-6 and Shikimic Acid-TNF did not undergo significant changes within 100 ns. In addition, the in vitro experiment showed that 250 μM SA significantly increased the survival rate of HL-1 cells exposed to 43°C for 2 h by 71.1% and promoted the proliferation of HL-1 cells by 30.7%. Overall, these findings provide substantial evidence for the therapeutic potential of SA in HS-induced myocardial damage.

Accurate stock market predictions following earnings reports are crucial for investors. Traditional methods, particularly classical machine learning models, struggle with these predictions because they cannot effectively process and interpret extensive textual data contained in earnings reports and often overlook nuances that influence market movements. This paper introduces an advanced approach by employing Large Language Models (LLMs) instruction fine-tuned with a novel combination of instruction-based techniques and quantized low-rank adaptation (QLoRA) compression. Our methodology integrates ‘base factors’, such as financial metric growth and earnings transcripts, with ‘external factors’, including recent market indices performances and analyst grades, to create a rich, supervised dataset. This comprehensive dataset enables our models to achieve superior predictive performance in terms of accuracy, weighted F1, and Matthews correlation coefficient (MCC), especially evident in the comparison with benchmarks such as GPT-4. We specifically highlight the efficacy of the llama-3-8b-Instruct-4bit model, which showcases significant improvements over baseline models. The paper also discusses the potential of expanding the output capabilities to include a ‘Hold’ option and extending the prediction horizon, aiming to accommodate various investment styles and time frames. This study not only demonstrates the power of integrating cutting-edge AI with fine-tuned financial data but also paves the way for future research in enhancing AI-driven financial analysis tools.

Introduction – Elections are a cornerstone of democratic societies, enabling citizens to express their preferences and shape the direction of their governments. Economic conditions, political dynamics, and social factors often play pivotal roles in influencing voter behavior and determining electoral outcomes, making elections a key area of study in understanding governance and policy change. Objectives – This study examines how key economic factors like inflation, unemployment, and poverty influence election results in Ghana. It also looks at how these economic elements shape voter choices and predicts the possible outcomes of the 2024 general elections using past data. Methodology – The study used historical economic and political data from 1992 to 2023, applying statistical models like ARIMA (Auto-Regressive Integrated Moving Average) to forecast future election outcomes. Economic indicators such as inflation and unemployment were included to study their relationship with voter behavior. Findings - The results show that economic factors significantly impact voter turnout and party vote share. High inflation and unemployment rates tend to reduce voter support for incumbent parties. The model also predicts possible outcomes for the 2024 election based on these economic trends. Conclusion and Recommendation – The study concludes that improving economic conditions, such as reducing unemployment, could positively influence electoral outcomes for incumbents. It is recommended that policymakers focus on stabilizing the economy to enhance electoral success.

Withania somnifera (WS), also known as ashwagandha, is a popular botanical supplement used to treat various conditions including memory loss, anxiety and depression. Previous studies from our group showed an aqueous extract of WS root (WSAq) enhances cognition and alleviates markers for depression in Drosophila. Here, we sought to confirm these effects in the 5xFAD mouse model of β-amyloid (Aβ) accumulation. Six- to seven-month-old male and female 5xFAD mice were treated with WSAq in their drinking water at 0 mg/mL, 0.5 mg/mL or 2.5 mg/mL for four weeks. In the fourth week of treatment, spatial memory, anxiety and depressive-like symptoms were evaluated. At the conclusion of behavioral testing, brain tissue was harvested, immunohistochemistry was performed, and the cortical expression of antioxidant response genes was evaluated. Both concentrations of WSAq improved spatial memory and reduced depressive and anxiety-related behavior. These improvements were accompanied by a reduction in Aβ plaque burden in the hippocampus and cortex and an attenuation of neuroinflammatory markers. Antioxidant response genes were upregulated in the cortex of WSAq treated mice. Oral WSAq treatment could be beneficial as a therapeutic option in AD for improving disease pathology and behavioral symptoms. Future studies focused on dose optimization of WSAq administration and further assessment of the mechanisms by which WSAq elicits its beneficial effects will help inform the clinical potential of this promising botanical therapy.

This study employs a comprehensive computational approach to identify and evaluate potential inhibitors of gamma D crystallin (CRYGD), a key protein implicated in cataract formation. A library of compounds, including natural products and FDA-approved drugs, was screened using molecular docking simulations and binding energy calculations. The top-scoring compounds were further assessed for drug-likeness properties and potential toxicity. Molecular dynamics simulations were conducted on the most promising candidates to investigate their interactions with CRYGD at the atomic level. The study identified CNP0185903, CID 3741, and CNP0151361 as the most promising compounds, with CNP0151361 demonstrating the most favorable combination of binding affinity, structural stabilization, and conformational modulation of CRYGD. These findings provide a foundation for the development of novel pharmacological interventions for cataract treatment, potentially offering alternatives or complements to current surgical approaches.

The cultural transmissions of a society like famous paintings, popular music, good poetry, and enduring religious rituals/doctrines/texts serve as mediums for the embodiment of algorithms that interact with the mind and body in looping, mostly unconscious interactions that are mutually beneficial to the participants of the society and the algorithms themselves. Useful algorithms are also found in the mind and bodies of other people, and individuals in a group can be used to outsource specialized tasks to benefit the society as a whole. Since useful algorithms must be embodied in a medium to work, there are physical limitations to the computations that can be performed in our universe.

The issue of climate change represents a significant global risk that demands immediate attention. However, higher education frequently fails to incorporate global perspectives and intricate transdisciplinary matters. In order to integrate global and transdisciplinary aspects into the curriculum of a technical university with a focus on computer science, a Master's programme has been developed. The programme's objective is to situate global risk at the core of the curriculum, thereby encouraging scientists to work beyond the boundaries of their respective disciplines. The paper considers the challenges that science and higher education must address to facilitate pathways towards a peaceful and sustainable future. It argues for a shift in focus from short-term and localised issues to a more holistic approach, emphasising the importance of considering universal problems. The curriculum, as developed, may be regarded as a guide for future university efforts in the fields of global risk and climate change.

Abstract: Background: We previously found that forest bathing significantly reduced the scores for negative emotions and in-creased the score for vigor in the profile of mood states (POMS) test in both males and females and increased the sero-tonin level in males, indicating the potential for beneficial effect on depressive status. In the present study, we investi-gated the effects of forest bathing on the levels of serotonin, oxytocin and insulin-like growth factor I (IGF-1) in blood, Self-Rating Depression Scale (SDS) scores, POMS, subjective fatigue symptoms and subjective sleep quality in female subjects with depression/depressive tendencies. Methods: Thirty-one female subjects aged 40.1±2.4 years with depression/depressive tendencies were recruited after obtaining informed consent. These subjects took day trips to a forest park, the birthplace of forest bathing in Japan named Akasawa Shizen Kyuyourin in Nagano Prefecture, and to a city area of Nagano Prefecture as a control in June 2023. On both trips, they walked 2.2-2.5 km for two hours each in the morning and afternoon on Saturday and Sunday, respectively. Blood was sampled in the afternoon before and after each trip. Concentrations of serotonin, oxytocin, IGF-1 and lactic acid in blood were measured. The SDS scores, POMS test, and questionnaires for subjective fatigue symptoms and subjective sleep quality were conducted before and after the trips. Ambient temperature and humidity were monitoring during the trips. The Nippon Medical School Central Ethics Committee approved this study. Results: Forest bathing significantly decreased SDS scores and the effect lasted for one week after forest bathing. Forest bath-ing also significantly increased the level of serotonin in serum in subjects who were not taking antidepressants, significantly increased the levels of oxytocin and IGF-1 in blood, significantly increased the scores for vigor-activity and friendliness and de-creased the scores for anger-hostility, confusion-bewilderment, fatigue-inertia, tension-anxiety, depression-dejection and total mood disturbance compared with city walking (all p

This study aimed to evaluate the effect of three yeast strains, Pichia guilliermondii (Levica 27), Candida norvegensis (Levazoot 15), and a commercial product (Levucell® SC 10) containing Saccharomyces cerevisiae, on in vitro rumen fermentation of two substrates: corn stover and a total mixed ration (TMR). Samples were taken 24 hours post-fermentation to measure pH, ammonia nitrogen (NH3-N), gas production, and volatile fatty acid (VFA) concentrations. The addition of yeasts did not affect gas production at 24 hours for either diet. For pH, differences between yeast strains were noted when TMR was used. The lowest NH3-N concentration (6.1 mM) was recorded with corn stover, showing similar results across strains. Conversely, NH3-N levels with TMR ranged from 47 mM to 66 mM. The inclusion of all three yeast strains increased the molar concentrations of total VFA, as well as acetic (C2), propionic (C3), and butyric (C4) acids in the rumen fluid. The C2:C3 ratio remained unchanged with corn stover but decreased with the inclusion of Levica 27 and Levazoot 15 in the TMR. With the commercial product Levucell® SC 10, this ratio was comparable to the control treatment. In conclusion, Levica 27 and Levazoot 15 demonstrated a stimulatory effect on in vitro rumen fermentation for the evaluated diets, evidenced by increased VFA concentrations.

Through the geometric relationships and force analysis of the main components of the panto-graph on high-speed trains, coefficients of aerodynamic forces and lift transmission between the pantograph and main components under crosswind conditions were derived. Based on the aer-odynamic forces acting on the pantograph at different crosswind speeds, wind angles, and oper-ating speeds, the aerodynamic lift of the pantograph and main components was ultimately de-termined. The results indicate that the aerodynamic lift of the pantograph is mainly distributed on the bow structure, with the aerodynamic lift of the upper frame all being negative values, while the absolute value of the aerodynamic lift of the lower arm rod is the smallest. The oper-ating speed of the pantograph and the wind angle of the crosswind have a significant impact on the aerodynamic lift of the main components, while the impact of the crosswind speed is rela-tively small. At the same operating speed of the pantograph, the lower the corresponding crosswind speed, the smaller the aerodynamic lift of the pantograph. The aerodynamic lift of the pantograph tends to decrease gradually with the increase of crosswind speed, and the impact of crosswind speed decreases gradually with the increase of pantograph operating speed. A com-prehensive relationship formula between the aerodynamic lift of the pantograph and the oper-ating speed, crosswind speed, and wind angle is obtained, and the empirical formula for the contact force of the bow net and train operating speed is modified. The research results are of great significance and value for the study and application of lift forces on pantographs under crosswind conditions.

As businesses increasingly adopt advanced technologies to enhance productivity and safety, the integration of human-robot collaboration (HRC) emerges as a transformative approach. This paper explores the role of GPU-accelerated computer vision and generative AI in optimizing HRC within business environments. By leveraging the computational power of GPUs, real-time data processing, and advanced machine learning algorithms, robots can better interpret complex visual cues and adapt to dynamic workspaces. Generative AI further enables the design of intelligent robotic systems capable of anticipating human actions, optimizing task allocation, and ensuring safety through predictive modeling. This research highlights the potential of these technologies to improve operational efficiency, reduce human error, and create safer, more adaptable workplaces. The implications of such advancements are discussed, providing insights into the future of HRC in various industries.

Photocatalytic ammonia decomposition to produce N2 and H2 was achieved using single-walled carbon nanotube (SWCNT) nanohybrids. Physical modification of ferrocene-dye-encapsulated CNTs by amphiphilic C60-dendron yielded nanohybrids with a dye/CNT/C60 coaxial heterojunction. Upon visible light irradiation, an aqueous solution of NH3 and dye@CNT/C60-dendron nanohybrids produced both N2 and H2 in a stoichiometric ratio of 1:3. Action spectra of this reaction clearly demonstrated that the encapsulated dye acted as the photosensitizer, with an apparent quantum yield (AQY) of 0.22% at 510 nm (the λmax of the dye). This study presents the first example of dye-sensitized ammonia decomposition and offers a new avenue for developing efficient and sustainable photocatalytic hydrogen production systems.

In the context of global climate change, the frequency of sudden natural disasters is increasing. Assessing traffic road damage post-disaster is crucial for emergency decision-making and disaster management. Traditional ground observation methods for evaluating traffic road damage are limited by the timeliness and coverage of data updates. Relying solely on these methods does not adequately support rapid assessment and emergency management during extreme natural disasters. Social media, a major source of big data, can effectively address these limitations by providing more timely and comprehensive disaster information. Motivated by this, we utilized multi-source heterogeneous data to assess the damage to traffic roads under extreme conditions and established a new framework for evaluating traffic roads in cities prone to flood disasters caused by rainstorms. The approach involves several steps: First, extracting the surface area affected by precipitation using a threshold method constrained by confidence intervals derived from microwave remote sensing images; Second, collecting disaster information from the Sina Weibo platform, where social media information is screened and cleaned. A quantification table for road traffic loss assessment was defined, and a social media disaster information classification model combining text convolutional neural networks and attention mechanisms was proposed (TextCNN-Attention Disaster Classification); Finally, matching traffic road information on social media with basic geographic data, visualizing the classification of traffic road disaster risk levels, and completing the assessment of traffic road disaster levels based on multi-source heterogeneous data. Using the "7.20" rainstorm event in Henan Province as an example, this study categorizes the disaster impact on traffic roads into five levels: particularly severe, severe, moderate, mild, and affected, as interpreted from remote sensing images. The evaluation framework for flood disaster traffic roads based on multi-source heterogeneous data provides important data support and methodological support for enhancing disaster management capabilities and systems.

The coronavirus SARS-CoV-2 is the causative agent for the COVID-19 first registered in Wuhan, China and responsible for more than 6 million deaths worldwide. Currently, RT-PCR is the gold standard method for diagnosing COVID-19. However, serological tests are needed for screening acute disease diagnosis and screening large populations during the COVID-19 outbreak. Herein we described the development of an in-house enzyme-linked immunosorbent assay (ELISA) for detecting levels of anti-Spike-1-RBD IgM antibody in well-defined serum/plasma panel for screening and identifying subjects infected with SARS-CoV-2 in a Latin population. We found that, compared to the RT-PCR as standard method, the in-house CovIgM-ELISA displayed sensitivities of 96.15% and 93.22% for samples collected up to 30 or 60 days after infection, respectively, as well as 95.59% specificity with 97.3% accuracy. The agreement kappa value (κ) of our CovIgM-ELISA was substantial when compared to RT-PCR (κ =0.873) and to SCoV-2 DetectTM IgM ELISA (κ = 0.684). The IgM levels detected in the population positively correlate with the neutralizing activity against the Wild-type, Alpha and Delta variants but failed to neutralize Omicron. These data indicate that our in-house CovIgM-ELISA is a compatible performing assay for the detection of SARS-CoV-2 infection.