Search Results (50)

Background: Physiological and psychological factors are important for determining static breath-hold duration. Preconditioning, such as preparatory apneas at functional residual capacity, is a potentially valuable method for prolonging breath-hold duration at total lung capacity. We investigated the physiological influence of preparatory apneas to determine the possible association with maximal apnea duration via diaphragmatic pressure and electromyographic measurements. Methods: Fourteen male breath-hold divers (39 ± 10 years; body mass, 87.2 ± 8.5 kg; body fat, 14.4 ± 3.8%; body height, 186.6 ± 3.9 cm; training experience, 14.2 ± 9.6 years) were included. We measured diaphragm activity during breath-holds via transdiaphragmatic pressure (Pdia) using balloon-tipped catheters in the stomach and esophagus and electromyographic (EMG) activity. From these, ∆EMG and ∆Pdia for every involuntary breathing movement (IBM) during all apneas were quantified. Furthermore, a pressure difference (difference between the first and last IBM Pdia value) and the respiratory neuromuscular output index (RNMI) (∆Pdia/∆EMG) were included as indirect parameters of the pressure perceived. These variables were measured during three preparatory breath-holds (average duration = 185 ± 69 s and range = 62–309 s, separated by 2.5 min) and three maximal breath-holds (average duration = 308 s and range = 179–733 s, separated by 5 min). Results: The preparatory apnea performed at FRC elicited significantly higher Pdia activity (p < 0.00) and a significantly lower RNMI (p = 0.00–0.01) compared to the maximal apneas. Furthermore, a higher and more pronounced increase in Pdia during the preparatory apnea at FRC was related to longer maximal apneas (Max 1, r = 0.65, p = 0.01; Max 2, r = 0.65, p = 0.02; Max 3, r = 0.66, p = 0.01). Conclusions: The results suggest an acute preconditioning effect of primarily the preparatory apnea at FRC on the duration of the subsequent maximal apneas. The implementation of preparatory apneas preceding maximal apneas during training sessions may elicit a longer breath-hold duration in trained divers. Full article

This study addresses the trade-off between accuracy and efficiency in predicting the aerodynamics and wakes of large wind turbines. We developed a unified immersed boundary–actuator line framework with large-eddy simulation. The actuator line efficiently represents blade loading, while the immersed boundary method (IBM) with a wall model resolves near-blade turbulence. The solver uses a staggered Cartesian discretization and is accelerated by a hybrid CPU/GPU implementation. An implicit signed-distance geometry treatment and a ghost cell wall function based on Spalding’s law reduce near-wall grid requirements and eliminate body-fitted meshing. Flow past a three-dimensional cylinder at Re = 3900 validates the accuracy and good grid convergence of the IBM. For the wind turbine, three meshes show converged thrust and torque, with differences below 1% between the two finer grids. At the rated condition (U_∞ = 11.4 m/s), thrust and torque agree with STAR-CCM+ and FAST, with deviations of 6.3% and 1.2%, respectively. Parametric cases at 4–10 m/s show thrust and torque increasing nonlinearly with inflow, approximately quadratically, in close agreement with reference models. As wind speed rises, the helical pitch tightens, the wake broadens, and breakdown occurs earlier, consistent with stronger shed vorticity. The framework delivers high fidelity and scalability without body-fitted meshes, offering a practical tool for turbine design studies and extensible wind plant simulations. Full article

Background and Objectives: Breast cancer is one of the most common cancers in women and the most common cause of cancer death. Hormone receptors, specifically the estrogen receptor (ER) and progesterone receptor (PR), as well as human epidermal growth factor receptor-2 (Her2), are tumor-specific markers used to guide breast cancer therapy. The purpose of this study is to evaluate the impact of tumor biology, including ER, PR, and Her2 expression, on survival in female breast cancer. Materials and Methods: This retrospective cohort study represents an analysis of 2016 female breast cancer cases using anonymized data. We reviewed cases of female breast cancer diagnosed from 1 January 2010 to 31 December 2021, in the Clinic of Obstetrics and Gynecology, Diakoneo Diak Klinikum Schwäbisch Hall, Germany. Data on clinical, pathology, immunohistochemistry, and follow-up characteristics were retrieved from the clinic’s database. To interpret the data, we used the software IBM SPSS Statistics 20, and, to account for multiple comparisons, we used a Bonferroni-adjusted significance level of 0.004. In the survival analysis, the Kaplan–Meier method and the log-rank test of equality of survival distributions were applied. Results: Among 2016 female breast cancer cases, 84.5% (1703/2016) were hormone receptor (HR)-positive. The 5-year overall survival was 0.873 (95% CI (0.851, 0.895); 99.6% CI (0.841, 0.905)) for HR-positive patients and 0.760 (95% CI (0.713, 0.807); 99.6% CI (0.691, 0.829)) for HR-negative patients (p < 0.001). Statistically significant differences were observed among HR+/HER2+, HR+/HER2−, HR−/HER2+, and triple-negative subtypes (p = 0.003). When comparing survival distributions based solely on HER2 expression (positive vs. negative), no statistically significant difference was observed (p = 0.29). Conclusions: Statistically significant differences in unadjusted overall survival distributions were observed among breast cancer molecular subtypes. HR-positive breast cancers demonstrated better overall survival than HR-negative cancers, while no statistically significant difference in unadjusted survival was observed between HER2-positive and HER2-negative groups. Full article

Background: Pediatric road traffic injuries (RTIs) represent a significant public health concern, particularly in countries like Romania, where road infrastructure and safety remain challenges. Despite recent economic reclassification, Romania continues to report high rates of pediatric traffic-related injuries. Non-fatal RTIs often result in long-term physical and psychological harm. This study aims to assess age- and gender-specific injury patterns and mechanisms of non-fatal RTIs in children and adolescents, using data from “St. Mary’s” Emergency Clinical Hospital for Children in Iași over a ten-year period to inform targeted prevention strategies. Methods: This 10-year retrospective study (2015–2024) was conducted at “St. Mary’s” Emergency Clinical Hospital for Children in Iași, Romania, a regional referral center. Data from 1074 pediatric patients (aged 1 month–17 years, 11 months) with RTIs were analyzed using ICD-10 codes and verified manually. Variables included demographics, injury type, mechanism, and treatment. Patients were stratified into four age groups. Statistical analysis was performed using IBM SPSS Statistics 25, with significance set at p < 0.05. Results: The highest incidence was observed among boys (77.7%) and children aged 10–14 years. Car passengers and cyclists constituted the most frequently affected groups, with only 11% of passengers appropriately restrained and 78% of cyclists not wearing helmets. Common injuries included excoriations, thoracic contusions, and abdominal trauma, with notable variations by age and sex. Thoracic injuries were more frequent among girls, whereas younger children exhibited a higher incidence of abdominal trauma. Conclusions: The findings emphasize critical safety gaps in child restraint and helmet use and highlight the urgent need for targeted, age-specific road safety interventions and improved public health education. Full article

To determine the association between FGFR4 (rs351855 and rs7708357) gene variants, serum levels, and immunohistochemical markers (Ki-67 and p53) in pituitary adenoma (PA), a case-control study was conducted involving 300 subjects divided into two groups: the control group (n = 200) and a group of PA (n = 100). The genotyping of FGFR4 rs351855 and rs7708357 was carried out using the real-time polymerase chain reaction (RT-PCR) method. The serum FGFR4 levels were measured using the ELISA method. Immunohistochemical analysis (Ki-67 and p53) was conducted. Statistical analysis of the data was performed using IBM SPSS Statistics 30.0 software. There were no statistically significant differences after analyzing the genotypes and alleles of FGFR4 rs351855 and rs7708357 in patients with PA and control groups (all p > 0.05). After evaluating the distribution of genotypes and alleles of FGFR4 rs351855 and rs7708357 in micro/macro, invasiveness, activity, and recurrence of PA and the control groups, the analysis showed no statistically significant differences between the groups (p > 0.05). Similarly, no significant differences in FGFR4 levels were observed between PA patients and control group (median (IQR): 3642.41 (1755.08) pg/mL vs. 3126.24 (1334.15) pg/mL, p = 0.121). Immunohistochemistry for Ki-67 revealed a labeling index (LI) of <1% in 25.5% of patients with PA, an LI of 1% in 10.9%, and an LI of >1% in 63.6% of patients. Further analyses showed no statistically significant associations with tumor size, invasiveness, activity, or recurrence. Immunohistochemistry for p53 revealed that macroadenomas had a significantly higher p53 H-score compared to microadenomas (median (IQR): 30.33 (28.68) vs. 18.34 (17.65), p = 0.005). Additionally, a moderate, statistically significant positive correlation between the Ki-67 LI and the p53 expression was found (Spearman’s ρ = 0.443, p = 0.003, n = 43). FGFR4 variants and serum protein levels were not significantly associated with PA risk or tumor features. Conversely, immunohistochemical markers Ki-67 and p53 were more informative, with higher p53 expression in macroadenomas and a moderate positive correlation between Ki-67 and p53, highlighting their potential relevance in tumor growth assessment. Full article

The electric vehicle (EV) business model integrates advanced battery technology, dynamic power train architectures, and intelligent energy management systems with ecosystem strategies and digital services. It incorporates environmental sustainability through lifecycle analysis and renewable energy integration. China, with 9.49 million EV sales in 2023 (33% market share), faces infrastructure gaps constraining further growth. China is strategically mitigating CO₂ emissions while fostering economic expansion, notwithstanding constraints such as suboptimal battery technology advancements, elevated production expenditure, and enduring ecological impacts. This Political, Economic, Social, Technological, Legal, Environmental (PESTLE) assessment, operationalized through a survey of 800 stakeholders and Statistical Package for the Social Sciences IBM SPSS SPSS (Version 28) quantitative analysis (factor loading = 0.73 for Technology; eigenvalue = 4.12), identifies infrastructure gaps as the dominant barrier (72% of stakeholders). Political factors (β = 0.82) emerged as the strongest adoption predictor, outweighing economic subsidies in significance. The adoption of EVs in China presents a significant prospect for reducing CO₂ emissions and advancing technology. However, economic barriers, market dynamics, inadequate infrastructure, regulatory uncertainty, and social acceptance issues are addressed in the assessment. The study recommends prioritizing infrastructure investment (e.g., 500 K fast-charging stations by 2027) and policy stability to overcome adoption barriers. This study provides three key advances: (1) quantification of PESTLE factor weights via factor analysis, revealing technological (infrastructure) and political factors as dominant; (2) identification of infrastructure gaps, not subsidies, as the primary adoption barrier; and (3) demonstration of infrastructure’s persistence post-subsidy cuts. These insights redefine EV adoption priorities in China. Full article

In response to the global trend of population aging, the issue of providing elderly individuals suitable leisure and entertainment has become increasingly important. In this study, it aims to utilize artificial intelligence (AI) technology to offer the elderly with a healthy and enjoyable exercise and leisure experience. A human–machine interactive system is designed using computer vision, a subfield of AI, to promote positive physical adaptation for the elderly. The relevant literature on the needs of the elderly, technology, exercise, leisure, and AI techniques is reviewed. Case studies of interactive devices for exercise and leisure for the elderly, both domestically and internationally, are summarized to establish the prototype concept for system design. The proposed interactive exercise and leisure system is developed by integrating motion-sensing interfaces and real-time object detection using the YOLO algorithm. The system’s effectiveness is evaluated through questionnaire surveys and participant interviews, with the collected survey data analyzed statistically using IBM SPSS 26 and AMOS 23. Findings indicate that (1) AI technology provides new and enjoyable interactive experiences for the elderly’s exercise and leisure; (2) positive impacts are made on the elderly’s health and well-being; and (3) the system’s acceptance and attractiveness increase when elements related to personal experiences are incorporated into the system. Full article

Background/Objectives: Romania remains endemic for measles due to suboptimal vaccine coverage rates. During the last three epidemics, the highest incidence of measles was recorded in children younger than 1 year, who should have been partially protected by maternal antibodies. A nationwide cross-sectional seroprevalence study was conducted on persons of fertile age, to evaluate potential immunity gaps in the population. Methods: Between June and October 2020, 959 serum samples were collected from individuals aged 25–44 years (46.5% females) from all the geographic regions in Romania. Measles IgG antibodies were assessed using an enzyme-linked immune assay (DIA.PRO-Diagnostic Bioprobes Srl, Italy). Statistical analysis was performed in IBM SPSS Statistics 27.0, using Fisher’s exact and chi-squared tests to test for associations between seropositivity and demographic factors, with p < 0.05 considered statistically significant. Results: The overall measles seroprevalence was 77%, without gender- or geographic region-related differences. Both the seropositivity rate and the measles antibodies titers increased with age, with the highest difference between the oldest and the youngest age group (p = 0.057), suggesting persistent immunity after natural infection in older individuals or anamnestic responses in vaccinated persons, caused by repeated exposures to the circulating virus. An additional confirmatory pilot study on 444 pregnant women confirmed the low level of measles seroprevalence (68.4%), with a significant upward trend in older ages (75% in those aged >40 years old vs. 65% in those aged 25–29 years, p = 0.018 and mean reactivity of measles antibodies 3.05 ± 1.75 in those aged >40 years vs. 2.28 ± 1.39 in those aged 25–29 years, p = 0.037). Conclusions: This study signals critical immunity gaps in the population that contribute to the accumulation of susceptible individuals and recurrent measles outbreaks. The absence of measles antibodies in women of childbearing age increases the newborn’s susceptibility to infection, with potentially severe complications. Full article

Background: Health inequalities between citizens of different European countries and between socially advantaged and disadvantaged groups are seen as challenges to the EU’s commitment to solidarity, social and economic cohesion, human rights, and equality of opportunity. This study aimed to assess inequity in the distribution of specific communicable and non-communicable diseases among the adult population of Pécs City, Hungary. Methods: This is an ecological study combined with health inequity analysis. The study population comprised adult patients living in the city of Pécs who were treated for circulatory, neoplasm, or respiratory diseases in 2019. Hospitalization and local socioeconomic data by zip codes were obtained from the National Health Insurance Fund of Hungary and the Hungarian Central Statistical Office. Diseases were classified according to the International Classification of Diseases, 10th revision (ICD-10). The differences and ratios of the proportions of treated diseases were calculated, along with the concentration index (C). Zip codes were ranked into categories 1 to 5 based on socio-demographic variables: education, employment status, and apartment ownership. A p-value of less than 0.05 was used as the cut-off point for statistical significance. IBM SPSS version 25 and STATA version 14.0 software were used for data analysis. Results: All 36 zip codes in the city of Pécs were included in this study. Analysis based on employment status showed a higher prevalence of neoplasms in areas with higher unemployment (C = −0.0528; 95% CI = −0.0975, −0.0080). In terms of apartment ownership, neoplasms (C = −0.0742; 95% CI = −0.1102, −0.0382) and circulatory diseases (C = −0.0280; 95% CI = −0.0520, −0.0039) were more prevalent in zip codes with fewer apartment owners. Conclusions: This study identified significant inequity in the distribution of neoplasms and circulatory diseases among the adult population of Pécs, especially in areas where the low socioeconomic segment of the population lives. Efforts should be made to establish tailored interventions such as targeted healthcare funding and employment programs for the unemployed and National Housing Trust Fund for those who do not own houses through multisectoral collaboration among healthcare providers, employers, governors, and policymakers. Full article

(1) Introduction: Indonesia is vulnerable to climate change’s impact. To adapt better, a study about community knowledge, attitudes, and behavior toward climate change is needed. This research aims to determine the perception of the health impacts caused and how urban communities (in the Jakarta area) respond to climate change, using a cross-sectional study to see the relationship between knowledge and attitudes towards behavior related to climate change. (2) Methods: This study used descriptive, analytical, observational, and quantitative methods with a cross-sectional design. In addition, the procedures were conducted in the Jakarta metropolitan area using a Google Form platform distributed through social media. Univariate, bivariate, and multivariate analyses were performed using IBM SPSS Statistics 22 software. (3) Results: The results showed that attitudes had a significant association with behavior toward climate change. Respondents had good knowledge of climate change but had negative attitudes and bad behavior. In addition, the impacts felt by the community were increasing heat waves, air pollution, and worsening health conditions. Most respondents were confident and believed that Indonesia could withstand these impacts. (4) Conclusions: Respondents in this study showed good knowledge of climate change and had a positive attitude towards adaptation and mitigation of climate change impacts. However, this is not reflected in their behavior because they predominantly show poor results. The government should be able to encourage changes in people’s behavior so that they are better prepared to deal with the impacts of climate change. Full article

This study aimed to develop and optimize an analytical method for profiling 21 amino acids in cerebrospinal fluid and plasma, addressing the need for improved diagnostic tools in leukemia research. Using high-performance liquid chromatography coupled with electrospray ionization mass spectrometry, the method achieved enhanced resolution, sensitivity, and specificity. Rigorous sample preparation, including liquid–liquid extraction, ensured high recovery rates, while validation confirmed the method’s accuracy and reproducibility. Clinical application in pediatric leukemia patients revealed significant variations in amino acid concentrations across treatment stages, providing insights into disease progression and therapeutic response. Statistical analysis with IBM SPSS Statistics 25 compared amino acid levels in patients to healthy controls, identifying distinct patterns on day 1, day 15, and day 33 of treatment. Correlation analysis highlighted relationships between amino acid levels and factors such as treatment duration, sex, age, and blood test results. Key amino acids, including proline, leucine, and hydroxyproline, emerged as significant predictors of white blood cell count, effectively distinguishing between patient and control groups. This method demonstrates robust potential for broader leukemia research applications, pending further validation on larger cohorts. Full article

In this paper, we use a simplified quantum counter to implement Grover’s algorithm-based quantum circuits to tackle the NP-hard exact cover problem (ECP). The ECP seeks a subcollection of sets such that every element is covered by exactly one set. Leveraging Grover’s algorithm, our quantum circuits achieve a quadratic speedup, querying the oracle O($\sqrt{N}$) times, compared to O(N) for classical methods, where $N=2^n$ is the total number of unstructured input instances and n is the number of input (quantum) bits. For the whole quantum circuit, the simplified quantum counter saves $(4mb-4m)\lfloor \pi /4\sqrt{N/M}\rfloor$ quantum gates and reduces the quantum circuit depth by $\left(2mb\right)\lfloor \pi /4\sqrt{N/M}\rfloor$ compared to Heidari et al.’s design, where $b=\lfloor logn\rfloor +1$ is the number of counting qubits used in a counter. Experimental results obtained using IBM Qiskit packages confirm the effectiveness of our quantum circuits. Full article

The advent of the Internet of Things (IoT) has revolutionized numerous sectors, with healthcare being particularly significant. Despite extensive studies addressing healthcare challenges, two persist: (1) the need for the swift detection of abnormalities in patients under medical care and timely notifications to patients or caregivers and (2) the accurate diagnosis of abnormalities tailored to the patient’s condition. Addressing these challenges, numerous studies have focused on developing healthcare systems, leveraging technologies like edge computing, which plays a pivotal role in enhancing system efficiency. Fog computing, situated at the edge of network hierarchies, leverages multiple nodes to expedite system processes. Furthermore, the wealth of data generated by sensors connected to patients presents invaluable insights for optimizing medical care. Data mining techniques, in this context, offer a means to enhance healthcare system performance by refining abnormality notifications and disease analysis. In this study, we present a system utilizing the K-Nearest Neighbor (KNN) algorithm and Raspberry Pi microcomputer within the fog layer for a diabetic patient data analysis. The KNN algorithm, trained on historical patient data, facilitates the real-time assessment of patient conditions based on past vital signs. A simulation using an IBM SPSS dataset and real-world testing on a diabetic patient demonstrate the system’s efficacy. The results manifest in prompt alerts or normal notifications, illustrating the system’s potential for enhancing patient care in healthcare settings. Full article

This work introduces a quantum K-Nearest Neighbor (K-NN) classifier algorithm. The algorithm utilizes angle encoding through a Quantum Random Access Memory (QRAM) using n number of qubit addresses with $\mathrm{O}(log(\mathrm{n}\left)\right)$ space complexity. It incorporates Grover’s algorithm and the quantum SWAP-Test to identify similar states and determine the nearest neighbors with high probability, achieving $\mathrm{O}\left(\sqrt{m}\right)$ search complexity, where m is the qubit address. We implement a simulation of the algorithm using IBM’s Qiskit with GPU support, applying it to the Iris and MNIST datasets with two different angle encodings. The experiments employ multiple QRAM cell sizes (8, 16, 32, 64, 128) and perform ten trials per size. According to the performance, accuracy values in the Iris dataset range from 89.3 ± 5.78% to 94.0 ± 1.56%. The MNIST dataset’s mean binary accuracy values range from 79.45 ± 18.84% to 94.00 ± 2.11% for classes 0 and 1. Additionally, a comparison of the results of this proposed approach with different state-of-the-art versions of QK-NN and the classical K-NN using Scikit-learn. This method achieves a 96.4 ± 2.22% accuracy in the Iris dataset. Finally, this proposal contributes an experimental result to the state of the art for the MNIST dataset, achieving an accuracy of 96.55 ± 2.00%. This work presents a new implementation proposal for QK-NN and conducts multiple experiments that yield more robust results than previous implementations. Although our average performance approaches still need to surpass the classic results, an experimental increase in the size of QRAM or the amount of data to encode is not achieved due to limitations. However, our results show promising improvement when considering working with more feature numbers and accommodating more data in the QRAM. Full article

The seasonal fluctuations of the copepod Eodiaptomus japonicus, which dominates the zooplankton community of Lake Biwa, have been disrupted several times over the past 45 years. The aim of this study was to clarify the primary environmental factor that caused the disrupted seasonal cycle in population density of E. japonicus. Here, we tested the hypothesis that the disruption in their seasonal cycle was due to the impacts of water temperature, food conditions, and predator pressure, using an individual-based model (IBM). Based on the experimental data from the literature, we described the growth and reproduction of E. japonicus using temperature- and food-dependent functions. Previously, the developmental time of this species was expressed using Bělehrádek’s equation. In this study, we applied the Kontodimas equation, which successfully reproduced the effects of food scarcity at higher temperatures. Additionally, the influence of predators was incorporated into the survival rate of adult individuals. The long-term data set of Lake Biwa was input into the developed model to simulate the population fluctuations during the disruption period (1975–1979) and stable period (1995–1999) of their seasonal cycle. The combination of environmental data to be input was (1) water temperature, food availability, and predators; (2) water temperature and food availability; and (3) water temperature and predators. Disruptions in the seasonal cycle of the population were only observed in scenario (1) during the disruption period simulation, suggesting that the disrupted seasonal cycle of this species in Lake Biwa may have been caused by the effects of both food condition and predators. The results of simulation scenarios (2) and (3) indicated that predators have a stronger impact on the population than food availability. This time, we used common and simple indicators to describe food conditions and predators, but the model can be improved to be more complex and accurate as more data become available. Such models are important tools for understanding the relationship between environmental factors and the dynamics of diaptomid copepod populations. Full article