This study examines the role of public procurement in stimulating innovation in the United States, with particular attention to small and medium‐sized enterprises (SMEs). Public procurement of innovation (PPI) is widely regarded as a demand‐side policy instrument that can generate lead markets for emerging technologies, yet its scope and structural dynamics remain underexplored in large economies. To address this gap, we analyze more than 46 million procurement contracts (2007–2021) from USAspending.gov using a text‐mining approach informed by disruptive technology keywords from Bloom et al. We find that innovation‐related contracts constitute only 0.12% of the total, with SMEs securing around 41%. Nonetheless, SMEs face persistent difficulties in winning follow‐on contracts, reflecting resource limitations. Moreover, results reveal an inverted U‐shaped relationship between competition and innovation procurement, indicating that moderate competition fosters innovation most effectively. The study contributes to theory by extending the Resource‐Based View (RBV) to public procurement, demonstrating how resource heterogeneity and immobility shape outcomes in government‐led markets. Practically, we provide policy insights to strengthen SME participation in PPI through targeted support and simplified procedures, thereby advancing both innovation policy and SME competitiveness.

BACKGROUND There is a lack of study on vitamin D and calcium levels in epileptic patients receiving therapy, despite the growing recognition of the importance of bone health in individuals with epilepsy. Associations one statistical method for finding correlations between variables in big datasets is called association rule mining (ARM). This technique finds patterns of common items or events in the data set, including associations. Through the analysis of patient data, including demographics, genetic information, and reactions with previous treatments, ARM can identify harmful drug reactions, possible novel combinations of medicines, and trends which connect particular individual features to treatment outcomes. AIM To investigate the evidence on the effects of anti-epileptic drugs (AEDs) on calcium metabolism and supplementing with vitamin D to help lower the likelihood of bone-related issues using ARM technique. METHODS ARM technique was used to analyze patients’ behavior on calcium metabolism, vitamin D and anti-epileptic medicines. Epileptic sufferers of both sexes who attended neurological outpatient and in patient department clinics were recruited for the study. There were three patient groups: Group 1 received one AED, group 2 received two AEDs, and group 3 received more than two AEDs. The researchers analyzed the alkaline phosphatase, ionized calcium, total calcium, phosphorus, vitamin D levels, or parathyroid hormone values. RESULTS A total of 150 patients, aged 12 years to 60 years, were studied, with 50 in each group (1, 2, and 3). 60% were men, this gender imbalance may affect the study’s findings, as women have different bone metabolism dynamics influenced by hormonal variations, including menopause. The results may not fully capture the distinct effects of AEDs on female patients. A greater equal distribution of women should be the goal of future studies in order to offer a complete comprehension of the metabolic alterations brought on by AEDs. 86 patients had generalized epilepsy, 64 partial. 42% of patients had AEDs for > 5 years. Polytherapy reduced calcium and vitamin D levels compared to mono and dual therapy. Polytherapy elevated alkaline phosphatase and phosphorus levels. CONCLUSION ARM revealed the possible effects of variables like age, gender, and polytherapy on parathyroid hormone levels in individuals taking antiepileptic medication.

Background: Correct body posture should be at the very top, because it is an essential prerequisite for good health, normal growth and development, and good looks of every student. Inadequate spending of free time leads to the appearance of obesity, improper posture, and the appearance of physical deformities. Aims: This research aims to determine the prevalence of obesity, improper body posture, body deformities, and the way of spending free time among primary school students according to gender. Methods: The research was carried out on a sample of a total of N=1,850 primary school students, of which there were N=989 (53%) male respondents and N=861 (47%) female respondents. Analyzing the results of morphological characteristics, it was determined that boys have a higher body mass index (BMI) than girls. Result: The obtained results are statistically significant at the level of less than 1% (p<0.01), and the results were in favor of girls. Obesity and excessive nutrition are more prevalent in boys than in girls. By measuring the lateral curve of the spinal column, the following results were obtained: 67.24% of boys have improper posture, and 37.52% of girls. Conclusion: Classroom data show children spend substantial free time on electronic devices, especially boys gaming on computers and tablets. Girls more often engage in sports, television viewing, reading, and mobile communication. Preventive action is needed through regular student monitoring, testing, and measurement to track developmental changes and promote healthier lifestyle patterns in school contexts.

Effective voice education promotes healthy voice use, particularly for kindergarten teachers in demanding environments. This study assessed the effectiveness of a brief, workplace-based voice education program for the preschool teachers. Teachers were quasi-randomly assigned based on work schedules to either the Direct Voice Training Group (N = 31), which received a four-hour face-to-face training session and an educational booklet, or the Educational Booklet Only Group (N = 45). Outcomes were measured using the Vocal Fatigue Index (VFI) and blinded perceptual voice quality assessments via the GRBAS scale at baseline and three months post-intervention. Nonparametric Wilcoxon signed-rank tests assessed statistical significance, with effect sizes (r) calculated. Both groups improved significantly, but the Direct Voice Training Group showed broader improvements, with small to medium effect sizes (r = 0.25-0.38), whereas the Booklet Only Group had limited improvements (r < 0.3). Blinded evaluations confirmed greater benefits for the direct training group. These findings highlight the value of brief, targeted interventions for occupational vocal health, though small to medium effect sizes suggest that more intensive interventions may be required for clinically significant outcomes.

Liver proton density fat fraction (PDFF), the ratio between fat-only and overall proton densities, is an extensively validated biomarker associated with several diseases. In recent years, numerous deep learning-based methods for estimating PDFF have been proposed to optimize acquisition and post-processing times without sacrificing accuracy, compared to conventional methods. However, the lack of interpretability and the often poor generalizability of these DL-based models undermine the adoption of such techniques in clinical practice. In this work, we propose an Artificial Intelligence-based Decomposition of water and fat with Echo Asymmetry and Least-squares (AI-DEAL) method, designed to estimate both proton density fat fraction (PDFF) and the associated uncertainty maps. Once trained, AI-DEAL performs a one-shot MRI water-fat separation by first calculating the nonlinear confounder variables, R2∗ and off-resonance field. It then employs a weighted least squares approach to compute water-only and fat-only signals, along with their corresponding covariance matrix, which are subsequently used to derive the PDFF and its associated uncertainty. We validated our method using in vivo liver CSE-MRI, a fat-water phantom, and a numerical phantom. AI-DEAL demonstrated PDFF biases of 0.25% and -0.12% at two liver ROIs, outperforming state-of-the-art deep learning-based techniques. Although trained using in vivo data, our method exhibited PDFF biases of -3.43% in the fat-water phantom and -0.22% in the numerical phantom with no added noise. The latter bias remained approximately constant when noise was introduced. Furthermore, the estimated uncertainties showed good agreement with the observed errors and the variations within each ROI, highlighting their potential value for assessing the reliability of the resulting PDFF maps.

Decoding how specific neuronal subtypes contribute to brain function requires linking extracellular electrophysiological features to underlying molecular identities, yet reliable in vivo electrophysiological signal classification remains a major challenge for neuroscience and clinical brain-computer interfaces (BCI). Here, we show that pretrained, general-purpose vision-language models (VLMs) can be repurposed as few-shot learners to classify neuronal cell types directly from electrophysiological features, without task-specific fine-tuning. Validated against optogenetically tagged datasets, this approach enables robust and generalizable subtype inference with minimal supervision. Building on this capability, we developed the BCI AI Agent (BCI-Agent), an autonomous AI framework that integrates vision-based cell-type inference, stable neuron tracking, and automated molecular atlas validation with real-time literature synthesis. BCI-Agent addresses three critical challenges for in vivo electrophysiology: (1) accurate, training-free cell-type classification; (2) automated cross-validation of predictions using molecular atlas references and peer-reviewed literature; and (3) embedding molecular identities within stable, low-dimensional neural manifolds for dynamic decoding. In rodent motor-learning tasks, BCI-Agent revealed stable, cell-type-specific neural trajectories across time that uncover previously inaccessible dimensions of neural computation. Additionally, when applied to human Neuropixels recordings–where direct ground-truth labeling is inherently unavailable–BCI-Agent inferred neuronal subtypes and validated them through integration with human single-cell atlases and literature. By enabling scalable, cell-type-specific inference of in vivo electrophysiology, BCI-Agent provides a new approach for dissecting the contributions of distinct neuronal populations to brain function and dysfunction.

This paper analyses the key aspects of the operation of geroler hydraulic motors, widely used rotary hydraulic motors. The focus is on understanding their performance, efficiency and durability in different working conditions. The work includes a theoretical analysis of the principles of work, supported by modelling. Furthermore, experimental tests conducted to characterize the motors in terms of torque, speed, flow and efficiency are considered. Special attention is paid to the analysis of factors that influence the wear of internal components. The results of the analysis provide deeper insight into the behaviour of geroler hydraulic motors, identify areas for potential improvements in design and application and for optimizing their operation and extending their service life in various industrial and mobile applications. In conclusion, the work contributes to a better understanding of hydraulic motors, which is crucial for engineers and technicians involved in the design, application and maintenance of hydraulic systems.

Based on an early 2020s survey of 3,500 police officers from nine countries, this paper explores perceived community and police adherence to the COVID-19 regulations. We propose that both public and police perceived adherence with the COVID-19 rules are related not only to individual-level factors (e.g., gender, concern for own health) but also to country-level factors (e.g., quality of governance, protection of citizens' rights). Our findings reveal that individual-level factors, such as the concerns for personal and family health, were strong and consistent predictors of perceived community and police adherence. While misinformation about the COVID-19 pandemic was associated with the perceived community adherence, it was not related to the perceived police adherence. Moreover, country-level factors, such as the stringency of COVID-19 rules and regulations and the protection of citizens' rights, were also significantly related to both perceived community and police adherence.

Introduction: Periarthritis humeroscapularis (PHS) is an inflammatory reaction of the muscle tendons of the shoulder joint and other adjacent tissues of the shoulder joint. The main clinical signs are pain and limitation of mobility, which endangers the daily life activities. The aim of this study is to determine the effectiveness of rehabilitation in reducing the pain and the effectiveness of rehabilitation in improving the daily life activities of respondents with PHS. Methods: This prospective, pre-post study, conducted from May to September 2023, included subjects of both sexes, different age groups, and different occupations, who were part of the rehabilitation program. The data were collected by filling out a questionnaires of the general data of the respondents and a the American Shoulder and Elbow Surgeon Score (ASES) questionnaire on activities of daily life and intensity of pain in the shoulder before and after the rehabilitation program. The rehabilitation program lasted an average of 14 days and included the following physical procedures: Electrotherapy, ultrasound, kinesitherapy, cryotherapy, short-wave diathermy, and manual massage. Results: Comparison of the average pain score on an ASES pain scale of 1-10 shows that the respondents rated pain with an average score of 7.32 before the treatment and with an average score of 2.14 after the treatment. The average scores on the activities of daily living subscale of the ASES questionnaire were worse before the treatment and amounted to 20.72 ± 10.46 (range 3.33-40.0) compared to the scores after the treatment when they averaged 40.95 ± 7.46 (range 21.67-50). The average total score on the ASES scale before the treatment was 34.10 ± 13.93 (range 8.33-58.33), and after the treatment, it was 80.27 ± 14.7 (range 48.33-100.0). Conclusion: The rehabilitation showed statistically significant improvement in the average score on the ASES subscales of pain and daily life activities.

Background Approximately 5–10% of patients with acute ischemic stroke (AIS) have known active cancer. These patients are at high risk for both recurrent AIS and major bleeding. The optimal antithrombotic strategy for cancer-related stroke is uncertain. This study compared clinical outcomes among patients with cancer-related stroke treated with anticoagulant versus antiplatelet therapy for secondary prevention. Methods We identified consecutive patients with AIS and active cancer hospitalized at our comprehensive stroke center from 2015 through 2020. Patients with cardioembolic mechanisms were excluded. We used Cox regression and inverse probability of treatment weighting (IPTW) analyses to evaluate the associations between type of antithrombotic therapy at discharge (anticoagulant versus antiplatelet therapy) and the main outcomes of 1-year mortality and long-term recurrent AIS. Results Among 5,012 AIS patients, 306 had active cancer. After applying study eligibility criteria, we analyzed 135 patients (median age 72 years; 44% women), of whom 58 (43%) were treated with anticoagulant and 77 (57%) with antiplatelet therapy. The median follow-up time was 495 days (IQR, 57–1,029). Patients treated with anticoagulants, compared to patients treated with antiplatelet therapy, were younger (median 69 versus 75 years), had more metastatic disease (72% versus 41%), and higher median baseline D-dimer levels (median 8,536 μg/L versus 1,010 μg/L). Anticoagulant versus antiplatelet therapy was associated with similar risks of 1-year mortality (adjusted hazard ratio [aHR], 0.76; 95% confidence interval [CI], 0.36–1.63) and long-term recurrent AIS (aHR 0.49; 95% CI 0.08–2.83). The IPTW analyses for 1-year mortality confirmed the results of the main analyses (HR 0.82; 95%CI: 0.39–1.72, p = 0.61). Conclusion Factors associated with anticoagulant use in patients with cancer-related stroke include younger age, more advanced cancer, and elevated D-dimer. Similar outcomes were seen with anticoagulant versus antiplatelet therapy in these patients highlighting the need for future randomized trials to determine the preferred antithrombotic strategy.

The original serial implementation of the segmentation algorithm exhibits suboptimal performance, motivating a systematic optimization effort without compromising accuracy.

Per- and polyfluoroalkyl substances (PFAS) are ubiquitous environmental pollutants that pose potential risks to ecosystems and human health. Prior to mass spectrometric analysis of environmental samples, it is necessary to separate PFAS from compounds that can cause ion suppression and compromise analyte identification and quantification accuracy. Although liquid chromatography-mass spectrometry (LC-MS) is the gold standard for PFAS trace analysis, some PFAS species still coelute in the LC dimension and could benefit from an orthogonal dimension of separation. Moreover, an additional orthogonal dimension of separation could potentially aid in the identification of unknown fluorinated species (e.g., those identified within a specified mass-defect range). Here, we investigate the sequential use of LC and differential mobility spectrometry (DMS) separation to analyze 34 PFAS species. Upon incorporating DMS in a two-dimensional (2D) separation scheme, we observed baseline resolution of 29 compounds in the 2D LC × DMS space, with partial resolution of the remaining five. In comparison, only five PFAS compounds were baseline-resolved in 1D LC experiments. Because DMS measurements can be acquired in milliseconds, targeted 2D LC × DMS-MS2 analyses operate on the same time scale as LC-MS2 analysis. However, the limits of quantitation for PFAS using the 2D LC × DMS-MS2 method are slightly higher than those achieved by the state-of-the-art LC-MS2 method owing to ion fragmentation within the energetic DMS environment. Nevertheless, distinct trends observed in the 2D separation space for the various PFAS subclasses will facilitate analyte identification of unknown species in future nontargeted analyses. Finally, we assessed the feasibility of our method for quantifying PFAS in a series of wastewater samples obtained from a Southern Ontario wastewater treatment plant. We were successful in quantifying PFOS, although the concentrations determined were consistently higher than those measured with LC-MS2.

The third-order difference equation yn+1=a1yn21+yn2+a2yn−121+yn−12+a3yn−221+yn−22, as a potential discrete time model of population dynamics with three generation involved, is studied. The parts of the basins of attraction of three equilibrium points that this equation admits are described. Some results about period-two and period-three solutions have been established.