Feelings behind words: A systematic review on how effective IS NLP-based assessment for mental health diagnosis in human studies.

Venous catheter selection is a crucial aspect of patient care, impacting treatment outcomes, patient safety and healthcare sustainability performance. While central venous catheters (CVCs) offer advantages for certain clinical situations, their safety profile compared to peripheral venous catheters (PVCs) remains a subject of debate. This study aimed to compare the safety profiles of CVCs and PVCs to inform evidence-based practice and improve sustainability performance in venous catheter selection. A two-group, prospective observational cohort study included adult patients (≥ 18 years) admitted to the emergency department and intensive care units of a university hospital in Assiut, Egypt. The safety profile was assessed using three key metrics: the Infection Probability Score (IPS), rates of Healthcare-Associated Infections and Sepsis (HAIS) and in-hospital mortality. While all 200 enrolled patients completed the study (160 ICU, 40 ED), the subgroup analysis was limited to 160 ICU patients due to the smaller ED group size. Central venous catheters were associated with a higher rate of catheter occlusion (40.0% (n=32) vs. 31.3% (n=25), p=0.248), while peripheral venous catheters were predominantly associated with phlebitis (18.8% (n=15) vs. 27.5% (n=22), p=0.189) and extravasation (0.0% (n=0) vs. 3.8% (n=3), p=0.080, OR = 7.2, 95% CI 2.7-140.2). Furthermore, patients with central venous catheters had a higher mean infection prevention score (17.6 ± 2.4 vs. 12.7 ± 5.1, p < 0.001, r = 0.520) and a significantly greater likelihood of healthcare-associated infections (91.3% (n=73) vs. 25.0% (n=29), p < 0.001, OR = 31.3, 95% CI 12.4-79.0). Notably, mortality was significantly higher among patients with central venous catheters (71.3% (n=57) vs. 6.3% (n=5), p < 0.001, OR = 37.2, 95% CI 13.3-103.8). These findings suggest that, despite the potential benefits of central venous catheters, the presence of a CVC is associated with a higher risk of severe complications, including infection, healthcare-associated infections, and mortality, compared to peripheral venous catheters in this observational cohort. This elevated risk profile may negatively impact sustainability performance by increasing resource utilization, treatment costs, and the burden of harm. The large observed effect sizes, supported by high post-hoc power, strengthen the evidence for this association within the study population. However, as this was an observational study without adjustment for potential confounding factors such as severity of illness or indication for catheter type, these results demonstrate association and cannot infer causation. The findings highlight the complex risk-benefit assessment required in clinical decision-making. These findings highlight the importance of a meticulous assessment of patient needs and risk factors when selecting a venous catheter. Clinicians should carefully weigh the potential benefits of central venous catheters against the increased risk of complications, particularly in patients with heightened susceptibility to infection or those requiring shorter-term intravenous access. Prioritising peripheral venous catheters whenever feasible may contribute to improved patient safety, outcomes and overall sustainability performance of healthcare delivery by preventing costly complications and enhancing resource efficiency.

Ensemble learning-driven hybrid prediction model for improved prenatal down's syndrome screening: a comparative study with laboratory-based median equations.

Large language modeling of hallucinatory problem mitigation based on the wheel of emotions.

Exploring environmental sustainability of artificial intelligence in radiology: A scoping review.

Bionic helical sensor array inspired by mammal olfactory recess.

Towards next-generation 5-hydroxytryptamine 2C receptor modulators: Greener synthesis and evaluation of novel isocoumarin derivatives as PAAMs of 5-HT2CR.

Underground hydrogen storage (UHS): Comparative analysis of key performance metrics

Risk analysis of pedestrian crosswalks in airport drop-off zones based on integrated VISSIM-SSAM model.

Effects of mobile health management model on the prevention of gestational diabetes mellitus in pregnant women at risk of gestational diabetes: A randomized controlled trial.

The rapid growth of High-Definition (HD) and Ultra-High-Definition (UHD) content, ranging from 2K to 10K resolutions, presents significant challenges for terrestrial broadcasting systems, particularly regarding spectrum utilization and reliable transmission. Digital Video Broadcasting-Terrestrial second generation (DVB T2) with Multi-Physical Layer Pipes (M-PLP) offers a flexible framework to deliver heterogeneous content streams efficiently. This study investigates spectrum-aware adaptive transmission strategies to optimize multi PLP DVB-T2 networks for high-resolution streaming, focusing on efficient multiplexing, error resilience and bandwidth management. Simulation analyses evaluate the performance of various multi-PLP configurations under different channel conditions, including low Signal to Noise Ratio (SNR) environments. Key metrics, such as throughput, Bit Error Rate (BER) and spectral efficiency, were assessed. Results indicate that adaptive allocation of PLPs according to stream resolution and channel conditions can significantly enhance system performance. For instance, multiplexing a 2K, 4K, 6K, 8K and 10K stream set using optimized PLP bandwidth allocations achieved a spectral efficiency improvement of 18% and a BER reduction from 10⁻³ to 10⁻⁵ at SNR levels of 3–5 dB compared to static PLP allocation schemes. Iterative modulation and coding adaptations further reduced transmission errors while maintaining low latency suitable for live broadcasting. The findings demonstrate that spectrum-aware adaptive transmission not only maximizes resource utilization but also ensures reliable reception across diverse resolutions. By dynamically matching PLP parameters to stream requirements and channel conditions, DVB-T2 broadcasters can efficiently deliver high-quality 2K-10K content even under constrained spectrum scenarios. This research provides a practical methodology for next-generation terrestrial broadcasting, contributing to improved spectrum efficiency, robust high-resolution transmission and enhanced user experience.

Objective This study evaluated the impact of a timeline-based management protocol on treatment efficiency and short-term outcomes in acute ischemic stroke (AIS) patients. Methods A quasi-experimental study compared two cohorts treated at a single emergency department: a control group (n = 138) receiving conventional care (January–June 2022) and an intervention group (n = 125) managed with a timeline-based protocol (August–December 2022). Key metrics included door-to-needle time (DTN), admission-to-puncture time, order-to-CT time, and order-to-platelet-result time. Thrombolysis rates, Glasgow Outcome Scale (GOS), Fugl-Meyer scores, and stroke-specific quality of life were also analyzed. Results The intervention group showed significantly shorter DTN, puncture, CT, and platelet result times (p < 0.05), along with a higher thrombolysis rate. Significant improvements were also observed in GOS, motor function, and quality of life scores (p < 0.05). Conclusion The timeline-based protocol, featuring parallel coordination and a rapid-response stroke team, reduced treatment delays and improved clinical outcomes in AIS patients. These results support the adoption of time-sensitive protocols to enhance stroke care efficiency and prognosis.

This paper presents the design and implementation of a high-precision photovoltaic emulator that utilises the Black-Winged Kite Optimisation algorithm for extracting parameters of the Single Diode Model (SDM). In contrast to traditional memory-intensive look-up table methods, the proposed emulator offers enhanced flexibility and reliability in simulating diverse environmental conditions. To ensure accuracy, the system integrates high-performance voltage, current, and temperature sensors, each equipped with detailed calibration curves. These sensors, interfaced with a microcontroller, significantly enhance the emulator's dynamic response and measurement precision, outperforming commercial emulators. Experimental validation is conducted by comparing the emulator's output with real-time measurements on a real photovoltaic panel under varying loads, temperatures, and irradiance levels. The Black-Winged Kite algorithm effectively extracts SDM parameters, resulting in a close match between simulated and experimental I–V and P–V characteristics. The emulator's performance is validated through key statistical metrics, including an RMSE of 2.65 E − 3 A, standard deviation of 5.66 E − 16 A, and NRMSE of 3.561 E − 3 A. To assess its robustness and generalisation capability, the emulator was tested on both monocrystalline (RTC France) and thin-film (STM6-40/36) PV modules. The results demonstrate its effectiveness in accurately reproducing photovoltaic behaviour across different technologies and operating scenarios, confirming its adaptability and performance under real-world conditions.

Purpose This study aims to assess and enhance the production efficiency and reliability of fertilizer manufacturing plants through the application of Markovian analysis. By integrating stochastic modeling with performance management theory, the research provides a predictive performance measurement framework that links reliability metrics to key business outcomes. The research develops a probabilistic framework to model system state transitions, evaluate system availability and optimize maintenance strategies to reduce downtime and improve overall plant performance. Design/methodology/approach A discrete-time Markov chain model is constructed to represent the operational dynamics of critical units within a fertilizer plant. Historical operational and maintenance data are analyzed to develop transition probability matrices that capture the likelihood of state transitions between operational, idle and failure conditions. Key performance metrics such as steady-state probabilities, mean first passage times and recurrence times are computed to assess long-term system behavior. Statistical hypothesis testing – including paired t-tests, Wilcoxon signed-rank tests and chi-square tests – is employed to validate improvements in reliability and efficiency following the implementation of Markov-based maintenance strategies. Regression analysis is also conducted to examine the relationships between operational parameters (e.g. downtime and failure frequency) and production output. Findings The analysis reveals that the production units remain in the operational (RUN) state approximately 61.01% of the time, compared to 53% prior to optimization. Markovian-based maintenance strategies significantly reduced average weekly downtime from 42 h to 29 h (p &amp;lt; 0.001). Weekly production output increased from an average of 1,250 tons to 1,375 tons (p &amp;lt; 0.001). A chi-square test confirmed statistically significant changes in system state transitions (p &amp;lt; 0.001), favoring increased operational continuity. Confidence intervals constructed for key reliability parameters further strengthened the robustness of the findings. Practical implications This study provides a data-driven methodology for improving maintenance planning and production reliability in fertilizer plants. By modeling system behavior through Markovian analysis and applying statistical validation techniques, maintenance managers can develop predictive strategies that reduce unplanned downtime and enhance production efficiency. The methodology is adaptable to other continuous process industries where uptime and reliability are operational priorities and can be integrated into existing performance management systems to support data-driven decision-making and strategic alignment of maintenance activities with productivity goals. Originality/value This study offers a novel academic contribution by applying discrete-time Markov chain modeling to fertilizer manufacturing using empirical operational data. It advances performance management research by integrating stochastic modeling with statistical validation to quantify production efficiency and system reliability. The linkage between probabilistic reliability metrics (e.g. steady-state probabilities and mean first passage times) and business key performance indicators (e.g. downtime and weekly output) provides a new data-driven framework for industrial performance evaluation. This work bridges theoretical modeling with applied maintenance strategies, offering a transferable methodology relevant to researchers and practitioners seeking to optimize reliability and productivity in continuous-process industries.

High-frequency electrocardiography (HF-ECG) enhances ischemia detection by capturing microvolt-level changes in the QRS complex; however, clinical adoption requires validating digital systems against Analog standards. We recorded HF-ECG signals simultaneously from 12 healthy subjects (84 beats total) using a five-stage Analog reference (100-500Hz band-pass, gold connectors) and Sydäntek's 10× capacitive sensors; both outputs were digitized using a Texas Instruments ADS1298. Signals underwent 10× amplification with a low-noise op-amp, the Analog output was scaled to match, and data were processed in PulseTek™ and stored in PulseVault™. Root mean square (RMS), Amplitude, Kurtosis, and Frequency content were compared using Bland-Altman analysis (Analog as the reference); values reported reflect pre-amplified measurements in microvolts (µV). Mean differences between the Analog setup and Sydäntek fell within the 95% limits of agreement (LOA): RMS, 6.39 µV (- 49.74 to 62.52 µV); amplitude, 1.82 µV (- 57.09 to 60.73 µV); kurtosis, 1.93 (- 5.13 to 1.54); and frequency, 2.1Hz (- 5.8 to 6.2Hz), all within a 5% clinical tolerance when scaled 10× (~ 10-20 mV). Sydäntek matched analog fidelity, with frequency peaks near ~ 150Hz, indicating digital HF-ECG performance equivalent to that of the Analog system on key metrics. Its wearable design and cloud integration provide a portable, reliable alternative for ischemia detection with broader clinical applicability.

Construction projects play a pivotal role in social development, influencing rural and urban communities through their execution and management. The complexity of these projects necessitates effective collaboration among diverse stakeholders to achieve sustainable social outcomes. While Building Information Modeling (BIM) has reshaped collaboration patterns, its influence on the dynamic evolution of stakeholder relationships across project phases remains underexplored. This study proposes a comprehensive analytical framework integrating Social Network Analysis (SNA) and Cognitive Social Structure (CSS) to examine stakeholder collaboration in construction projects. By incorporating a Work Breakdown Structure (WBS) for task-level relationship quantification, the framework captures structural and temporal dynamics of collaboration. A novel network model is also developed to assess BIM’s impact on information exchange networks. The framework is empirically validated through a case study, incorporating stakeholder identification, and network analysis across design and construction preparation phases. Key metrics such as network density, centrality, and collaboration frequency are quantified and analyzed. The findings offer actionable insights for enhancing collaborative efficiency and project performance, contributing to socially sustainable and digitally advanced construction management practices.

The purpose of this study was to evaluate the safety and efficacy of perioperative outcomes of the KangDuo Surgical Robot System versus the Da Vinci System in robot-assisted renal surgery. We conducted a comprehensive systematic search in key databases including PubMed, the Cochrane Library, Embase, and Web of Science, encompassing studies in all languages, with the final search date being October 2025. We also excluded articles comprising reviews, letters, and single-arm studies. Variables were assessed using metrics of Weighted Mean Difference (WMD), Standardized Mean Difference (SMD), and Odds Ratio (OR). The statistical synthesis of the data, encompassing diverse outcome measures, was conducted utilizing the Review Manager software. Moreover, the protocol for this systematic review and meta-analysis is publicly available on PROSPERO (Registration number: CRD420251181530). In a pooled analysis of 370 participants from five trials, the KangDuo Surgical Robot System demonstrated a significantly extended docking time relative to the Da Vinci System (SMD = 1.55, 95% CI: 1.21-1.90; p < 0.00001). Conversely, comparisons of operative time, estimated blood loss, incidence of minor complications, ischemia time, and postoperative renal function (assessed by 4-week serum creatinine and eGFR) yielded comparable results, with no statistically significant differences. In this meta-analysis with limited sample size, geographic focus on Chinese centers, and short-term follow-up, the pooled data indicate that the KangDuo (KD) Surgical Robot System achieved non-inferiority to the Da Vinci System on several key perioperative metrics in robot-assisted renal surgery, although with a longer docking time. These findings support the KD system as a promising alternative in comparable settings, yet they should be interpreted within the constraints of the available evidence. Future research, particularly large-scale, multicenter randomized trials with long-term monitoring of survival and renal function, is necessary to confirm its efficacy and safety profile for widespread clinical adoption.

This article was inspired by the five-year anniversary of the journal “DEMIS. Demographic Research”. During this time, 20 volumes of the journal were published, featuring prominent and emerging Russian and international researchers whose work covered demographic theory, research methods, analysis of demographic situations and processes, demographic forecasts, demographic policies, family policies, migration policies, economic and social implications of demographic changes, spatial demographics, historical demographics, and more. All papers are published in open access to demonstrate the editorial team's commitment to scientific collaboration, dissemination of demographic knowledge, and further development. “DEMIS. Demographic Research” aims to be a platform for sharing experiences and ideas between leading demographers, historians, social scientists, philosophers, economists from Russia and abroad, as well as younger scholars just starting their careers. This review article offers a general overview of the magazine, highlights significant milestones in its history, some of its published articles, and key bibliometric metrics. Additionally, the authors suggest potential future directions for the publication's development.

BackgroundBlood culture is an essential diagnostic tool for detecting bloodstream infections, particularly in patients with suspected sepsis. Routine implementation in healthcare settings necessitates adherence to standardized protocols to ensure diagnostic accuracy. We assessed blood culture practices in university-affiliated hospitals across Korea by analyzing key performance metrics and related indicators.MethodsIn 2024, a standardized questionnaire was distributed to 14 university-affiliated hospitals. The survey collected data on blood culture practices during 2023, including the total number of cultures performed, the number of sets obtained per episode, the frequency of repeated cultures, the volume of blood collected per bottle, discrepancies in culture results, the types of organisms isolated, and post-test reporting practices.ResultsIn 2023, the median number of blood cultures requested per 1,000 hospitalized patients was 166.6 (43,699 cultures among 241,053 admissions). Two-set collections were most common (median 68.9%), followed by single-set collections (13.0%). Repeat cultures were more frequently performed following negative results (29.2%) than positive ones (5.5%). The median volume collected was 4.0 and 4.3 mL for aerobic and anaerobic bottles, respectively. Only 16.7% of Gram stain results were reported within 2 h.ConclusionsBlood culture practices, particularly regarding test ordering and the use of two or more sets per episode, appear generally appropriate. However, specific aspects—including the indications for repeat cultures, adequacy of collected blood volume, and timeliness of Gram stain reporting—require further evaluation and targeted quality improvement efforts.

The construction of regional medical centers represents a strategic initiative for improving healthcare quality and accessibility in public hospitals. Advanced imaging platforms integrating computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography-computed tomography (PET-CT) technologies are crucial for achieving high-quality diagnostic services. This study aimed to develop and evaluate an integrated advanced imaging platform model for a public hospital transitioning to a regional medical center, focusing on quality improvement, resource optimization, and clinical outcomes. A prospective implementation study was conducted from January 2021 to December 2023 in a tertiary public hospital. Implementation followed three phases: infrastructure preparation (months 1-12), technology deployment (months 13-24), and full-scale operations (months 25-36). Quality control included daily calibration, monthly phantom imaging, and quarterly physics evaluations. Key metrics measured were diagnostic accuracy (peer review), turnaround times, patient satisfaction (Press Ganey surveys), equipment utilization, and financial performance. AI systems underwent retrospective validation on 10000 cases before clinical deployment. All AI tools utilized in this study were FDA-cleared or CE-marked commercial software platforms with established validation for clinical use. Following platform implementation, diagnostic accuracy improved by 23.7% (95% CI: 21.4%-26.0%, p<0.001), examination turnaround time decreased by 42.3% (from 48.6±12.3 to 28.1±8.7h, p<0.001), and patient satisfaction scores increased from 72.4% to 91.8% (p<0.001). The centralized imaging center model achieved a 31.5% reduction in operational costs while expanding service capacity by 58.2%. The integrated advanced imaging platform successfully enhanced diagnostic capabilities, operational efficiency, and patient care quality in a public hospital. This model provides a replicable framework for healthcare institutions pursuing regional medical center development.