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[Display omitted] • Highly spatially differentiated characterization factors capture the impact on three ecosystem services. • Developed characterization factors allow to differentiate between types and intensities of agricultural practices. • The stepwise procedure provides guidance to derive tailored characterization factors. The Life Cycle Assessment (LCA) methodology is a scientifically sound tool, endorsed by the European Commission, for conducting environmental assessments. Despite its standardized approach, certain areas require improvement, particularly concerning impacts on ecosystem structure, function, and associated ecosystem services. Given the significant pressure of agriculture on global ecosystems, research has attempted to establish characterization factors (CFs) that reflect the impact of agricultural practices on ecosystem services. However, the current CFs lack precision and are unable to differentiate between the various types and intensities of agricultural management. This study aims to contribute to the evolving LCA field by introducing novel CFs that allow for the assessment of the impacts and benefits of growing wheat under different agricultural practices on three ecosystem services: water purification, soil erosion control, and carbon sequestration. Spatially explicit, nonlinear models (InVEST and RothC) were used, with the Galician region of northwestern Spain as the spatial setting. The quantified CFs indicated that wheat cultivated with conventional tillage, straw removal and without cover crop exhibited the least favorable outcomes in terms of ecosystem service supply, specifically water purification (13.29 kg N·ha−1·y-1), soil erosion control (11.71 t soil·ha−1·y-1), and carbon sequestration (45.41 t C·ha−1·y-1). In contrast, the combination of conservation tillage, straw return and cover crop resulted in the highest ecosystem services gains (−0.46 kg N·ha−1·y-1, 2.64 t soil·ha−1·y-1, 7.05 t C·ha−1·y-1, respectively). The geographical specificity of the CFs presented here, combined with the detailed classification of agricultural land use, offers essential insights into the potential gains and losses of ecosystem services resulting from various farming practices. This information can assist industry stakeholders and policymakers in making well-informed agricultural management decisions, and in developing strategies that prevent unintended environmental burden shifting. [ABSTRACT FROM AUTHOR]

This study investigates the potential for integrating AI and journalistic networks to create real-time, priority-driven maps of infrastructure damage during natural disasters. Focusing on Hurricane Florence in 2018, we collected over a million tweets using the REST Twitter API and extracted 11,638 images for analysis. Tweets were categorized by source, including news organizations and citizen journalists. We applied the OpenAI CLIP unsupervised machine learning model for image classification, splitting the data into 80 % for training, 10 % for validation, and 10 % for testing. The model achieved an average precision of 92 %, recall of 78 %, and an F1 score of 85 %. When compared to other models such as ViT and DeiT, which achieved F1 scores of 82.9 and 81.2, respectively, CLIP performed similarly but stood out due to its accessibility and zero-shot learning capabilities, making it ideal for rapid deployment in newsrooms and crisis scenarios. The framework's success was further demonstrated by cross-referencing model predictions with geotagged metadata and journalist sources, which linked damage locations with credible information. By leveraging this AI-based framework, journalists can significantly reduce the time needed to identify disaster-response targets, helping to focus relief and recovery efforts in real time. This approach enhances disaster data collection, analysis, and dissemination, ultimately saving lives and reducing harm by providing more efficient and accurate damage assessments. The study highlights how AI and journalistic networks can collaborate to improve crisis response efforts. [ABSTRACT FROM AUTHOR]

• Fungal culture and Next-Generation Sequencing (Next Gen Seq) had 80% agreement. • Kappa coefficient for fungal culture and Next Gen Seq was 0.49 (moderate correlation). • Bacteria culture and Next Gen Seq had a 74% agreement. • Kappa coefficient for bacteria culture and Next Gen Seq was 0.46 (moderate correlation). • Next Gen Seq is a potential diagnostic modality for equine uterine infections. Fungal and bacterial culture is currently the primary method for pathogen detection and identification. Next-generation sequencing is a powerful method for detecting and identifying the presence of microbial DNA in samples. We evaluated the correlation between fungal and bacterial culture with next-generation sequencing in equine uterine samples. Fungal cultures (n=63) were evaluated based on their culture results. In culture positive (n=16) samples, next-generation sequencing identified the same organism in 10 samples (62.5%), 5 samples did not identify fungal agents, and 1 sample identified other species of fungal agents. In no growth samples (n=42), next-generation sequencing did not identify fungal agents in 37 samples (88.1%), 4 samples had a potential fungal pathogen identified, and one sample identified only non-pathogenic fungal organisms. Fungal culture and next-generation sequencing had an 80% agreement and moderate correlation by Kappa coefficient (0.508). Bacterial culture (n=57) was also evaluated based on bacterial culture results. In bacterial culture Positive samples (n=32), next-generation sequencing identified the same organism in 25 (78.1%) samples, and identified different organisms from the remaining 7 samples. In bacterial no growth samples (n=14), next-generation sequencing detected bacterial presence in 5 samples, and 9 samples had no bacterial DNA identified. The agreement between bacterial culture and next-generation sequencing was 74% with a moderate correlation by Kappa coefficient (0.46). In conclusion, culture and next-generation sequencing have moderate correlation, and next-generation sequencing has the potential as a diagnostic option for enhancing pathogen detection for equine endometritis. [ABSTRACT FROM AUTHOR]

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Purpose: The purpose of this study is to evaluate the effect of three different preexercise meals: high-carbohydrate, low-glycemic index (LGI), high-carbohydrate, high-GI (HGI) and low-carbohydrate high fat (LCHO) on substrate oxidation during an incremental cycling test (ICT) in recreationally active adults. Design/methodology/approach: This was a parallel, randomized study in which participants ingested one of three meals (LGI, HGI or LCHO) 3 h prior to exercise testing. Testing included ICT to exhaustion with continuous ergospirometry measurement. Findings: Fat oxidation rate was significantly higher in LCHO compared to HGI (p = 0.039). Carbohydrate contribution to energy production was significantly lower and fat contribution higher in LCHO compared to HGI (p = 0.034). Fat-to-carbohydrates crossover point was achieved at significantly higher heart rate in LCHO group compared to LGI and HGI (p = 0.046 and p = 0.049, respectively). Peak fat oxidation occurred significantly later during exercises in LCHO group compared to HGI (p = 0.025). In conclusion, LCHO meal results in a higher fat oxidation, reduced carbohydrates contribution-to-energy production, delayed peak fat oxidation point and altered fat-to-carbohydrates crossover dynamics. There are no differences in substrate oxidation between high-carbohydrate preexercise meals that differ only in GI. Originality/value: To the best of the authors' knowledge, this is the first study to compare the acute effect of both the amount of carbohydrates and the GI in a preexercise meal on substrate utilization during ICT. [ABSTRACT FROM AUTHOR]

Background: Patent ductus arteriosus is one of the most common cardiac conditions affecting the neonates. Considering the lack of studies done on this topic in healthcare settings in Khyber Pakhtunkhwa province, this study aims to find out the comparative effectiveness of paracetamol and ibuprofen in management of PDA in our healthcare setting to conclude a better management option for the condition. Objective: To find and compare the effectiveness of paracetamol and ibuprofen in the closure of patent ductus arteriosus in preterm neonates. Methodology: This randomized controlled trial was conducted in the Department of Nursery and Neonatal Intensive Care Unit, Khyber Teaching Hospital, Peshawar, Pakistan, from 10th April 2024 to 10th October 2024. A total of 256 neonates of both genders with patent ductus arteriosus were included. Group A received oral paracetamol, and Group B received oral ibuprofen. The effectiveness of the treatments was evaluated at the end of the treatment period. Results: The age range in this study was from 48 to 96 h, with a mean age of 71.79 ± 13.10 h in Group A and 73.40 ± 11.81 h in Group B. Efficacy was observed in 107 (83.6%) patients in Group A compared to 90 (70.3%) patients in Group B, showing a statistically significant difference (P = 0.011). Conclusion: Our study has concluded that paracetamol is more effective than ibuprofen in closing patent ductus arteriosus. The trials were retrospectively registered at NIH Trial Registry (NCT06601114) https://clinicaltrials.gov/study/NCT06601114 dated 15/09/2024. Key points: • This study was conducted to find and compare the effectiveness of paracetamol and ibuprofen in the closure of patent ductus arteriosus in preterm neonates within the healthcare setting of Khyber Pakhtunkhwa Province, Pakistan. • This study conclude that paracetamol is more effective than ibuprofen in closing patent ductus arteriosus (PDA) in preterm neonates. • The higher efficacy rate observed with paracetamol suggests that it may be a preferable first-line treatment for PDA in this population. [ABSTRACT FROM AUTHOR]

Deep learning (DL)-based Human Activity Recognition (HAR) using wearable inertial measurement unit (IMU) sensors can revolutionize continuous health monitoring and early disease prediction. However, most DL HAR models are untested in their robustness to real-world variability, as they are trained on limited lab-controlled data. In this study, we isolated and analyzed the effects of the subject, device, position, and orientation variabilities on DL HAR models using the HARVAR and REALDISP datasets. The Maximum Mean Discrepancy (MMD) was used to quantify shifts in the data distribution caused by these variabilities, and the relationship between the distribution shifts and model performance was drawn. Our HARVAR results show that different types of variability significantly degraded the DL model performance, with an inverse relationship between the data distribution shifts and performance. The compounding effect of multiple variabilities studied using REALDISP further underscores the challenges of generalizing DL HAR models to real-world conditions. Analyzing these impacts highlights the need for more robust models that generalize effectively to real-world settings. The MMD proved valuable for explaining the performance drops, emphasizing its utility in evaluating distribution shifts in HAR data. [ABSTRACT FROM AUTHOR]

Introduction:The strength of inspiratory muscles is one of the essential factors in preventing postoperative pulmonary complications (POPC). One of the new tools to safely measure the strength of the inspiratory muscles in heart patients dynamically and without breath holding is the strength-index (S-index). This study aims to evaluate the immediate effects of a respiratory warm-up (RWU) session on the S-index and other lung parameters in cardiac surgery candidates, a subject with limited existing research. Materials and Methods: This study was conducted as a randomized controlled trial. Forty participants scheduled for heart surgeries were randomly assigned to either the study (RWU between two tests) or control (without RWU) groups. RWU consists of threshold loading inspiratory muscle training (TL-IMT) exercises at 30% of the S-index with 30 breathing cycles. Respiratory tests, including S-index, peak inspiratory flow (PIF) and vital capacity (VC), were assessed twice using an electronic respiratory device. Results: Covariance analysis showed no significant difference in the average and best values of the S-index, PIF or VC indices at the second tests, between two groups (P>0.05), or in the independent t test and Mann-Whitney U test for the "rate of changes," between two tests (P>0.05). Finally, intragroup changes, assessed with paired sample t test between two tests, were mostly non-significant for these indices (P>0.05), except for the best VC in the study group (P=0.03). Conclusion: The study results suggest that a RWU session does not significantly impact cardiac surgery candidates' S-index or other respiratory parameters. Thus, incorporating RWU before S-index testing may not be necessary. [ABSTRACT FROM AUTHOR]

Background: Failure mode and effect analysis (FMEA) is a valuable risk analysis tool aimed at predicting the potential failures of a system and preventing them from occurring. Since its initial use, it has also recently been applied to the healthcare setting, which has been made progressively more complex by technological developments and new challenges. Infection prevention and control (IPC) is an area that requires effective strategies. The aim of this study is to review the literature on the employment of FMEA in the healthcare environment, with special consideration for its application in the infectious disease setting. Methods: An extensive search was carried out in two international and public databases, PUBMED and EMBASE; we included all studies regarding the use of FMEA in hospital settings and human patient care processes. Results: A total of 163 studies published over the period from 2003 to 2023 were included for data extraction. These studies were analyzed regarding bibliometric data (publication year and country of origin), the healthcare issues to be addressed, the application fields, and the utilized FMEA methods. Among these, 13 studies were found that took an interest in infectious diseases. Conclusions: FMEA can be effectively used for healthcare risk assessment. Its implementation as a standard tool in healthcare settings, though demanding, may serve as an important tool for preventing the risk of biohazard incidents, epidemics, and environmental contamination, thereby improving safety for both patients and healthcare workers. [ABSTRACT FROM AUTHOR]

Purpose: Inland shipping has been extensively recognised as a sustainable, efficient and good alternative to rail and road modes of transportation. In recent years, various authorities and academic researchers have advocated shifting from road to other sustainable modes like inland waterway transport (IWT) or rail transport. Academic work on modernisation and technological innovations to enhance the effectiveness and efficiency of waterborne transportation is becoming apparent as a growing body of literature caused by the need to achieve a sustainable transport system. Thus, it became apparent to explore the research trends on IWT. Design/methodology/approach: A systematic and structured literature review study was employed in this paper to identify the challenges and concepts in modernising inland waterways for freight transportation. The review analysed 94 articles published in 54 journals from six well-known databases between 2010 and 2022. Findings: The key findings of this review are that despite various challenges confronting the sector, there have been successful cases of technological advancement in the industry. The main interest among scholars is improving technical and economic performance, digitalisation, and safety and environmental issues. The review revealed that most of the literature is fragmented despite growing interest from practitioners and academic scholars. Academic research to address the strategic objectives, including strengthening competitiveness (shipbuilding, hydrodynamics, incorporating artificial intelligence into the decision-making process, adopting blockchain technology to ensure transparency and security in the transactions, new technologies for fleets adaptation to climate change, more effective handling, maintenance and rehabilitation technologies), matching growth and changing trade patterns (intermodal solutions and new logistics approaches) are major causes of concerns. Originality/value: By employing the approach of reviewing previously available literature on IWT review papers, this review complements the existing body of literature in the field of IWT by providing in a single paper a consolidation of recent state-of-the-art research on technological developments and challenges for inland waterways freight transport in the intermodal supply chain that can act as a single resource to keep researchers up to date with the most recent advancements in research in the domain of inland waterway freight transport. Additionally, this review identified gaps in the literature that may inspire new research themes in the field of IWT. [ABSTRACT FROM AUTHOR]

The limited imaging depth of optical endoscope restrains the identification of tissues under surface during the minimally invasive spine surgery (MISS), thus increasing the risk of critical tissue damage. This study is proposed to improve the accuracy and effectiveness of automatic spinal soft tissue identification using a forward-oriented ultrasound endoscopic system. Total 758 ex-vivo soft tissue samples were collected from ovine spines to create a dataset with four categories including spinal cord, nucleus pulposus, adipose tissue, and nerve root. Three conventional methods including Gray-level co-occurrence matrix (GLCM), Empirical Wavelet Transform (EWT), Variational Mode Decomposition (VMD) and two deep-learning based methods including Densely Connected Neural Network (DenseNet) model, one-dimensional Vision Transformer (ViT) model, were applied to identify the spinal tissues. The two deep learning methods outperformed the conventional methods with both accuracy over 95%. Especially the signal-based method (ViT) achieved an accuracy of 98.31% and a specificity of 99.2%, and the inference latency was only 0.0025 s. It illustrated the feasibility of applying the forward-oriented ultrasound endoscopic system for real-time intraoperative recognition of critical spinal tissues to enhance the precision and safety of minimally invasive spine surgery. [ABSTRACT FROM AUTHOR]