bluuebunny/crossref_metadata_2025 huggingface dataset:
Does the Use of Prehospital Emergency Care Units in Inter-Hospital Transfers Affect Regional Prehospital Emergency Care Readiness?—A Finnish Pilot Study
Authors: Lehtimäki Simon , Lahelma Joonas , Aunola Anssi , Nordquist Hilla
Published: September 2024

Inter-hospital transfers remain a significant part of emergency care service missions, even though efforts have been made to change this. We examined the use of prehospital emergency care units in inter-hospital patient transfers in one wellbeing services county in Finland. We evaluated the potential strain they place on regional prehospital emergency care readiness, and examined how these transfers arise between different regions, populations, and healthcare centers. This was a register-based pilot study using prehospital emergency care inter-hospital transfer mission statistics within the wellbeing services county of Pirkanmaa, Finland during 2020 and 2021. The data were extracted from the emergency care service’s field management program. A descriptive analysis of the data was performed, in which interdependencies between several variables were examined. During the two years, there were 5812 prehospital emergency care inter-hospital transfer missions dispatched to prehospital emergency care units. The number of prehospital emergency care inter-hospital transfer missions was especially notable in rural regions, where there were also fewer units available. Based on the results, the criteria for prehospital emergency care use in inter-hospital transfers require clarification, since there is an observable strain caused by these transfers on regional emergency care readiness. The results of this pilot study encourage further studies on the use of prehospital emergency care units in inter-hospital patient transfers.
Multi-Objective Optimal Configuration of Hydrogen Fuel Cell-Based Multi-Energy Microgrid System Considering Exergy
Authors: Li Ji , Xu Lei , Kou Yang , Liang Weile , Wang Yunshan , Yuan Zhi
Published: September 2024

Relying solely on electrical energy storage for energy regulation makes it difficult to provide a stable and efficient energy supply for microgrid systems currently. Additionally, the economic cost of microgrids and the rate of energy use present a challenge that must be addressed. A strategy for allocating capacity for multi-energy microgrids that takes energy efficiency and hydrogen energy into account is offered as a solution to the aforementioned issues. Initially, the construction of the multi-energy microgrid system takes into account the thermoelectric coupling properties of hydrogen energy devices. Second, the system’s energy utilization level is measured using the exergy efficiency analysis. Next, the multi-objective capacity optimization allocation model of the multi-energy microgrid system is established, with the exergy efficiency and system economic cost serving as the objective functions. Lastly, the multi-objective model is solved using the ε-constraint approach to find the Pareto frontier, and Technique for Order Preference by Similarity to an Ideal Solution is employed for decision-making. The example results demonstrate that, when compared to a traditional microgrid using electric energy storage, the proposed model can effectively lower the system’s economic cost and improve exergy efficiency. Additionally, multi-objective capacity optimization can be used to strike a balance between exergy efficiency and the system’s economic cost. For relevant studies on the capacity allocation of multi-energy microgrids, this work can be a helpful resource.
An Improved Prevention Strategy Based on Fault Probability Detection for Commutation Failure in Line-Commutated Converter-Based High-Voltage Direct Current Transmission Systems
Authors: Xu Ying , Zou Yi , Liu Lei , Li Xiaopeng , Teng Yufei , Gao Zijian
Published: September 2024

Commutation failure (CF) is one of the most prevalent events in line-commutated converter-based high-voltage direct current (LCC–HVDC) systems. The frequent occurrence of CF poses a significant threat to the safe and stable operation of power grids. The commutation failure prevention control (CFPREV) is the main method to prevent the initial CF, which relies on the detection of a drop in AC voltage. However, its slow fault detection hinders the rapid response of post-fault control, thereby affecting the effectiveness of CF suppression. Therefore, this paper proposes a fast fault detection method based on Bayesian theory. This algorithm can calculate the conditional probability of each variable in a given dataset, effectively mitigating the impact of noise and errors in data to yield precise and dependable results. By processing the collected continuous data and calculating the probability of the existence of a fault point, it determines whether a fault occurs. Based on this method, an improved prevention strategy is proposed, which can effectively enhance the CF resilience of LCC–HVDC systems under AC faults. Finally, using the power systems computer-aided design (PSCAD) platform, the accuracy of the fault probability detection algorithm was verified based on actual engineering data. The effectiveness of the proposed strategy was further validated under three typical fault scenarios, leading to significant improvements: a 64.12% reduction in detection time for three-phase grounding faults, a 69.88% decrease for single line-to-ground faults, and a 36.84% improvement in phase-to-phase fault detection. Additionally, the overall performance of the strategy was thoroughly assessed through extensive simulations covering various fault cases within a selected range of typical faults. The simulations demonstrated the superiority of the proposed strategy in CF mitigation, with a significant reduction in incidents from 89 to 34 out of 150 tested scenarios. This highlights the robustness and reliability of the proposed strategy.
Analysis of Power Electronic Traction Transformer under Non-Single Frequency PWM Control
Authors: Hou Bingbing , Li Yan , Teng Yun
Published: September 2024

In the context of locomotive traction systems, the power electronic traction transformer (PETT) represents a pivotal component, fulfilling essential functions pertaining to electrical isolation and power control. The majority of existing PETT prototypes employ a combination of a cascade structure and an ISOP structure. The aforementioned scheme effectively reduces the voltage withstand level of the power devices on the input side, yet it also necessitates the utilisation of a greater number of power devices and passive components within the PETT. In order to further reduce the number of power devices used, this paper proposes a new cascaded PETT design. The proposed PETT will adopt a scheme combining a cascade structure and a hardware circuit multiplexing. This paper first analyses the operation principle of the new circuit under non-single frequency PWM control. It then derives the design equations for some hardware parameters and control circuits. Finally, it verifies the effectiveness of the proposed PETT through simulation analysis.
Game-Based Intelligent Jamming Strategy without Prior Information in Wireless Communications
Authors: Li Yongcheng , Wang Jinchi , Gao Zhenzhen , Lv Gangming
Published: September 2024

Traditional jamming technologies have become less effective with the development of anti-jamming technologies, especially with the appearance of intelligent transmitters, which can adaptively adjust their transmission strategies. To deal with intelligent transmitters, in this paper, a game-based intelligent jamming scheme is proposed. Considering that the intelligent transmitter has multiple transmission strategy sets whose prior probabilities are unknown to the jammer, we first model the interaction between the transmitter and the jammer as a dynamic game with incomplete information. Then the perfect Bayesian equilibrium is derived based on assumptions of some prior information. For more practical applications when no prior information about the transmitter is available at the jammer, a Q-learning-based method is proposed to find an intelligent jamming strategy by exploiting the sensing results of the wireless communications. The design of the jammer’s reward function is guided by the game utility and the reward is calculated based on the Acknowledgement/Negative Acknowledgement feedback of the receiver. Simulation results show that the proposed scheme has only 0.5% loss in jamming utility compared to that of the perfect Bayesian equilibrium strategy. Compared to existing jamming schemes, a higher packet error rate can be achieved by the proposed scheme by consuming less jamming power.
The Energy Potential of Firewood and By-Products of Round Wood Processing—Economic and Technical Aspects
Authors: Mydlarz Katarzyna , Wieruszewski Marek
Published: September 2024

According to most energy demand forecasts, woody biomass has the potential to become an important source of renewable energy, especially during the transitional period of energy transition. The aim of this article was to estimate the energy potential of the biomass from the forest and the biomass generated by the mechanical processing of wood raw material and also to show the spectrum of possibilities for the potential use of the biomass for energy production in Poland. This research used available statistical and literature data on the species structure of harvested wood and the qualitative and assortment structures of woody biomass. The basic parameters of the raw material were evaluated in accordance with the EU classification of energy wood. This study confirmed the relationship between the energy potential of woody biomass and energy demand in Poland. The correlation coefficient for these variables was r = 0.984. This correlation was reflected in the significant shares of biomass in the production of electricity (more than 9%) or heat (almost 14%). Energy wood resources in Poland are smaller than in other European Union countries, which affects the scale of the potential use of woody biomass for energy purposes. Nevertheless, the use of such a biomass is fully justified from the point of view of possible development.
Artificial-Intelligence-Based Detection of Defects and Faults in Photovoltaic Systems: A Survey
Authors: Thakfan Ali , Bin Salamah Yasser
Published: September 2024

The global shift towards sustainable energy has positioned photovoltaic (PV) systems as a critical component in the renewable energy landscape. However, maintaining the efficiency and longevity of these systems requires effective fault detection and diagnosis mechanisms. Traditional methods, relying on manual inspections and standard electrical measurements, have proven inadequate, especially for large-scale solar installations. The emergence of machine learning (ML) and deep learning (DL) has sparked significant interest in developing computational strategies to enhance the identification and classification of PV system faults. Despite these advancements, challenges remain, particularly due to the limited availability of public datasets for PV fault detection and the complexity of existing artificial-intelligence (AI)-based methods. This study distinguishes itself by proposing a novel AI-based approach that optimizes fault detection and classification in PV systems, addressing existing gaps in AI-driven fault detection, especially in terms of thermal imaging and current–voltage (I-V) curve analysis. This comprehensive survey identifies emerging trends in AI-driven PV fault detection, highlights the most advanced methodologies, and proposes a novel AI-based approach to enhance fault detection and classification capabilities. The findings aim to advance the state of technology in this field, offering insights into more efficient and practical solutions for PV system fault management.
Energy Hub and Micro-Energy Hub Architecture in Integrated Local Energy Communities: Enabling Technologies and Energy Planning Tools
Authors: Rossi Mosè , Jin Lingkang , Monforti Ferrario Andrea , Di Somma Marialaura , Buonanno Amedeo , Papadimitriou Christina , Morch Andrei , Graditi Giorgio , Comodi Gabriele
Published: September 2024

The combination of different energy vectors like electrical energy, hydrogen, methane, and water is a crucial aspect to deal with in integrated local energy communities (ILECs). The ILEC stands for a set of active energy users that maximise benefits and minimise costs using optimisation procedures in producing and sharing energy. In particular, the proper management of different energy vectors is fundamental for achieving the best operating conditions of ILECs in terms of both energy and economic perspectives. To this end, different solutions have been developed, including advanced control and monitoring systems, distributed energy resources, and storage. Energy management planning software plays a pivotal role in developing ILECs in terms of performance evaluation and optimisation within a multi-carrier concept. In this paper, the state-of-the-art of ILECs is further enhanced by providing important details on the critical aspects related to the overall value chain for constituting an ILEC (e.g., conceptualisation, connecting technologies, barriers/limitations, control, and monitoring systems, and modelling tools for planning phases). By providing a clear understanding of the technical solutions and energy planning software, this paper can support the energy system transition towards cleaner systems by identifying the most suitable solutions and fostering the advancement of ILECs.
Mathematical Models and Dynamic Global Warming Potential Calculation for Estimating the Role of Organic Amendment in Net-Zero Goal Achievement
Authors: Chowdhury Raja , Agarwal Vivek
Published: September 2024

This study aimed to assess the potential of soil organic carbon (SOC) production through organic amendments. SOC sequestration would help to achieve the net-zero emissions targets set by the Intergovernmental Panel on Climate Change (IPCC). Given the urgency to reduce greenhouse gas emissions, traditional methods that estimate SOC over 100 years must be revised. Hence, a novel fate transport numerical model was developed to forecast SOC levels relevant to individual countries’ net-zero targets in various time frames. The simulation results revealed that most countries had sufficient organic amendment to mitigate the CO2 emission of that country for a year if the organic amendment was applied on 20% of the arable land. However, if a significant fraction of the total CO2 emissions needs to be mitigated before reaching the net zero target, the requirements of organic amendments need to be increased several folds. All the available agricultural land should also be brought under the organic amendment regime. Later, the dynamic LCA approach was undertaken for estimating Global Warming (GWP) from land-applied organic residue. It was observed that, depending on the dynamic LCA model, the estimated GWP was different. However, the estimated dynamic GWP was very close to the residual SOC calculated through the fate transport model. The mass of organic residues generated from a biorefinery was examined by employing a waste biorefinery model to explore further the routes of acquiring additional organic amendment. Simulated results showed that while a waste biorefinery could not provide additional organic residue compared to the original organic waste input, it was highly efficient for nutrient recovery and its uses. This study demonstrated that organic amendment-based carbon sequestration adequately mitigated residual GHG at the net-zero target.
Addressing the Renewable Energy Challenges through the Lens of Monetary Policy—Insights from the Literature
Authors: Lupu Iulia , Criste Adina , Ciumara Tudor , Milea Camelia , Lupu Radu
Published: September 2024

This review explores the interplay between renewable energy and monetary policy, highlighting how central banks can contribute to renewable energy development. Although the shift towards renewable energy is tremendous for sustainable development, it also comes with notable economic and financial challenges. Supervenient, the energy transition has raised significant interest among decision-makers and academia, prompting them to explore new innovative policies and strategies; as a result, these actions acknowledged that research in this field is essential for identifying optimal solutions. Moreover, recent global crises, including the energy crisis, have emphasised the important role of macroeconomic policies in crisis management. Within this framework, it’s essential to investigate how monetary policy, as the main tool of central banks, can foster renewable energy development. This comprehensive review systematically examines existing literature through a semi-structured literature analysis, which allows for a more flexible, complex, and thorough approach to identifying key issues and providing insights into the potential of monetary policy to address renewable energy challenges. We identified four main clusters of research: sustainability and development, economic growth and energy, monetary policy and investment, and emissions and renewable energy. Furthermore, exploring the interaction between monetary policy and renewable energy objectives to uncover paths for harmonizing monetary strategies with the goals of renewable energy development contributes to highlighting the existing gaps in the field and represents a starting point for further research topics. This study provides a comprehensive overview of the existing knowledge, identifies gaps in the literature, and suggests directions for future research.
Model Predictive Control of Heat Pumps with Thermal Energy Storages in Industrial Processes
Authors: Agner Raphael , Gruber Peter , Wellig Beat
Published: September 2024

Integration of heat pumps combined with thermal energy storage provides a key pathway to decarbonizing the energy supply in the industry when the processes are not operated continuously. Yet, this integration of such novel systems introduces control challenges due to added dependencies between different process streams. This work investigates the control problem of heat pumps coupled to stratified thermal energy storage that is integrated into non-continuous industrial processes. A two-layer control strategy is proposed, where, in the higher level, a model predictive controller is developed for energy management using a linear model of the non-linear process. The resulting optimization problem is a mixed integer quadratic program. The low-level control layer is defined with standard industry controllers. The overall system is tested using a dynamic simulation model for the entire process, demonstrating its performance in three different cases. The control strategy optimizes heat recovery while ensuring system operability. The study demonstrates successful disturbance rejection and cold starts, wherein 100% of the targeted heat recovery can be confirmed under nominal conditions. Further evaluation in laboratory or field trials is recommended, and alternative, yet-to-be-defined, control concepts may be compared to the proposed approach.
An Overview of Silica Scaling Reduction Technologies in the Geothermal Market
Authors: Longval Rochelle , Meirbekova Rauan , Fisher Jason , Maignot Audrey
Published: September 2024

Renewable energy sources play a vital role in the energy mix with geothermal energy providing an opportunity to harness the natural heat coming from the Earth for sustainable power production. As innovative drilling technologies come to market, it is easier to extract heat from various localities across the globe, leading to significant development in the geothermal sector. The economic viability of this resource can be significantly impacted when energy output declines due to scale deposition. Scale formation is a major challenge in the exploitation of geothermal wells, particularly in liquid-dominated geothermal fields. One of the most robust forms of scale build-up common to higher temperature geothermal wellbores and surface equipment for power production is silica scaling. Silica is one of the Earth’s most abundant elements that can precipitate from brine due to various factors. The accumulation of scale deposits significantly impacts the lifespan and efficiency of surface equipment and geothermal wells by restricting fluid flow, thus reducing efficiency and performance. To guarantee the peak performance and longevity of geothermal systems, it is essential to implement a strategic maintenance plan for scaling reduction in geothermal systems. Throughout this review, relevant case studies highlight scaling reduction methods for silica scale in subsurface wellbores and surface facilities.
Novel Recuperated Power Cycles for Cost-Effective Integration of Variable Renewable Energy
Authors: Arnaiz del Pozo Carlos , Cloete Schalk , Chiesa Paolo , Jiménez Álvaro Ángel
Published: September 2024

The ongoing transition to energy systems with high shares of variable renewables motivates the development of novel thermal power cycles that operate economically at low capacity factors to accommodate wind and solar intermittency. This study presents two recuperated power cycles with low capital costs for this market segment: (1) the near-isothermal hydrogen turbine (NIHT) concept, capable of achieving combined cycle efficiencies without a bottoming cycle through fuel combustion in the expansion path, and (2) the intercooled recuperated water-injected (IRWI) power cycle that employs conventional combustion technology at an efficiency cost of only 4% points. The economic assessment carried out in this work reveals that the proposed cycles increasingly outperform combined cycle benchmarks with and without CO2 capture as the plant capacity factor reduces below 50%. When the cost of fuel storage and delivery by pipelines is included in the evaluation, however, plants fired by hydrogen lose competitiveness relative to natural gas-fired plants due to the high fuel delivery costs caused by the low volumetric energy density of hydrogen. This important but uncertain cost component could erode the business case for future hydrogen-fired power plants, in which case the IRWI concept powered by natural gas emerges as a promising solution.
Socio-Economic and Health Literacy Inequalities as Determinants of Women’s Knowledge about Their Reproductive System: A Cross-Sectional Study
Authors: Prémusz Viktória , Kovács Kálmán András , Skriba Eszter , Tándor Zoltán , Szmatona Gábor , Dózsa-Juhász Olívia
Published: September 2024

Background/Objectives: To support women’s informed decisions and reproductive self-care, confident reproductive health-related knowledge is needed, supported by adequate health literacy (HL). No corresponding survey has been carried out in Hungary on inequalities to provide information addressing education. Materials and Methods: In the current cross-sectional online survey, 301 women of reproductive age (27.16 ± 0.36 years) were asked with the Hungarian versions of validated and standardised questionnaires about reproductive knowledge on hormones, ovulation, menstrual cycle, pregnancy signs and birth control (Knowledge of Female Body Scale—KFB), and HL (Brief Health Literacy Screening Tool—BRIEF). Spearman correlation and multivariable linear regression analyses were utilised, with a significance level set at p < 0.05. IBM SPSS version 28.0 (IBM SPSS, Armonk, NY, USA: IBM Corp.) and G*Power (version 3.1.9.7; Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany) software. The STROBE checklist was followed. The Clinical Trial Registry Nr. is NCT06146673. Results: The KFB composite score was high (20.01 ± 2.33); 86.374% had “high knowledge”. Still, lacking information was identified for the mechanisms of certain contraceptive methods and early physical signs of pregnancy. A significant difference was also found in the KFB scores in the case of higher age (p = 0.019), higher education level (p = 0.018) and previous live birth (p = 0.028). A positive correlation was found between KFB and HL (p < 0.001), education (p = 0.005), and age (p = 0.021). A multiple regression analysis (R2 = 0.087, p < 0.001) indicated that both HL (p < 0.001) and age (p = 0.003) are potential positive predictors of adequate reproductive knowledge, whereas induced abortion (p = 0.013) might serve as an inverse predictor. Conclusions: Inequalities in women’s knowledge about their reproductive system and HL were found, and it was significantly the lowest in their highest conception probability age. Therefore, in addition to targeted education, HL also needs improvement.
Managing Trees Species of High Social and Cultural Value: Forest Manager Attitudes towards Pest and Disease Risks to Oak in Britain
Authors: O’Brien Liz , Marzano Mariella , Dandy Norman , Bates Seumas , Hemery Gabriel , Petrokofsky Gillian , Dunn Mike , Forster Jack
Published: September 2024

The values of forests have been extensively researched by focusing on general public perspectives with different frameworks used to categorise them. Studies have also explored forest manager values; however, there is limited evidence on the values they associate with specific tree species. Understanding more about managers’ values regarding a particular species is important when considering how they make decisions and might respond to tree pests and disease threats. In this study, we explored forest managers’ values regarding oak trees and the effects of a particular pest and disease risk known as Acute Oak Decline on these. This paper outlines the results from interviews with forest managers in England and a survey of private forest managers in Britain to capture the ways in which they value the oak trees they own, manage, or influence. Forest manager types included private owners of single or multiple properties, forestry professionals, businesses, and tenants. The results show that oaks were highly valued by forest managers as an iconic cultural species in the landscape and for their timber. Veteran and ancient oak trees were considered very important, and managers were more likely to spend time and resources attempting to conserve these oaks due to their perceived cultural value. Those who had trees that were suffering from Acute Oak Decline were also more likely to spend resources on them to save the trees or try to reduce the impact of the disease. Gaining a better understanding of forest managers’ attitudes towards protecting species they value is important as it has implications for their decision-making and management behaviours. It can also help to provide relevant bodies with information on how best to develop and communicate guidance and advice on monitoring and reporting disease symptoms, as well as managing oak tree health.
A Preliminary System of Equations for Predicting Merchantable Whole-Tree Volume for the Decurrent Non-Native Quercus rubra L. Grown in Navarra (Northern Spain)
Authors: Gómez-García Esteban , Alonso Ponce Rafael , Pérez-Rodríguez Fernando , Molina Terrén Cristobal
Published: September 2024

Estimation of tree volume typically focuses on excurrent forms, with less attention given to decurrent forms. Species with a decurrent form, particularly hardwoods, lack a dominant stem and have large diameter branches that can be included in the merchantable wood volume. We developed a preliminary two-equation system comprising a taper equation and a merchantable whole-tree volume (stem and branches) equation for Quercus rubra L. growing in Navarra (Northern Spain). The equation system includes the diameter at breast height and total tree height as independent variables, along with merchantable height—the height up to which the stem maintains a well-defined excurrent form—as an additional variable. After estimating the stem volume, the branch volume is estimated by subtracting the stem volume from the merchantable whole-tree volume. A second order continuous autoregressive error structure was used to correct for autocorrelation between residuals from the fitted taper equation. The equations explained 90% of the observed variability in diameter and 86% of the observed variability in merchantable whole-tree volume. Both equations have been implemented in the Cubica Navarra 3.0 software for use as a system of equations. These equations are considered preliminary and will be refitted or validated as additional data becomes available from new locations.
Electricity Consumption Forecasting: An Approach Using Cooperative Ensemble Learning with SHapley Additive exPlanations
Authors: Alba Eduardo Luiz , Oliveira Gilson Adamczuk , Ribeiro Matheus Henrique Dal Molin , Rodrigues Érick Oliveira
Published: September 2024

Electricity expense management presents significant challenges, as this resource is susceptible to various influencing factors. In universities, the demand for this resource is rapidly growing with institutional expansion and has a significant environmental impact. In this study, the machine learning models long short-term memory (LSTM), random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGBoost) were trained with historical consumption data from the Federal Institute of Paraná (IFPR) over the last seven years and climatic variables to forecast electricity consumption 12 months ahead. Datasets from two campuses were adopted. To improve model performance, feature selection was performed using Shapley additive explanations (SHAP), and hyperparameter optimization was carried out using genetic algorithm (GA) and particle swarm optimization (PSO). The results indicate that the proposed cooperative ensemble learning approach named Weaker Separator Booster (WSB) exhibited the best performance for datasets. Specifically, it achieved an sMAPE of 13.90% and MAE of 1990.87 kWh for the IFPR–Palmas Campus and an sMAPE of 18.72% and MAE of 465.02 kWh for the Coronel Vivida Campus. The SHAP analysis revealed distinct feature importance patterns across the two IFPR campuses. A commonality that emerged was the strong influence of lagged time-series values and a minimal influence of climatic variables.
Analysis of Caputo-Type Non-Linear Fractional Differential Equations and Their Ulam–Hyers Stability
Authors: Girgin Ekber , Büyükkaya Abdurrahman , Kuru Neslihan Kaplan , Younis Mudasir , Öztürk Mahpeyker
Published: September 2024

This study presents two novel frameworks, termed a quasi-modular b-metric space and a non-Archimedean quasi-modular b-metric space, and various topological properties are provided. Using comparison and simulation functions, this paper rigorously proves several fixed point theorems in the non-Archimedean quasi-modular b-metric space. As a useful application, it also establishes Ulam–Hyers stability for the fixed point problem. Finally, this study concludes with a unique solution to a non-linear fractional differential equation, making a substantial contribution to the discipline.
Diversity, Distribution and Structural Prediction of the Pathogenic Bacterial Effectors EspN and EspS
Authors: Li Zhan , Hu Yuru , Song Yuan , Li Deyu , Yang Xiaolan , Zhang Liangyan , Li Tao , Wang Hui
Published: September 2024

Background: Many Gram-negative enterobacteria translocate virulence proteins (effectors) into intestinal epithelial cells using a type III secretion system (T3SS) to subvert the activity of various cell functions possess. Many T3SS effectors have been extensively characterized, but there are still some effector proteins whose functional information is completely unknown. Methods: In this study, two predicted effectors of unknown function, EspN and EspS (Escherichia coli secreted protein N and S), were selected for analysis of translocation, distribution and structure prediction. Results: The TEM1 (β-lactamase) translocation assay was performed, which showed that EspN and EspS are translocated into host cells in a T3SS-dependent manner during bacterial infection. A phylogenetic tree analysis revealed that homologs of EspN and EspS are widely distributed in pathogenic bacteria. Multiple sequence alignment revealed that EspN and its homologs share a conserved C-terminal region (673–1133 a.a.). Furthermore, the structure of EspN (673–1133 a.a.) was also predicted and well-defined, which showed that it has three subdomains connected by a loop region. EspS and its homologs share a sequence-conserved C-terminal (146–291 a.a.). The predicted structure of EspS (146–291 a.a.) is composed of a β-sheet consisting of four β-strands and several short helices, which has a TM score of 0.5014 with the structure of the Vibrio cholerae RTX cysteine protease domain (PDBID: 3eeb). Conclusions: These results suggest that EspN and EspS may represent two important classes of T3SS effectors associated with pathogen virulence, and our findings provide important clues to understanding the potential functions of EspN and EspS.
HybridQC: A SNP-Based Quality Control Application for Rapid Hybridity Verification in Diploid Plants
Authors: Ongom Patrick Obia , Ajibade Yakub Adebare , Mohammed Saba Baba , Dieng Ibnou , Fatokun Christian , Boukar Ousmane
Published: September 2024

Background/Objectives: Hybridity authentication is an important component of quality assurance and control (QA/QC) in breeding programs. Here, we introduce HybridQC v1.0, a QA/QC software program specially designed for parental purity and hybridity determination. HybridQC rapidly detects molecular marker polymorphism between parents of a cross and utilizes only the informative markers for hybridity authentication. Methods: HybridQC is written in Python and designed with a graphical user interface (GUI) compatible with Windows operating systems. We demonstrated the QA/QC analysis workflow and functionality of HybridQC using Kompetitive allele-specific PCR (KASP) SNP genotype data for cowpea (Vigna unguiculata). Its performance was validated in other crop data, including sorghum (Sorghum bicolor) and maize (Zea mays). Results: The application efficiently analyzed low-density SNP data from multiple cowpea bi-parental crosses embedded in a single Microsoft Excel file. HybridQC is optimized for the auto-generation of key summary statistics and visualization patterns for marker polymorphism, parental heterozygosity, non-parental alleles, missing data, and F1 hybridity. An added graphical interface correctly depicted marker efficiency and the proportions of true F1 versus self-fertilized progenies in the data sets used. The output of HybridQC was consistent with the results of manual hybridity discernment in sorghum and maize data sets. Conclusions: This application uses QA/QC SNP markers to rapidly verify true F1 progeny. It eliminates the extensive time often required to manually curate and process QA/QC data. This tool will enhance the optimization efforts in breeding programs, contributing to increased genetic gain.
Lymphadenitis/Reactive-Hyperplasia, Mimickers of Lymphomas, Low-Grade B-Cell Lymphomas, and Hodgkin Lymphoma
Authors: Nicolae A. , Sabattini E. , Ponzoni M. , Paulli M. , Lucioni M. , Salviato T. , Carbone A.
Published: September 2024

A two-day meeting on controversial topics in hematopathology was held in Bologna, Italy, on 19–20 January 2024. The meeting primarily targeted pathologists lacking experience in hematological neoplasms and pathologists in training. The course aimed to highlight practical diagnostic challenges faced by pathologists and discuss solutions through the application of conventional histology, along with appropriate immunohistological, genetic, and molecular findings. The teaching program included lectures and slide seminars presented by a team of expert hematopathologists who were co-authors of the WHO classification of hematolymphoid tumors. Special interest revolved around “lymphadenitis and lymphoma mimickers”, “a rational approach to low-grade B-cell lymphomas”, and “advancements in defining Hodgkin lymphoma”. A key aspect emphasized by the faculty team was the use of the fifth edition of the WHO Bluebook and the International Consensus Classification (ICC 2022) of lymphomas.
The Role of Machine Learning in the Most Common Hematological Malignancies: A Narrative Review
Authors: Perillo Teresa , de Giorgi Marco , Giorgio Claudia , Frasca Carmine , Cuocolo Renato , Pinto Antonio
Published: September 2024

Background: Hematologic malignancies are a group of heterogeneous neoplasms which originate from hematopoietic cells. The most common among them are leukemia, lymphoma, and multiple myeloma. Machine learning (ML) is a subfield of artificial intelligence that enables the analysis of large amounts of data, possibly finding hidden patterns. Methods: We performed a narrative review about recent applications of ML in the most common hematological malignancies. We focused on the most recent scientific literature about this topic. Results: ML tools have proved useful in the most common hematological malignancies, in particular to enhance diagnostic work-up and guide treatment. Conclusions: Although ML has multiple possible applications in this field, there are some issue that have to be fixed before they can be used in daily clinical practice.
Padua Prediction Score and Hospital-Acquired Proximal and Isolated Distal Deep Vein Thrombosis in Symptomatic Patients
Authors: Sartori Michelangelo , Fiocca Miriam , Soldati Mario , Borgese Laura , Favaretto Elisabetta , Cosmi Benilde
Published: September 2024

Background: Hospital-acquired deep vein thrombosis (DVT) is an important cause of morbidity and mortality. Objectives: The purpose of this study was to evaluate the prevalence of proximal lower limb DVT and isolated distal DVT (IDDVT) and their relationship to the Padua Prediction Score (PPS) in acutely ill, hospitalized patients. Methods: In a single-center cross-sectional study, all inpatients from medical departments with suspected lower-extremity DVT were evaluated with whole-leg ultrasonography during 183 days from 2016 to 2017. Results: Among the 505 inpatients (age 78.0 ± 13.3, females 59.2%) from medical departments, 204 (40.2%) had PPS ≥ 4, but only 54.4% of them underwent pharmacological thrombo-prophylaxis. Whole-leg ultrasonography detected 47 proximal DVTs (9.3%) and 65 IDDVTs (12.8%). Proximal DVT prevalence was higher in patients with high PPS vs. those with low PPS (12.7% vs. 7.0% p = 0.029, respectively), whereas IDDVT prevalence was similar in patients with high and low PPS (14.7% vs. 11.6% p = 0.311, respectively). The area under the receiver operating curve (AUC) for the PPS was 0.62 ± 0.03 for all DVTs, 0.64 ± 0.04 for proximal DVTs, and 0.58 ± 0.04 for IDDVTs. Conclusions: In hospitalized patients, IDDVT had similar prevalence regardless of PPS risk stratification. Adherence to thrombo-prophylaxis in patients was still far from optimal.
Lactation in Primates: Understanding the Physiology of Lactation from an Evolutionary Perspective
Authors: Hassler Michelle Pascale , Fabre Alexandre , Moulin Valérie , Faccin Lucie , Gullstrand Julie , Cermolacce Alexia , Frémondière Pierre
Published: September 2024

Lactation in humans is complex. Understanding the cultural and biological patterns of human breastfeeding requires a global evolutionary analysis that includes observations of other primates. Human breastfeeding may have several specificities, but some features could be shared with other non-human primates. The purpose of this work is to determine what makes human breastfeeding unique from an evolutionary perspective. We consider behavioral as well as biological variables. Human and non-human primates share behavioral characteristics, such as the need to learn breastfeeding skills, and they display an adaptation of the energy density of the milk according to the type of mothering. However, despite having slow-growing, secondarily altricial offspring and rather diluted milk, modern humans spend less time breastfeeding than the great apes, and consequently have shorter interbirth intervals. Milk composition in macro- and micro-constituents changes during lactation, demonstrating evolutionary and ecological adaptation. Among the great apes, the milk of modern humans contains a higher proportion of fats, an equivalent proportion of carbohydrates and proteins, and a greater variety of oligosaccharides involved in brain and immune system development. The microbiome of modern man is less diverse than those of non-human primates, but the presence of HMOs and immunoglobulin A suggests that human milk is particularly adapted to prevent neonatal infections.
Global Health Alert: Racing to Control Antimicrobial-Resistant Candida auris and Healthcare Waste Disinfection Using UVC LED Technology
Authors: Reedy Jamie M. , Fernando Theekshana , Awuor Silas O. , Omwenga Eric Omori , Koutchma Tatiana , Mariita Richard M.
Published: September 2024

Emerging antimicrobial-resistant (AMR) Candida auris presents a formidable global health challenge, causing severe healthcare-associated infections (HAIs) with high mortality rates. Its ability to colonize surfaces and resist standard disinfectants undermines traditional hygiene practices, prompting an urgent need for new strategies. Ultraviolet C (UVC) light offers a promising approach with rapid and broad-spectrum germicidal efficacy. This review examines current literature on UVC LED technology in combating C. auris, highlighting its effectiveness, limitations, and applications in healthcare hygiene. UVC light has potent activity against C. auris, with up to 99.9999% inactivation depending on certain conditions such as microbial load, type of organism, surface, environmental, equipment, and UVC radiation factors. UVC LEDs can effectively combat C. auris, driving down healthcare costs and reducing attributable global mortality. Here, we explore implementation strategies for the targeted disinfection of high-risk areas and equipment, air handling units (AHUs), and water treatment systems. Challenges associated with UVC LED disinfection devices in healthcare settings, current performance limitations, and radiation safety are discussed. This will help in optimizing application protocols for effective disinfection and radiation safety. To further strengthen healthcare facility hygiene practices and curb the global spread of C. auris, recommendations for integrating UVC LED disinfection into infection control programs are shared.
Design and Development of Virtual Reality Application Implementing Age-Friendly Care and the 4Ms: A Quality Improvement Project
Authors: Tewary Sweta , Shnayder Oksana , Dezine Marie , Pandya Naushira
Published: September 2024

Introduction: With an increase in the aging population, the application of evidence-based practice in geriatric education can strengthen knowledge, skills, and clinical experience for healthcare students caring for older adults. The Age-Friendly Health System (AFHS) is one of the frameworks founded on providing evidence-based and low-risk care centered on what matters most to older adults, their families, and caregivers. Virtual reality (VR) platforms are gaining popularity due to their ability to provide an immersive, hands-on learning experience resembling an actual medical practice or care setting. Immersive learning enhances students’ sensory perceptions, promoting an innovative and engaging way of acquiring concepts that are difficult to teach in real life. This study aimed to design, develop, implement, and evaluate a case-based training module highlighting AFHS and educating medical students on the 4Ms approach in geriatric care (What Matters, Medication, Mentation, and Mobility). Methodology: The project was a feasibility study completed in two phases. Phase one included planning and developing a case-based scenario incorporating the 4Ms of AFHS. Phase two included implementing and evaluating the VR training module into the geriatric curriculum for medical students. Results: The final VR case displays a hospital and post-acute setting where an elderly patient is admitted for a hip fracture. Students learn how to triage and treat patients from admission to discharge while demonstrating their knowledge of AFHSs. Approximately 10% of students completed the evaluation survey, and preliminary results indicate significant knowledge change in pre-post scenario-based training on an AFHS. Conclusions: The VR education platform and embedded scenario promise an innovative adaptation of technology in learning the concepts of the 4Ms of AFHSs. However, future studies should explore VR education with clinical assessment evaluation to ensure competence in providing age-friendly care.
The Status of the Implementation of the Building Information Modeling Mandate in Poland: A Literature Review
Authors: Borkowski Andrzej Szymon , Drozd Wojciech , Zima Krzysztof
Published: September 2024

BIM is being strongly implemented in design companies. General contractors are using it during investment projects, and boards are using it for the maintenance and operation of buildings or infrastructure. Without the so-called BIM mandate (mandatory in public procurement), this is hard to imagine, even though it has already been implemented in many countries. In Poland, work in this direction is still being carried out. Due to the high complexity of investment and construction processes, the multiplicity of stakeholder groups, and conflicting interests, work on BIM adoption at the national level is hampered. The paper conducts an in-depth literature review of BIM implementation in Poland and presents a critical analysis of the current state of work. As a result of the literature research, proposals for changes in the processes of implementing the BIM mandate in Poland were formulated. This paper presents an excerpt from a potential BIM strategy and the necessary steps on the road to making BIM use mandatory. The results of the study indicate strong grassroots activity conducted by NGOs, which, independent of government actions, lead to measurable results. The authors propose that these activities must be coordinated by a single leading entity at the government level. The study could influence decisions made in other countries in the region or with similar levels of BIM adoption. BIM is the basis of the idea of the digital twin, and its implementation is necessary to achieve the goals of the doctrine of sustainable development and circular economy.
MicroRNAs in Lung Cancer Brain Metastasis
Authors: Martínez-Espinosa Israel , Serrato José A. , Ortiz-Quintero Blanca
Published: September 2024

Brain metastasis is a significant clinical challenge for patients with advanced lung cancer, occurring in about 20–40% of cases. Brain metastasis causes severe neurological symptoms, leading to a poor prognosis and contributing significantly to lung cancer-related mortality. However, the underlying molecular mechanism behind brain metastasis remains largely unknown. MicroRNAs (miRNAs) are small, non-coding RNAs linked to several aspects of cancer progression, including metastasis. In the context of lung cancer, significant research has shown the involvement of miRNAs in regulating critical pathways related to metastatic spread to the brain. This review summarizes the scientific evidence regarding the regulatory roles of intra- and extracellular miRNAs, which specifically drive the spread of lung cancer cells to the brain. It also revises the known molecular mechanisms of brain metastasis, focusing on those from lung cancer as the primary tumor to better understand the complex mechanisms underlying this regulation. Understanding these complex regulatory mechanisms holds promise for developing novel diagnostic biomarkers and potential therapeutic strategies in brain metastasis.