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This article presents the results of experimental investigations on an advanced model of a polymer exchange membrane fuel cell (PEMFC) stack designed to serve as a charging unit for electric vehicle batteries or off-grid distributed power sources. The assembled 720 W PEMFC stack comprises two 360 W modules that can be electrically connected in series or parallel. A liquid cooling system for the PEMFC stack has also been constructed. The dependencies – voltage (U) versus current (I) and current (I) versus electrical power (P) – for single modules, as well as for the electrically connected two-module PEMFC stack, are determined. Additionally, the hydrogen utilization versus the electrical power (P) of the PEMFC stack is examined. The electrical efficiency of the PEMFC stack varies between 42% and 50%, depending on the electrical power. An adjustable DC/DC converter, operating in two modes (step-down or stepup), is proposed as a device to integrate a lithium-ion (Li-ion) battery pack with approximately 720 W PEMFC stack. The electrical architecture of the integrated system, comprising the PEMFC stack, DC/DC converter, and Li-ion battery pack, is investigated and discussed in this paper. It was found that the electrical efficiency of the proposed DC/DC converter varies depending on the electrical power, reaching a peak efficiency of 95%– 98%. The environmental benefit, in terms of reducing CO2 emissions when charging the battery, was also identified.

Background: Chest X-rays (CXRs) are pivotal in clinical diagnostics, particularly in assessing cardiomegaly through the cardiothoracic ratio (CTR). This systematic review and meta-analysis evaluate the efficacy of artificial intelligence (AI) in automating CTR determination to enhance patient care and streamline diagnostic processes. They are concentrated on comparing the performance of AI models in determining the CTR against human assessments, identifying the most effective models for potential clinical implementation. This study was registered with PROSPERO (no. CRD42023437459). No funding was received. Methods: A comprehensive search of medical databases was conducted in June 2023. The search strategy adhered to the PICO framework. Inclusion criteria encompassed original articles from the last decade focusing on AI-assisted CTR assessment from standing-position CXRs. Exclusion criteria included systematic reviews, meta-analyses, conference abstracts, paediatric studies, non-original articles, and studies using imaging techniques other than X-rays. After initial screening, 117 articles were reviewed, with 14 studies meeting the final inclusion criteria. Data extraction was performed by three independent investigators, and quality assessment followed PRISMA 2020 guidelines, using tools such as the JBI Checklist, AMSTAR 2, and CASP Diagnostic Study Checklist. Risk of bias was assessed according to the Cochrane Handbook guidelines. Results: Fourteen studies, comprising a total of 70,472 CXR images, met the inclusion criteria. Various AI models were evaluated, with differences in dataset characteristics and AI technology used. Common preprocessing techniques included resizing and normalization. The pooled AUC for cardiomegaly detection was 0.959 (95% CI 0.944–0.975). The pooled standardized mean difference for CTR measurement was 0.0353 (95% CI 0.147–0.0760). Significant heterogeneity was found between studies (I2 89.97%, p < 0.0001), with no publication bias detected. Conclusions: Standardizing methodologies is crucial to avoid interpretational errors and advance AI in medical imaging diagnostics. Uniform reporting standards are essential for the further development of AI in CTR measurement and broader medical imaging applications. [ABSTRACT FROM AUTHOR]

Cerebral vascular angiography, or digital subtraction angiography (DSA), is essential for diagnosing neurological conditions but poses radiation risks. This study aims to analyze the impact of examination parameters and patient characteristics on the radiation dose received during DSA to optimize safety and minimize exposure. A retrospective analysis of 251 DSA procedures using the GE Innova IGS 630 dual-plane instrument was conducted. Data on dose area product (DAP) and air kerma (KERMA), along with patient and examination details, were collected. Statistical analyses, including Mann–Whitney, Kruskal–Wallis, and Spearman rank correlation tests, assessed the relationships between variables and radiation dose outcomes. Significant correlations were found between the sides examined (left, right, or both) and DAP (p < 0.0001) and KERMA (p < 0.0001) values, with bilateral studies showing the highest values. The post hoc Dunn tests showed that the 'L + P' group significantly differs from both the right group (p < 0.0001 and the left group (p < 0.0001). There is no significant difference between the 'P' group and the 'L' group (p-value = 0.53). These results suggest that the right and left (both) group have unique KERMA mGy values compared to the other two groups. A strong correlation (rS = 0.87) existed between DAP and KERMA. The number of projections significantly impacted radiation dose (rS = 0.61). Tube parameters (kV and mA) and skull size had low correlations with DAP and KERMA. Optimizing imaging protocols and individualizing parameters can significantly enhance patient safety and diagnostic efficacy while also reducing occupational exposure for medical staff. [ABSTRACT FROM AUTHOR]

Objectives: The purpose of this study is to evaluate the performance of our deep learning algorithm in calculating cardiothoracic ratio (CTR) and thus in the assessment of cardiomegaly or pericardial effusion occurrences on chest radiography (CXR). Methods: From a database of 8000 CXRs, 13 folders with a comparable number of images were created. Then, 1020 images were chosen randomly, in proportion to the number of images in each folder. Afterward, CTR was calculated using RadiAnt Digital Imaging and Communications in Medicine (DICOM) Viewer software (2023.1). Next, heart and lung anatomical areas were marked in 3D Slicer. From these data, we trained an AI model which segmented heart and lung anatomy and determined the CTR value. Results: Our model achieved an Intersection over Union metric of 88.28% for the augmented training subset and 83.06% for the validation subset. F1-score for subsets were accordingly 90.22% and 90.67%. In the comparative analysis of artificial intelligence (AI) vs. humans, significantly lower transverse thoracic diameter (TTD) (p < 0.001), transverse cardiac diameter (TCD) (p < 0.001), and CTR (p < 0.001) values obtained using the neural network were observed. Conclusions: Results confirm that there is a significant correlation between the measurements made by human observers and the neural network. After validation in clinical conditions, our method may be used as a screening test or advisory tool when a specialist is not available, especially on Intensive Care Units (ICUs) or Emergency Departments (ERs) where time plays a key role. [ABSTRACT FROM AUTHOR]

This paper presents a study of the characteristic effects of the physicochemical properties of microcrystalline cellulose and a series of biocarbon samples produced from this raw material through thermal conversion at temperatures ranging from 200 °C to 850 °C. Structural studies revealed that the biocarbon samples produced from cellulose had a relatively low degree of graphitization of the carbon and an isometric shape of the carbon particles. Based on thermal investigations using the differential thermal analysis/differential scanning calorimeter method, obtaining fully formed biocarbon samples from cellulose feedstock was possible at about 400 °C. The highest direct carbon solid oxide fuel cell (DC-SOFC) performance was found for biochar samples obtained via thermal treatment at 400–600 °C. The pyrolytic gases from cellulose decomposition had a considerable impact on the achieved current density and power density of the DC-SOFCs supplied by pure cellulose samples or biochars derived from cellulose feedstock at a lower temperature range of 200–400 °C. For the DC-SOFCs supplied by biochars synthesised at higher temperatures of 600–850 °C, the "shuttle delivery mechanism" had a substantial effect. The impact of the carbon oxide concentration in the anode or carbon bed was important for the performance of the DC-SOFCs. Carbon oxide oxidised at the anode to form carbon dioxide, which interacted with the carbon bed to form more carbon oxide. The application of biochar obtained from cellulose alone without an additional catalyst led to moderate electrochemical power output from the DC-SOFCs. The results show that catalysts for the reverse Boudouard reactions occurring in a biocarbon bed are critical to ensuring high performance and stable operation under electrical load, which is crucial for DC-SOFC development. [ABSTRACT FROM AUTHOR]

(1) Background. Digital subtraction angiography (DSA) is indispensable for diagnosing cerebral aneurysms due to its superior imaging precision. However, optimizing X-ray parameters is crucial for accurate diagnosis, with X-ray tube settings significantly influencing image quality. Understanding the relationship between skull dimensions and X-ray parameters is pivotal for tailoring imaging protocols to individual patients. (2) Methods. A retrospective analysis of DSA data from a single center was conducted, involving 251 patients. Cephalometric measurements and statistical analyses were performed to assess correlations between skull dimensions and X-ray tube parameters (voltage and current). (3) Results. The study revealed significant correlations between skull dimensions and X-ray tube parameters, highlighting the importance of considering individual anatomical variations. Gender-based differences in X-ray parameters were observed, emphasizing the need for personalized imaging protocols. (4) Conclusions. Personalized approaches to DSA imaging, integrating individual anatomical variations and gender-specific differences, are essential for optimizing diagnostic outcomes. While this study provides valuable insights, further research across multiple centers and diverse imaging equipment is warranted to validate these findings. [ABSTRACT FROM AUTHOR]

Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain-machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin-Huxley models to bi-dimensional generalized adaptive integrate and fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips.

In the underground mines of the Legnica–Głogów Copper District (LGOM) the main way to protect the room excavation is the use of a rock bolt support. For many years, it has proven to be an efficient security measure in excavations which met all safety standards and requirements. The article presents the consumption of the rock bolt support in the Mining Department “Polkowice–Sieroszowice” in the years 2010–2015 as well as the number of bolt supports that were used to secure the excavations. In addition, it shows the percentage of bolt supports that were used to conduct rebuilding work and cover the surface of exposed roofs. One of the factors contributing to the loss of the functionality of bolt supports is corrosion whose occurrence may lead directly to a reduction in the diameter of rock bolt support parts, in particular rods, bearing plates and nuts. The phenomenon of the corrosion of the bolt support and its elements in underground mining is an extremely common phenomenon due to the favorable conditions for its development in mines, namely high temperature and humidity, as well as the presence of highly aggressive water. This involves primarily a decrease in the capacity of bolt support construction, which entails the need for its strengthening, and often the need to perform the reconstruction of the excavation.

In this paper series, we will examine the relation between the smart grid and the next generation of buildings. The new objective is now to design buildings so that the energy exchange takes place in time suitable for both sides. The building takes energy from the grid and give it back at different time because in the next generation of buildings they are equipped in advanced control system that controls energy storage for several hours and the new or retrofitted building can eliminate energy peaks and valleys for itself and assist the smart grid in equalization of the load. The control systems include monitoring and modeling of energy use and indoor environment to arrive to a weather-based prediction, Therefore, in part 1 of the paper series we address collecting, processing and analysis of the measurement data that can be done by the new modular statistical software alone or in conjunction with a dedicated neural network. We want to minimize energy use and maximize the thermal comfort of the occupants. Using the modular structure of the database, data transformation technologies and other existing tools, the modular statistical software (MSS) has been created to process large amounts of data as input to the decision-making algorithm. This is to enable buildings automated control (BAC) to take over the control of heating/cooling, ventilation, illumination and other performance aspects that relate to sustainability of built environment.

The publication presents the results of aerodynamic characteristics of selected profile blades for applications in wind turbines. Considering the potential of energy resources and investors’ preferences, the amount of energy produced in wind farms in the total amount of electricity generated will be systematically growing and probably, in the next few years, wind energy will be the first in the field of electricity production from all types of power plants. Harnessing the power of moving air masses is now a global phenomenon. Rotor wheel converts wind energy into mechanical energy when using blades with chosen shape and oriented in the terms of the optimum performance. The aim of the measurements was to determine the impact of blade shape and blade angle of attack on the efficiency of conversion of wind energy into mechanical energy on the rotor wheel. The obtained power coefficients were presented as results.