16168 research outputs found
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Cycling operation of a LiFePO4 battery and investigation into the influence on equivalent electrical circuit elements /
No full textThis study explores the significant effects of charge–discharge cycling on lithium iron phosphate (LiFePO4)-based electrochemical cells, with a particular focus on the Sinopoly SP-LFP040AHA cell. As lithium-ion batteries undergo repeated charging and discharging cycles, their internal characteristics evolve, influencing performance, efficiency, and longevity. Understanding these changes is crucial for optimizing battery management strategies and ensuring reliable operation across various applications. To analyze these effects, the study utilizes equivalent electrical circuits (EEC) to model the internal behavior of the battery. The individual components of the EEC—such as its resistive, capacitive, and inductive elements—are examined through 3D waveforms, offering a comprehensive visualization of how each parameter responds to cycling. One of the key contributions of this research is the development and implementation of an EEC identification approach that enables a systematic assessment of battery parameter evolution. This technique provides insights into the general trends and variations in electrical behavior based on the state of charge (SoC) of the cell. By analyzing data across a wide range of SoC values—from 0% (fully discharged) to 100% (fully charged)—and tracking changes over 100 charge– discharge cycles, the study highlights the progressive alterations in battery performance. The findings of this investigation offer valuable implications for battery health monitoring, predictive maintenance, and the refinement of state estimation models
Ultra-short term PV power forecasting under diverse environmental conditions: a case study of Norway /
No full textAccurate short-term solar forecasting is critical for power plant operations, grid balancing, real-time dispatching, automatic generation control, and energy trading. In Norway, where solar radiation is limited in winter and highly variable in summer, accurate predictions are essential. This study focuses on ultra-short-term forecasting of solar radiation and power output from a 37.8 kWp solar photovoltaic (PV) power plant at the University of Agder (UiA), Grimstad (58.335322°N, 8.577718°E), in southern Norway. We propose a novel forecasting model that integrates Spatial Attention, Temporal Attention, Self-Attention, CNN, and BiLSTM architectures to enhance prediction accuracy. Using a custom dataset collected from the UiA PV plant, the model's effectiveness was validated through comprehensive ablation studies and comparative analysis with state-of-the-art methods. The proposed model achieved a low RMSE of 0.162 kW using seven days of data, demonstrating its superiority in predicting short-term PV power outputs and associated uncertainties, outperforming conventional forecasting techniques
Development and research of the laser engraving process in packaging manufacturing technology /
No full textThe areas of application of carbon dioxide lasers in the printing industry are given. A technological process for laser engraving of packaging made of wood-fiber materials has been developed. Experimental studies of the influence of the parameters of CO2 laser radiation on the engraving process of HDF material, which is widely used in the packaging industry, have been conducted. The dependence of the engraving depth on changes in the speed and power of laser radiation has been studied. Based on the experimental studies conducted, the main operating, technological and operational factors affecting the quality of engraving have been identified
A novel approach to non-invasive intracranial pressure wave monitoring: a pilot healthy brain study /
No full textIntracranial pressure (ICP) pulse wave morphology, including the ratios of the three characteristic peaks (P1, P2, and P3), offers valuable insights into intracranial dynamics and brain compliance. Traditional invasive methods for ICP pulse wave monitoring pose significant risks, highlighting the need for non-invasive alternatives. This pilot study investigates a novel non-invasive method for monitoring ICP pulse waves through closed eyelids, using a specially designed, liquid-filled, fully passive sensor system named ‘Archimedes 02’. To our knowledge, this is the first technological approach that enables the non-invasive monitoring of ICP pulse waveforms via closed eyelids. This study involved 10 healthy volunteers, aged 26–39 years, who underwent resting-state non-invasive ICP pulse wave monitoring sessions using the ‘Archimedes 02’ device while in the supine position. The recorded signals were processed to extract pulse waves and evaluate their morphological characteristics. The results indicated successful detection of pressure pulse waves, showing the expected three peaks (P1, P2, and P3) in all subjects. The calculated P2/P1 ratios were 0.762 (SD = ±0.229) for the left eye and 0.808 (SD = ±0.310) for the right eye, suggesting normal intracranial compliance across the cohort, despite variations observed in some individuals. Physiological tests—the Valsalva maneuver and the Queckenstedt test, both performed in the supine position—induced statistically significant increases in the P2/P1 and P3/P1 ratios, supporting the notion that non-invasively recorded pressure pulse waves, measured through closed eyelids, reflect intracranial volume and pressure dynamics. Additionally, a transient hypoemic/hyperemic response test performed in the upright position induced signal changes in pressure recordings from the ‘Archimedes 02’ sensor that were consistent with intact cerebral blood flow autoregulation, aligning with established physiological principles. These findings indicate that ICP pulse waves and their dynamic changes can be monitored non-invasively through closed eyelids, offering a potential method for brain monitoring in patients for whom invasive procedures are not feasible
Novel derivatives of 3-amino-4-hydroxy-benzenesulfonamide: synthesis, binding to carbonic anhydrases, and activity in cancer cell 2D and 3D cultures /
No full textA series of novel derivatives of 3-amino-4-hydroxybenzenesulfonamide was synthesized. As the analyzed compounds possess a sulfonamide group, the affinity of these compounds for human carbonic anhydrases (CAs) was measured by fluorescent thermal shift assay, and compound selectivity for different isoenzymes was identified. The crystal structures of the complexes of compound 25 with CAI and CAII were determined. Additionally, the activity of compounds on the viability of three cancer cell lines—human glioblastoma U-87, triple-negative breast cancer MDA-MB-231, and prostate adenocarcinoma PPC-1—was established using the MTT assay and compared to CAIX-selective and non-selective comparative compounds U-104 and acetazolamide. The half-maximal concentration (EC50) was determined for the identified most active compounds, and their selectivity over fibroblasts was established. Compound 9 (inhibitor of multi-CAs) and compound 21 (not binding to CAs), considered the most promising candidates, were tested in cancer cell 3D cultures (cancer spheroids) by assessing their effect on spheroid growth and viability. Both compounds reduced the viability of spheroids from all cancer cell lines. U-87 and PPC-1 spheroids became looser in the presence of compound 9, while the growth of MDA-MB-231 spheroids was slower compared to the control. Compound 21 reduced the growth of U-87 and MDA-MB-231 3D cultures, with no significant effect on PPC-1 spheroids
Biorefining of walnut shells into polyphenol-rich extracts using ultrasound-assisted, enzyme-assisted, and pressurized liquid extraction coupled with chemometrics /
No full textWalnut (Juglans regia L.) shells are valuable agro-industrial by-products rich in polyphenols. This study investigated traditional (maceration) and advanced extraction techniques—ultrasound-assisted extraction (UAE), enzyme-assisted extraction (EAE), pressurized liquid extraction (PLE), and combined ultrasound–enzyme extraction (US-EAE)—to recover bioactive compounds from walnut shells. Extraction efficiency, total phenolic content (TPC), antioxidant capacity (ABTS•+, DPPH•), and polyphenol composition were evaluated. UPLC-ESI-MS/MS identified key polyphenols including ellagic acid, 4-hydroxybenzoic acid, vanillin, taxifolin, and quercitrin. The highest TPC (5625 mg GAE/100 g dw) was found in extracts subjected to US-EAE, in which ultrasound pretreatment (200 W, 10 min) was followed by enzymatic extraction using 0.06 mL/g Viscozyme® L at pH 3.5 and 45 °C. Under the same extraction conditions, UAE alone yielded the second highest TPC (4129 mg GAE/100 g dw). The highest ABTS•+ scavenging activity (14,478 mg TE/100 g dw) and enhanced DPPH• activity (45.38 mg TE/100 g dw) were also observed in US-EAE extracts. Chemometric techniques (PCA and HCA) revealed meaningful clustering and variation patterns among methods. These findings highlight the potential of walnut shells as a sustainable source of polyphenols and demonstrate the effectiveness of innovative extraction technologies in maximizing bioactive compound recovery for potential functional applications
Efficient soil temperature profile estimation for thermoelectric powered sensors /
No full textInternet of Things (IoT) sensors designed for environmental and agricultural purposes can offer significant contributions to creating a sustainable and green environment. However, powering these sensors remains a challenge, and exploiting the temperature difference between air and soil appears to be a promising solution. For energy-harvesting technologies, accurate soil temperature profile data are needed. This study uses meteorological and soil temperature profile data collected in the Czech Republic to train machine learning models based on Polynomial Regression (PR), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) to predict the soil temperature profile. The results of the study indicate an error of 0.79 °C, which is approximately 10.9% lower than the temperature error reported in state-of-the-art studies. Beyond achieving a lower temperature prediction error, the proposed solution simplifies the input parameters of the model to only ambient temperature and solar irradiance. This improvement significantly reduces the computational costs associated with the regression model, offering a more efficient approach to predicting soil temperature for the purpose of optimizing energy harvesting in IoT sensors
Enhancing digital collaboration through ai integration: a holistic framework for sustainable digital transformation :
No full textThe significance of the interplay between digital collaboration and digital transformation is largely recognised. With a rapid adoption of digital tools, but without a systematic framework for doing so, organisations face challenges in utilising diverse technological solutions. This leads to digital fragmentation, tool redundancy and deficient collaboration maturity. Addressing these gaps, the article aims to develop a holistic framework for enhancing digital collaboration while integrating artificial intelligence (AI). The proposed framework includes four phases and 31 factors categorised into four dimensions: business strategy & structure, management & processes, technology, and culture & behaviour. The framework moves beyond diagnostic assessment by guiding the formulation of digital collaboration improvement plans and embedding AI into each phase of digital collaboration improvement management. Although the framework is validated through a case study at an organisation that requested the results to remain confidential, the research design and selected analogous results are presented to demonstrate the flow of the validation and application of the framework. Furthermore, the integration of AI-supported collaboration practices is systematically mapped to specific Sustainable Development Goals (SDGs), offering a structured approach for aligning digital collaboration strategies with broader sustainability objectives. This research contributes a comprehensive tool for assessing and improving digital collaboration maturity while addressing the growing need for sustainable and strategically aligned digital transformation initiatives
Material flow analysis of the selected hazardous substances (TCPP, diuron, 6:2 diPAP) used in construction materials.
No full textThis study examined three chemical substances widely used in construction products – TCPP, diuron, and 6:2 diPAP – which serve important technical functions due to their specific properties but simultaneously pose risks to the environment and human health. All the investigated substances exhibit hazardous properties and persistence in various environmental compartments. The material flow analysis (MFA) method was applied to assess the movement of these substances within the construction sector – from their import into the European Union to potential emissions into the environment compartments. The analysis was conducted using STAN software. A quantitative flow analysis was carried out for TCPP and diuron, while due to data limitations, a conceptual model was developed for 6:2 diPAP. The results for TCPP showed that the highest emissions occur on construction sites during the spraying of PUR foams (approximately 32.3 tonnes per year), with significant accumulation in water (13.7 t/year) and soil (45.9 t/year). The substance is persistent; this accumulation potentially poses a long-term risk to the environment and human health. Diuron analysis revealed that most emissions occur early in the product use phase, with the majority of the substance accumulating in soil (74.29 or 132.66 t/year). Diuron is highly toxic to aquatic organisms and exhibits high persistence, meaning that even small quantities may cause long-lasting negative effects on ecosystems. Although only a conceptual flow model was created for 6:2 diPAP, it was found that the substance is frequently detected in indoor dust, indicating a potential for chronic human exposure in indoor environments. The study results revealed that information on the presence of these substances in construction products is often not publicly available or clearly disclosed. Therefore, it is necessary to tighten transparency requirements, and if their use is unavoidable, to implement measures that reduce emissions to the environment. Only through such actions can the risks associated with TCPP, diuron, and 6:2 diPAP be effectively managed and ensure that construction sector parties have access to hazard-free building solutions