bluuebunny/crossref_metadata_2025 huggingface dataset:
Potential Benefits of Combining Proton or Carbon Ion Therapy with DNA Damage Repair Inhibitors
Authors: Rødland Gro Elise , Temelie Mihaela , Eek Mariampillai Adrian , Hauge Sissel , Gilbert Antoine , Chevalier François , Savu Diana I. , Syljuåsen Randi G.
Published: June 2024

The use of charged particle radiotherapy is currently increasing, but combination therapy with DNA repair inhibitors remains to be exploited in the clinic. The high-linear energy transfer (LET) radiation delivered by charged particles causes clustered DNA damage, which is particularly effective in destroying cancer cells. Whether the DNA damage response to this type of damage is different from that elicited in response to low-LET radiation, and if and how it can be targeted to increase treatment efficacy, is not fully understood. Although several preclinical studies have reported radiosensitizing effects when proton or carbon ion irradiation is combined with inhibitors of, e.g., PARP, ATR, ATM, or DNA-PKcs, further exploration is required to determine the most effective treatments. Here, we examine what is known about repair pathway choice in response to high- versus low-LET irradiation, and we discuss the effects of inhibitors of these pathways when combined with protons and carbon ions. Additionally, we explore the potential effects of DNA repair inhibitors on antitumor immune signaling upon proton and carbon ion irradiation. Due to the reduced effect on healthy tissue and better immune preservation, particle therapy may be particularly well suited for combination with DNA repair inhibitors.
Scaffold Application for Bone Regeneration with Stem Cells in Dentistry: Literature Review
Authors: Saberian Elham , Jenča Andrej , Zafari Yaser , Jenča Andrej , Petrášová Adriána , Zare-Zardini Hadi , Jenčová Janka
Published: June 2024

Bone tissue injuries within oral and dental contexts often present considerable challenges because traditional treatments may not be able to fully restore lost or damaged bone tissue. Novel approaches involving stem cells and targeted 3D scaffolds have been investigated in the search for workable solutions. The use of scaffolds in stem cell-assisted bone regeneration is a crucial component of tissue engineering techniques designed to overcome the drawbacks of traditional bone grafts. This study provides a detailed review of scaffold applications for bone regeneration with stem cells in dentistry. This review focuses on scaffolds and stem cells while covering a broad range of studies explaining bone regeneration in dentistry through the presentation of studies conducted in this field. The role of different stem cells in regenerative medicine is covered in great detail in the reviewed literature. These studies have addressed a wide range of subjects, including the effects of platelet concentrates during dental surgery or specific combinations, such as human dental pulp stem cells with scaffolds for animal model bone regeneration, to promote bone regeneration in animal models. Noting developments, research works consider methods to improve vascularization and explore the use of 3D-printed scaffolds, secretome applications, mesenchymal stem cells, and biomaterials for oral bone tissue regeneration. This thorough assessment outlines possible developments within these crucial regenerative dentistry cycles and provides insights and suggestions for additional study. Furthermore, alternative creative methods for regenerating bone tissue include biophysical stimuli, mechanical stimulation, magnetic field therapy, laser therapy, nutritional supplements and diet, gene therapy, and biomimetic materials. These innovative approaches offer promising avenues for future research and development in the field of bone tissue regeneration in dentistry.
Deep Convolutional Generative Adversarial Networks in Image-Based Android Malware Detection
Authors: Mercaldo Francesco , Martinelli Fabio , Santone Antonella
Published: June 2024

The recent advancements in generative adversarial networks have showcased their remarkable ability to create images that are indistinguishable from real ones. This has prompted both the academic and industrial communities to tackle the challenge of distinguishing fake images from genuine ones. We introduce a method to assess whether images generated by generative adversarial networks, using a dataset of real-world Android malware applications, can be distinguished from actual images. Our experiments involved two types of deep convolutional generative adversarial networks, and utilize images derived from both static analysis (which does not require running the application) and dynamic analysis (which does require running the application). After generating the images, we trained several supervised machine learning models to determine if these classifiers can differentiate between real and generated malicious applications. Our results indicate that, despite being visually indistinguishable to the human eye, the generated images were correctly identified by a classifier with an F-measure of approximately 0.8. While most generated images were accurately recognized as fake, some were not, leading them to be considered as images produced by real applications.
Investigating the Role of CNP and CNP Aggregates in the Rheological Breakdown of Triglyceride Systems
Authors: Penagos Ivana A. , De Witte Fien , Rimaux Tom , Dewettinck Koen , Van Bockstaele Filip
Published: June 2024

In many food applications, the mechanical properties of fats play a critical role in determining the processing performance of fat-rich products. In fact, fat crystal networks form a particular class of soft materials that exhibit viscoelastic properties. The uniqueness of the mechanical response is intricately linked to the hierarchical nature of the system, as fats possess a complex architecture encompassing features at different scale levels (i.e., length scales). Since the discovery of crystalline nanoplatelets (CNPs), it has been hypothesized that CNPs are the basic building blocks of lipid networks and that CNPs are the responsible units for the mechanical properties of fats. This hypothesis, however, has only been partially tested. In this article, we examine which units could be responsible (e.g., lamellae, CNP, CNP aggregates) for the mechanical breakdown of fat crystal networks, through Rheo-USAXS in beamline ID02 (ESRF, Grenoble, France). Time-resolved USAXS profiles were acquired during the three steps of a three-interval thixotropy test (3iTT), namely, pre-shear, shear and recovery. The results were then utilized to evidence which specific length scale is arranged (i.e., orientated) during rheological breakdown. The findings suggest that, at the tested shear rates, orientation is only visible from 250 nm onwards, suggesting that the rheological breakdown of triglycerides is primarily driven by the orientation, and possible disruption, of CNP aggregates. These results reveal the critical role of CNP aggregates in the mechanical properties of fats. In the longer term, we believe this study will steer future research toward a more focused understanding of CNP aggregation and disaggregation dynamics.
Implications of Ecological Drivers on Roan Antelope Populations in Mokala National Park, South Africa
Authors: Maruping-Mzileni Nkabeng Thato , Bezuidenhout Hugo , Ferreira Sam , Ramoelo Abel , Mapuru Morena , Munyai Lufuno , Erusan Roxanne
Published: June 2024

Climate change has massive global impacts and affects a wide range of species. Threatened species such as the roan antelope (Hippotragus equinus) are particularly vulnerable to these changes because of their ecological requirements. Attempts to address concerns about the roan’s vulnerability have not been well documented in South African protected areas. This study identifies the landscape use and distribution of the roan as well as habitat and forage suitability changes to help inform management decisions for the conservation of roan. We used fine- and broad-scale data from Mokala National Park, South Africa that includes roan occurrence data, vegetation condition indices, vegetation (structure and plant species composition), elevation and temperature differences, and precipitation strata to construct a suitability framework using the Maximum Entropy (Maxent) and Random Forest statistical package. In Mokala National Park, roan occurred in the Schmidtia pappophoroides–Vachellia erioloba sparse woodland, Senegalia mellifera–Vachellia erioloba closed woodland, Senegalia mellifera–Vachellia tortilis open shrubland, Vachellia erioloba–V. tortilis closed woodland and Rhigozum obovatum–Senegalia mellifera open shrubland. The veld (vegetation) condition index (VCI) improved from 2019 (VCI < 50%) to 2021 (VCI > 60%), with the proportion of palatable grass species (Schmidtia pappophoroides and Eragrostis lehmanniana) also increasing. This study identified four key climatic conditions affecting roan distribution, namely annual mean daily temperature range, temperature seasonality, minimum temperatures of the coldest month, and precipitation of the wettest month. These results suggest that the conservation of roan antelope should consider these key variables that affect their survival in preferred habitats and foraging areas in anticipation of changing ecological conditions.
Biochemical Markers for Neuroendocrine Tumors: Traditional Circulating Markers and Recent Development—A Comprehensive Review
Authors: Franchina Marianna , Cavalcoli Federica , Falco Olga , La Milia Marta , Elvevi Alessandra , Massironi Sara
Published: June 2024

Neuroendocrine neoplasms (NENs) are a heterogeneous group of neoplasms presenting unique challenges in diagnosis and management. Traditional markers such as chromogranin A (CgA), pancreatic polypeptide (PP), and neuron-specific enolase (NSE) have limitations in terms of specificity and sensitivity. Specific circulating markers such as serotonin and its metabolite 5-hydroxyindoleacetic acid (5-HIAA) and various gastrointestinal hormones such as gastrin, glucagon, somatostatin, and vasoactive intestinal peptide (VIP) have a role in identifying functional NENs. Recent advances in molecular and biochemical markers, also accounting for novel genomic and proteomic markers, have significantly improved the landscape for the diagnosis and monitoring of NENs. This review discusses these developments, focusing on both traditional markers such as CgA and NSE, as well as specific hormones like gastrin, insulin, somatostatin, glucagon, and VIP. Additionally, it covers emerging genomic and proteomic markers that are shaping current research. The clinical applicability of these markers is highlighted, and their role in improving diagnostic accuracy, predicting surgical outcomes, and monitoring response to treatment is demonstrated. The review also highlights the need for further research, including validation of these markers in larger studies, development of standardized assays, and integration with imaging techniques. The evolving field of biochemical markers holds promise for improving patient outcomes in the treatment of NENs, although challenges in standardization and validation remain.
A Comparison of the Clinical Characteristics of Short-, Mid-, and Long-Term Mortality in Patients Attended by the Emergency Medical Services: An Observational Study
Authors: Enriquez de Salamanca Gambara Rodrigo , Sanz-García Ancor , del Pozo Vegas Carlos , López-Izquierdo Raúl , Sánchez Soberón Irene , Delgado Benito Juan F. , Martínez Diaz Raquel , Pérez-Oleaga Cristina Mazas , López Nohora Milena Martínez , Domínguez Azpíroz Irma , Martín-Rodríguez Francisco
Published: June 2024

Aim: The development of predictive models for patients treated by emergency medical services (EMS) is on the rise in the emergency field. However, how these models evolve over time has not been studied. The objective of the present work is to compare the characteristics of patients who present mortality in the short, medium and long term, and to derive and validate a predictive model for each mortality time. Methods: A prospective multicenter study was conducted, which included adult patients with unselected acute illness who were treated by EMS. The primary outcome was noncumulative mortality from all causes by time windows including 30-day mortality, 31- to 180-day mortality, and 181- to 365-day mortality. Prehospital predictors included demographic variables, standard vital signs, prehospital laboratory tests, and comorbidities. Results: A total of 4830 patients were enrolled. The noncumulative mortalities at 30, 180, and 365 days were 10.8%, 6.6%, and 3.5%, respectively. The best predictive value was shown for 30-day mortality (AUC = 0.930; 95% CI: 0.919–0.940), followed by 180-day (AUC = 0.852; 95% CI: 0.832–0.871) and 365-day (AUC = 0.806; 95% CI: 0.778–0.833) mortality. Discussion: Rapid characterization of patients at risk of short-, medium-, or long-term mortality could help EMS to improve the treatment of patients suffering from acute illnesses.
Predicting Sudden Sensorineural Hearing Loss Recovery with Patient-Personalized Seigel’s Criteria Using Machine Learning
Authors: Shon Sanghyun , Lim Kanghyeon , Chae Minsu , Lee Hwamin , Choi June
Published: June 2024

Background: Accurate prognostic prediction is crucial for managing Idiopathic Sudden Sensorineural Hearing Loss (ISSHL). Previous studies developing ISSHL prognosis models often overlooked individual variability in hearing damage by relying on fixed frequency domains. This study aims to develop models predicting ISSHL prognosis one month after treatment, focusing on patient-specific hearing impairments. Methods: Patient-Personalized Seigel’s Criteria (PPSC) were developed considering patient-specific hearing impairment related to ISSHL criteria. We performed a statistical test to assess the shift in the recovery assessment when applying PPSC. The utilized dataset of 581 patients comprised demographic information, health records, laboratory testing, onset and treatment, and hearing levels. To reduce the model’s reliance on hearing level features, we used only the averages of hearing levels of the impaired frequencies. Then, model development, evaluation, and interpretation proceeded. Results: The chi-square test (p-value: 0.106) indicated that the shift in recovery assessment is not statistically significant. The soft-voting ensemble model was most effective, achieving an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.864 (95% CI: 0.801–0.927), with model interpretation based on the SHapley Additive exPlanations value. Conclusions: With PPSC, providing a hearing assessment comparable to traditional Seigel’s criteria, the developed models successfully predicted ISSHL recovery one month post-treatment by considering patient-specific impairments.
MRI-Derived Dural Sac and Lumbar Vertebrae 3D Volumetry Has Potential for Detection of Marfan Syndrome
Authors: Naas Omar , Norajitra Tobias , Lückerath Christian , Fink Matthias A. , Maier-Hein Klaus , Kauczor Hans-Ulrich , Rengier Fabian
Published: June 2024

Purpose: To assess the feasibility and diagnostic accuracy of MRI-derived 3D volumetry of lower lumbar vertebrae and dural sac segments using shape-based machine learning for the detection of Marfan syndrome (MFS) compared with dural sac diameter ratios (the current clinical standard). Materials and methods: The final study sample was 144 patients being evaluated for MFS from 01/2012 to 12/2016, of whom 81 were non-MFS patients (46 [67%] female, 36 ± 16 years) and 63 were MFS patients (36 [57%] female, 35 ± 11 years) according to the 2010 Revised Ghent Nosology. All patients underwent 1.5T MRI with isotropic 1 × 1 × 1 mm3 3D T2-weighted acquisition of the lumbosacral spine. Segmentation and quantification of vertebral bodies L3-L5 and dural sac segments L3-S1 were performed using a shape-based machine learning algorithm. For comparison with the current clinical standard, anteroposterior diameters of vertebral bodies and dural sac were measured. Ratios between dural sac volume/diameter at the respective level and vertebral body volume/diameter were calculated. Results: Three-dimensional volumetry revealed larger dural sac volumes (p < 0.001) and volume ratios (p < 0.001) at L3-S1 levels in MFS patients compared with non-MFS patients. For the detection of MFS, 3D volumetry achieved higher AUCs at L3-S1 levels (0.743, 0.752, 0.808, and 0.824) compared with dural sac diameter ratios (0.673, 0.707, 0.791, and 0.848); a significant difference was observed only for L3 (p < 0.001). Conclusion: MRI-derived 3D volumetry of the lumbosacral dural sac and vertebral bodies is a feasible method for quantifying dural ectasia using shape-based machine learning. Non-inferior diagnostic accuracy was observed compared with dural sac diameter ratio (the current clinical standard for MFS detection).
Design of a Robust Synchronization-Based Topology Observer for Complex Delayed Networks with Fixed and Adaptive Coupling Strength
Authors: Sun Yanqin , Wu Huaiyu , Chen Zhihuan , Chen Yang , Zheng Xiujuan
Published: June 2024

Network topology plays a key role in determining the characteristics and dynamical behaviors of a network. But in practice, network topology is sometimes hidden or uncertain ahead of time because of network complexity. In this paper, a robust-synchronization-based topology observer (STO) is proposed and applied to solve the problem of identifying the topology of complex delayed networks (TICDNs). In comparison to the existing literature, the proposed STO does not require any prior knowledge about the range of topological parameters and does not have strict limits on topology type. Furthermore, the proposed STO is suitable not only for networks with fixed coupling strength, but also for networks with adaptive coupling strength. Finally, a few comparison examples for TICDNs are used to verify the feasibility and efficiency of the proposed STO, and the results show that the proposed STO outperforms the other methods.
Time-Dependent Effective Hamiltonians for Light–Matter Interactions
Authors: Santos Aroaldo S. , Pereira Pedro H. , Abrantes Patrícia P. , Farina Carlos , Maia Neto Paulo A. , de Melo e Souza Reinaldo
Published: June 2024

In this paper, we present a systematic approach to building useful time-dependent effective Hamiltonians in molecular quantum electrodynamics. The method is based on considering part of the system as an open quantum system and choosing a convenient unitary transformation based on the evolution operator. We illustrate our formalism by obtaining four Hamiltonians, each suitable to a different class of applications. We show that we may treat several effects of molecular quantum electrodynamics with a direct first-order perturbation theory. In addition, our effective Hamiltonians shed light on interesting physical aspects that are not explicit when employing more standard approaches. As applications, we discuss three examples: two-photon spontaneous emission, resonance energy transfer, and dispersion interactions.
Evaluating Public Library Services in Taiwan through User-Generated Content: Analyzing Google Maps Reviews
Authors: Chen Chao-Chen , Chang Chen-Chi
Published: June 2024

This study explores the public library service evaluation domain through user-generated content on Google Maps, highlighting digital feedback’s significant yet underexplored potential in understanding public library patronage across Taiwan’s six major cities. Utilizing a mixed-methods research design, this study integrates Google Maps review content analysis with social network analysis to delineate public perceptions and identify areas for service enhancement in public libraries. It innovatively leverages personal experiences extracted from over 60,000 Google Maps reviews to evaluate public library services in cities such as Taipei, New Taipei, Taoyuan, Taichung, Tainan, and Kaohsiung. The research taps into the National Library of Taiwan’s National Library Statistics System to provide a robust analysis of library performance and user satisfaction, offering a novel perspective by emphasizing user-centric feedback from Google Maps as a primary data source. This approach provides quantitative data on library usage and geographic distribution and enriches our understanding of the qualitative experiences of library users. In analyzing the keywords from Google Maps reviews of public libraries, we categorize and interpret these under the three core LibQUAL+ dimensions—Affect of Service, Information Control, and Library as Place. The findings expose variances in perceived service quality among the cities, with Kaohsiung and Taichung receiving the highest accolades for service satisfaction. Simultaneously, the study identifies potential areas for improvement, particularly in cities with lower satisfaction ratings like Taipei. This personalized feedback illustrates the intimate relationship between public libraries and their communities, offering invaluable insights for policymakers and library management to enhance service delivery and user experience.
Enhancing Multi-Class Attack Detection in Graph Neural Network through Feature Rearrangement
Authors: Le Hong-Dang , Park Minho
Published: June 2024

As network sizes grow, attack schemes not only become more varied but also increase in complexity. This diversification leads to a proliferation of attack variants, complicating the identification and differentiation of potential threats. Enhancing system security necessitates the implementation of multi-class intrusion detection systems. This approach enables the categorization of incoming network traffic into distinct intrusion types and illustrates the specific attack encountered within the Internet. Numerous studies have leveraged deep learning (DL) for Network-based Intrusion Detection Systems (NIDS), aiming to improve intrusion detection. Among these DL algorithms, Graph Neural Networks (GNN) stand out for their ability to efficiently process unstructured data, especially network traffic, making them particularly suitable for NIDS applications. Although NIDS usually monitors incoming and outgoing flows in a network, represented as edge features in graph format, traditional GNN studies only consider node features, overlooking edge features. This oversight can result in losing important flow data and diminish the system’s ability to detect attacks effectively. To address this limitation, our research makes several key contributions: (1) Emphasize the significance of edge features for enhancing GNN for multi-class intrusion detection, (2) Utilize port information, which is essential for identifying attacks but often overlooked during training, (3) Reorganize features embedded within the graph. By doing this, the graph can represent close to the actual network, which is the node showing endpoint identification information such as IP addresses and ports; the edge contains information related to flow such as Duration, Number of Packet/s, and Length…; (4) Compared to traditional methods, our experiments demonstrate significant performance improvements on both CIC-IDS-2017 (98.32%) and UNSW-NB15 (96.71%) datasets.
LACTNet: A Lightweight Real-Time Semantic Segmentation Network Based on an Aggregated Convolutional Neural Network and Transformer
Authors: Zhang Xiangyue , Li Hexiao , Ru Jingyu , Ji Peng , Wu Chengdong
Published: June 2024

Transformers have demonstrated a significant advantage over CNNs in modeling long-range dependencies, leading to increasing attention being paid towards their application in semantic segmentation tasks. In the present work, a novel semantic segmentation model, LACTNet, is introduced, which synergistically combines Transformer and CNN architectures for the real-time processing of local and global contextual features. LACTNet is designed with a lightweight Transformer, which integrates a specially designed gated convolutional feedforward network, to establish feature dependencies across distant regions. A Lightweight Average Feature Bottleneck (LAFB) module is designed to effectively capture spatial detail information within the features, thereby enhancing segmentation accuracy. To address the issue of spatial feature loss in the decoder, a long skip-connection approach is employed through the designed Feature Fusion Enhancement Module (FFEM), which enhances the integrity of spatial features and the feature interaction capability in the decoder. LACTNet is evaluated on two datasets, achieving a segmentation accuracy of 74.8% mIoU and a frame rate of 90 FPS on the Cityscapes dataset, and a segmentation accuracy of 71.8% mIoU with a frame rate of 126 FPS on the CamVid dataset.
Rasterized Data Image Processing (RDIP) Techniques for Photovoltaic (PV) Data Cleaning and Application in Power Prediction
Authors: Zang Ning , Tao Yong , Yuan Zuoteng , Yuan Chen , Jing Bailin , Liu Renfeng
Published: June 2024

Photovoltaic (PV) power generation has attracted widespread interest as a clean and sustainable energy source, with increasing global attention given to renewable energy. However, the operation and monitoring of PV power generation systems often result in large amounts of data containing missing values, outliers, and noise, posing challenges for data analysis and application. Therefore, PV data cleaning plays a crucial role in ensuring data quality, enhancing data availability and reliability. This study proposes a PV data cleaning method based on Rasterized Data Image Processing (RDIP) technology, which integrates rasterization and image processing techniques to select optimal contours and extract essential data. To validate the effectiveness of our method, we conducted comparative experiments using three data cleaning methods, including our RDIP algorithm, the Pearson correlation coefficient interpolation method, and cubic spline interpolation method. Subsequently, the cleaned datasets from these methods were utilized for power prediction using two linear regression models and two neural network models. The experimental results demonstrated that data cleaned using the RDIP algorithm improved the short-term forecast accuracy by approximately 1.0% and 3.7%, respectively, compared to the other two methods, indicating the feasibility and effectiveness of the RDIP approach. However, it is worth noting that the RDIP technique has limitations due to its reliance on integer parameters for grid division, potentially leading to coarse grid divisions. Future research efforts could focus on optimizing the selection of binarization thresholds to achieve better cleaning results and exploring other potential applications of RDIP in PV data analysis.
Paleoenvironment and Hydrocarbon Potential of Salinized Lacustrine Shale with High Terrigenous Input in the Paleogene Biyang Depression (East China): Evidence from Organic Petrography and Geochemistry
Authors: Song Yu , Paerhati Paerzhana , Xu Shilin , Jiang Shu , Gao Bo , Li Shuifu , Cao Qiang , Li Zhonghui , Wan Li , Li Chuang
Published: June 2024

Salinized lacustrine shale (SLS) represents a frontier in the global quest for unconventional hydrocarbon resources. The impact of terrigenous input, which includes terrigenous organic matter (OM) and detrital matter, on the deposition and hydrocarbon potential of SLS is still controversial. Here, we examine this issue using the newly discovered SLS within the Paleogene Biyang Depression, employing a combination of organic petrographic and geochemical analyses. A high influx of terrigenous input (terrigenous OM and detrital matter) promotes the formation of SLS. On the one hand, terrigenous higher plants emerge as the primary source of OM in the SLS, as indicated by the dominance of terrigenous macerals (e.g., terrigenous liptinite) and the abundance of plant-derived biomarkers (e.g., tricyclic terpanes). Additionally, a portion of the OM may originate from bacteria. On the other hand, the rapid input of detrital matter improves the preservation of OM, resulting in the deposition of SLS with high total organic carbon (TOC) contents and low hydrogen index (HI) values. The findings of this study contribute to a deeper understanding of SLS deposition and provide guidance for regional hydrocarbon exploration.
Visualisation Testing of the Vertex Angle of the Spray Formed by Injected Diesel–Ethanol Fuel Blends
Authors: Krzemiński Artur , Ustrzycki Adam
Published: June 2024

The internal combustion engine continues to be the main source of power in various modes of transport and industrial machines. This is due to its numerous advantages, such as easy adaptability, high efficiency, reliability and low fuel consumption. Despite these beneficial qualities of internal combustion engines, growing concerns are related to their negative environmental impacts. As a result, environmental protection has become a major factor determining advancements in the automotive industry in recent years, with the search for alternative fuels being one of the priorities in research and development activities. Among these, fuels of plant origin, mainly alcohols, are attracting a lot of attention due to their high oxygen content (around 35%). These fuels differ from diesel oil, for instance, in kinematic viscosity and density, which can affect the formation of the fuel spray and, consequently, the proper functioning of the compression–ignition engine, as well as the performance and purity of the exhaust gases emitted into the environment. The process of spray formation in direct injection compression–ignition engines is extremely complicated and requires detailed analysis of the fast-changing variables. This explains the need for using complicated research equipment enabling visualisation tests and making it possible to gain a more accurate understanding of the processes that take place. The present article aims to present the methodology for alternative fuel visualisation tests. To achieve this purpose, sprays formed by diesel–ethanol blends were recorded. A visualisation chamber and a high-speed camera were used for this purpose. The acquired video provided the material for the analysis of the changes in the vertex angle of the spray formed by the fuel blends. The test was carried out under reproducible conditions in line with the test methodology. The shape of the fuel spray is impacted by an increase in the proportional content of ethanol in the diesel and dodecanol blend. Based on the present findings, it is possible to note that the values of the vertex angle in the spray produced by the diesel–ethanol blend with the addition of dodecanol are most similar to those produced by diesel oil at an injection pressure of 100 MPa. The proposed methodology enables an analysis of the injection process based on the spray macrostructure parameters, and it can be applied in the testing of alternative fuels.
Adaptive Feedforward Control Based on Estimated Electromagnetic Parameters upon a Novel Two-Degrees-of-Freedom Actuator with a Unibody Magnetic Circuit
Authors: Yi Zhaoxi , Luo Meizhu , Duan Ji-an , Jiang Yan
Published: June 2024

This paper focuses on the study of the tracking performance of a novel two-degrees-of-freedom (two-DOF) voice coil motor (VCM). Intelligent control algorithms estimate the motor control model by the relationship between the input and output values, and it is hard to achieve excellent tracking performance due to the wider variational range of motor electromagnetic parameters (MEPs). This paper proposes estimated methods for MEPs and an adaptive feedforward control algorithm based on estimated MEPs (ACBE). ACBE can alter its control parameters from inside out in time with the change in MEPs. Experiments are carried out to verify that the proposed ACBE realized excellent tracking performance upon the novel two-DOF VCM. This study indicates that an adaptive control algorithm with high-precision control parameters has better tracking performance upon a motor control model with large variation.
Economic and Energy Efficiency Analysis of the Biogas Plant Digestate Management Methods
Authors: Nowak Mateusz , Bojarski Wiktor , Czekała Wojciech
Published: June 2024

The aim of this study was to conduct a comprehensive economic and energy efficiency analysis of selected digestate management methods, considering their implications on operational costs and resource management. To achieve this aim, the study focuses on a comparative assessment of different digestate management methods, including land application, mechanical separation, the composting process and pellet production. The economic analysis involves the evaluation of the initial investment, operational expenses, and potential revenue streams associated with each method. The most economical and popular solution of digestate management is direct use as fertilizer, with total costs of 1.98 EUR·Mg−1. All of the other methods involve higher digestate management costs, respectively; for separation it is 2.42 EUR·Mg−1, for composting it is 2.81 EUR·Mg−1. The process that is the most energy-intensive, but profitable, is the production of pellets from digestate, resulting in profits of 334,926 EUR·year−1. It should be noted that the other analyzed methods of digestate management also bring many environmental benefits, affecting sustainability and reducing emissions. The results of this research will contribute unique data on the feasibility of managing the digestate and its fractions. The calculations of economic and energy values for different strategies will allow for the optimization of the overall performance of the biogas plant, thus promoting a circular economy.
Optimal Electrification Using Renewable Energies: Microgrid Installation Model with Combined Mixture k-Means Clustering Algorithm, Mixed Integer Linear Programming, and Onsset Method
Authors: Kabe Moyème , Bokovi Yao , Sedzro Kwami Senam , Takouda Pidéname , Lare Yendoubé
Published: June 2024

Optimal planning and design of microgrids are priorities in the electrification of off-grid areas. Indeed, in one of the Sustainable Development Goals (SDG 7), the UN recommends universal access to electricity for all at the lowest cost. Several optimization methods with different strategies have been proposed in the literature as ways to achieve this goal. This paper proposes a microgrid installation and planning model based on a combination of several techniques. The programming language Python 3.10 was used in conjunction with machine learning techniques such as unsupervised learning based on K-means clustering and deterministic optimization methods based on mixed linear programming. These methods were complemented by the open-source spatial method for optimal electrification planning: onsset. Four levels of study were carried out. The first level consisted of simulating the model obtained with a cluster, which is considered based on the elbow and k-means clustering method as a case study. The second level involved sizing the microgrid with a capacity of 40 kW and optimizing all the resources available on site. The example of the different resources in the Togo case was considered. At the third level, the work consisted of proposing an optimal connection model for the microgrid based on voltage stability constraints and considering, above all, the capacity limit of the source substation. Finally, the fourth level involved a planning study of electrification strategies based mainly on microgrids according to the study scenario. The results of the first level of study enabled us to obtain an optimal location for the centroid of the cluster under consideration, according to the different load positions of this cluster. Then, the results of the second level of study were used to highlight the optimal resources obtained and proposed by the optimization model formulated based on the various technology costs, such as investment, maintenance, and operating costs, which were based on the technical limits of the various technologies. In these results, solar systems account for 80% of the maximum load considered, compared to 7.5% for wind systems and 12.5% for battery systems. Next, an optimal microgrid connection model was proposed based on the constraints of a voltage stability limit estimated to be 10% of the maximum voltage drop. The results obtained for the third level of study enabled us to present selective results for load nodes in relation to the source station node. Finally, the last results made it possible to plan electrification using different network technologies and systems in the short and long term. The case study of Togo was taken into account. The various results obtained from the different techniques provide the necessary leads for a feasibility study for optimal electrification of off-grid areas using microgrid systems.
Analysis of the Relationship between Fuel Prices and Vehicle Numbers in Urban Road Networks
Authors: Ziemska-Osuch Monika
Published: June 2024

The article presents an analysis of the relationship between the price of fuel and diesel oil and traffic intensity in the city. First, an analysis of fuel prices was prepared over fourteen months and the number of passenger cars and trucks per day was examined at the same time. From the results obtained, the highest, lowest, and average values were obtained. On this basis, it was noticed that when the price of fuel was the highest, there were fewer passenger cars, but when the price dropped, the number of vehicles increased. Another conclusion from the analysis is that when the price of fuel dropped even more than the average, there were no more cars. Based on the analysis, it was noticed that the number of vehicles may vary by up to 8000 passenger vehicles per day within one intersection. Then, a microsimulation model was performed in the PTV Vissim program to check the amount of pollution generated by vehicles in three variants: the highest, lowest, and average traffic intensities. The results show that the average daily CO pollution at the moment of the lowest traffic intensity is 15,000 g lower than the average, so the high price of fuel causes much less pollution for the consumer.
Transient Flow-Induced Stress Investigation on a Prototype Reversible Pump–Turbine Runner
Authors: Zhang Dehao , Quan Qiang , Huang Xingxing , Wang Zhengwei , Wang Biao , Xiao Yunfeng
Published: June 2024

Pump–turbine units with high heads are subjected to strong pressure pulsations from the unsteady transient flow in fluid channels, which can produce severe vibrations and high stresses on the pump–turbine structural components. Therefore, reducing transient flow-induced stresses on prototype reversible pump–turbine units is an important measure for ensuring their safe and efficient operation. A high-head prototype reversible pump–turbine with a rated head of 440 m was used to investigate the transient flow characteristics and the flow-induced-stresses in this study. First, the flow passages of the pump–turbine unit and the structure of the reversible pump–turbine runner were constructed with CAD tools. Next, CFD simulations at the full load were performed to investigate the pressure pulsation characteristics of the pump turbine in both the time domain and the frequency domain. After this, the pressure files calculated by the CFD were exported and applied to a finite element model of the pump–turbine runner to calculate the transient flow-induced dynamic stresses. The results show that the pressure pulsations in the flow passage are closely related to the rotational speed, the guide vane number, and the runner blade number of the pump–turbine unit. The maximum flow-induced stresses on the pump–turbine runner at the full load were below 2 MPa and lower than the allowable value, which reveals that the designs of the pump–turbine runner and the flow passage are acceptable. The conclusions can be used as a reference to evaluate the design of high-head pump–turbines units. The approaches used to carry out the transient flow-induced stress calculations can be applied not only to pump–turbines units but also to other types of fluid turbomachinery such as pumps, turbines, fans, compressors, turbochargers, etc.
Elucidating Synergetic Effects of Anaerobic Co-Digestion of Slaughterhouse Waste with Livestock Manures
Authors: Jo Sangyeol , Kadam Rahul , Jang Heewon , Seo Dongyun , Park Jungyu
Published: June 2024

This study quantitatively analyzed the synergistic effects of co-digestion of slaughterhouse waste (SHW) with cattle manure (CM) and pig manure (PM) on methane production by applying statistical methods. The biochemical methane potential of volatile solid concentration-based mixtures showed that the biodegradability (BD) of the co-substrates was improved as the mixing proportion of the highly biodegradable SHW increased. Furthermore, mathematical analysis using the modified Gompertz model showed that an increase in the SHW mixture ratio shortened the lag phase at the initial period by more than 58%. The synergy index (SI) analysis revealed that co-digestion of CM and SHW mixed at an equal ratio of 1:1 in sample S4 resulted in a higher SI of 1.18 compared to 1.10 for PM and SHW in sample S5. An overlay plot based on BD and SI identified the optimal mixture ratio as 26.9:31.0:42.1 (CM/PM/SHW), where both BD and SI reached their maximum values. The study successfully demonstrated that co-digestion of SHW with livestock manure enhances BD through a synergistic effect.
Exploring Blockchain for Nuclear Material Tracking: A Scoping Review and Innovative Model Proposal
Authors: Ecemis Irem Nur , Ekinci Fatih , Acici Koray , Guzel Mehmet Serdar , Medeni Ihsan Tolga , Asuroglu Tunc
Published: June 2024

Ensuring safe and transparent tracking of nuclear materials in the modern era is critical for global security and compliance with international regulations. Blockchain technology, a decentralized and immutable ledger, offers a new approach to recording transactions, increasing trust without intermediaries. In this study, it was investigated whether nuclear material tracking was performed with advanced technology blockchain from past to present; it was seen that there needed to be a study on this subject in the literature, and that there was a gap. Search results proving this are presented. The authors present a model that can enable nuclear material tracking with blockchain technology, which will create a solid structure for recording and verifying every process step in the nuclear supply chain, from the creation of the first product to destruction. This model discusses how nuclear materials, which are very important to track from the beginning until they become waste, can be tracked with blockchain technology, and the contributions they can make nationally and internationally are explained. As a result of the research, it is shown that blockchain technology has the potential to pave the way for more resilient and precise nuclear supply chains by significantly increasing the security and efficiency of nuclear material tracking.
Comprehensive Analysis of Factors Underpinning the Superior Performance of Ducted Horizontal-Axis Helical Wind Turbines
Authors: Suheel Shaikh Zishan , Fazlizan Ahmad , Razali Halim , Wong Kok Hoe , Molla Altaf Hossain , Rathore Rajkumar Singh , Lipu M. S. Hossain , Sarker Mahidur R.
Published: June 2024

The societal and economic reliance on non-renewable energy sources, primarily fossil fuels, has raised concerns about an imminent energy crisis and climate change. The transition towards renewable energy sources faces challenges, notably in understanding turbine shear forces within wind technology. To address this gap, a novel solution emerges in the form of the ducted horizontal-axis helical wind turbine. This innovative design aims to improve airflow dynamics and mitigate adverse forces. Computational fluid dynamics and experimental assessments were employed to evaluate its performance. The results indicate a promising technology, showcasing the turbine’s potential to harness energy from diverse wind sources. The venturi duct aided in the augmentation of the velocity, thereby increasing the maximum energy content of the wind by 179.16%. In addition, 12.16% of the augmented energy was recovered by the turbine. Notably, the integration of a honeycomb structure demonstrated increased revolutions per minute (RPM) by rectifying the flow and reducing the circular wind, suggesting the impact of circular wind components on turbine performance. The absence of the honeycomb structure allows the turbine to encounter more turbulent wind (circular wind), which is the result of the movement of the fan. Strikingly, the downwash velocity of the turbine was observed to be equal to the incoming velocity, suggesting the absence of an axial induction factor and, consequently, no back force on the system. However, limitations persist in the transient modelling and in determining optimal performance across varying wind speeds due to experimental constraints. Despite these challenges, this turbine marks a significant stride in wind technology, highlighting its adaptability and potential for heightened efficiency, particularly at higher speeds. Further refinement and exploration are imperative for broadening the turbine’s application in renewable energy generation. This research emphasizes the turbine’s capacity to adapt to different wind velocities, signaling a promising avenue for more efficient and sustainable energy production.
Effect of Multi-Mode Divergent Ultrasound Pretreatment on Hardness, Microstructure and Digestion of Acid-Induced Whey Protein Gels
Authors: Cheng Yu , Shi Xiaolong , Yeboah Georgina Benewaa , Chen Lihong , Wu Juan
Published: June 2024

Whey protein was pretreated with multi-frequency ultrasound in mono-, dual-, and tri-frequency modes. The effect of multi-frequency ultrasound pretreatment on the hardness, chemical forces, and microstructure of acid-induced whey protein gel was investigated. Whey protein gels pretreated with dual- and tri-frequency ultrasound showed higher hardness (p < 0.05) and a denser network than mono-frequency ultrasound and control. Moreover, they had higher hydrophobic interaction and lower disulfide bonds than the control (p < 0.05). The effect of gel properties on digestion was evaluated using an in vitro static model. Whey protein gels pretreated with dual- and tri-frequency ultrasound had a higher proportion of large fragments in the oral boluses than in the control. Large fragments (>3.35 mm) in those samples were resistant to gastric digestion. Moreover, the tri-frequency ultrasound pretreatment of whey protein gel released the least free amino group during gastric digestion. In contrast, whey protein gel with the mono-frequency ultrasound pretreatment released the highest amount of free amino acid group during intestinal digestion. Findings from this study suggests that gel hardness and network density could modulate the digestion behaviors of protein gels.
Effect of Spirulina and Fish Processing By-Products Extracts on Citrinin-Induced Cytotoxicity in SH-SY5Y Cells
Authors: Martí-Quijal Francisco J. , Castagnini Juan Manuel , Barba Francisco J. , Ruiz María José
Published: June 2024

Citrinin (CIT) is a mycotoxin commonly found in grains, fruits, herbs, and spices. Its toxicity primarily affects the kidney and liver. Meanwhile, food industry by-products, particularly from fishing and aquaculture, contribute significantly to environmental concerns but can also serve as valuable sources of nutrients and bioactive compounds. Additionally, microalgae like spirulina (Arthrospira platensis) offer interesting high-added-value compounds with potential biological and cytoprotective properties. This study aims to reduce CIT’s toxicity on SH-SY5Y cells using natural extracts from the microalgae spirulina and fish processing by-products (sea bass head). The combination of these extracts with CIT has shown increased cell viability up to 15% for fish by-products extract and about 10% for spirulina extract compared to CIT alone. Furthermore, a notable reduction of up to 63.2% in apoptosis has been observed when fish by-products extracts were combined with CIT, counteracting the effects of CIT alone. However, the extracts’ effectiveness in preventing CIT toxicity in the cell cycle remains unclear. Overall, considering these nutrient and bioactive compound sources is crucial for enhancing food safety and mitigating the harmful effects of contaminants such as mycotoxins. Nevertheless, further studies are needed to investigate their mechanisms of action and better understand their protective effects more comprehensively.