bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparison of the Effects of NaOH and Deep Eutectic Solvent Catalyzed Tobacco Stock Lignin Isolation: Chemical Structure and Thermal Characteristics
Authors: Liu Zhichang , Wang Ziwei , Li Yichen , Wang Wanxia , Liu Xiongbin , Shu Hao , Jiang Jungang
Published: October 2024

Uncovering the structure of lignin from biorefinery has an important effect on lignin catalytic depolymerization and the production of bioenergy. In this study, two biorefinery lignins were isolated from tobacco stalks via alkaline and deep eutectic solvent (DES) catalyzed delignification processes, and the lignin heterogeneity structural characteristics were elucidated by gel permeation chromatography, 2D-HSQC, FT-IR, etc., to understand the relationship between the structure and the thermal characteristics of lignin. It was found that the lignins presented various structural characteristics and components, in which the predominant interunit linkages of black liquor lignin are β-O-4 and β-β linkages, and the β-O-4 linkages disappeared by DES treatment. DES lignins exhibited lower molecular weights and yields than black liquor lignin. Thermogravimetric analysis and fixed-bed pyrolysis were also performed to investigate the lignin thermal behavior. The results show that the DES approach can improve the bio-oil production from lignin and highlight the potential of DES lignin as a promising feedstock in the lignin pyrolysis process. This work provides a valuable example of the conversion of biorefinery lignin into pyrolysis products.
MPS@BWO with High Adsorption Capacity for Efficient Photocatalytic Reduction of CO2
Authors: Chen Peng , Du Tao , Li Yingnan , Jia He , Cao Gemeng , Zhang Junxu , Wang Yisong
Published: October 2024

Photocatalysis can reduce CO2 to available energy by means of light energy, which is considered to be an effective solution to alleviate energy and environmental problems. In this paper, an MPS@Bi2WO6 composite photocatalyst was prepared by in situ hydrothermal method. BWO grew on the surface of MPS, which increased the CO2 absorption capacity of the photocatalyst and improved the microstructure. Under the synergistic effect of the two aspects, BWS achieves the enhancement of light energy absorption capacity and can effectively excite electron-hole pairs. The transition electrons with high reduction ability migrate to the surface and contact with high concentrations of CO2, achieving efficient CO2 reduction under visible light. Among the photocatalysts in this paper, BWS-1 (BWO: MPS = 1:1) has efficient CO2 gas phase reduction ability under visible light, and the CO yield reaches 29.51 μmol/g. The MPS@BWO photocatalyst is a low-cost and efficient CO2 photoreduction catalyst with broad application prospects.
Harnessing Biomass for a Sustainable Future: The Role of Starch and Lignin
Authors: Vadivel Dhanalakshmi , Ferraro Francesco , Dondi Daniele
Published: October 2024

The global climate crisis, driven by unchecked industrialization and ecological negligence, compels humanity to seek alternative ways to either avert or mitigate the disastrous environmental phenomena encountered, particularly in recent years. The significant quantities of biomass generated by human activities may serve as important resources for technological applications, and biomass valorization offers dual benefits. This review emphasizes the potential of starch and lignin as adaptable materials for the advancement of sustainable and eco-friendly technologies. By investigating catalytic alterations, we may advance a more sustainable future and tackle the escalating issues of environmental pollution and sustainability. Catalytic alterations of lignin and starch have become essential techniques for their valorization. Biopolymers can be changed into useful chemicals and materials, like levulinic acid, lactic acid, 5-HMF and modified starch, which are used in the paper, textile, and coatings industries. Besides transforming into chemicals, lignin and starch can produce reactive carbon compounds that find application in both classical chemistry and photocatalysis. Additionally, we can use their highly functionalized polymeric matrices as catalysts. We can change the polymeric matrices’ chemical backbone to make them better at speeding up reactions like cross-coupling and multicomponent reactions.
Enhanced Collaborative Filtering: Combining Autoencoder and Opposite User Inference to Solve Sparsity and Gray Sheep Issues
Authors: El Youbi El Idrissi Lamyae , Akharraz Ismail , El Ouaazizi Aziza , Ahaitouf Abdelaziz
Published: October 2024

In recent years, the study of recommendation systems has become crucial, capturing the interest of scientists and academics worldwide. Music, books, movies, news, conferences, courses, and learning materials are some examples of using the recommender system. Among the various strategies employed, collaborative filtering stands out as one of the most common and effective approaches. This method identifies similar active users to make item recommendations. However, collaborative filtering has two major challenges: sparsity and gray sheep. Inspired by the remarkable success of deep learning across a multitude of application areas, we have integrated deep learning techniques into our proposed method to effectively address the aforementioned challenges. In this paper, we present a new method called Enriched_AE, focused on autoencoder, a well-regarded unsupervised deep learning technique renowned for its superior ability in data dimensionality reduction, feature extraction, and data reconstruction, with an augmented rating matrix. This matrix not only includes real users but also incorporates virtual users inferred from opposing ratings given by real users. By doing so, we aim to enhance the accuracy of predictions, thus enabling more effective recommendation generation. Through experimental analysis of the MovieLens 100K dataset, we observe that our method achieves notable reductions in both RMSE (Root Mean Squared Error) and MAE (Mean Absolute Error), underscoring its superiority over the state-of-the-art collaborative filtering models.
Genetic Characterization and Breeding Habitats of Black Fly (Diptera, Simuliidae) Vector Species in Laos
Authors: Thanee Isara , Gomontean Bhuvadol , Jumpato Waraporn , Namtaku San , Wongpakam Komgrit , Wannasingha Wannachai , Inkhavilay Khamla , Malavong Banchai , Pramual Pairot
Published: October 2024

Genetic diversity and ecology are two important aspects of vector species crucial to a full understanding of disease epidemiology. In this study, we examined genetic diversity, genetic structure and the physiochemical parameters of the breeding habitats of the two significant black fly vector species, Simulium chumpornense Takaoka and Kuvangkadilok and S. nodosum Puri, from Laos. Genetic diversity of S. chumpornense in Laos was relatively high with maximum genetic divergence of 3.05% but no significant genetic differentiation between populations. Comparisons with conspecific populations from Thailand also found no genetic differentiation between the two countries. This possibly related to the recent history of this species, as a very recent (31,000 years ago) historical population expansion was detected. Physicochemical parameters of the breeding habitats suggest the ability to utilize diverse stream sizes from small flows (1 m wide) to huge rivers (290 m or more wide). Populations of S. nodosum from Laos had low genetic diversity with maximum genetic divergence of 2.56% and no genetic structuring among populations. Comparisons with those reported from other countries revealed five genetically divergent lineages (I–V) with minimum genetic divergence of 1.36%. The majority (42 of 52) of specimens from Laos belonged to lineage I and the remaining (10) comprised lineage II. Lineage I is the largest and representative of specimens from Thailand, Myanmar, Vietnam, Taiwan and Laos. Population history analysis revealed that lineage I had undergone recent demographic expansion dating back to 7000 years ago. This very recent population expansion resembles others reported from Thailand and possibly indicates a response to increasing human and domestic animals following the agricultural revolution. Breeding sites of S. nodosum are diverse in respect to elevation, velocity, water conductivity and streambed particle size. The ability to utilize a wide variety of breeding sites could promote the production of a large adult population, which can be a pest for humans and other animals.
The Association Between Obesity, Chronic Inflammation, Metabolic Disorders and Mood Disorders Among Patients up to 12 Months After Hospitalization for SARS-CoV-2
Authors: Rachubińska Kamila , Mińko Alicja , Rotter Iwona , Sołek-Pastuszka Joanna , Ustianowski Przemysław , Skonieczna-Żydecka Karolina , Grochans Elżbieta
Published: October 2024

Background/Objectives: The relationship between BMI, inflammation, and mental health is complex. A high BMI, especially obesity, is associated with chronic inflammation, which can lead to mental disorders such as depression. Inflammatory cytokines affect neurotransmitters and the stress axis, worsening mental health. Obesity and mental disorders can mutually reinforce each other. New findings show that inflammation can lead to neurobiological changes, and the gut microbiota may play a key role. Obesity has been implicated as a factor in the high mortality and duration of influenza-like illnesses, even in people who do not have other chronic diseases that may increase the risk of complications. The aim of this study was to determine the associations between BMI and chronic inflammation, metabolic disorders, depression, and anxiety in patients hospitalized with COVID-19 up to 12 months after hospitalization. Methods: The study included 248 participants previously hospitalized for SARS-CoV-2 infection up to 12 months after hospitalization. The study was conducted in a multistage design using a diagnostic survey, anthropometric measurements, and laboratory methods. Results: A statistically significantly higher BDI-II score was observed among women. Statistical analysis showed a statistically significant higher GAD-7 score among women and those over 75 years of age. Conclusions: Higher BMI among subjects is often associated with elevated values of inflammatory markers and immune cells, such as WBC, neutrophils, monocytes, and CRP, as well as higher blood glucose levels. These associations may be related to the chronic inflammation and metabolic disorders that often accompany obesity. Lymphocytes and eosinophils may show more varied relationships depending on individual factors and specific health conditions. It is therefore important to continue research in this area.
U-Net and Its Variants Based Automatic Tracking of Radial Artery in Ultrasonic Short-Axis Views: A Pilot Study
Authors: Tian Yuan , Gao Ruiyang , Shi Xinran , Lang Jiaxin , Xue Yang , Wang Chunrong , Zhang Yuelun , Shen Le , Yu Chunhua , Zhou Zhuhuang
Published: October 2024

Background/Objectives: Radial artery tracking (RAT) in the short-axis view is a pivotal step for ultrasound-guided radial artery catheterization (RAC), which is widely employed in various clinical settings. To eliminate disparities and lay the foundations for automated procedures, a pilot study was conducted to explore the feasibility of U-Net and its variants in automatic RAT. Methods: Approved by the institutional ethics committee, patients as potential RAC candidates were enrolled, and the radial arteries were continuously scanned by B-mode ultrasonography. All acquired videos were processed into standardized images, and randomly divided into training, validation, and test sets in an 8:1:1 ratio. Deep learning models, including U-Net and its variants, such as Attention U-Net, UNet++, Res-UNet, TransUNet, and UNeXt, were utilized for automatic RAT. The performance of the deep learning architectures was assessed using loss functions, dice similarity coefficient (DSC), and Jaccard similarity coefficient (JSC). Performance differences were analyzed using the Kruskal–Wallis test. Results: The independent datasets comprised 7233 images extracted from 178 videos of 135 patients (53.3% women; mean age: 41.6 years). Consistent convergence of loss functions between the training and validation sets was achieved for all models except Attention U-Net. Res-UNet emerged as the optimal architecture in terms of DSC and JSC (93.14% and 87.93%), indicating a significant improvement compared to U-Net (91.79% vs. 86.19%, p < 0.05) and Attention U-Net (91.20% vs. 85.02%, p < 0.05). Conclusions: This pilot study validates the feasibility of U-Net and its variants in automatic RAT, highlighting the predominant performance of Res-UNet among the evaluated architectures.
Deep Learning Models for Anatomical Location Classification in Esophagogastroduodenoscopy Images and Videos: A Quantitative Evaluation with Clinical Data
Authors: Kang Seong Min , Lee Gi Pyo , Kim Young Jae , Kim Kyoung Oh , Kim Kwang Gi
Published: October 2024

Background/Objectives: During gastroscopy, accurately identifying the anatomical locations of the gastrointestinal tract is crucial for developing diagnostic aids, such as lesion localization and blind spot alerts. Methods: This study utilized a dataset of 31,403 still images from 1000 patients with normal findings to annotate the anatomical locations within the images and develop a classification model. The model was then applied to videos of 20 esophagogastroduodenoscopy procedures, where it was validated for real-time location prediction. To address instability of predictions caused by independent frame-by-frame assessment, we implemented a hard-voting-based post-processing algorithm that aggregates results from seven consecutive frames, improving the overall accuracy. Results: Among the tested models, InceptionV3 demonstrated superior performance for still images, achieving an F1 score of 79.79%, precision of 80.57%, and recall of 80.08%. For video data, the InceptionResNetV2 model performed best, achieving an F1 score of 61.37%, precision of 73.08%, and recall of 57.21%. These results indicate that the deep learning models not only achieved high accuracy in position recognition for still images but also performed well on video data. Additionally, the post-processing algorithm effectively stabilized the predictions, highlighting its potential for real-time endoscopic applications. Conclusions: This study demonstrates the feasibility of predicting the gastrointestinal tract locations during gastroscopy and suggests a promising path for the development of advanced diagnostic aids to assist clinicians. Furthermore, the location information generated by this model can be leveraged in future technologies, such as automated report generation and supporting follow-up examinations for patients.
Perfusion Computed Tomography for Assessing Pancreas Graft Volumetric Perfusion After Simultaneous Pancreas and Kidney Transplantation
Authors: Dmitriev Ilya V. , Muslimov Rustam Sh. , Anisimov Yuriy A. , Shchelykalina Svetlana P. , Grigorieva Elena V. , Shchekoturov Igor O. , Serova Natalya S. , Ternovoy Sergey K.
Published: October 2024

Background: There is paucity of data in the available medical literature regarding the parameters of the volumetric perfusion of pancreas grafts. Methods: From 5 February 2016 to 23 December 2021, we performed perfusion computed tomography in 41 patients at different times after simultaneous pancreas and kidney transplantation. The study group consisted of 18 men (44%) and 23 women (56%) with a long history of type 1 diabetes mellitus complicated by terminal chronic renal failure. The results of the perfusion computed tomography of the pancreas graft were studied, and the effects of post-transplantation timing and graft revascularization peculiarities on volumetric perfusion parameters were evaluated. Results: The median arterial blood flow, arterial blood volume, and permeability of the pancreas graft were 115.1 [99.7;130.3] mL/100 mL/min, 46.7 [37.4;56.9] mL/min, and 8.6 [4.1;11.4] mL/100 mL/min, respectively. No statistically significant differences in the averaged perfusion values were found in the head, body, and tail of the pancreas graft. The post-transplantation timing and the number of arteries involved in graft revascularization did not have a significant effect on the volumetric perfusion of the graft. Conclusion: The volumetric perfusion results of the pancreas graft correspond to those obtained in the study of pancreatic perfusion in healthy participants.
Rare Head and Neck Cancers and Pathological Diagnosis Challenges: A Comprehensive Literature Review
Authors: Filippini Daria Maria , Carosi Francesca , Querzoli Giulia , Fermi Matteo , Ricciotti Ilaria , Molteni Gabriele , Presutti Livio , Foschini Maria Pia , Locati Laura Deborah
Published: October 2024

Head and neck cancers (HNCs) arise from anatomically adjacent sites and subsites, with varying etiological factors, diagnostic strategies, prognoses, and treatment approaches. While conventional squamous cell carcinoma (SCC) is the most common histology in the head and neck district, HNCs encompass a variety of rare histopathological entities, categorized into epithelial tumors such as salivary gland cancers, sinonasal tumors, neuroendocrine tumors, malignant odontogenic tumors, and SCC variants versus non-epithelial tumors including soft tissue sarcomas, mucosal melanomas, and hematological malignancies. Rare HNCs (R-HNCs) represent a diagnostic and clinical challenge, requiring histopathological expertise, the availability of peculiar molecular analysis, and the personalization of local and systemic treatments, all guided by a multidisciplinary tumor board. Here, we provide a comprehensive literature review on R-HNCs, emphasizing key histopathological and molecular characteristics that are crucial for guiding treatment decisions. An insight about the latest developments in systemic treatments is also reported.
Generalized XY Models with Arbitrary Number of Phase Transitions
Authors: Žukovič Milan
Published: October 2024

We propose spin models that can display an arbitrary number of phase transitions. The models are based on the standard XY model, which is generalized by including higher-order nematic terms with exponentially increasing order and linearly increasing interaction strength. By employing Monte Carlo simulation we demonstrate that under certain conditions the number of phase transitions in such models is equal to the number of terms in the generalized Hamiltonian and, thus, it can be predetermined by construction. The proposed models produce the desirable number of phase transitions by solely varying the temperature. With decreasing temperature the system passes through a sequence of different phases with gradually decreasing symmetries. The corresponding phase transitions start with a presumably BKT transition that breaks the U(1) symmetry of the paramagnetic phase, and they proceed through a sequence of discrete Z2 symmetry-breaking transitions between different nematic phases down to the lowest-temperature ferromagnetic phase.
Communication Complexity of Entanglement-Assisted Multi-Party Computation
Authors: Meng Ruoyu , Ramamoorthy Aditya
Published: October 2024

We consider a quantum and a classical version of a multi-party function computation problem with n players, where players 2,…,n need to communicate appropriate information to player 1 so that a “generalized” inner product function with an appropriate promise can be calculated. In the quantum version of the protocol, the players have access to entangled qudits but the communication is still classical. The communication complexity of a given protocol is the total number of classical bits that need to be communicated. When n is prime, and for our chosen function, we exhibit a quantum protocol (with complexity (n−1)(logn) bits) and a classical protocol (with complexity ((n−1)2(logn2) bits)). Furthermore, we present an integer linear programming formulation for determining a lower bound on the classical communication complexity. This demonstrates that our quantum protocol is strictly better than classical protocols.
Study on the Microstructure, Mechanical Properties, and Corrosion Behavior of 900 °C-Annealed CoCrFeMnNiSix (X = 0, 0.3, 0.6, 0.9) High-Entropy Alloys
Authors: Jiang Chunxia , Li Rongbin , Zong Zaikang , Li Wenge , Zhang Yong , Li Tongyao
Published: October 2024

In this study, a series of CoCrFeMnNiSix (x = 0, 0.3, 0.6, 0.9) high-entropy alloys (HEAs) were prepared by suspension melting of cold crucible, annealed at 1000 °C, and then quenched at 900 °C. The changes in the microstructure of the HEAs after the addition of Si were analyzed using X-ray diffraction (XRD), metallographic microscope, scanning electron microscopy (SEM) with energy dispersive spectroscopy (EDS), and electron backscatter diffraction (EBSD). The hardness, room-temperature friction, and wear behavior, room-temperature compressive properties, and corrosion resistance of the annealed CoCrFeMnNiSix HEAs were also studied. The results show that when the Si content is 0 and 0.3, the annealed CoCrFeMnNiSix HEA exhibits a single face-centered cubic (FCC) structure. As the silicon content increases, a face-centered orthorhombic (FCO) phase appears. At a Si content of 0.9, a hexagonal close-packed (HCP) phase is observed. After heat treatment, the hardness of the CoCrFeMnNiSix HEAs increases continuously with the addition of Si. The HEA with a Si content of 0.9 achieves the highest hardness of 974.8 ± 30.2 HV. The HEA with a Si content of 0.6 reaches the highest compressive strength and yield strength, which are 1990.3 MPa and 1327.5 MPa. When the Si content is 0.9, the HEA shows the smoothest surface after wear, with the best wear resistance, achieving a value of 0.21 mm−1. In the CoCrFeMnNiSix HEAs after 900 °C heat treatment, the HEA with a Si content of 0.6 exhibits the lowest self-corrosion current density of 0.23 µA/cm2 and the highest pitting potential of 157.65 mV, indicating the best corrosion resistance.
Preparing Teachers for the ‘Ummah’: A Case Study of Hayat Foundation
Authors: Suddahazai Imran H. Khan
Published: October 2024

This preliminary study initiates an exploration into a novel informal Islamic teacher education programme developed by a community organisation. The presented case study explores the Afghan Muslim inspired Hayat Foundation’s communal educational initiative, the Marefat programme. The programme, derived from Islamic educational theoriesis ‘imbued with notions of reflective, critical, and reflexive thinking’, which are designed to recognise the individual levels of development for every educator and simultaneously elevate their efficacy, agency, and person. Adopting an interpretivist-narrative methodology conveyed through a dialogical approach, this study focuses on the lived experiences of this social-welfare organisation by seeking to decipher the teleological nature of the programme. This examination is guided by three questions which seek to identify its intended audience, the specificity of the issue it attempts to address, and the approaches it has adapted to implement its programme. The findings suggest that the overarching purpose of this educational initiative is to foster empowering liberatory pedagogical responses to counter oppressive narratives in the reading of the Islamic tradition.
Course Success Prediction and Early Identification of At-Risk Students Using Explainable Artificial Intelligence
Authors: Ujkani Berat , Minkovska Daniela , Hinov Nikolay
Published: October 2024

Artificial Intelligence (AI) is increasingly used in online education platforms to provide valuable insights into students’ performance and success. However, the complexity of AI models makes it challenging for educators to interpret the specific factors that influence whether a student is going to pass or fail. Utilizing the Open University Learning Analytics Dataset (OULAD), this study employs various machine learning and deep learning techniques for predicting students’ success, along with SHapley Additive exPlanations (SHAP) as an Explainable Artificial Intelligence (XAI) technique, to understand the key factors behind success or failure. Unlike traditional statistical methods that explore variable relationships, this AI-driven approach uses advanced deep learning techniques to identify patterns and insights, allowing for a better understanding of the factors influencing student success. Additionally, this study focuses on identifying students at risk of failure using XAI techniques, specifically SHAP, to interpret model outputs by breaking down how specific factors contribute to a student’s success. This method enables targeted interventions to support their success. Results reveal that student engagement and registration timelines are critical factors affecting performance. The customized models achieve up to 94% accuracy for the designed tasks, outperforming traditional approaches. This study contributes to the use of AI in education and offers practical insights not only for educators but also for administrators and policymakers to enhance the quality and effectiveness of online learning.
The Design of Fast Type-V Discrete Cosine Transform Algorithms for Short-Length Input Sequences
Authors: Polyakova Marina , Witenberg Anna , Cariow Aleksandr
Published: October 2024

Fast algorithms for type-five discrete cosine transform (DCT-V) for sequences of input data of short length in the range of two to eight are elaborated in the paper. A matrix–vector product representation of the DCT-V is the starting point for designing the algorithms. In each specific case, the DCT-V matrices have remarkable structural properties that follow from the localization of identical entries within the matrices. Each matrix of the DCT-V has only a few distinct entries that are repeated at different positions in its structure. Using simple transformations such as permutations of the rows and/or columns of this matrix or its favorable decomposition into two or more matrix components, it is possible to obtain efficient matrix structures that lead to useful factorization schemes. Based on the suitable factorization schemes we obtained, we developed fast algorithms that reduce the number of arithmetic operations when calculating the DCT-V. The correctness of the obtained algorithmic solutions was justified theoretically using a strict mathematical background of each of them. The developed algorithms were then further tested using MATLAB R2023b software to finally confirm their correctness. Finally, an evaluation of the computational complexity for each obtained solution is presented. The evaluation results were compared with the computational complexity of the direct calculation of matrix–vector products. The resulting factorizations of the matrices of the DCT-V reduce the average number of multiplications by 57% but increase the number of additions by 29%.
Virtual Development of a Single-Cylinder Hydrogen Opposed Piston Engine
Authors: Mattarelli Enrico , Caprioli Stefano , Savioli Tommaso , Volza Antonello , Di Gaetano Iftene Claudiu Marcu , Rinaldini Carlo Alberto
Published: October 2024

A significant challenge in utilizing hydrogen in conventional internal combustion engines is achieving a balance between NOx emissions and brake power output. A lean premixed charge (Lambda ≈ 2.5) allows for efficient and stable combustion with minimal NOx emissions. However, this comes at the cost of reduced power density due to the higher air requirements of the thermodynamic process. While supercharging can mitigate this drawback, it introduces increased complexity, cost, and size. An intriguing alternative is the 2-stroke cycle, particularly in an opposed piston (OP) configuration. This study presents the virtual development of a single-cylinder 2-stroke OP engine with a total displacement of 0.95 L, designed to deliver 25 kW at 3000 rpm. Thanks to its compact size, high thermal efficiency, robustness, modularity, and low manufacturing cost, this engine is intended for use either as an industrial power unit or in combination with electric motors in hybrid vehicles. The overarching goal of this project is to demonstrate that internal combustion engines can offer a practical and cost-effective alternative to hydrogen fuel cells without significant penalties in terms of efficiency and pollutant emissions. The design of this novel engine started from scratch, and both 1D and 3D CFD simulations were employed, with particular focus on optimizing the cylinder’s geometry and developing an efficient low-pressure injection system. The numerical methodology was based on state-of-the-art commercial codes, in line with established engineering practices. The numerical results indicated that the optimized engine configuration slightly surpasses the target performance, achieving 29 kW at 3000 rpm, while maintaining near-zero NOx emissions (<20 ppm) and high brake thermal efficiency (~40%) over a wide power range. Additionally, the cost of this engine is projected to be lower than an equivalent 4-stroke engine, due to fewer components (e.g., no cylinder head, poppet valves, or camshafts) and a lighter construction.
Coal Phase-Out and Carbon Tax Analysis with Long-Term Planning Models: A Case Study for the Chilean Electric Power System
Authors: Castillo Patricio , Aguad Matias , Lorca Álvaro , Cordova Samuel , Negrete-Pincetic Matias
Published: October 2024

Large CO2 emissions constitute a significant problem today due to their effect on climate change, and the need to design appropriate energy policies to mitigate their consequences and reduce emissions requires a detailed analysis of one of the main sources of such emissions: the electricity system. Thus, this paper presents a study on the effects of energy policies on decarbonization by comparing the detailed phase-out of coal-fired power plants across a range of cases with the implementation of a carbon tax to meet Nationally Determined Contributions (NDCs). The case study focuses on the Chilean electricity system, using a long-term generation and transmission expansion planning model (GTEP) that incorporates a wide range of generation technologies. The study examines the long-term effects of these policies, including costs, investments, and CO2 emissions, as well as their impact on consumer prices reflected in the marginal costs of the system. The transmission system modeling covers various regions of Chile and significant projections for renewable energy sources. It evaluates three economic scenarios based on generation technology costs, fuel prices, and electricity demand under four different closure schemes and fourteen different carbon tax levels. The results indicate that implementing a carbon tax can be more cost-effective for the system than the implementation of a phase-out schedule for coal plants, taking the form of reduced CO2 emission and overall system costs, with an optimal carbon tax value of 37 USD/tCO2. Additionally, the study reveals significant effects on consumer prices, showing that a carbon tax as an energy policy leads to lower prices compared to a phase-out scheme.
Optimal Scheduling of Energy Storage and Shiftable Loads in Grid-Connected Residential Buildings with Photovoltaic Micro-Installations
Authors: Korab Roman , Połomski Marcin , Naczyński Tomasz
Published: October 2024

Photovoltaic (PV) systems are becoming increasingly popular, especially in residential buildings. However, the high penetration of prosumer PV micro-installations can have a negative impact on the operation of distribution networks due to the low self-consumption of the energy produced. One way to mitigate this problem is to use a residential energy storage system (RESS) and load shifting under a demand-side management (DSM) scheme. Energy management systems (EMSs) are used to control the operation of RESSs and to implement DSM. There are two main categories of EMSs: rule-based and optimization-based. Optimization-based EMSs provide better results than rule-based EMSs but can be computationally expensive. This article proposes an optimization-based EMS that is designed specifically for residential buildings. The proposed home energy management system (HEMS) uses a particle swarm optimization method to maximize the prosumer’s financial neutrality, which is calculated based on dynamic energy prices. Simulation-based evaluation using the measurements taken in a building equipped with a PV source, RESS, and shiftable loads shows the improved performance of the proposed HEMS compared to rule-based RESS control. The results show that the designed HEMS increases self-consumption, thus reducing the impact of the prosumer’s PV micro-installations on the distribution grid.
A Study of Heat Recovery and Hydrogen Generation Systems for Methanol Engines
Authors: Kryshtopa Sviatoslav , Smigins Ruslans , Kryshtopa Liudmyla
Published: October 2024

Biofuels are the most essential types of alternative fuels, which currently have significant potential to reduce CO2 emissions compared to fossil fuels. Methanol is a more efficient fuel than petrol due to its physicochemical properties, such as a higher latent heat of vaporization, research octane number, and heat of combustion of the fuel–air mixture. Also, biomethanol is cheaper than traditional petrol and diesel fuel for agricultural countries. The authors have proposed a new approach to improve the characteristics and efficiency of methanol diesel engines by using biomethanol mixed with hydrogen instead of pure biomethanol. Using a hydrogen–biomethanol mixture in modern engines is an effective method because hydrogen is a carbon-free, low-ignition, highest-flame-rate, high-octane fuel. A small quantity of hydrogen added to biomethanol and its combustion in an engine with a heat exchanger increases the combustion temperature and heat release, increases engine power, and reduces fuel consumption. This article presents experimental results of methanol combustion and a hydrogen-in-methanol mixture if hydrogen was retained due to the utilization of the heat of the exhaust gases. The tests were carried on a single-cylinder experimental engine with an injection of liquid methanol and gaseous hydrogen mixtures. The experiments showed that green hydrogen generated onboard the car due to the utilization of heat significantly reduced fuel costs of engines of vehicles and technological installations. It was established a hydrogen gaseous mixture addition of up to 5% by mass to methanol requires a corresponding change in the coefficient of excess air to λ = 1.25. Also, using an additional hydrogen mixture requires adjustment at the ignition moment in the direction of its decrease by 4–5 degrees of the engine crankshaft. Hydrogen gas mixture addition reduced methanol consumption, reaching a maximum reduction of 24%. The maximum increase in power was 30.5% based on experimental data. The reduction in the specified fuel consumption, obtained after experimental tests of the methanol research engine on the stand, can be implemented on the vehicle engines and technological installations equipped with an onboard heat recovery system. Such a system, due to the utilization of heat and the supply of additional hydrogen, can be implemented for engines that work on any alternative or traditional fuels.
A Joint Estimation Method of Distribution Network Topology and Line Parameters Based on Power Flow Graph Convolutional Networks
Authors: Wang Yu , Shen Xiaodong , Tang Xisheng , Liu Junyong
Published: October 2024

Accurate identification of network topology and line parameters is essential for effective management of distribution systems. An innovative joint estimation method for distribution network topology and line parameters is presented, utilizing a power flow graph convolutional network (PFGCN). This approach addresses the limitations of traditional methods that rely on costly voltage phase angle measurements. The node correlation principle is applied to construct a node correlation matrix, and a minimum distance iteration algorithm is proposed to generate candidate topologies, which serve as graph inputs for the parameter estimation model. Based on the topological dependencies and convolutional properties of AC power flow equations, a PFGCN model is designed for line parameter estimation. Parameter refinement is achieved through an alternating iterative process of pseudo-trend calculation and neural network training. Training convergence and loss function values are used as feedback to filter and validate candidate topologies, enabling precise joint estimation of both topologies and parameters. The proposed method’s accuracy, transferability, and robustness are demonstrated through experiments on the IEEE-33 and modified IEEE-69 distribution systems. Multiple metrics, including MAPE, IAE, MAE, and R2, highlight the proposed method’s advantages over Adaptive Ridge Regression (ARR). In the C33 scenario, the proposed method achieves MAPEs of 4.6% for g and 5.7% for b, outperforming the ARR method with MAPEs of 7.1% and 7.9%, respectively. Similarly, in the IC69 scenario, the proposed method records MAPEs of 3.0% for g and 5.9% for b, surpassing the ARR method’s 5.1% and 8.3%.
A Multi-Functional Integrated Onboard Charger for Dual-Motor Driving EVs
Authors: Tong Minghao , Liu Xiaoqiang , Chen Yudong , Sun Le , Xu Zhiyuan
Published: October 2024

In this paper, to achieve versatile, cost-effective charging for dual-motor EVs, a multi-functional integrated onboard charger is constructed using a dual-motor driving system. In the driving mode, a five-phase flux-switching permanent-magnet (FSPM) motor powers the front, while a three-phase FSPM motor drives the rear. While in the charging mode, different topologies are adopted for different application scenarios, such as the single-phase AC charging mode, the three-phase AC charging mode, and the DC charging mode. The five-phase FSPM motor and its inverters serve as a boost-based AC/DC converter in both single-phase and three-phase AC charging modes, transforming grid power to DC. In the DC charging mode, they are reconfigured to function as a buck converter. During the three-phase AC charging mode, the three-phase FSPM motor and its inverters take on the role of a rear-stage buck converter. They function to regulate the rectified DC voltage, ensuring it meets battery charging needs. The performance of the integrated charger is validated through simulation and experiment results.
Assessing the Influence of Occupancy Factors on Energy Performance in US Small Office Buildings
Authors: Norouziasl Seddigheh , Vosoughkhosravi Sorena , Jafari Amirhosein , Pang Zhihong
Published: October 2024

Office buildings are responsible for about 35% of the total electricity in the US and over 70% of building energy consumption occurs during occupancy periods. Therefore, understanding occupancy behavior is crucial for reducing building energy consumption. However, given the stochastic nature of occupant behavior, identifying which occupancy parameters have the most impact on energy consumption poses a considerable challenge. This study aims to investigate and quantify the impact of various occupancy parameters on the energy performance of a US small-sized office building using an EnergyPlus-based nationwide energy simulation. First, dynamic occupancy schedules are created based on different occupancy parameters using an agent-based model. Next, the generated dynamic occupancy schedules are integrated into a small office building model from the Department of Energy’s prototypes. This creates a dataset of occupancy parameters and building energy performance across various climate zones. Finally, various feature selection and statistical analysis methods are applied to the generated dataset. This helps identify significant occupancy parameters and quantify their impact on building energy performance across different climate zones. According to the results of the study, buildings located in cool marine, mixed marine, and warm marine climate zones had lower total energy consumption compared to other zones. Additionally, feature selection methods identified “Occupant Density” as the primary significant variable impacting energy consumption, across all climate zones. These findings offer valuable insights into the influential occupancy parameters across various climate zones, highlighting the importance of tailoring occupancy schedules to enhance energy efficiency. They provide practical guidance that can be directly applied to optimize energy consumption and achieve significant energy savings in small office settings with different weather conditions.
Optimal Energy Management of EVs at Workplaces and Residential Buildings Using Heuristic Graph-Search Algorithm
Authors: Shohan Md Jamal Ahmed , Islam Md Maidul , Owais Sophia , Faruque Md Omar
Published: October 2024

As the adoption of electric vehicles (EVs) continues to rise, efficient scheduling methods that minimize operational costs are critical. This paper introduces a novel EV scheduling method utilizing a heuristic graph-search algorithm for cost minimization due to its admissible nature. The approach optimizes EV charging and discharging schedules by considering real-time energy prices and battery degradation costs. The method is tested on systems with solar generation, electric loads, and EVs featuring vehicle-to-grid (V2G) connections. Various charging rates, such as standard, fast, and supercharging, along with uncertainties in EV arrival and departure times, are factored into the analysis. Results from various case studies demonstrate that the proposed method outperforms popular heuristic optimization techniques, such as particle swarm optimization and genetic algorithms, by 3–5% for different real-time energy prices. Additionally, the method’s effectiveness in reducing operational costs for workplace EVs is confirmed through extensive case studies under varying uncertain conditions. Finally, the system is implemented on a digital real-time simulator with DNP3 communication, where real-time results align closely with offline simulations, confirming the algorithm’s efficacy for real-world applications.
Examination of Exercise Physiological Traits According to Usage Grade of National Forest Trails
Authors: Choi Yeji , Lee Sugwang , Yun Somi , Lee Dae Taek
Published: October 2024

Forest trail difficulty levels must be investigated to promote their safe usage. However, exercise-based physiological changes are difficult to estimate in a forest environment. Hence, studies comparing indoor and outdoor environmental activities are required. We aimed to investigate the physiological traits associated with different usage grades of national forest trails in the Republic of Korea. We conducted both field and indoor experiments to measure different variables of 20 healthy participants aged 40–50 years to evaluate the exercise intensity and physiological effects of hiking on these trails. Significant variations in physiological responses were observed based on trail difficulty, with the highest heart rate and energy expenditure recorded during the most challenging uphill segments. Specifically, the heart rate increased from 115.4 ± 13.7 bpm in easy segments to 140.3 ± 15.6 bpm in difficult segments, whereas energy expenditure ranged from 404.1 ± 112.2 kcal/h to 518.1 ± 131.0 kcal/h. This study highlights the importance of considering both objective trail difficulty and users’ physiological reactions, including perceived exertion, to enhance user safety and optimize the health benefits of national forest trails. These findings provide essential data for developing comprehensive exercise programs and improving the national forest trail usage grade system.
Comparison of Nature Tourism in Two Hungarian Forest-Dominated Areas—Results of Visitor Surveys
Authors: Ferencz-Havel Alexandra , Saláta Dénes , Orosz György , Halász Gergely , Tormáné Kovács Eszter
Published: October 2024

Recreation and nature-based tourism targeting forests are important cultural services provided by forests, and are also considered non-productive social functions of forests. Many factors influence the demand for forest recreation and tourism that are worth exploring for each forest area. The main aim of our study was to compare the results of visitor surveys related to two mountainous forested areas (Börzsöny and Cserhát) that are both located near to the capital city of Budapest but have different characteristics regarding the forests, accessibility, and the level of tourism infrastructure and services. The questionnaires focused on the characteristics of the visits, perceptions of visitors regarding the values of the areas, and the development needs besides the characteristics of the respondents. In addition to basic statistics, Chi2 and Fisher’s exact tests were used to detect the differences between the two sites. Despite the different characteristics of the study areas, the main results were quite similar at both sites. Most respondents came from Budapest or within a 60 km distance of the sites by car with family and friends, mainly for hiking and nature walks, and spent less than a day in the areas. The state of forests was perceived as good in both areas. Landscape and fresh air were the most attracting factors for visiting both sites. There was a demand for more guided tours, and regarding tourism infrastructure development needs, benches and toilets ranked high at both sites. However, there were also some slight differences between the sites. For example, Börzsöny was visited more frequently, and railway and bicycle were more often used to access this site. Pleasant climate, easy access and fresh air were more important reasons to visit Börzsöny, and it was more associated with wilderness. In Cserhát, low prices and cultural values seemed more important reasons to visit; Hollókő as a world heritage site was highlighted, and more nature-related and other tourism development needs were mentioned regarding this site. These differences were probably due to the different characteristics of the forests (more mosaic forests in Cserhát), the level of the public transportation network, tourist infrastructure and services (higher in Börzsöny), and cultural heritage (more important in Cserhát). Based on our results, Cserhát needs more development in tourism infrastructure and services, while in Börzsöny, the development of a visitor management plan would be useful to harmonize the different uses of the forest.