bluuebunny/crossref_metadata_2025 huggingface dataset:
Functionalized Palygorskite as a Delivery Platforms for Bioactive Asymmetric Beta-Diketone Dyes
Authors: Raduly Florentina Monica , Raditoiu Valentin , Raditoiu Alina , Grapin Maria , Fierascu Radu Claudiu , Raut Iuliana , Constantin Mariana
Published: July 2024

Natural clay minerals are among the most versatile materials used in the biomedical field. Palygorskite has found various applications in this field, from the treatment of diarrheal diseases in the past to materials with antibacterial properties and platforms carrying bioactive compounds used in the treatment of diseases, cosmetic and healthcare products in the present. In this study, a possible delivery method of some bioactive asymmetric β-diketonic compounds is presented. Palygorskite modified with amphionic groups (P) and copper ions (PCu) was used as a platform to load bioactive curcumin derivatives (1 and 2). By varying the copper ions, the amounts of charged active compounds were monitored. Studies have shown that the hybrid materials resulting from the loading of 1 and 2 compounds on palygorskite with 30% copper ions (PCu30) can be used as delivery methods for these asymmetric curcumin derivatives, while palygorskite with 50% copper ions(PCu50) loaded with the same type of bioactive compounds has antibacterial properties.
A New Intriguing Teleost from the Albian Muhi Quarry, Central Mexico, and Early Euteleostean Diversification
Authors: Arratia Gloria , González-Rodríguez Katia A.
Published: July 2024

The Albian Muhi Quarry of Central Mexico has produced a variety of fishes, among which the teleosts are the most diversified, including representatives of stem groups together with a variety of crown groups. A new genus and species, †Xeneichthys yanesi, is described based on a unique combination of characters, such as a dorsoventral elongation and narrowing of infraorbitals 2 and 3, preopercle, opercle, and cleithrum; absence of some bones, such as the infraorbital 5, interopercle, and pelvic plate and fin; presence of an elongated urostyle and a membranous outgrowth or stegural on the first uroneural; and cycloid and ctenoid scales on the flanks. Due to its combination of characters, †Xeneichthys yanesi is interpreted as belonging to a new extinct family, †Xeneichthyidae, which is considered as an Euteleostei or Euteleosteomorpha incertae sedis. The fish faunas of another Albian quarry, Tlayúa of Tepexi de Rodriguez, Puebla, and Muhi Quarry are compared, and although both contain taxa that can be assigned to stem teleosts (e.g., †ichthyodectiforms and †crossognathiforms), both also contain crown teleosts (e.g., elopomorphs, clupeomorphs, and euteleosts)—all of which are endemic to their respective quarries and make them important centers of diversification of fish faunas during the Early Cretaceous.
Age Determination of Rocky Mountain Ridged Mussels (Gonidea angulata) in the Okanagan Basin, Canada
Authors: Grant Paul , Wade Joy , Lewis Todd R. , Castañeda Rowshyra A. , Branquinho Emma , MacConnachie Sean , Wischniowski Stephen , Campbell Barbara
Published: July 2024

Freshwater ecosystems and the biodiversity they support are facing unprecedented threats, exemplified by broad declines of freshwater mussels within a global biodiversity hotspot. The Rocky Mountain Ridged Mussel (Gonidea angulata) is an at-risk species in Canada, with limited information on population age structure. Maximum age of the species was found to be 50 years, by counting winter annuli and validated by isotopic oxygen analysis. Employing a Bayesian Generalized Linear Mixed Model (GLMM), results showed mussels from river habitat were predicted to have faster growth rates than mussels from lake habitats, highlighting the impact of local environmental conditions, including temperature variations, primary productivity, and water quality, on mussel growth dynamics. Of concern was the limited evidence of juvenile recruitment, with the majority of specimens potentially representing an ageing population. This pattern potentially signals an early warning of impending population decline. Our results underscore the necessity of monitoring age structure as a vital component of assessing population health of freshwater mussels and the importance of understanding local environmental conditions when determining age.
An Updated Checklist of the Fishes from the Upper Malagarazi (Lake Tanganyika Basin) in Burundi: Implications for an under Implementation Malagarazi Nature Reserve
Authors: Bigirimana Anatole , Kisekelwa Tchalondawa , da Costa Luis M. , Muzumani Donatien R. , Mukweze Mulelenu Christian , Abwe Emmanuel , Banyankimbona Gaspard , Vreven Emmanuel
Published: July 2024

The upper Malagarazi (uM) Basin is situated in Southeastern Burundi and Northwestern Tanzania, and partially covered by the Malagarazi Nature Reserve (MNR). A checklist of fishes from the uM, in Burundi, is presented based on a literature review, a re-examination of historical collections, and a study of new collections (2013–2022). A total of 74 native species, including 14 endemics and two introduced Oreochromis, distributed over 38 genera and 16 families, are reported. Of the aforementioned species, 60 native (81%) and one introduced are present in the MNR. The most important families in the uM and the MNR are the Cyprinidae (21 versus 17 species, respectively) and Cichlidae (12 versus 11). Other families are represented by less than 10 species in both the uM and the MNR. Furthermore, of the 14 species endemic to the uM (19%), only eight (57%) are reported from the MNR; the others are confined to some non-included affluent rivers. Moreover, eight taxa still await formal description. Finally, as some endemic and native species are not included within the current borders of the MNR, adjustments are proposed and the need for a new protected area is considered.
The Role of Crop, Livestock, and Farmed Aquatic Intraspecific Diversity in Maintaining Ecosystem Services
Authors: Bernis-Fonteneau Agnès , Jarvis Devra I. , Scherf Beate , Schütz Lukas , Zhang Yanxin , Attorre Fabio , Collette Linda
Published: July 2024

Most of the attention given to intraspecific crop, livestock, and aquaculture diversity in agricultural production systems has been targeted at their roles in providing provisioning services, such as food and fibre or their cultural services, providing non-material benefits, such as places for recreation and inspiration. The additional role that intraspecific crop, livestock, and aquaculture diversity has in providing regulating and supporting ecosystem services for agricultural productivity and ecosystem resilience has been largely neglected. A literature review was carried out across sectors (crop, livestock, aquaculture), both on the counterfactual, i.e., the lack of intraspecific diversity in the production system and on the direct and indirect roles that intraspecific diversity plays in maintaining seven of the regulating and supporting ecosystem services: (i) regulating pest and diseases; (ii) maintaining and regulating water and soil quality; (iii) regulating and improving the flow of reproductive diversity; (iv) buffering excess or lack of water; (v) regulating soil erosion; (vi) nutrient cycling in water and soil; and (vii) supporting habitat maintenance. Benefits from the use of intraspecific diversity, diversity per se, and adaptive traits include a limited use of chemical inputs and unsustainable practices and their negative impact on livelihoods, ecosystem functioning, and productivity. All sectors (crop, livestock, and aquaculture) should be examined in the agricultural production system to understand the provision of the different ecosystem services by intraspecific diversity. Differences in structure, functioning, and temporal and spatial scales of these sectors should also be considered. Supporting and regulating ecosystem services often have relatively longer-term processes than food provisioning and rely not only on the current diversity but also on its presence over time. The presented regulating and supporting ecosystem services rely on the presence of the diversity from the farm to the landscape and to agroecological zone. Neglecting the additional role that intraspecific crop, livestock, and aquaculture diversity has in providing regulating and supporting ecosystem services is shown in this review to be detrimental to agricultural productivity and landscape resilience.
Hybrid Positron Emission Tomography and Magnetic Resonance Imaging Guided Microsurgical Management of Glial Tumors: Case Series and Review of the Literature
Authors: Caglar Yusuf Sukru , Buyuktepe Murat , Sayaci Emre Yagiz , Dogan Ihsan , Bozkurt Melih , Peker Elif , Soydal Cigdem , Ozkan Elgin , Kucuk Nuriye Ozlem
Published: July 2024

In this case series, we aimed to report our clinical experience with hybrid positron emission tomography (PET) and magnetic resonance imaging (MRI) navigation in the management of recurrent glial brain tumors. Consecutive recurrent neuroglial brain tumor patients who underwent PET/MRI at preoperative or intraoperative periods were included, whereas patients with non-glial intracranial tumors including metastasis, lymphoma and meningioma were excluded from the study. A total of eight patients (mean age 50.1 ± 11.0 years) with suspicion of recurrent glioma tumor were evaluated. Gross total tumor resection of the PET/MRI-positive area was achieved in seven patients, whereas one patient was diagnosed with radiation necrosis, and surgery was avoided. All patients survived at 1-year follow-up. Five (71.4%) of the recurrent patients remained free of recurrence for the entire follow-up period. Two patients with glioblastoma had tumor recurrence at the postoperative sixth and eighth months. According to our results, hybrid PET/MRI provides reliable and accurate information to distinguish recurrent glial tumor from radiation necrosis. With the help of this differential diagnosis, hybrid imaging may provide the gross total resection of recurrent tumors without harming eloquent brain areas.
Dental Age Assessment Based on Developmental Stages and Maturity Index of Third Molars for Medico-Legal Purposes
Authors: Švábová Petra , Soták Michal , Galis Branislav , Kroupová Patrícia , Bundová Lucia , Vojtušová Adriana , Masnicová Soňa , Beňuš Radoslav
Published: July 2024

As results may vary depending on the method of examination, this paper analyzes methods of age estimation based on the maturation index of third molars (I3M) and Kohler’s developmental stages in living individuals. A total of 1475 orthopantomograms were analyzed. The results showed that the development of third molars tended to be more advanced in males than in females. Regression equations that included the value of the developmental stage of the left third molar most accurately predicted chronologic age in males and females. Using the I3M method, there were no statistically significant bilateral differences between the mandibular right and left third molars. Overall, 82.92% of males and only 72.82% of females were correctly classified with the cut-off value (0.08) for the left mandibular third molar index. In addition, 81.97% of individuals were correctly classified as minors and adults using the Kohler method, while only 78.62% of individuals were correctly classified as minors and adults using the I3M method. Based on the misclassification of minors as adults, both methods should be used with caution and overestimation of age should be considered, especially for those close to 18 years of age.
Vision-Based Anti-UAV Detection Based on YOLOv7-GS in Complex Backgrounds
Authors: Bo Chunjuan , Wei Yuntao , Wang Xiujia , Shi Zhan , Xiao Ying
Published: July 2024

Unauthorized unmanned aerial vehicles (UAVs) pose threats to public safety and individual privacy. Traditional object-detection approaches often fall short during their application in anti-UAV technologies. To address this issue, we propose the YOLOv7-GS model, which is designed specifically for the identification of small UAVs in complex and low-altitude environments. This research primarily aims to improve the model’s detection capabilities for small UAVs in complex backgrounds. Enhancements were applied to the YOLOv7-tiny model, including adjustments to the sizes of prior boxes, incorporation of the InceptionNeXt module at the end of the neck section, and introduction of the SPPFCSPC-SR and Get-and-Send modules. These modifications aid in the preservation of details about small UAVs and heighten the model’s focus on them. The YOLOv7-GS model achieves commendable results on the DUT Anti-UAV and the Amateur Unmanned Air Vehicle Detection datasets and performs to be competitive against other mainstream algorithms.
The Role of Comparative Advantage in Enhancing Trade in Value-Added Using a Dynamic GMM Model
Authors: Wuri Josephine
Published: July 2024

Currently, international trade has evolved into international production fragmentation captured in GVCs. Countries must enhance intermediate exports in comparative advantage sectors to increase their trade in value-added (TVA) in global production chains. However, traditional measurements of revealed comparative advantage (RCA) based on gross exports need to be updated due to overvaluation, double counting, and implicit distortions in international trade. This study uses a new comparative advantage measure, “new revealed symmetric comparative advantage” (NRSCA). Using a dynamic General Method of Moment (GMM) approach, we investigate the role of comparative advantage in driving TVA regarding backward and forward linkages and examine the impact of the COVID-19 pandemic. We use data from the current Asian Development Bank multi-regional input–output database for 2010–2020. Our findings reveal that comparative advantage significantly impacted international TVA, along with the support of quality institutional services in each country. Implementing a new comparative advantage measure, NRSCA, provided accurate estimation results to overcome the overvaluation problem. Moreover, the COVID-19 pandemic disrupted value-added trade.
Challenges Faced by International Students in Understanding British Accents and Their Mitigation Strategies—A Mixed Methods Study
Authors: Vasquez Diaz Katherine Regina , Iqbal Jamshed
Published: July 2024

The massive relocation of international students calls for a thorough investigation of diverse difficulties faced by them, among which language-related barriers are reported to have serious consequences. The main goal of this research is to investigate accent-related challenges as barriers to comprehension and effective communication faced by international students in the United Kingdom (UK), along with the factors that helped or could help the students in terms of having better experiences. The scope of this study is limited to native British accents. The study relies on data collected to analyse the impact of native-accented speech, both qualitatively and quantitatively, on the listening experiences of currently enrolled or recently graduated international students in a British university. The underlying mixed-method approach is comprised of a survey and an interview. Analysis of data collected from the survey (n = 33 participants) revealed that 42% of the participants considered native-accented speech as the biggest factor affecting their listening comprehension. This is followed by a fast speech rate, which was selected by 36% of the participants. Regarding mitigation of the difficulties, participants showed mixed responses in terms of adopting various strategies. During the interview, participants (n = 6) shared their listening comprehension experiences, particularly those encountered during the initial months after their arrival in the UK. The results obtained are potentially useful in terms of students’ support, English as a Second Language (ESL) curriculum design, English language teachers’ training and establishing learning pedagogies.
Advanced Convolutional Neural Networks for Precise White Blood Cell Subtype Classification in Medical Diagnostics
Authors: Kanavos Athanasios , Papadimitriou Orestis , Al-Hussaeni Khalil , Maragoudakis Manolis , Karamitsos Ioannis
Published: July 2024

White blood cell (WBC) classification is pivotal in medical image analysis, playing a critical role in the precise diagnosis and monitoring of diseases. This paper presents a novel convolutional neural network (CNN) architecture designed specifically for the classification of WBC images. Our model, trained on an extensive dataset, automates the extraction of discriminative features essential for accurate subtype identification. We conducted comprehensive experiments on a publicly available image dataset to validate the efficacy of our methodology. Comparative analysis with state-of-the-art methods shows that our approach significantly outperforms existing models in accurately categorizing WBCs into their respective subtypes. An in-depth analysis of the features learned by the CNN reveals key insights into the morphological traits—such as shape, size, and texture—that contribute to its classification accuracy. Importantly, the model demonstrates robust generalization capabilities, suggesting its high potential for real-world clinical implementation. Our findings indicate that the proposed CNN architecture can substantially enhance the precision and efficiency of WBC subtype identification, offering significant improvements in medical diagnostics and patient care.
Privacy-Preserving Real-Time Action Detection in Intelligent Vehicles Using Federated Learning-Based Temporal Recurrent Network
Authors: Gökcen Alpaslan , Boyacı Ali
Published: July 2024

This study introduces a privacy-preserving approach for the real-time action detection in intelligent vehicles using a federated learning (FL)-based temporal recurrent network (TRN). This approach enables edge devices to independently train models, enhancing data privacy and scalability by eliminating central data consolidation. Our FL-based TRN effectively captures temporal dependencies, anticipating future actions with high precision. Extensive testing on the Honda HDD and TVSeries datasets demonstrated robust performance in centralized and decentralized settings, with competitive mean average precision (mAP) scores. The experimental results highlighted that our FL-based TRN achieved an mAP of 40.0% in decentralized settings, closely matching the 40.1% in centralized configurations. Notably, the model excelled in detecting complex driving maneuvers, with mAPs of 80.7% for intersection passing and 78.1% for right turns. These outcomes affirm the model’s accuracy in action localization and identification. The system showed significant scalability and adaptability, maintaining robust performance across increased client device counts. The integration of a temporal decoder enabled predictions of future actions up to 2 s ahead, enhancing the responsiveness. Our research advances intelligent vehicle technology, promoting safety and efficiency while maintaining strict privacy standards.
Performance Analysis of Dual Three-Phase Synchronous Reluctance Motor According to Winding Configuration
Authors: Jeong Chaelim
Published: July 2024

This manuscript examines the output characteristics of a dual three-phase synchronous reluctance motor (DT-SynRM) according to two winding arrangements under normal and half-control modes. In the case of the DT-SynRM, it can operate by using all of the dual three-phase systems (the normal control) or one of the dual three-phase systems (the half control). In this paper, conventional winding function theory (WFT) is applied, because the output characteristic can be predicted by the inductance behavior. According to the WFT, the inductance value can be affected by the winding function, the turn function, and the inverse air gap function. As a result, the rotor barrier shape as well as the winding configuration are the most important factors that have an effect on the performance of the DT-SynRM. Therefore, the effect of the rotor barrier design on the performance is analyzed when the winding configuration and control mode are different. Finally, the validity of the torque characteristic is substantiated through experimental verification.
Energy and Precision Evaluation of a Systolic Array Accelerator Using a Quantization Approach for Edge Computing
Authors: Sanchez-Flores Alejandra , Fornt Jordi , Alvarez Lluc , Alorda-Ladaria Bartomeu
Published: July 2024

This paper focuses on the implementation of a neural network accelerator optimized for speed and energy efficiency, for use in embedded machine learning. Specifically, we explore power reduction at the hardware level through systolic array and low-precision data systems, including quantized approaches. We present a comprehensive analysis comparing a full precision (FP16) accelerator with a quantized (INT16) version on an FPGA. We upgraded the FP16 modules to handle INT16 values, employing data shifts to enhance value density while maintaining accuracy. Through single convolution experiments, we assess the energy consumption and error minimization. The paper’s structure includes a detailed description of the FP16 accelerator, the transition to quantization, mathematical and implementation insights, instrumentation for power measurement, and a comparative analysis of power consumption and convolution error. Our results attempt to identify a pattern in 16-bit quantization to achieve significant power savings with minimal loss of accuracy.
Design and Analysis of a Superconducting Homopolar Inductor Machine for Aerospace Application
Authors: Wang Jiabao , Guo Chao , Zhou Wanyu , Wan Qin
Published: July 2024

The electrically excited homopolar inductor machine has a static excitation coil as well as a robust rotor, which makes it attractive in the field of high-speed superconducting machines. This paper designed and analyzed a megawatt class superconducting homopolar inductor machine for aerospace application. To improve the power density, a mass-reduced rotor structure is proposed. Firstly, the main structure parameters of the superconducting homopolar inductor machine are derived based on the required power and speed. Secondly, the electromagnetic performance of the superconducting homopolar inductor machine is analyzed based on the finite element method. Thirdly, a mass-reduced rotor is proposed to improve its power density. The structural performance of the rotor and the electromagnetic performance of the superconducting homopolar inductor machine before and after rotor-mass reduction are evaluated. Compared with the initial rotor, the mass of the mass-reduced rotor is reduced from 66.56 kg to 50.02 kg, which increases the power density by 14.3%. The result shows that a superconducting homopolar inductor machine with a mass-reduced rotor can effectively improve its power density without affecting its output power.
UAV Inspections of Power Transmission Networks with AI Technology: A Case Study of Lesvos Island in Greece
Authors: Chatzargyros Georgios , Papakonstantinou Apostolos , Kotoula Vasiliki , Stimoniaris Dimitrios , Tsiamitros Dimitrios
Published: July 2024

The inspection of overhead power transmission lines is of the utmost importance to ensure the power network’s uninterrupted, safe, and reliable operation. The increased demand for frequent inspections implementing efficient and cost-effective methods has emerged, since conventional manual inspections are highly inaccurate, time-consuming, and costly and have geographical and weather restrictions. Unmanned Aerial Vehicles are a promising solution for managing automatic inspections of power transmission networks. The project “ALTITUDE (Automatic Aerial Network Inspection using Drones and Machine Learning)” has been developed to automatically inspect the power transmission network of Lesvos Island in Greece. The project combines drones, 5G data transmission, and state-of-the-art machine learning algorithms to replicate the power transmission inspection process using high-resolution UAV data. This paper introduces the ALTITUDE platform, created within the frame of the ALTITUDE project. The platform is a web-based, responsive Geographic Information System (GIS) that allows registered users to upload bespoke drone imagery of medium-voltage structures fed into a deep learning algorithm for detecting defects, which can be either exported as report spreadsheets or viewed on a map. Multiple experiments have been carried out to train artificial intelligence (AI) algorithms to detect faults automatically.
Multiphase Flow’s Volume Fractions Intelligent Measurement by a Compound Method Employing Cesium-137, Photon Attenuation Sensor, and Capacitance-Based Sensor
Authors: Mayet Abdulilah Mohammad , Fouladinia Farhad , Hanus Robert , Parayangat Muneer , Raja M. Ramkumar , Muqeet Mohammed Abdul , Mohammed Salman Arafath
Published: July 2024

Multiphase fluids are common in many industries, such as oil and petrochemical, and volume fraction measurement of their phases is a vital subject. Hence, there are lots of scientists and researchers who have introduced many methods and equipment in this regard, for example, photon attenuation sensors, capacitance-based sensors, and so on. These approaches are non-invasive and for this reason, are very popular and widely used. In addition, nowadays, artificial neural networks (ANN) are very attractive in a lot of fields and this is because of their accuracy. Therefore, in this paper, to estimate volume proportion of a three-phase homogeneous fluid, a new system is proposed that contains an MLP ANN, standing for multilayer perceptron artificial neural network, a capacitance-based sensor, and a photon attenuation sensor. Through computational methods, capacities and mass attenuation coefficients are obtained, which act as inputs for the proposed network. All of these inputs were divided randomly in two main groups to train and test the presented model. To opt for a suitable network with the lowest rate of mean absolute error (MAE), a number of architectures with different factors were tested in MATLAB software R2023b. After receiving MAEs equal to 0.29, 1.60, and 1.67 for the water, gas, and oil phases, respectively, the network was chosen to be presented in the paper. Hence, based on outcomes, the proposed approach’s novelty is being able to predict all phases of a homogeneous flow with very low error.
A Novel Biogenic Silicon-Based Anode Material for Lithium-Ion Batteries: A Review
Authors: Seroka Ntalane Sello , Luo Hongze , Khotseng Lindiwe
Published: July 2024

Silicon possesses a 10-fold specific capacity compared to commonly used carbon-based anodes. The volume instability, among other impediments for practical use of silicon anodes, leads to the rapid decay of the capacity because of poor cyclability. Urgent mechanisms are required to improve lithium-ion storage during cycling and prevent volume variation in the silicon structure. Biogenic silicon derived from sugarcane bagasse can be used in nanoelectronic devices. Over the years, electrode materials have been an essential part of battery components. Moreover, electrode materials are favourable for highly portable nanoelectronics, hybrid as well as pure electric vehicles, etc. Furthermore, the biogenic silicon chosen for this study was based on natural abundance, environmental friendliness, and affordability. However, most silicon anodes are hindered by unstable volume expansion, variation in solid electrolyte interface films, and poor electrical conductivity. The focus is on silicon anodes, recent developments, and the potential of biogenic silicon from sugarcane waste, exploring its physicochemical properties to meet the requirements of a suitable anode material.
Transition to Low-Carbon Vehicle Market: Characterization, System Dynamics Modeling, and Forecasting
Authors: Pourmatin Mohammad , Moeini-Aghtaie Moein , Hassannayebi Erfan , Hewitt Elizabeth
Published: July 2024

Rapid growth in vehicle ownership in the developing world and the evolution of transportation technologies have spurred a number of new challenges for policymakers. To address these challenges, this study develops a system dynamics (SD) model to project the future composition of Iran’s vehicle fleet, and to forecast fuel consumption and CO2 emissions through 2040. The model facilitates the exploration of system behaviors and the formulation of effective policies by equipping decision-makers with predictive insights. Under various scenarios, this study simulates the penetration of five distinct vehicle types, highlighting that an increase in fuel prices does not constitute a sustainable long-term intervention for reducing fuel consumption. Additionally, the model demonstrates that investments aimed at the rapid adoption of electric transportation technologies yield limited short-term reductions in CO2 emissions from transportation. The projections indicate that the number of vehicles in Iran is expected to surpass 30 million by 2040, with plug-in and hybrid electric vehicles (EVs and PHEVs) comprising up to approximately 2.2 million units in the base scenario. It is anticipated that annual gasoline consumption and CO2 emissions from passenger cars will escalate to 30,000 million liters and 77 million tons, respectively, over the next two decades. These findings highlight the need for a strategic approach in policy development to effectively manage the transition towards a lower-carbon vehicle fleet.
Simulation of a Continuous Pyrolysis Reactor for a Heat Self-Sufficient Process and Liquid Fuel Production
Authors: Chavando Antonio , Silva Valter Bruno , Tarelho Luís A. C. , Cardoso João Sousa , Eusebio Daniela
Published: July 2024

This study investigates the potential of utilizing pyrolysis byproducts, including char and non-condensable gases, as an energy source to promote autothermal pyrolysis. A total of six pyrolysis experiments were conducted at three distinct cracking temperatures, namely, 450 °C, 500 °C, and 550 °C. The experiments utilized two types of biomasses, i.e., 100% pine chips and 75% pine chips mixed with 25% refuse-derived fuels (RDF). The findings from the experiments were subsequently incorporated into a process simulation conducted on Aspen Plus for an energy balance and a techno-economic analysis. The results of the experiments revealed that the energy produced by the byproducts utilizing only pine chips is 1.453 kW/kg, which is enough to fulfill the energy demand of the pyrolysis reactor (1.298 kW/kg). However, when 25% of RDF is added, the energy demand of the reactor decreases to 1.220 kW/kg, and the produced energy increases to 1.750 kW/kg. Furthermore, adding RDF increases bio-oil’s lower heating value (LHV). The techno-economic study proposed three scenarios: optimistic, conservative, and tragic. The optimistic has a payback period (PBP) of 7.5 years and a positive net present value (NPV). However, the other two scenarios were unfavorable, resulting in unfeasibility.
Impact of Stationarizing Solar Inputs on Very-Short-Term Spatio-Temporal Global Horizontal Irradiance (GHI) Forecasting
Authors: Amaro e Silva Rodrigo , Benavides Cesar Llinet , Manso Callejo Miguel Ángel , Cira Calimanut-Ionut
Published: July 2024

In solar forecasting, it is common practice for solar data (be it irradiance or photovoltaic power) to be converted into a stationary index (e.g., clear-sky or clearness index) before being used as inputs for solar-forecasting models. However, its actual impact is rarely quantified. Thus, this paper aims to study the impact of including this processing step in the modeling workflow within the scope of very-short-term spatio-temporal forecasting. Several forecasting models are considered, and the observed impact is shown to be model-dependent. Persistence does not benefit from this for such short timescales; however, the statistical models achieve an additional 0.5 to 2.5 percentual points (PPs) in terms of the forecasting skill. Machine-learning (ML) models achieve 0.9 to 1.9 more PPs compared to a linear regression, indicating that stationarization reveals non-linear patterns in the data. The exception is Random Forest, which underperforms in comparison with the other models. Lastly, the inclusion of solar elevation and azimuth angles as inputs is tested since these are easy to compute and can inform the model on time-dependent patterns. Only the cases where the input is not made stationary, or the underperforming Random Forest model, seem to benefit from this. This indicates that the apparent Sun position data can compensate for the lack of stationarization in the solar inputs and can help the models to differentiate the daily and seasonal variability from the shorter-term, weather-driven variability.
Numerical Analysis of Optimal Hybridization in Parallel Hybrid Electric Powertrains for Tracked Vehicles
Authors: Milićević Stefan , Blagojević Ivan , Milojević Saša , Bukvić Milan , Stojanović Blaža
Published: July 2024

Tracked vehicles are integral for maneuvering diverse terrains, with hybrid propulsion systems offering potential benefits in terms of fuel efficiency and performance. However, research in hybrid electric tracked vehicles remains limited, thus necessitating a comprehensive analysis to maximize their advantages. This study presents a numerical analysis focusing on optimizing hybridization in speed-coupled parallel hybrid electric powertrains for tracked vehicles. A dynamic programming algorithm and custom drive cycle are utilized to determine optimal hybridization factors and assess parameter sensitivities. The study reveals that a hybridization factor of 0.48 is optimal for speed-coupled parallel configurations. Furthermore, the sensitivity analysis underscores the substantial impact of factors such as the engine displacement and bore-to-stroke ratio on the fuel economy, with a 10% change in these parameters potentially influencing the fuel economy by up to 2%, thus emphasizing the importance of thorough consideration during powertrain sizing. Parallel hybrid configurations exhibit considerable potential for tracked vehicles, thus highlighting the viability of choosing them over series configurations.
Review of Various Sensor Technologies in Monitoring the Condition of Power Transformers
Authors: Beheshti Asl Meysam , Fofana Issouf , Meghnefi Fethi
Published: July 2024

Modern power grids are undergoing a significant transformation with the massive integration of renewable, decentralized, and electronically interfaced energy sources, alongside new digital and wireless communication technologies. This transition necessitates the widespread adoption of robust online diagnostic and monitoring tools. Sensors, known for their intuitive and smart capabilities, play a crucial role in efficient condition monitoring, aiding in the prediction of power outages and facilitating the digital twinning of power equipment. This review comprehensively analyzes various sensor technologies used for monitoring power transformers, focusing on the critical need for reliable and efficient fault detection. The study explores the application of fiber Bragg grating (FBG) sensors, optical fiber sensors, wireless sensing networks, chemical sensors, ultra-high-frequency (UHF) sensors, and piezoelectric sensors in detecting parameters such as partial discharges, core condition, temperature, and dissolved gases. Through an extensive literature review, the sensitivity, accuracy, and practical implementation challenges of these sensor technologies are evaluated. Significant advances in real-time monitoring capabilities and improved diagnostic precision are highlighted in the review. It also identifies key challenges such as environmental susceptibility and the long-term stability of sensors. By synthesizing the current research and methodologies, this paper provides valuable insights into the integration and optimization of sensor technologies for enhancing transformer condition monitoring and reliability in modern power systems.
Ethical Issues in Researching Higher Education Teaching and Learning
Authors: Golding Jennie , Ince Amanda
Published: July 2024

Higher education here is taken to be education beyond school level: often at an institution (a university) that has degree-awarding powers, though some programmes may lead to a diploma, certificate or other award or qualification. Nomenclature around “research” in the associated teaching and learning is contested: professional enquiry, scholarship of teaching and learning, pedagogical research… are some of the terms used. Authors discuss and distinguish such terms. An ethical issue is a circumstance in which a moral conflict arises or is implicit and should be resolved in a morally acceptable way. These include privacy, informed consent, insider research and power relationships within higher education research; there are also fluid challenges around the use of digital contributions. Authors present a narrative exegesis of work in the associated areas, which authors argue will be of use to universities promoting such research, and of particular interest to two groups of academics wishing to systematically develop their practice in ways that contribute to the field: those on teaching-only contracts, who would also demonstrate their research credentials; and researchers from non-education-cognate fields. Authors identify directions for further work.
Spatiotemporal Changes in Ecological Quality and Its Response to Forest Landscape Connectivity—A Study from the Perspective of Landscape Structural and Functional Connectivity
Authors: Liu Miaomiao , Liang Guanmin , Wu Ziyi , Zuo Xueman , Hu Xisheng , Lin Sen , Wu Zhilong
Published: July 2024

Understanding the response of ecological quality (EQ) to forest landscape connectivity is essential to global biodiversity conservation and national ecological security. However, quantitatively measuring the properties and intensities within these relationships from a spatial heterogeneity perspective remains challenging. This study takes the Fujian Delta region as its case study. The Google Earth Engine platform was employed to compute the remote sensing ecological index (RSEI), the landscape metrics were applied to represent the structural connectivity of the forest landscape, and the minimum cumulative resistance model was adopted to measure the cost distance index representing the functional connectivity of the forest landscape. Then, the spatial correlation and heterogeneity between the EQ and forest landscape connectivity were analyzed based on spatial autocorrelation and geographical weighted regression at three scales (3, 4, and 5 km). The results showed the following: (1) from 2000 to 2020, the overall EQ increased, improving in 37.5% of the region and deteriorating in 13.8% of the region; (2) the forest landscape structural and functional connectivity showed a small decreasing trend from 2000 to 2020, decreasing by 1.3% and 0.9%, respectively; (3) eight forest landscape structural and functional connectivity change modes were detected under the conditions of an improving or degrading EQ based on the change in RSEI and forest landscape structural and functional connectivity; (4) the geographical weighted regression results showed that compared with the forest landscape structural connectivity index, the cost distance index had the highest explanatory power to RSEI in different scales. The effect of forest landscape functional connectivity on EQ is greater than that of structural connectivity. It provides a scientific reference for ecological environmental monitoring and the ecological conservation decision-making of managers.
Trait Assessment of 1122 Populus deltoides Clones: Unveiling Correlations among Growth, Wood Properties, and Disease Susceptibility
Authors: Ma Tianyu , Hou Jing
Published: July 2024

This study aimed to evaluate the growth, wood properties, disease susceptibility, and sex traits of 1122 Populus deltoides clones to reveal the trait variability and correlations, providing a basis for genetic improvement and breeding. The measurements included the diameter at breast height (DBH), leaf area, basic wood density (BWD), content of cellulose, hemicellulose, lignin, and disease susceptibility index (DSI). The coefficients of variation ranged 6.91%–41.96%, with the BWD showing the lowest variability. Significant sexual dimorphism was observed, with male clones exhibiting higher DBH and hemicellulose content, and female clones displaying larger leaf areas and greater phenotypic variability. Correlation analysis revealed that the leaf area was positively correlated with the BWD and hemicellulose, and it was negatively correlated with the DBH and lignin; lignin was negatively correlated with cellulose. PCA confirmed these relationships and additionally highlighted a positive correlation between the DSI and DBH. These findings established links between the growth traits and wood properties, enhancing our understanding of trait diversity in P. deltoides and providing insights for breeding strategies to develop high-quality, high-yielding cultivars.
Biogeochemical Migration of Some Rare Elements in the “Leaf Debris–Soil” System of the Catenary Landscapes in Tropical Mountainous Forests in Southern Vietnam
Authors: Lebedev Yaroslav , Drygval Anna , Pham Cam Nhung , Gorbunov Roman , Gorbunova Tatiana , Kuznetsov Andrei , Kuznetsova Svetlana , Nguyen Van Thinh , Tabunshchik Vladimir
Published: July 2024

Expeditionary studies of the functioning of landscapes of mid-mountain monsoon (including fog) forests have been being conducted within the landscape and ecological station in the territory of the Bidoup-Nui Ba National Park and the adjacent Hon Giao since 2018 and are currently underway. One of the research objectives is to clarify the biogeochemical migrations of the material composition of soils in the “leaf debris–soil” system. We have consistently studied natural objects for their material composition as well as the intensity and rate of involvement of chemical elements in physicochemical migration processes in the “leaf debris–soil” system. Our findings indicate an active influx of a select group of examined elements (Se, Pd, Ag, Cd, Sn, Bi), particularly Bi, Pd, Se, and Cd, through the leaf debris and the detachment of aboveground plant organs, warranting their integration into organogenic soil horizons. Subsequently, lateral migration (Pd, Cd, Se) ensues. Slope processes within subordinate landscape facets, in addition to soil moisture and aeration processes, contribute to the subsequent redistribution of elemental volumes introduced into organogenic soil horizons.
Mapping Characteristics in Vaccinium uliginosum Populations Predicted Using Filtered Machine Learning Modeling
Authors: Duan Yadong , Wei Xin , Wang Ning , Zang Dandan , Zhao Wenbo , Yang Yuchun , Wang Xingdong , Xu Yige , Zhang Xiaoyan , Liu Cheng
Published: July 2024

Bog bilberry (Vaccinium uliginosum L.) is considered a highly valued non-wood forest product (NWFP) species with edible and medicinal uses in East Asia. It grows in the northeastern forests of China, where stand attributes and structure jointly determine its population characteristics and individuals’ growth. Mapping the regional distributions of its population characteristics can be beneficial in the management of its natural resources, and this mapping should be predicted using machine learning modeling to obtain accurate results. In this study, a total of 60 stands were randomly chosen and screened to investigate natural bog bilberry populations in the eastern mountains of Heilongjiang and Jilin provinces in northeastern China. Individual height, canopy cover area, and fresh weight all increased in stands at higher latitudes, and shoot height was also higher in the eastern stands. The rootstock grove density showed a polynomial quadratic distribution pattern along increasing topographical gradients, resulting in a minimum density of 0.43–0.52 groves m−2 in stands in the southern part (44.3016° N, 129.4558° E) of Heilongjiang. Multivariate linear regression indicated that the bog bilberry density was depressed by host forest tree species diversity; this was assessed using both the Simpson and Shannon–Wiener indices, which also showed polynomial quadratic distribution patterns (with a modeling minimum of 0.27 and a maximum of 1.21, respectively) in response to the increase in latitude. Structural equation models identified positive contributions of tree diameter at breast height and latitude to shoot height and a negative contribution of longitude to the bog bilberry canopy area. Random forest modeling indicated that dense populations with heavy individuals were distributed in eastern Heilongjiang, and large-canopy individuals were distributed in Mudanjiang and Tonghua. In conclusion, bog bilberry populations showed better attributes in northeastern stands where host forest trees had low species diversity, but the dominant species had strong trunks.
Phenotypic Characterization of Fermentation Performance and Stress Tolerance in Commercial Ale Yeast Strains
Authors: Chen Anqi , Si Qiqi , Xu Qingyun , Pan Chenwei , Cheng Yuhan , Chen Jian
Published: July 2024

Yeast plays a crucial role in the fermentation industry, particularly in alcoholic beverage production, where robustness and metabolic flexibility are essential. This study aimed to investigate the stress tolerance and metabolic capabilities of seven commercial ale yeast strains under various stress conditions, including temperature, pH, osmotic pressure, glucose starvation, and ethanol concentration. Detailed growth assays and stress tolerance tests were utilized to evaluate fermentation efficiency, carbon source utilization, and stress adaptation. Significant variability was observed among the strains. ACY169 and ACY150 demonstrated high overall stress tolerance, making them suitable for high-gravity brewing and processes involving extreme temperature fluctuations. ACY10 showed robust performance under acid stress, making it ideal for sour beer production. In contrast, ACY5 exhibited limited adaptability under stress, with longer doubling times and reduced metabolic activity. The study also revealed differences in carbon source utilization, with ACY169 displaying exceptional metabolic versatility by efficiently fermenting various sugars, including glucose, fructose, maltose, and raffinose. ACY10 and ACY150 exhibited balanced fermentation profiles with high ethanol production rates, while ACY9 demonstrated the highest glucose consumption rate but lower ethanol yields and significant acidification.
Insight into the Effects of Norfloxacin on Bacterial Community and Antibiotic Resistance Genes during Chicken Manure Composting
Authors: Feng Yao , Shi Huading , Fei Yang , Zhao Quansheng , Li Zhaojun
Published: July 2024

Composting emerges as an effective strategy to eliminate antibiotics and antibiotic resistance genes (ARGs) in animal manure. In this study, chicken manure with the addition of wheat straw and sawdust was used as composting raw materials, and different concentrations of norfloxacin were added to investigate its effects on physicochemical properties, bacterial community, and ARGs during the composting process. Results show that the presence of norfloxacin has obvious effects on the composting physicochemical properties and germination index (GI). A high concentration of norfloxacin influences the succession direction of the bacterial community and promotes the transfers of gyrA, gyrB, parC, qepA, and qnrB. The composting physicochemical properties alter bacterial communities and further influence the fate of ARGs. These results suggest that meticulous management of antibiotic usage and compost conditions are vital strategies for mitigating the influx of antibiotics and ARGs into the environment, both at the source and on the path.