bluuebunny/crossref_metadata_2025 huggingface dataset:
Introduction to Reproducible Geospatial Analysis and Figures in R: A Tutorial Article
Authors: Maesen Philippe , Salingros Edouard
Published: April 2024

The present article is intended to serve an educational purpose for data scientists and students who already have experience with the R language and which to start using it for geospatial analysis and map creation. The basic concepts of raster data, vector data, CRS and datum are first presented along with a basic workflow to conduct reproducible geospatial research in R. Examples of important types of maps (scatter, bubble, choropleth, hexbin and faceted) created from open-source environmental data are illustrated and their practical implementation in R is discussed. Through these examples, essential manipulations on geospatial vector data are demonstrated (reading, transforming CRS, creating geometries from scratch, buffer zones around existing geometries and intersections between geometries).
Predicting Academic Success of College Students Using Machine Learning Techniques
Authors: Guanin-Fajardo Jorge Humberto , Guaña-Moya Javier , Casillas Jorge
Published: April 2024

College context and academic performance are important determinants of academic success; using students’ prior experience with machine learning techniques to predict academic success before the end of the first year reinforces college self-efficacy. Dropout prediction is related to student retention and has been studied extensively in recent work; however, there is little literature on predicting academic success using educational machine learning. For this reason, CRISP-DM methodology was applied to extract relevant knowledge and features from the data. The dataset examined consists of 6690 records and 21 variables with academic and socioeconomic information. Preprocessing techniques and classification algorithms were analyzed. The area under the curve was used to measure the effectiveness of the algorithm; XGBoost had an AUC = 87.75% and correctly classified eight out of ten cases, while the decision tree improved interpretation with ten rules in seven out of ten cases. Recognizing the gaps in the study and that on-time completion of college consolidates college self-efficacy, creating intervention and support strategies to retain students is a priority for decision makers. Assessing the fairness and discrimination of the algorithms was the main limitation of this work. In the future, we intend to apply the extracted knowledge and learn about its influence of on university management.
Investigation on the Microstructure and Micro-Mechanical Properties of Thermal-Sprayed NiCoCrAlY High Entropy Alloy Coating
Authors: Basak Animesh Kumar , Radhika Nachimuthu , Prakash Chander , Pramanik Alokesh
Published: April 2024

NiCoCrAlY high entropy alloy (HEA) coating (47.1 wt.% Ni, 23 wt.% Co, 17 wt.% Cr, 12.5 wt.% Al, and 0.4 wt.% Y) was deposited on a stainless steel subtract by atmospheric plasma spraying (APS). The as-deposited coating was about 300 μm thickness with <1% porosity. The microstructure of the coating consisted of dispersed secondary phases/intermetallics in the solid solution. The stress–strain behaviour of this coating was investigated in micro-scale with the help of in situ micro-pillar compression. The experimental results show that yield and compressive stress in the cross-section of the coating was higher (1.27 ± 0.10 MPa and 2.19 ± 0.10 GPa, respectively) than that of the planar direction (0.85 ± 0.09 MPa and 1.20 ± 0.08 GPa, respectively). The various secondary/intermetallic phases (γ′–Ni3Al, β–NiAl) that were present in the coating microstructure hinder the lattice movement during compression, according to Orowan mechanism. In addition to that, the direction of the loading to that of the orientation of the phase/splat boundaries dictate the crack propagation architecture, which results in difference in the micro-mechanical properties.
Advanced Respiratory Failure Requiring Tracheostomy—A Marker of Unfavourable Prognosis after Heart Transplantation
Authors: Załęska-Kocięcka Marta , Morosin Marco , Dutton Jonathan , Garda Rita Fernandez , Piotrowska Katarzyna , Lees Nicholas , Aw Tuan-Chen , Saez Diana Garcia , Doce Ana Hurtado
Published: April 2024

Advanced respiratory failure with tracheostomy requirement is common in heart recipients. The aim of the study is to assess the tracheostomy rate after orthotopic heart transplantation and identify the subgroups of patients with the highest need for tracheostomy and these groups’ association with mortality at a single centre through a retrospective analysis of 140 consecutive patients transplanted between December 2012 and July 2018. As many as 28.6% heart recipients suffered from advanced respiratory failure with a need for tracheostomy that was performed after a median time of 11.5 days post-transplant. Tracheostomy was associated with a history of stroke (OR 3.4; 95% CI) 1.32–8.86; p = 0.012), previous sternotomy (OR 2.5; 95% CI 1.18–5.32; p = 0.017), longer cardiopulmonary bypass time (OR 1.01; 95% CI 1.00–1.01; p = 0.007) as well as primary graft failure (OR 6.79; 95% CI2.93–15.71; p < 0.001), need of renal replacement therapy (OR 19.2; 95% 2.53–146; p = 0.004) and daily mean SOFA score up to 72 h (OR 1.50; 95% 1.23–1.71; p < 0.01). One-year mortality was significantly higher in patients requiring a tracheostomy vs. those not requiring one during their hospital stay (50% vs. 16%, p < 0.001). The need for tracheostomy in heart transplant recipients was 30% in our study. Advanced respiratory failure was associated with over 3-fold greater 1-year mortality. Thus, tracheostomy placement may be regarded as a marker of unfavourable prognosis.
Endoscopic Retrograde Cholangiopancreatography (ERCP) for Suspected Mirizzi Syndrome Type IV as Both a Diagnostic and Bridge-to-Surgery Procedure
Authors: Rizzo Giacomo Emanuele Maria , Caruso Settimo , Tarantino Ilaria
Published: April 2024

Mirizzi syndrome (MS) is a challenging diagnosis due to its similar presentation with other biliary diseases; thus, the role of endoscopy is sometimes unclear, especially in altered anatomy. Radiological examinations may usually suspect it, but deeper examinations could be necessary to confirm it. Endoscopic retrograde cholangiopancreatography (ERCP) certainly has a therapeutic role in cases of jaundice, cholangitis or concurrent choledocolithiasis, although surgery is without doubt the definitive treatment in most of the cases. Therefore, surgeons may have a clearer picture of the condition of the biliary tree with respect to fistulas thanks to ERCP, particularly in patients with a higher grade of MS (type higher than 2 in the Csendes classification). Therefore, a complete removal of biliary stones is sometimes not possible due to size and location, so biliary stenting becomes the only option, even if transitory. Our brief report is a further demonstration of the fundamental role of ERCP in managing MS, even when it has no long-term therapeutic aim but is performed as bridge-to-surgery, especially in cases with a more difficult biliary anatomy due to the type of fistula. Moreover, we truly suggest discussing patients affected with MS in a multidisciplinary board, preferably in tertiary hepatobiliary centers.
Impact of Systemic Treatments on Outcomes and Quality of Life in Patients with RAS-Positive Stage IV Colorectal Cancer: A Systematic Review
Authors: Braicu Vlad , Stelian Pantea , Fulger Lazar , Verdes Gabriel , Brebu Dan , Duta Ciprian , Fizedean Camelia , Ignuta Flavia , Danila Alexandra Ioana , Cozma Gabriel Veniamin
Published: April 2024

This systematic review critically evaluates the impact of systemic treatments on outcomes and quality of life (QoL) in patients with RAS-positive stage IV colorectal cancer, with studies published up to December 2023 across PubMed, Scopus, and Web of Science. From an initial pool of 1345 articles, 11 relevant studies were selected for inclusion, encompassing a diverse range of systemic treatments, including panitumumab combined with FOLFOX4 and FOLFIRI, irinotecan paired with panitumumab, regorafenib followed by cetuximab ± irinotecan and vice versa, and panitumumab as a maintenance therapy post-induction. Patient demographics predominantly included middle-aged to elderly individuals, with a slight male predominance. Racial composition, where reported, showed a majority of Caucasian participants, highlighting the need for broader demographic inclusivity in future research. Key findings revealed that the addition of panitumumab to chemotherapy (FOLFOX4 or FOLFIRI) did not significantly compromise QoL while notably improving disease-free survival, with baseline EQ-5D HSI mean scores ranging from 0.76 to 0.78 and VAS mean scores from 70.1 to 74.1. Improvements in FACT-C scores and EQ-5D Index scores particularly favored panitumumab plus best supportive care in KRAS wild-type mCRC, with early dropout rates of 38–42% for panitumumab + BSC. Notably, cetuximab + FOLFIRI was associated with a median survival of 25.7 months versus 16.4 months for FOLFIRI alone, emphasizing the potential benefits of integrating targeted therapies with chemotherapy. In conclusion, the review underscores the significant impact of systemic treatments, particularly targeted therapies and their combinations with chemotherapy, on survival outcomes and QoL in patients with RAS-positive stage IV colorectal cancer, and the need for personalized treatment.
Extended State Observer-Based Sliding-Mode Control for Aircraft in Tight Formation Considering Wake Vortices and Uncertainty
Authors: Zheng Ruiping , Zhu Qi , Huang Shan , Du Zhihui , Shi Jingping , Lyu Yongxi
Published: April 2024

The tight formation of unmanned aerial vehicles (UAVs) provides numerous advantages in practical applications, increasing not only their range but also their efficiency during missions. However, the wingman aerodynamics are affected by the tail vortices generated by the leading aircraft in a tight formation, resulting in unpredictable interference. In this study, a mathematical model of wake vortex was developed, and the aerodynamic characteristics of a tight formation were simulated using Xflow software. A robust control method for tight formations was constructed, in which the disturbance is first estimated with an extended state observer, and then a sliding mode controller (SMC) was designed, enabling the wingman to accurately track the position under conditions of wake vortex from the leading aircraft. The stability of the designed controller was confirmed. Finally, the controller was simulated and verified in mathematical simulation and semi-physical simulation platforms, and the experimental results showed that the controller has high tight formation accuracy and is robust.
Enhancing Pre-Service EFL Teachers’ Self-Efficacy through the Use of ELF in a Multilingual World
Authors: Matsumura Shoichi , Tatsuyama Taichi
Published: April 2024

The teaching practicum, a psychologically demanding phase of professional development, yields diverse self-efficacy outcomes for pre-service teachers. While it is crucial to view the practicum as a cornerstone for shaping teaching beliefs, there exists a research gap in understanding its influence on the self-efficacy of pre-service English-as-a-foreign-language (EFL) teachers in East Asia and delving into the cognitive processes during this period. This mixed-methods study (n = 18) aimed to address this gap. The quantitative results revealed an overall increase in participants’ self-efficacy, notably in classroom management. The qualitative findings uncovered challenges faced by those with lower self-efficacy, particularly when discrepancies arose with mentor teachers over teaching English through a communicative approach. Conversely, individuals with linguistically and culturally diverse pre-practicum experiences exhibited resilience while maintaining robust beliefs about their own teaching. The findings suggest the necessity for tailored teacher preparation programs aiming at nurturing a multilingual perspective through interaction in English as a lingua franca.
Temporal Fluctuations of Suicide Mortality in Japan from 2009 to 2023 Using Government Databases
Authors: Matsumoto Ryusuke , Motomura Eishi , Okada Motohiro
Published: April 2024

In Japan, suicide mortalities consistently decreased before the COVID-19 pandemic (from 2009 to 2019) but, conversely, increased after the pandemic outbreak from 2020 to 2022. To provide up-to-date suicide statistics in Japan, this study determined the temporal fluctuations of standardized suicide mortalities (SMRs), disaggregated by sex and age, by joinpoint regression analysis using the government suicide database, named the “Basic Data on Suicide in Region”. From January 2009 to December 2023, three temporal fluctuation patterns of SMRs pertaining to working age and older adults were detected, such as attenuations of decreasing trends before the COVID-19 pandemic (from around the mid-2010s), a sharply increasing trend that coincided with the pandemic outbreak, and gradually decreased during the pandemic, but no changes at the end of the COVID-19 pandemic. In particular, the SMRs of working-age females sharply increased concurrently with the pandemic outbreak, whereas those of males did not change. However, before the pandemic, decreasing trends of the SMRs of working-age males diminished in the mid-2010s, but those of females consistently decreased. The SMRs of working-age males indicated non-significant but sharply increasing trends in early 2022, a trend that was not observed for females. In contrast to working-age adults, the SMRs of adolescents already began to increase in the mid-2010s and also indicated consistently increasing trends between the periods during and after the pandemic. These results suggest, contrary to our expectations, that the impacts of both the outbreak and end of the COVID-19 pandemic were limited regarding the increase in SMRs from 2020. Therefore, when revising suicide prevention programs in the post-COVID-19 era, it should be noted that focusing on pandemic-associated factors alone is not sufficient.
Design and Evaluation of Device Authentication and Secure Communication System with PQC for AIoT Environments
Authors: Chen Yu-Jen , Hsu Chien-Lung , Lin Tzu-Wei , Lee Jung-San
Published: April 2024

With the rapid development of Internet of Things (IoT) technology, the number of IoT users is growing year after year. IoT will become a part of our daily lives, so it is likely that the security of these devices will be an important issue in the future. Quantum computing is maturing, and the security threat associated with quantum computing will be faced in the transmissions of IoT devices, which mainly use wireless communication technologies. Therefore, to ensure the protection of transmitted data, a cryptographic algorithm that is efficient in defeating quantum computer attacks needs to be developed. In this paper, we propose a device authentication and secure communication system with post-quantum cryptography (PQC) for AIoT environments using the NTRU and Falcon signature mechanism, which can resist quantum computer attacks and be used in AIoT environments to effectively protect the confidentiality, integrity, and non-repudiation of transmitted data. We also used Raspberry Pi to simulate AIoT devices for implementation.
Revealing IoT Cryptographic Settings through Electromagnetic Side-Channel Analysis
Authors: Zunaidi Muhammad Rusyaidi , Sayakkara Asanka , Scanlon Mark
Published: April 2024

The advancement of cryptographic systems presents both opportunities and challenges in the realm of digital forensics. In an era where the security of digital information is crucial, the ability to non-invasively detect and analyze cryptographic configurations has become significant. As cryptographic algorithms become more robust with longer key lengths, they provide higher levels of security. However, non-invasive side channels, specifically through electromagnetic (EM) emanations, can expose confidential cryptographic details, thus presenting a novel solution to the pressing forensic challenge. This research delves into the capabilities of EM side-channel analysis (EM-SCA), specifically focusing on detecting both cryptographic key lengths and the algorithms employed utilizing a machine-learning-based approach, which can be instrumental for digital forensic experts during their investigations. Through meticulous data processing and analysis, the Support Vector Machine (SVM) model, among others, demonstrated a notable accuracy of 94.55% in distinguishing between AES and ECC cryptographic operations. This capability significantly enhances digital forensic methodologies, offering a novel avenue for noninvasively uncovering encrypted data’s cryptographic settings. By identifying key lengths and algorithms without invasive procedures, this research contributes substantially to the advancement of forensic investigations in encrypted environments.
WSN-Driven Advances in Soil Moisture Estimation: A Machine Learning Approach
Authors: Singh Tinku , Kundroo Majid , Kim Taehong
Published: April 2024

Soil moisture estimation is crucial for agricultural productivity and environmental management. This study explores the integration of Wireless Sensor Networks (WSNs) with machine learning (ML) and deep learning (DL) techniques to optimize soil moisture estimation. By combining data from WSN nodes with satellite and climate data, this research aims to enhance the accuracy and resolution of soil moisture estimation, enabling more effective agricultural planning, irrigation management, and environmental monitoring. Five ML models, including linear regression, support vector machines, decision trees, random forests, and long short-term memory networks (LSTM), are evaluated and compared using real-world data from multiple geographical regions, which includes a dataset from NASA’s SMAP project, supplemented by climate data, which employs both active and passive sensors for data collection. The outcomes demonstrate that the LSTM model consistently outperforms other ML algorithms across various evaluation metrics, highlighting the effectiveness of WSN-driven approaches to soil moisture estimation. The study contributes to the advancement of soil moisture monitoring technologies, offering insights into the potential of WSNs combined with ML and DL for sustainable agriculture and environmental management practices.
Trajectory Planning for UAV-Assisted Data Collection in IoT Network: A Double Deep Q Network Approach
Authors: Wang Shuqi , Qi Nan , Jiang Hua , Xiao Ming , Liu Haoxuan , Jia Luliang , Zhao Dan
Published: April 2024

Unmanned aerial vehicles (UAVs) are becoming increasingly valuable as a new type of mobile communication device and autonomous decision-making device in many application areas, including the Internet of Things (IoT). UAVs have advantages over other stationary devices in terms of high flexibility. However, a UAV, as a mobile device, still faces some challenges in optimizing its trajectory for data collection. Firstly, the high complexity of the movement action and state space of the UAV’s 3D trajectory is not negligible. Secondly, in unknown urban environments, a UAV must avoid obstacles accurately in order to ensure a safe flight. Furthermore, without a priori wireless channel characterization and ground device locations, a UAV must reliably and safely complete the data collection from the ground devices under the threat of unknown interference. All of these require the proposing of intelligent and automatic onboard trajectory optimization techniques. This paper transforms the trajectory optimization problem into a Markov decision process (MDP), and deep reinforcement learning (DRL) is applied to the data collection scenario. Specifically, the double deep Q-network (DDQN) algorithm is designed to address intelligent UAV trajectory planning that enables energy-efficient and safe data collection. Compared with the traditional algorithm, the DDQN algorithm is much better than the traditional Q-Learning algorithm, and the training time of the network is shorter than that of the deep Q-network (DQN) algorithm.
Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence
Authors: Kuizinienė Dovilė , Krilavičius Tomas
Published: April 2024

Imbalanced datasets are one of the main issues encountered by artificial intelligence researchers, as machine learning (ML) algorithms can become biased toward the majority class and perform insufficiently on the minority classes. Financial distress (FD) is one of the numerous real-world applications of ML, struggling with this issue. Furthermore, the topic of financial distress holds considerable interest for both academics and practitioners due to the non-determined indicators of condition states. This research focuses on the involvement of balancing techniques according to different FD condition states. Moreover, this research was expanded by implementing ML models and dimensionality reduction techniques. During the course of this study, a Combined FD was constructed using five distinct conditions, ten distinct class balancing techniques, five distinct dimensionality reduction techniques, two features selection strategies, eleven machine learning models, and twelve weighted majority algorithms (WMAs). Results revealed that the highest area under the receiver operating characteristic (ROC) curve (AUC) score was achieved when using the extreme gradient boosting machine (XGBoost) feature selection technique, the experimental max number strategy, the undersampling methods, and the WMA 3.1 weighted majority algorithm (i.e., with categorical boosting (CatBoost), XGBoost, and random forest (RF) having equal voting weights). Moreover, this research has introduced a novel approach for setting the condition states of financial distress, including perspectives from debt and change in employment. These outcomes have been achieved utilizing authentic enterprise data from small and medium Lithuanian enterprises.
Building Resilient Cities: A Comprehensive Review of Climate Change Adaptation Indicators for Urban Design
Authors: Díaz Carlota García , Zambrana-Vasquez David , Bartolomé Carmen
Published: April 2024

Urban areas generate more than 70% of the world’s climate change emissions, mainly CO2, produced by the combustion of fossil fuels. Climate change is increasing cities’ exposure to climate hazards, such as heat waves or floods. Therefore, there is a need to improve risk management with the inclusion of climate resilience in urban policy design. Despite improved urban climate monitoring, there are still relatively few scientific publications on climate change adaptation in urban areas. Adaptation to climate change is not achieved through specific action, but rather through the adoption and continuous implementation of adaptation actions such as housing rehabilitation, green space management and protection measures for vulnerable groups. This variety of actions makes it difficult not only to identify different indicators, but also to use common benchmarks. Considering the role of municipalities in adapting to climate change, it is crucial to identify adaptation indicators that serve as a basis for decision making, as well as evaluation methods that allow the effectiveness of planned and implemented measures in municipalities. It can be used to determine which measures increase the level of adaptation or lead to poor adaptation. Therefore, monitoring indicators makes it possible to evaluate the effectiveness of the measures, in addition to formulating new ones. This paper includes a literature review of existing index designed to address climate hazards and mitigate their impacts in urban areas.
Research on Capacity Optimization Configuration of Renewable Energy Off Grid Hydrogen Production System Considering Collaborative Electrolysis
Authors: Kang Zhongjian , Liu Shijie
Published: April 2024

This study proposes a multitype electrolytic collaborative hydrogen production model for optimizing the capacity configuration of renewable energy off grid hydrogen production systems. The electrolytic hydrogen production process utilizes the synergistic electrolysis of an alkaline electrolyzer (AEL) and proton exchange membrane electrolyzer (PEMEL), fully leveraging the advantages of the low cost of the AEL and strong regulation characteristics of the PEMEL. For the convenience of the optimization solution, the article constructs a mixed linear optimization model that considers the constraints during system operation, with the objective function of minimizing total costs while meeting industrial production requirements. Gurobi is used for the optimal solution to obtain the optimal configuration of a renewable energy off grid hydrogen production system. By comparing and analyzing the optimal configuration under conventional load and high-load conditions, it is concluded that collaborative electrolysis has advantages in improving resource consumption and reducing hydrogen production costs. This is of great significance for optimizing the capacity configuration of off grid hydrogen production systems and improving the overall economic benefits of the system.
Application and Verification of a Leg-Transfer Method for Three-Level Active Neutral-Point-Clamped Inverters for Railway Vehicles
Authors: Lee Hyunjae , Kim Gildong , Shon Jingeun
Published: April 2024

In this paper, a two-leg-transfer switch structure method that can continuously supply three-phase power even when an accident occurs in a power semiconductor of a three-level active neutral-point-clamped (ANPC) inverter for railway vehicles is presented. The proposed method can minimize the ripple effect caused by power semiconductor faults by separating the faulty leg from the main circuit and connecting the load-side circuit to a neutral point. As a result of simulations, the average values of MAE and RMSE can be reduced by 1.53 [A] and 1.77 [A], respectively, when using the proposed leg-transfer switch structure compared to using the conventional structure. In the IGBT failure experiment, when the proposed method was applied to a three-level ANPC inverter, there was only a 0.21 [%] difference from the THD under normal conditions. As a result, the magnitude, phase, and total harmonic distortion of the three-phase current waveforms measured before and after the fault were identical. Thus, normal three-phase power could be effectively supplied to the load when the proposed leg-transfer switch method was applied after a power semiconductor fault occurred in the three-level ANPC inverter. If this leg-transfer switch method is applied in three-level ANPC inverterd for railway vehicles, track schedule errors can be minimized by continuously supplying three-phase power to the electric motor even when an accident occurs in a power semiconductor.
Indoor Parameters of Museum Buildings for Guaranteeing Artworks Preservation and People’s Comfort: Compatibilities, Constraints, and Suggestions
Authors: Cirrincione Laura , La Gennusa Maria , Peri Giorgia , Rizzo Gianfranco , Scaccianoce Gianluca
Published: April 2024

Since people tend to spend more and more time visiting museums, more accurate requirements are needed for the indoor environmental conditions of these confined spaces where two primary requisites coincide in defining their optimal indoor microclimate: the need for the appropriate artwork preservation and suitable levels of indoor comfort conditions for people visiting the exhibition buildings and/or working there. Regrettably, people and artwork requirements are sometimes characterized by different reference limits of the environmental parameters that, not rarely, could potentially conflict. Another important point to consider is that museums hosted by heritage buildings (particularly in Mediterranean climates, as is often the case in Italy) are often not equipped with climatization systems because of difficulty in installing generally bulky equipment such as HVAC systems. This circumstance represents another important limit for achieving suitable conditions for the two requisites. In addition, the recent pandemic-related occurrences are pushing technicians and designers to rethink the criteria for controlling the microclimate of public buildings, and museums among them. In this paper, this issue is addressed by reviewing current regulations, standards, and handbooks (and by means of a real case example related to the Italian context) in order to ascertain whether such documentation could facilitate the development of effective rules/guidelines for proper management of indoor parameters in museums.
Study of Energy Flow Mechanisms in High Power Device Converters
Authors: Zhao Zheyuan , Jin Qianzheng , Wu Yingjie , Li Gen , Xiang Tiange
Published: April 2024

The work in this paper is applied to the Zhangbei Power grid. In the flexible direct current (DC) power system, the fault current rises extremely fast when a DC fault occurs. The requirements for the peak of breaking current and fault energy absorption of DC circuit breakers (DCCBs) increase linearly, which significantly increases the cost of the equipment. Therefore, in order to reduce the design difficulty of DCCBs, this paper proposes a strategy to control energy after the fault occurs. Firstly, the energy dimension is added on the basis of the traditional vector control of MMC, which constitutes a three-dimensional energy direct control. Subsequently, the architectures of energy fluctuation control and feedforward control are proposed. The influencing mechanisms for the peak fault current, peak fault voltage and energy dissipation are analyzed. Finally, the simulation of energy fluctuation control and feedforward control is constructed on PSCAD/EMTDC. The simulation results show that the energy fluctuation control is obviously better than the conventional three-dimensional energy control, and the feedforward energy control is further improved on this basis. Compared with the conventional vector control, the peak energy is reduced by 45.43% and the peak current is reduced by 25.39%, which helps to simplify the equipment design and reduce the equipment cost.
A Targeted Review on Revisiting and Augmenting the Framework for Technology Acceptance in the Renewable Energy Context
Authors: Bhatia Tanvi , Bharathy Gnana , Prasad Mukesh
Published: April 2024

Given that the implementation of renewable technologies has some key bottlenecks in adoption, this topic has been explored. Particularly, we are reviewing existing theories and models to understand their fit for changing social structures and evolving world contexts. This review begins with an introduction followed by a background study on renewable energy technology (RET). We have employed a mixed-approach methodology to synthesize the relevant literature. The review comprises a summary and comparison of some existing theories and models such as TAM, TRA, and UTAUT, elucidating factors influencing technology adoption processes. Additionally, the review discusses the scope for future research, emphasizing the need for more nuanced frameworks that account for contextual intricacies and emerging trends in renewable energy adoption. Ultimately, the review concludes with insights into the ongoing discourse surrounding energy technology acceptance and recommendations on the inclusion of current world views in the scope for future study.
Effect of the Hole Diameter in Mechanical Properties of Wood: Experimental and Numerical Approaches
Authors: Guidoti Arthur B. , Aramburu Arthur B. , Acosta Andrey P. , Gatto Darci A. , Missio André L. , Beltrame Rafael , Tonatto Maikson L. P. , Delucis Rafael A.
Published: April 2024

Introducing openings or holes into wooden structures is a common practice for providing utility services. However, this practice leads to stress concentration, resulting in a reduction in stiffness and load-carrying capacity. Therefore, understanding the effects of holes on beam properties is important for design considerations. This study investigates the mechanical behavior of a wooden beam made from juvenile Pinus elliottii containing open cylindrical holes with three different diameters: 4, 8, and 12 mm. The mechanical properties were evaluated for compression parallel to the fibers, quasi-static bending, and tension perpendicular to the fibers. Numerical simulations were conducted using a finite element (FE) model, considering the orthotropic elastic properties determined from experimental tests and elastic ratios reported in the literature. The experimental results indicated that the influence of hole diameter was not significant on the compressive properties; however, longitudinal crack failures began to form for holes with diameters of 8–12 mm. Regardless of hole size, the compressive and bending characteristics revealed that hole location did not affect the stiffness, strength, or damage mechanisms.
Predicting Fine Dead Fuel Load of Forest Floors Based on Image Euler Numbers
Authors: Zhang Yunlin , Tian Lingling
Published: April 2024

The fine dead fuel load on forest floors is the most critical classification feature in fuel description systems, affecting several parameters in the manifestation of wildland fires. An accurate determination of this fine dead fuel load contributes substantially to effective wildland fire prevention, monitoring, and suppression. This study investigated the viability of incorporating image Euler numbers into predictive models of fine dead fuel load via the taking photos method. Pinus massoniana needles and Quercus fabri broad leaves—typical fuel types in karst areas—served as the research subjects. Accurate field data were collected in the Tianhe Mountain forests, China, while artificial fine dead fuelbeds of differing loads were constructed in the laboratory. Images of the artificial fuelbeds were captured and uniformly digitized according to various conversion thresholds. Thereafter, the Euler numbers were extracted, their relationship with fuel load was analyzed, and this relationship was applied to generate three load-prediction models based on stepwise regression, nonlinear fitting, and random forest algorithms. The Euler number had a significant relationship with both P. massoniana and Q. fabri fuel loads. At low conversion thresholds, the Euler number was negatively correlated with fuel load, whereas a positive correlation was recorded when this threshold exceeded a certain value. The random forest model showed the best prediction performance, with mean relative errors of 9.35% and 14.54% for P. massoniana and Q. fabri, respectively. The nonlinear fitting model displayed the next best performance, while the stepwise regression model exhibited the largest error, which was significantly different from that of the random forest model. This study is the first to propose the use of image features to predict the fine fuel load on a surface. The results are more objective, accurate, and time-saving than current fuel load estimates, benefiting fuel load research and the scientific management of wildland fires.
Review on the Application of Nature-Based Solutions in Urban Forest Planning and Sustainable Management
Authors: Zhao Jiajia , Davies Clive , Veal Charlotte , Xu Chengyang , Zhang Xinna , Yu Fengzhen
Published: April 2024

Despite growing recognition of nature-based solutions (NBS), there remains a research gap in understanding their implementation in urban areas, which poses a significant challenge for urban forest development. Therefore, our paper aims to explore the intersection of NBS with urban forests (UF), identify current barriers, propose strategies to maximize the potential of urban forests as nature-based solutions (UF-NBS) in effectively improving the resilience of urban forests, and enhance the service capacity of urban forest ecosystems. To achieve our objective, we conducted a comprehensive analysis that included a bibliometric review to summarize the evolution of the UF-NBS literature and classify UF-NBS types for the first time. Subsequently, we identified and organized current challenges faced by UF-NBS. Additionally, we proposed an original technological framework system for urban forest development based on NBS principles. The results show the significance of UF-NBS for enhancing urban resilience and human wellbeing, with multiple successful implementations in both China and Europe, validating their effectiveness. However, the implementation of UF-NBS faces several challenges, including inadequate financing, the gap between scientific knowledge and practical implementation, the absence of region-specific information, and the need for interdisciplinary collaboration. This study contributes to establishing a scientific theoretical basis for integrating UF and NBS and provides a systematic approach for decision-makers in urban forest management. Future research should focus on exploring the integration of UF within the NBS framework and prioritize knowledge sharing, international cooperation, and education initiatives to promote the global adoption of UF-NBS and address pressing urban challenges.
Interacting Sentinel-2A, Sentinel 1A, and GF-2 Imagery to Improve the Accuracy of Forest Aboveground Biomass Estimation in a Dry-Hot Valley
Authors: Liu Zihao , Huang Tianbao , Zhang Xiaoli , Wu Yong , Xu Xiongwei , Wang Zhenhui , Zou Fuyan , Zhang Chen , Xu Can , Ou Guanglong
Published: April 2024

Carbon absorption and storage in forests is one of the important ways to mitigate climate change. Therefore, it is essential to use a variety of remote-sensing resources to accurately estimate forest aboveground biomass (AGB) in dry-hot valley regions. In this study, satellite images from the Sentinel-1A, Sentinel-2A, and Gaofen-2 satellites were utilized to estimate the forest AGB in Yuanmou County, Yunnan Province, China. Different combinations of image data, based on selected variables of stepwise regression and their performance in constructing linear stepwise regression (LSR) and random forest (RF) models, were explored. The results showed that: (1) after adding the polarized values of the synthetic aperture radar backscatter coefficients, the combination fitting effect was significantly improved; (2) the fitting effect of the Sentinel-1A + Sentinel-2A + Gaofen-2 data combination was superior to the other combinations, indicating that the effective extraction of forest horizon and vertical information can improve the estimation effect of the forest AGB; and (3) the RF model exhibited superior fitting performance compared to the LSR model across all permutations of remotely sensed image datasets, with R2 values of 0.71 and 0.65, and RMSE values of 30.67 and 33.79 Mg/ha, respectively. These findings lay the groundwork for enhancing the precision of AGB estimation in dry-hot valley areas by integrating Sentinel-2A, Sentinel-1A, and GF-2 imagery, providing valuable insights for future research and applications.
Edge Federated Optimization for Heterogeneous Data
Authors: Lin Hsin-Tung , Wen Chih-Yu
Published: April 2024

This study focuses on optimizing federated learning in heterogeneous data environments. We implement the FedProx and a baseline algorithm (i.e., the FedAvg) with advanced optimization strategies to tackle non-IID data issues in distributed learning. Model freezing and pruning techniques are explored to showcase the effective operations of deep learning models on resource-constrained edge devices. Experimental results show that at a pruning rate of 10%, the FedProx with structured pruning in the MIT-BIH and ST databases achieved the best F1 scores, reaching 96.01% and 77.81%, respectively, which achieves a good balance between system efficiency and model accuracy compared to those of the FedProx with the original configuration, reaching F1 scores of 66.12% and 89.90%, respectively. Similarly, with layer freezing technique, unstructured pruning method, and a pruning rate of 20%, the FedAvg algorithm effectively balances classification performance and degradation of pruned model accuracy, achieving F1 scores of 88.75% and 72.75%, respectively, compared to those of the FedAvg with the original configuration, reaching 56.82% and 85.80%, respectively. By adopting model optimization strategies, a practical solution is developed for deploying complex models in edge federated learning, vital for its efficient implementation.
A New Method for the Determination of Fire Risk Zones in High-Bay Warehouses
Authors: Bošković Goran , Todorović Marko , Ubavin Dejan , Stepanov Borivoj , Mihajlović Višnja , Perović Marija , Čepić Zoran
Published: April 2024

Considering that the determination of fire hazard zones in warehouses is not sufficiently researched and studied, this paper aims to present a new methodological approach concerning the mentioned issue. Based on the COPRAS multi-criteria decision-making method, a new method was developed for the precise determination of potential zones where there is a risk of fire. The advantage of the described method is that it allows the quick and easy determination of all-orientation fire risk zones. The method requires fewer hardware resources compared to the existing ones and enables the display of the warehouse space in the form of a 3D model with calculated fire hazard zones. The mentioned procedure represents the first step when planning the layout and arrangement in the warehouse itself. The effectiveness of the proposed method was confirmed through a suitable numerical example.
Deep Learning-Based Forest Fire Risk Research on Monitoring and Early Warning Algorithms
Authors: Shang Dongfang , Zhang Fan , Yuan Diping , Hong Le , Zheng Haoze , Yang Fenghao
Published: April 2024

With the development of image processing technology and video analysis technology, forest fire monitoring technology based on video recognition is more and more important in the field of forest fire prevention and control. The objects currently applied to forest fire video image monitoring system monitoring are mainly flames and smoke. This paper proposes a forest fire risk monitoring and early warning algorithm, which integrates a deep learning model, infrared monitoring and early warning, and forest fire weather index. The algorithm first obtains the current visible image and infrared image of the same forest area, utilizing a smoke detection model based on deep learning to detect smoke in the visible image, and obtains the confidence level of the occurrence of fire in said visible image. Then, it determines whether the local temperature value of said infrared image exceeds a preset warning value, and obtains a judgment result based on the infrared image. It calculates again a current FWI based on environmental data, and determines a current fire danger level based on the current FWI. Finally, it determines whether or not to carry out a fire warning based on said fire danger level, said confidence level of the occurrence of fire in said visible image, and said judgment result based on the infrared image. The experimental results show that the accuracy of the algorithm in this paper reaches 94.12%, precision is 96.1%, recall is 93.67, and F1-score is 94.87. The algorithm in this paper can improve the accuracy of smoke identification at the early stage of forest fire danger occurrence, especially by excluding the interference caused by clouds, fog, dust, and so on, thus improving the fire danger warning accuracy.
Health Insights from Nematode Larval Characterization in Greater Lizardfish, Saurida tumbil (Bloch, 1795) (Teleostei, Synodontidae)
Authors: Ghahvei Younes , Mirzaei Mohammad , Azizi Shahrzad , Hashemnia Shadi , Shamsi Shokoofeh
Published: April 2024

Fish health is of significant ecological and economic importance. In response to public observations of parasite-like structures in a popular edible fish, this study aimed to characterize nematode larvae commonly found in the muscle and body cavity of Saurida tumbil (Bloch, 1795), a commercially important fish species inhabiting the Persian Gulf and Oman Sea. This fish, locally known as Hasoom, holds substantial culinary importance, being a staple in the diets of millions residing in countries around the Persian Gulf. A total of 458 Saurida tumbil specimens were obtained from fish markets between June 2022 and May 2023. Subsequent examination revealed the presence of a total of 6132 nematode larvae. Nematodes found in the body cavity were identified as belonging to the genus Hysterothylacium sp., family Raphidascarididae, while those in the muscle were identified as Anisakis sp. larval type, family Anisakidae. Histopathology results suggested that these parasites may have adverse health impacts on their fish host. Notably, both nematode genera were found in the third larval stage, which is known to be the infective stage for anisakidosis. Given the reported cases of anisakidosis among people living in the study region, it is strongly recommended that fish be properly cooked before consumption to mitigate health risks.
Biochemical Composition of Eggs, Larvae and Tissues of Macrobrachium tenellum Females Fed Diets with Different Lipid and Protein Levels
Authors: Peña-Almaraz Omar Alejandro , Vargas-Ceballos Manuel Alejandro , Cortés-Jacinto Edilmar , Vega-Villasante Fernando
Published: April 2024

One way to approach the nutritional requirements of native shrimp, necessary to consolidate their culture, is to know their biochemical composition. The effect of feeding two levels of lipids (4 and 12% L) and four levels of proteins (30, 35, 40 and 45% P) in M. tenellum females was evaluated with respect to the biochemical composition of their eggs (EG), larvae (LR), gonad (GO) and hepatopancreas (HP). Total protein (TP), total carbohydrate (TC) and total lipid (TL) were estimated. In EG, L and P levels influence TP and TL; TP increases in diets higher than P35. In LR, there are no differences (p > 0.05) in TP and in TL, only diets L4P40 and L12P30 were different (p < 0.05). In GO, there is no trend in TP differences; in TC there was variation in the range of the data and TL was higher in L4P30 and L4P35. In HP, the diets with L4 obtained the highest TP values (p < 0.05); the L12 diets were higher in TL (p < 0.05). In general, diets with an inclusion of L12 showed the highest TP, TC and TL means, within this lipid level the P30 diet stood out; therefore, it is recommended to use a diet with L12P30 in the formulation of balanced feed for the species.
Safety, Processing, and Utilization of Fishery Products
Authors: Samarajeewa Upali
Published: April 2024

Global marine fish harvests have reached a plateau over the last decade, and efforts to increase aquaculture tend to face limitations in terms of water resources and contamination problems. Of the current fish harvest, at least 50% is discarded as waste. The current situation requires efforts to process, preserve, and utilize the fish capture to minimize waste. Chemical and microbiological contamination limit the utilization of harvested fish. There is a need to improve fish preservation to minimize spoilage and to process them into more appealing products. Instead of resorting to individual food-processing methods, the efficiency of processing could best be increased by a combination of conventional and modern processing methods or by combinations of modern processing methods. Fish waste is a rich source of oils containing essential fatty acids, polypeptides, and amino polysaccharides that could be utilized through the upscaling of current scientifically proven methods to new processing technologies. The separation of collagens, gelatins, bioactive peptides, edible fish oils, and chitosan form the primary stages in the utilization of fish waste. The products need purification to meet food quality and safety standards and to have desirable industrial characteristics. The diversity of information and products generated through new methods require advanced data handling and prediction systems, such as artificial intelligence, to address food safety and to derive the best out of fish processing and utilization.