bluuebunny/crossref_metadata_2025 huggingface dataset:
Coastal Vulnerability Impact Assessment under Climate Change in the Arctic Coasts of Tromsø, Norway
Authors: Toumasi Polyxeni , Petropoulos George P. , Detsikas Spyridon E. , Kalogeropoulos Kleomenis , Tselos Nektarios Georgios
Published: October 2024

Arctic coastlines are the most vulnerable regions of the Earth, and local communities in those areas are being affected by rising sea levels and temperature. Therefore, Earth Observation combined with up-to-date geoinformation tools offers a dependable, cost-effective, and time-efficient approach to understanding the socioeconomic impact of climate changes in Arctic coastal areas. A promising approach is the Coastal Vulnerability Index (CVI), which takes into account different factors such as geomorphology, sea factors, and shoreline retreat or advance, to estimate the grade of vulnerability of a coastal area. Notwithstanding its potential, its application in the Arctic is still challenging. This study targets to estimate CVI to value the vulnerability of the coastal areas of Norway located in the Arctic. For the application of CVI and specifically for geomorphological and sea factors, data were acquired from international and national institutes. After the collection of all the necessary parameters for CVI was completed, all datasets were imported into a GIS software program (ArcGIS Pro) where the vulnerability classes of CVI were estimated. The results show that most of the coast of Northern Norway is characterized by a low to high degree of vulnerability, while in the island of Tromsø the vulnerability is mainly high and very high.
Climate Benefit Assessment of Doubling the Extent of Windbreak Plantations in Hungary
Authors: Király Éva , Bidló András , Keserű Zsolt , Borovics Attila
Published: October 2024

Agroforestry systems are recognized as sustainable land use practices that foster environmental health and promote adaptive responses to global change. By harnessing the synergies between trees and agricultural activities, agroforestry systems provide multiple benefits, including soil conservation, biodiversity enhancement, and carbon sequestration. Windbreaks form integral elements of Hungarian agricultural landscapes, and the enhanced agroforestry subsidy framework might have a favorable impact on their expansion, underscoring the importance of evaluating their potential for carbon sequestration. In the present study, we assess the implications of doubling the extent of windbreak plantations in Hungary by planting an additional 14,256 hectares of windbreaks. We evaluate the total carbon sequestration and the annual climate change mitigation potential of the new plantations up to 2050. For the modeling, we use the recently developed Windbreak module of the Forest Industry Carbon Model, which is a yield table-based model specific to Hungary and allows for the estimation of living biomass, dead organic matter, and soil carbon balance. We project that new windbreak plantations will sequester 913 kt C by 2050, representing an average annual climate change mitigation potential of 144 kt CO2 eq. Our findings reveal that doubling the extent of windbreak plantations could achieve an extra 5% carbon sequestration in forested areas as compared to business-as-usual (BAU) conditions. We conclude that new windbreak plantations on agricultural field boundaries have substantial climate change mitigation potential, underscoring agroforestry’s contribution to agricultural resilience and achieving Hungary’s climate goals set for the land-use (LULUCF) sector.
Insights from a Decade of Optimizing Emergency Medical Services Across Three Major Regions in Switzerland
Authors: Strauss Christoph , Schmid Michael , Kliem Daniel , Müller Martin
Published: October 2024

Prehospital care, and especially emergency medical services (EMSs), are facing an increasing demand, while experiencing financial pressure. Strategies aimed at improving prehospital care management emphasize the efficient utilization of resources, but often overlook the long-term implications for the prehospital healthcare system, encompassing emergency medical services, emergency departments, and healthcare authorities. This paper draws on almost ten years of improvement in EMS’ key performance indicators such as response time or overtime in Switzerland, using Discrete Event Simulation. Three representative simulation studies are used to reflect on the optimization potential of alternative bases and rosters, methodological limitations, and the uptake of the derived recommendations. The results demonstrate that EMSs’ efficiency gains in resource utilization increasingly come into conflict with emergency departments’ and healthcare authorities’ policies, indicating a need to enrich Discrete Event Simulation with a systemic perspective. A new methodology that conceptualizes long-term prehospital care planning as an interdisciplinary and iterative process utilizing a novel combination of System Dynamics and Discrete Event Simulation is proposed.
Prediction of Environmentally Suitable Areas for Zephyranthes (Amaryllidaceae) in Mexico
Authors: Rodríguez Flores Zayner Edin , Moredia Rosete Yanet , Ruiz Valencia Jesús Alejandro , Fernández Pavía Yolanda Leticia
Published: October 2024

The genus Zephyranthes is widely represented in Mexico, with 37 species of ornamental and medical importance. However, basic aspects of the genus, such as the environmental variables that determine its presence in certain sites, have not yet been addressed, which limits the knowledge of its ecology, potential applications and possible conservation strategies. Potential distribution models were generated with data on the presence of 13 species of the genus Zephyranthes, using 28 bioclimatic and edaphic variables with the maximum entropy method (Maxent). Of these variables, the most important and least correlated for each species were chosen by principal component analysis (PCA); the occurrence data were obtained from digital platforms and filtered to reduce spatial autocorrelation. The resulting models, had AUC values > 0.90 and Kappa index values > 0.6, in addition to being significant according to the results of the binomial test applied (p < 0.05). Maximum temperatures and humidity, as well as annual precipitation, are relevant environmental variables for the niche models. Most species are distributed in the biogeographic province of the Transmexican Volcanic Belt. Zephyranthes concolor and Zephyranthes lindleyana were the species with the largest potential range. The species with the most restricted potential distribution were Zephyranthes citrina and Zephyranthes sessilis. The most determinant variables for species with neotropical affinity are different from those identified for Nearctic species, reflecting niche differentiation, congruent with the evolutionary history of Zephyranthes.
Empowering Women in Tech Innovation and Entrepreneurship: A Qualitative Approach
Authors: Felgueira Teresa , Paiva Teresa , Alves Catarina , Gomes Natália
Published: October 2024

Women’s participation and leadership in technology and entrepreneurship are crucial for advancing innovation and ensuring inclusive economic development. Despite progress in gender equality, women in tech face challenges like under-representation and systemic biases. This research delves into the multifaceted experiences of women in tech innovation and entrepreneurship, highlighting the barriers they face and the strategies they use to overcome them. This study used a qualitative, observational design with semi-structured interviews and thematic analysis to explore the experiences and perceptions of entrepreneurial women in the Science, Technology, Engineering, and Mathematics (STEM) field. It was possible to conclude that entrepreneurial women exhibit a strong sense of autonomy, competence, and leadership, enabling them to manage their ventures independently and effectively. Also, the success of entrepreneurial women is multifaceted, combining personal fulfilment, professional achievements, and financial stability, with positive feedback and recognition playing a key role. Building strong social and supportive relationships is vital for entrepreneurial women, providing a sense of belonging, community, and emotional support. In this context, to empower women in STEM and entrepreneurship, Higher Education Institutions (HEIs) should promote entrepreneurship as a career path, provide practical and value-based education, and foster a work environment of trust, communication, and continuous learning.
The Mental Suffering of Cocaine-Addicted Patients: A Retrospective Analysis of Personality Disorders’ Prevalence and Their Association with Psychopathological Symptoms
Authors: Giordano Francesca , Guidotti Sara , Scategni Francesco Cassio , Cuzzola Domenico , Pruneti Carlo
Published: October 2024

(1) Background: The observational retrospective study aimed to investigate the prevalence of personality disorders (PDs) and their association with psychopathological symptoms in a group of patients with cocaine addiction. (2) Methods: Ninety-five medical records of the Pathological Addictions Service of the National Health Service of Lecce (Italy) were analyzed. PDs were diagnosed using the Structured Clinical Interview for DSM-IV Axis II Personality Disorders (SCID-II) and psychopathological symptoms were investigated through the Symptom Checklist 90-Revised (SCL-90-R). (3) Results: Ninety-two out of ninety-five patients met the criteria for at least one PD (96.8%), almost 35% had two Cluster B PDs, and over 25% had three Cluster B PDs. Nine out of ninety-five people met the criteria for all Cluster B PDs. Among the Cluster B PDs, it emerged that the most frequent diagnosis was that of narcissistic–borderline–antisocial (over 20% of the total sample). The analysis highlighted that mental suffering is prevalent in those with multiple comorbid Cluster B PDs. Specifically, depression and psychoticism exceed the clinical cut-off (T score > 63) in all patient groups. At the same time, anxiety and obsessions–compulsions are complained of only by those with more than two PDs as well as the level of general distress. Furthermore, anxiety, hostility, and paranoid ideation are significantly higher in the group of patients with more than three PDs. (4) Conclusions: Further studies should better investigate the relationship between the two aspects and describe the causal effects of PDs on psychopathological symptoms or, on the contrary, the effects of the substance on mental health and the worsening of personality alterations.
Pseudospherical Bismuth Oxychloride-Modified Carbon Paste Electrode for the Determination of Quinine in Beverages
Authors: Mutić Tijana , Stanković Vesna , Ognjanović Miloš , Nikolić Vladimir B. , Gao Guanyue , Sojic Neso , Stanković Dalibor
Published: October 2024

The extensive use of the alkaloid quinine (QN) in the cosmetic and food industries has induced major concerns relating to its impact on human health, considering its potential toxicity. Therefore, developing sensitive and selective electrochemical sensors is crucial for monitoring QN in environmental, food, and pharmaceutical samples. To respond to this need, a surfactant-supported green synthesis approach, based on a straightforward, organic solvent-free hydrothermal method was employed to synthesize highly crystalline pseudospherical bismuth oxychloride (BiOCl) nanoparticles. This material was used for the enrichment of carbon paste electrodes and its further utilization for the detection and quantification of quinine. They have superior electrocatalytic performance, due to their size and morphology, and facilitate the interactions of the target with the electrode surface. Under optimal operating conditions, differential pulse voltammetry demonstrated a remarkable feature: a broad linear working range of 10 to 140 μM, a detection limit of 0.14 μM, and a high sensitivity of 1.995 μA μM−1 cm−2. The suggested method’s satisfactory sensitivity, along with its good stability, repeatability, and reproducibility, strongly point to a possible use for identifying quinine in real samples.
Intelligent Semantic Communication System Based on Kolmogorov–Arnold Networks Driven by Dynamic Terminal-Side Computing Power Network
Authors: Liu Wang , Zeng Qingtao , Lu Likun , Abdul Waheed
Published: October 2024

With the advent of the 6G era, the number of IoT devices has experienced explosive growth, leading to the generation of massive amounts of data at the network edge. Semantic communication, as an innovative solution to handling this data deluge, can significantly enhance communication efficiency. However, the limited storage and computational resources of terminal devices constrain the widespread application of semantic communication in 6G networks. To address this issue, we propose a terminal-side-computing-driven intelligent semantic communication solution. Specifically, we introduce a semantic communication model based on Kolmogorov–Arnold Networks (KANs), named K-DeepSC. Using image-reconstruction tasks as an example, the proposed K-DeepSC reduces the number of model parameters by 44% compared to semantic communication models based on Multi-Layer Perceptrons (MLPs), while maintaining similar performance. Furthermore, to fully leverage idle terminal computing power for semantic tasks, we explore computation offloading in dynamic Terminal-Side Computing Power Networks. By optimizing task delay minimization, a deep reinforcement learning algorithm is employed to determine the optimal offloading strategy. Simulation results demonstrate that our proposed solution effectively reduces semantic task processing delay.
Measurements of Geometrical Quantities and Selection of Parameters in the Robotic Grinding Process of an Aircraft Engine
Authors: Kurc Krzysztof , Burghardt Andrzej , Muszyńska Magdalena , Pietruś Paulina , Szybicki Dariusz
Published: October 2024

Aircraft engine blades are produced through various techniques, one of which is precise electrochemical machining (ECM), commonly applied in the aerospace, automotive, and electromechanical industries. This method achieves machining accuracy ranging from 0.1 to 0.3 mm. However, components with complex shapes still require grinding and polishing. During the grinding of aircraft blades, achieving high precision and maintaining strict dimensional control are essential. This involves monitoring the thickness of the blade at key cross-sections, as well as the radii of the leading and trailing edges, chord lengths, twist angles, and more. The paper introduces a developed robotic blade grinding process featuring iterative laser measurement of geometric parameters. A custom measuring device with laser heads was designed, calibrated, and tested for repeatability. The measurement data were then used to determine the blade feed rate and machining path via a fuzzy logic decision system. The proposed method was validated on a series of PT6 aircraft engine blades in collaboration with Pratt and Whitney Rzeszów.
DDMAFN: A Progressive Dual-Domain Super-Resolution Network for Digital Elevation Model Based on Multi-Scale Feature Fusion
Authors: He Bing , Ma Xuebing , Kong Bo , Wang Bingchao , Wang Xiaoxue
Published: October 2024

This paper examines the multi-scale super-resolution challenge of digital elevation models in remote sensing. A dual-domain multi-scale attention fusion network is proposed, which reconstructs digital elevation image details step-by-step using cascading sub-networks. This model incorporates components like the wavelet guidance and separation module, multi-scale attention fusion blocks, dilated convolutional inception module, and edge enhancement module to improve feature extraction and fusion capabilities. A new loss function is designed to enhance the model’s robustness and stability. Experiments indicate that the proposed model outperforms 15 benchmark models in PSNR, RMSE, MAE, RMSEslope, and RMSEaspect metrics. In HMA data, The proposed model’s PSNR increases by 0.89 dB (~1.81%), and RMSE decreases by 1.22 m (~8.6%) compared to a state-of-the-art model. Compared to EDEM, which has the best elevation index, RMSEslope decreases by 0.79° (~16%). Additionally, the effectiveness and contribution of each DDMAFN component were verified through ablation experiments. Finally, on the SRTM dataset, The proposed model demonstrates superior performance even with interpolated degradation.
AMTCN: An Attention-Based Multivariate Temporal Convolutional Network for Electricity Consumption Prediction
Authors: Zhang Wei , Liu Jiaxuan , Deng Wendi , Tang Siyu , Yang Fan , Han Ying , Liu Min , Wan Renzhuo
Published: October 2024

Accurate prediction of electricity consumption is crucial for energy management and allocation. This study introduces a novel approach, named Attention-based Multivariate Temporal Convolutional Network (AMTCN), for electricity consumption forecasting by integrating attention mechanisms with multivariate temporal convolutional networks. The method involves feature extraction from diverse time series of different feature variables using dilated convolutional networks. Subsequently, attention mechanisms are employed to capture the correlation and contextually important information among various features, thereby enhancing the model’s predictive accuracy. The AMTCN method exhibits universality, making it applicable to various prediction tasks in different scenarios. Experimental evaluations are conducted on four distinct datasets, encompassing electricity consumption and weather temperature aspects. Comparative experiments with LSTM, ConvLSTM, GRU, and TCN—widely-used deep learning methods—demonstrate that our AMTCN model achieves significant improvements of 57% in MSE, 37% in MAE, 35% in RRSE, and 12% in CORR metrics, respectively. This research contributes a promising approach to accurate electricity consumption prediction, leveraging the synergy of attention mechanisms and multivariate temporal convolutional networks, with broad applicability in diverse forecasting scenarios.
Fast CU Partition Decision Algorithm Based on Bayesian and Texture Features
Authors: Tian Erlin , Yang Yifan , Zhang Qiuwen
Published: October 2024

As internet speeds increase and user demands for video quality grow, video coding standards continue to evolve. H.266/Versatile Video Coding (VVC), as the new generation of video coding standards, further improves compression efficiency but also brings higher computational complexity. Despite the significant advancements VVC has made in compression ratio and video quality, the introduction of new coding techniques and complex coding unit (CU) partitioning methods have also led to increased encoding complexity. This complexity not only extends encoding time but also increases hardware resource consumption, limiting the application of VVC in real-time video processing and low-power devices.To alleviate the encoding complexity of VVC, this paper puts forward a Bayesian and texture-feature-based fast splitting algorithm for coding intraframe bloc of VVC, which aims to reduce unnecessary computational steps, enhance encoding efficiency, and maintain video quality as much as possible. In the stage of rapid coding, the video frames are coded by the original VVC test model (VTM), and Joint Rough Mode Decision (JRMD) evaluation cost is used to update the parameter in the Bayesian algorithm to come and set the two thresholds to judge whether the current coding block continues to be split or not. Then, for coding blocks larger than those satisfying the above threshold conditions, the predominant direction of the texture within the coding block is ascertained by calculating the standard deviations along both the horizontal and vertical axes so as to skip some unnecessary splits in the current coding block patterns. The findings from our experiments demonstrate that our proposed approach improves the encoding rate by 1.40% on average, and the execution time of the encoder has been reduced by 49.50%. The overall algorithm has optimized the VVC intraframe coding technology and reduced the coding complexity of VVC.
The Study of an Improved Particle Swarm Optimization Algorithm Applied to Economic Dispatch in Microgrids
Authors: Dong Ang , Lee Seon-Keun
Published: October 2024

With the widespread use of fossil fuels, the Earth’s environment is facing a severe threat of degradation. Traditional large-scale power grids have struggled to meet the ever-growing demands of modern society. The implementation and functioning of microgrids not only enhance the use of renewable energy sources but also considerably diminish the environmental damage resulting from fossil fuel consumption. However, the inherent instability of renewable energy presents a major challenge to the reliability of microgrids. To address the uncertainties of wind and photovoltaic power generation, it is urgent to adopt effective operational control methods to adjust power distribution, thereby achieving an economically efficient system operation and ensuring a reliable power supply. This paper utilizes a microgrid system consisting of wind power, photovoltaic power generation, thermal power units, and energy storage devices as the research object, establishing an economic dispatch model aimed at minimizing the total operating cost of the system. To solve this problem, the paper introduces second-order oscillatory particles and improves the Particle Swarm Optimization algorithm, proposing a second-order oscillatory chaotic mapping particle swarm optimization (SCMPSO). The simulation results show that this method can effectively optimize system operating costs while ensuring the stable operation of the microgrid.
Utilizing Convolutional Neural Networks for the Effective Classification of Rice Leaf Diseases Through a Deep Learning Approach
Authors: Akter Salma , Sumon Rashadul Islam , Ali Haider , Kim Hee-Cheol
Published: October 2024

Rice is the primary staple food in many Asian countries, and ensuring the quality of rice crops is vital for food security. Effective crop management depends on the early and precise detection of common rice diseases such as bacterial blight, blast, brown spot, and tungro. This work presents a convolutional neural network model for classifying rice leaf disease. Four distinct diseases, bacterial blight, blast, brown spot, and tungro, are the main targets of the model. Previously, leaf pathologies in crops were mostly identified manually using specialized equipment, which was time-consuming and inefficient. This study offers a remedy for accurately diagnosing and classifying rice leaf diseases through deep learning techniques. Using this dataset, the proposed CNN model was trained to identify complex patterns and attributes linked to each disease using its deep learning capabilities. This CNN model achieved an exceptional accuracy of 99.99%, surpassing the benchmarks set by existing state-of-the-art models. The proposed model can be a useful diagnostic and early warning system for rice leaf diseases. It could help farmers and other agricultural professionals reduce crop losses and enhance the quality of their yields.
Smith Chart-Based Design of High-Frequency Broadband Power Amplifiers
Authors: Angarita Malaver Edison Ferney , Barrera Lombana Nelson , Moreno Rubio Jorge Julian
Published: October 2024

This paper presents a comprehensive study on the design and performance of a high-power amplifier (PA) covering a broad frequency band from 0.1 to 4.8 GHz. Leveraging a 10 W GaN device, the amplifier achieves output power levels surpassing 10 W across the entire frequency band. Furthermore, the power-added efficiency (PAE) of the amplifier ranges from 47% to 59%, indicating its energy-efficient operation. With consistent gain characteristics varying between 7 and 15 dB, the design ensures reliable signal amplification for diverse applications. Notably, the approach introduces a simplified output matching network based on an LC network, prioritizing practicality without sacrificing performance. Additionally, comprehensive guidance is provided on utilizing the Smith chart for streamlined amplifier design, enabling engineers with an accessible methodology. Through a meticulous analysis, this work contributes to advancing the field of high-power amplification, offering enhanced performance and usability for next-generation wireless communication systems.
Structural Market Power in the Presence of Renewable Energy Sources
Authors: Sirjani Bahareh , Akbari Foroud Asghar , Bazmohammadi Najmeh , Vasquez Juan C.
Published: October 2024

Assessing market power in the presence of different production technologies such as renewable energies, including wind and solar power, is crucial for electric market analysis and operation. This paper investigates structural market power by incorporating wind farms and solar generation over a short-term period. The study examines the issue of market concentration boundaries to assess structural market power by calculating the minimum and maximum market concentration index values in the day-ahead market. It models the technical specifications of power plants, such as the maximum and minimum production limits, ramp-up and ramp-down rates, and minimum required up and down times. By extracting the spatiotemporal correlation of wind power generation from real data, the uncertainty of renewable power generation is represented through a set of scenarios. The analysis explores the correlation effects of wind farms, solar generation, and wind penetration levels under different ownership structures. Simulation results using a modified PJM five-bus system illustrate the effectiveness of the developed method. Our results indicate that integrating renewable energy can reduce the Herfindahl–Hirschman Index (HHI) by up to 30% as wind penetration levels rise from 0% to 40%, fostering a more competitive market structure. However, the correlation between wind farms also increases market volatility, with the standard deviation of the HHI rising by about 25% during peak load periods. These findings demonstrate the practical applicability of the developed methodology for assessing market dynamics in the presence of renewable energy sources.
Optimization of Impact Ionization in Metal–Oxide–Semiconductor Field-Effect Transistors for Improvement of Breakdown Voltage and Specific On-Resistance
Authors: Chen Yanning , Song Yixian , Wu Bo , Liu Fang , Deng Yongfeng , Kang Pingrui , Huang Xiaoyun , Wu Yongyu , Gao Dawei , Xu Kai
Published: October 2024

For the past few decades, metal–oxide–semiconductor field-effect transistors (MOSFETs) have been the most important application in IC circuits. In certain circuit applications, the breakdown voltage and specific on-resistance serve as key electrical parameters. This article introduces a readily accessible approach to enhance the source–drain breakdown voltage (BVDS) of MOSFETs based on the Bipolar-CMOS-DMOS (BCD) platform without extra costs. By attentively refining the process steps and intricacies of the doping procedures, the breakdown voltages of NMOS and PMOS experienced increments of 3.4 V and 4.6 V, translating to enhancements of 31.5% and 50.3%. Parallel simulations offer insightful mechanistic explanations through simulation tools, facilitating superior outcomes. This initiative lays significant groundwork for the advancement of a comprehensive BCD process development framework.
Fuzzy Decision-Making Valuation Model for Urban Green Infrastructure Implementation
Authors: Bačić Samanta , Tomić Hrvoje , Rogulj Katarina , Andlar Goran
Published: October 2024

Urban green infrastructure plays a significant role in sustainable development and requires proper land management during planning. This study develops a valuation model for urban green infrastructure in land management, focusing on Zagreb’s 17 city districts. The fuzzy AHP method was used to calculate the weighting coefficients for a suitable set of criteria, and the TOPSIS method was used to select the priority city districts for implementing green infrastructure. The research results are relevant to decision makers, who can utilize them to prioritize areas for the development and implementation of green infrastructure. The green infrastructure index calculated in this study can be compared with other spatial and land data for effective spatial planning.
A Machine Learning Approach to Forecasting Hydropower Generation
Authors: Di Grande Sarah , Berlotti Mariaelena , Cavalieri Salvatore , Gueli Roberto
Published: October 2024

In light of challenges like climate change, pollution, and depletion of fossil fuel reserves, governments and businesses prioritize renewable energy sources such as solar, wind, and hydroelectric power. Renewable energy forecasting models play a crucial role for energy market operators and prosumers, aiding in planning, decision-making, optimization of energy sales, and evaluation of investments. This study aimed to develop machine learning models for hydropower forecasting in plants integrated into Water Distribution Systems, where energy is generated from water flow used for municipal water supply. The study involved developing and comparing monthly and two-week forecasting models, utilizing both one-step-ahead and two-step-ahead forecasting methodologies, along with different missing data imputation techniques. The tested algorithms—Seasonal Autoregressive Integrated Moving Average, Random Forest, Temporal Convolutional Network, and Neural Basis Expansion Analysis for Time Series—produced varying levels of performance. The Random Forest model proved to be the most effective for monthly forecasting, while the Temporal Convolutional Network delivered the best results for two-week forecasting. Across all scenarios, the seasonal–trend decomposition using the LOESS technique emerged as the most successful for missing data imputation. The accurate predictions obtained demonstrate the effectiveness of using these models for energy planning and decision-making.
Premixed Combustion Characteristics of Hydrogen/Air in a Micro-Cylindrical Combustor with Double Ribs
Authors: Ma Yi , Yuan Wenhua , Zhao Shaomin , Fang Hongru
Published: October 2024

Hydrogen is a promising zero-carbon fuel, and its application in the micro-combustor can promote carbon reduction. The structural design of micro-combustors is crucial for combustion characteristics and thermal performance improvement. This study investigates the premixed combustion characteristics of hydrogen/air in a micro-cylindrical combustor with double ribs, using an orthogonal design method to assess the impact of various geometric parameters on thermal performance. The results indicate that the impact of rib height, rib position, and inclined angle is greater than rib width and their interactions, while their influence decreases in that order. Increased rib height improves mean wall temperature and exergy efficiency due to an expanded recirculation region and increased flame–wall contact, but negatively affects temperature uniformity and combustion efficiency. Although double ribs enhance performance, placing them too close may reduce heat transfer due to the low-temperature region between the ribs. When the double ribs are positioned at the axial third equinoxes of the micro-combustor, the highest mean wall temperature is achieved. Meanwhile, with a rib height of 0.3 and an inclined angle of 45°, the micro-combustor achieves optimal thermal performance, with the mean wall temperature increasing by 61.32 K.
Evidence of Microbial Activity in Coal Seam Production Water and Hydrochemical Constraints
Authors: Bao Yuan , Chen Xueru , Guo Zhidong , Li Zhengyan , Zhuang Yufei , Gao Min
Published: October 2024

This study aims to explore microbial activity evidence, composition of archaeal communities, and environmental constraints in coalbed-produced waters from the Hancheng Block, a representative region for coalbed methane development on the eastern margin of Ordos Basin, China. The investigation involves analyzing microbial community composition using 16S rRNA sequencing analysis as well as examining hydrogeochemical parameters. The results indicate that Euryarchaeota and Thaumarchaeota are predominant phyla within archaeal communities present in coalbed-produced water from the Hancheng Block. Among these communities, Methanobacterium is identified as the most abundant genus, followed by Methanothrix and Methanoregula. Moreover, a positive correlation is observed between the abundance of Methanobacterium and the levels of total dissolved solids as well as Mn; conversely, there is a negative correlation with dissolved organic carbon, Zn concentrations, and pH. The abundance of Co and Ni primarily influence Methanothrix while pH and Zn play significant roles in controlling Methanoregula. Additionally, No. 5 coal seam waters exhibit greater species diversity in the archaeal community compared to No. 11 counterparts. The higher abundance of archaea in the No. 5 coal seam promotes biogas generation due to the correlation between bicarbonate and dissolved inorganic carbon isotope. These research findings hold scientific significance in guiding the exploration and development of biogas within coal seams.
Investigation into the Effect of Permeable Boundary Sealing on the Behavior of Hydrate Exploitation via Depressurization Combined with Heat Injection
Authors: Wang Yihan , Zeng Yunshuang , Zhong Xiuping , Pan Dongbin , Chen Chen
Published: October 2024

Depressurization combined with heat (mainly hot water) injection is an important technique for exploiting natural gas hydrate (NGH). To overcome the problems of pore water intrusion and hot water energy loss in the technique, this paper employs a method of setting sealing boundaries in permeable overburden and underburden to exploit NGH. The influence of the presence of sealing boundaries on NGH exploitation performances was numerically investigated. The results indicate that the sealing boundaries in permeable overburden and underburden can inhibit water intrusion and reduce heat loss, significantly improving the efficiency of hydrate dissociation and gas production. Specifically, the hydrate dissociation and gas production efficiency increased by 22.0–30.1% and 63.9–85.1%, respectively. Moreover, there is an optimal sealing vertical distance within the range of 0–15 m, maximizing the mining efficiency of NGH at the end of production. On the other hand, the presence of sealing boundaries effectively limits the escape range of CH4 in the permeable overburden and underburden, resulting in an increasing gas-to-water ratio and an increasing energy efficiency. These findings provide theoretical and technical support for the mining of NGH by depressurization combined with heat injection.
Fusion Forecasting Algorithm for Short-Term Load in Power System
Authors: Yu Tao , Wang Ye , Zhao Yuchong , Luo Gang , Yue Shihong
Published: October 2024

Short-term load forecasting plays an important role in power system scheduling, optimization, and maintenance, but no existing typical method can consistently maintain high prediction accuracy. Hence, fusing different complementary methods is increasingly focused on. To improve forecasting accuracy and stability, these features that affect the short-term power system are firstly extracted as prior knowledge, and the advantages and disadvantages of existing methods are analyzed. Then, three typically methods are used for short-term power load forecasting, and their interaction and complementarity are studied. Finally, the Choquet integral (CI) is used to fuse the three existing complementarity methods. Different from other fusion methods, the CI can fully utilize the interactions and complementarity among different methods to achieve consistent forecasting results, and reduce the disadvantages of a single forecasting method. Essentially, a CI with n inputs is equivalent to n! constrained feedforward neural networks, leading to a strong generalization ability in the load prediction process. Consequently, the CI-based method provides an effective way for the fusion forecasting of short-term load in power systems.
The Effect of a Reduction in the Catalyst Loading on a Mini Passive Direct Methanol Fuel Cell
Authors: Moreira C. S. , Pinto A. M. F. R. , Oliveira V. B.
Published: October 2024

Mini passive direct methanol fuel cells (mpDMFCs) appear to be a promising alternative for powering portable devices, since they use a liquid fuel, have a fast refuelling time, have a high efficiency and have a low environmental impact. However, some issues need to be solved before their commercialization, such as methanol crossover, short lifetime and high costs. The present work studies the effect of reducing the anode and cathode catalyst loading on the performance of a mpDMFC towards a reduction in the system costs and the characterization of the system losses. The undesirable losses that affect the fuel cell performance were identified and quantified using the electrochemical impedance spectroscopy (EIS) technique. Accordingly, a novel equivalent electric circuit (EEC) was proposed, accurately reproducing the mini pDMFC. In this work, a maximum power density of 7.07 mW cm−2 was obtained, with a methanol concentration of 5 M, using 2 mg cm−2 Pt-RuB and 4 mg cm−2 PtB. The mpDMFC allowed the cell to work with high methanol concentrations and reduced anode catalyst loadings.
A Survey of Computer Vision Detection, Visual SLAM Algorithms, and Their Applications in Energy-Efficient Autonomous Systems
Authors: Chen Lu , Li Gun , Xie Weisi , Tan Jie , Li Yang , Pu Junfeng , Chen Lizhu , Gan Decheng , Shi Weimin
Published: October 2024

Within the area of environmental perception, automatic navigation, object detection, and computer vision are crucial and demanding fields with many applications in modern industries, such as multi-target long-term visual tracking in automated production, defect detection, and driverless robotic vehicles. The performance of computer vision has greatly improved recently thanks to developments in deep learning algorithms and hardware computing capabilities, which have spawned the creation of a large number of related applications. At the same time, with the rapid increase in autonomous systems in the market, energy consumption has become an increasingly critical issue in computer vision and SLAM (Simultaneous Localization and Mapping) algorithms. This paper presents the results of a detailed review of over 100 papers published over the course of two decades (1999–2024), with a primary focus on the technical advancement in computer vision. To elucidate the foundational principles, an examination of typical visual algorithms based on traditional correlation filtering was initially conducted. Subsequently, a comprehensive overview of the state-of-the-art advancements in deep learning-based computer vision techniques was compiled. Furthermore, a comparative analysis of conventional and novel algorithms was undertaken to discuss the future trends and directions of computer vision. Lastly, the feasibility of employing visual SLAM algorithms in the context of autonomous vehicles was explored. Additionally, in the context of intelligent robots for low-carbon, unmanned factories, we discussed model optimization techniques such as pruning and quantization, highlighting their importance in enhancing energy efficiency. We conducted a comprehensive comparison of the performance and energy consumption of various computer vision algorithms, with a detailed exploration of how to balance these factors and a discussion of potential future development trends.
Multiple Load Forecasting of Integrated Renewable Energy System Based on TCN-FECAM-Informer
Authors: Li Mingxiang , Zhang Tianyi , Yang Haizhu , Liu Kun
Published: October 2024

In order to solve the problem of complex coupling characteristics between multivariate load sequences and the difficulty in accurate multiple load forecasting for integrated renewable energy systems (IRESs), which include low-carbon emission renewable energy sources, in this paper, the TCN-FECAM-Informer multivariate load forecasting model is proposed. First, the maximum information coefficient (MIC) is used to correlate the multivariate loads with the weather factors to filter the appropriate features. Then, effective information of the screened features is extracted and the frequency sequence is constructed using the frequency-enhanced channel attention mechanism (FECAM)-improved temporal convolutional network (TCN). Finally, the processed feature sequences are sent to the Informer network for multivariate load forecasting. Experiments are conducted with measured load data from the IRES of Arizona State University, and the experimental results show that the TCN and FECAM can greatly improve the multivariate load prediction accuracy and, at the same time, demonstrate the superiority of the Informer network, which is dominated by the attentional mechanism, compared with recurrent neural networks in multivariate load prediction.
Housing Defect Assessment through Household Scale and General Contractor Level
Authors: Park Junmo , Seo Deokseok
Published: October 2024

Consumer dissatisfaction and damage are increasing worldwide due to the increase in defects caused by the decline in housing quality, and disputes over housing defects are expanding. The number of housing units, a representative standard related to housing quality, is used in Canada, Japan, and Korea. Generally, quality costs increase as the number of housing units increases, and each country’s laws apply stricter management standards. Therefore, the quality is expected to be better as the number of units increases. In 2020, South Korea added a new regulation requiring inspections by a quality inspection team by a public institution only when building housing complexes with more than 300 households. There is a debate about whether this direction of regulation is appropriate. This study examines whether the number of households is being used appropriately as a criterion related to housing quality. It aims to determine whether the limit of 300 households is appropriate for distinguishing housing quality. In addition, since the contractor’s role is vital in housing construction, the contractor’s capabilities and supply–demand relationship were also considered as factors affecting housing quality. The ratio of defect repair costs to construction costs was used as a quality measure for 285 housing complexes in Korea. Generally, the lower the defect repair–construction costs ratio, the better the quality. A comparative study was conducted through a variance analysis on the scale of 300 households and the status of the contractor’s capability, whether they were among the top 10 construction companies with excellent construction performance, and whether a sole contract was made. The results showed that the quality was better in the cases with 300 or more households than in the cases with fewer than 300 households. The quality was better in the cases built by higher-ranking contractors than in those built by other contractors, but there was no difference according to supply-and-demand relationships. The results of the comprehensive analysis indicated that the quality was better when higher-ranking contractors built housing complexes with 300 or more households than when lower-ranking contractors built housing complexes with fewer than 300 households. Therefore, the direction of the Korean regulation requiring quality inspections for housing complexes with more than 300 households is incorrect and should be improved to regulate housing complexes with fewer than 300 households, and of low quality. In addition, the standard of determining housing quality based solely on the number of households should be revised, and the direction should be changed to strengthen quality control and the public supervision of housing built by low-capacity contractors. If the results of this study are utilized with this view in mind, a reasonable system to protect housing consumers will be promoted.
Study on the Effects of Temperature and Relative Humidity on the Hygroscopic Properties of Palm Leaf Manuscripts
Authors: Zhang Wenjie , Wang Shan , Guo Hong
Published: October 2024

Palm Leaf Manuscripts are an invaluable part of the cultural heritage of the world. Due to their strong hygroscopic properties, Palm Leaf Manuscripts are particularly susceptible to fluctuations in environmental humidity. Therefore, studying the hygroscopic behavior of these manuscripts and understanding how it changes under different environmental conditions is crucial for exploring their intrinsic properties, aging mechanisms, and the development of effective conservation strategies. In this study, we employed the dynamic vapor sorption (DVS) method to investigate the changes in the hygroscopic properties of Palm Leaf Manuscripts under conditions of high temperature and high humidity. The results indicate that high-temperature environments reduce the number of adsorption sites and the hygroscopicity of palm leaves, weakening their physical adsorption and capillary action towards moisture while increasing the hysteresis in desorption, thereby enhancing the stability of palm leaves in fluctuating humidity conditions. Conversely, high-humidity environments increase the number of adsorption sites and the hygroscopicity of the leaves, strengthening their physical adsorption and capillary action, and reducing the hysteresis in desorption. The extent of these changes increases with the aging time of the manuscripts. The surface energy test results of the samples similarly indicate that exposure to high-temperature environments reduces both surface energy and hygroscopicity, while high-humidity environments increase surface energy and hygroscopicity. The infrared spectroscopy results show that in high-temperature environments, the crystallinity of cellulose within the palm leaves increases, whereas in high-humidity environments and under the influence of fungal erosion, both the cellulose and hemicellulose in the Palm Leaf Manuscripts undergo significant degradation, with the amorphous regions of cellulose degrading more than the crystalline regions.
Evaluation of Carbon Sequestration and Oxygen-Release Potential of Six Mulberry Tree Varieties During Summer
Authors: Zhang Kaixin , Yang Yujie , Zhai Zeyang , Fang Yao , He Meiyu , Cheng Jialing , Tian Yue , Cao Xu , Liu Li
Published: October 2024

Human activities lead to an increase in greenhouse gases in the environment, among which carbon dioxide (CO2) is one of the most prominent, giving rise to global warming and climate change. Climate change, along with the resulting environmental degradation, is one of the most challenging difficulties faced by humanity in the twenty-first century. The forest ecosystem, with plants being its most important component, plays a pivotal role in regulating climate. Carbon sequestration and oxygen release (CSOR) by plants are major ecological service functions that play an important role in mitigating the negative impacts of the greenhouse effect and help to achieve carbon peaking and neutrality. The CSOR of mulberry (Morus spp.), a species of economic and ecological significance, is not yet understood. Six mulberry tree varieties were selected to evaluate their CSOR potential during summer. We took into consideration whole-plant diurnal assimilation amounts (P), carbon sequestration per unit leaf area (WCO2), oxygen release per unit leaf area (WO2), carbon sequestration per unit land area (PCO2), and oxygen release per unit land area (PO2). Zhongsang 1302 showed the greatest potential for CSOR among the six mulberry varieties. The PCO2 value of the Zhongsang 1302 variety was 1531.84 g·m−2 during summer, followed by Suhu 16 (1380.12 g·m−2), Husang 32 (1005.63 g·m−2), Zhongsang 9703 (990.01 g·m−2), Yu 711 (940.43 g·m−2), and Jin 10 (848.29 g·m−2). Moreover, the Pearson correlation and path analyses showed that photosynthetic rate (PN) and leaf area index (LAI) mainly affect the overall CSOR potential in mulberry. These findings not only enrich theoretical research on CSOR in mulberry, but also serve as an important reference for the use of different mulberry tree varieties in improving climate conditions and achieving carbon peaking and neutrality.