bluuebunny/crossref_metadata_2025 huggingface dataset:
Insights into Daily Dynamics of Fish Migration during Spring in the Konda River
Authors: Chemagin Andrey A. , Schletterer Martin
Published: December 2023

Hydrology and temperature are known as key drivers for fish migration in floodplain-channel systems of large rivers. The Lower Irtysh contains valuable species of whitefish and sturgeon. Thus, along the Konda River, a complex study was carried out in order to investigate fish migration in spring, with a focus on daily and monthly dynamics. To estimate the number of fish passing up- and downstream, a hydroacoustic system with a scanning beam frequency of 455 kHz was deployed in May 2017. The survey revealed the presence of three peaks in migration activity, as well as differences between a location close to the shore and another in the main channel. Regression analysis revealed a high degree of reliability of the influence of water temperature on the number of migrating fish (p < 0.001). The dataset also showed a daily rhythm of fish migration. An analysis of the daily variation in the illumination index and the intensity of fish migration revealed the presence of noticeable and high correlations for upstream (RS = 0.55; p < 0.05) and downstream migration (RS = 0.71; p < 0.001), respectively. Our data underline the importance of temperature as a trigger for fish migration and reveal diurnal patterns related to illumination.
Computational-Intelligence-Based Scheduling with Edge Computing in Cyber–Physical Production Systems
Authors: Xia Changqing , Jin Xi , Xu Chi , Zeng Peng
Published: December 2023

Real-time performance and reliability are two critical indicators in cyber–physical production systems (CPPS). To meet strict requirements in terms of these indicators, it is necessary to solve complex job-shop scheduling problems (JSPs) and reserve considerable redundant resources for unexpected jobs before production. However, traditional job-shop methods are difficult to apply under dynamic conditions due to the uncertain time cost of transmission and computation. Edge computing offers an efficient solution to this issue. By deploying edge servers around the equipment, smart factories can achieve localized decisions based on computational intelligence (CI) methods offloaded from the cloud. Most works on edge computing have studied task offloading and dispatching scheduling based on CI. However, few of the existing methods can be used for behavior-level control due to the corresponding requirements for ultralow latency (10 ms) and ultrahigh reliability (99.9999% in wireless transmission), especially when unexpected computing jobs arise. Therefore, this paper proposes a dynamic resource prediction scheduling (DRPS) method based on CI to achieve real-time localized behavior-level control. The proposed DRPS method primarily focuses on the schedulability of unexpected computing jobs, and its core ideas are (1) to predict job arrival times based on a backpropagation neural network and (2) to perform real-time migration in the form of human–computer interaction based on the results of resource analysis. An experimental comparison with existing schemes shows that our DRPS method improves the acceptance ratio by 25.9% compared to the earliest deadline first scheme.
Conditioning in Tropical Probability Theory
Authors: Matveev Rostislav , Portegies Jacobus W.
Published: December 2023

We define a natural operation of conditioning of tropical diagrams of probability spaces and show that it is Lipschitz continuous with respect to the asymptotic entropy distance.
Signal and Information Processing in Networks
Authors: Feng Minyu , Deng Liang-Jian , Chen Feng
Published: December 2023

Networks are omnipresent in the realm of science, serving as a central focus in our modern world [...]
Scale-Free Chaos in the 2D Harmonically Confined Vicsek Model
Authors: González-Albaladejo Rafael , Bonilla Luis L.
Published: December 2023

Animal motion and flocking are ubiquitous nonequilibrium phenomena that are often studied within active matter. In examples such as insect swarms, macroscopic quantities exhibit power laws with measurable critical exponents and ideas from phase transitions and statistical mechanics have been explored to explain them. The widely used Vicsek model with periodic boundary conditions has an ordering phase transition but the corresponding homogeneous ordered or disordered phases are different from observations of natural swarms. If a harmonic potential (instead of a periodic box) is used to confine particles, then the numerical simulations of the Vicsek model display periodic, quasiperiodic, and chaotic attractors. The latter are scale-free on critical curves that produce power laws and critical exponents. Here, we investigate the scale-free chaos phase transition in two space dimensions. We show that the shape of the chaotic swarm on the critical curve reflects the split between the core and the vapor of insects observed in midge swarms and that the dynamic correlation function collapses only for a finite interval of small scaled times. We explain the algorithms used to calculate the largest Lyapunov exponents, the static and dynamic critical exponents, and compare them to those of the three-dimensional model.
EEG Topography Amplification Using FastGAN-ASP Method
Authors: Zhao Min , Zhang Shuai , Mao Xiuqing , Sun Lei
Published: December 2023

Electroencephalogram (EEG) signals are bioelectrical activities generated by the central nervous system. As a unique information factor, they are correlated with the genetic information of the subjects, exhibiting robustness against forgery. The development of biometric identity recognition based on EEG signals has significantly improved the security and accuracy of biometric recognition. However, EEG signals obtained from incompatible acquisition devices have low universality and are prone to noise, making them challenging for direct use in practical identity recognition scenarios. Employing deep learning network models for data augmentation can address the issue of data scarcity. Yet, the time–frequency–space characteristics of EEG signals pose challenges for extracting features and efficiently generating data with deep learning models. To tackle these challenges, this paper proposes a data generation method based on channel attention normalization and spatial pyramid in a generative adversative network (FastGAN-ASP). The method introduces attention mechanisms in both the generator and discriminator to locate crucial feature information, enhancing the training performance of the generative model for EEG data augmentation. The EEG data used here are preprocessed EEG topographic maps, effectively representing the spatial characteristics of EEG data. Experiments were conducted using the BCI Competition IV-Ⅰ and BCI Competition IV-2b standard datasets. Quantitative and usability evaluations were performed using the Fréchet inception distance (FID) metric and ResNet-18 classification network, validating the quality and usability of the generated data from both theoretical and applied perspectives. The FID metric confirmed that FastGAN-ASP outperforms FastGAN, WGAN-GP, and WGAN-GP-ASP in terms of performance. Moreover, utilizing the dataset augmented with this method for classification recognition achieved an accuracy of 95.47% and 92.43%.
Advancing Hydrogen: A Closer Look at Implementation Factors, Current Status and Future Potential
Authors: Kaheel Sultan , Ibrahim Khalifa Aliyu , Fallatah Gasem , Lakshminarayanan Venkatasubramanian , Luk Patrick , Luo Zhenhua
Published: December 2023

This review article provides a comprehensive analysis of the hydrogen landscape, outlining the imperative for enhanced hydrogen production, implementation, and utilisation. It places the question of how to accelerate hydrogen adoption within the broader context of sustainable energy transitions and international commitments to reduce carbon emissions. It discusses influencing factors and policies for best practices in hydrogen energy application. Through an in-depth exploration of key factors affecting hydrogen implementation, this study provides insights into the complex interplay of both technical and logistical factors. It also discusses the challenges of planning, constructing infrastructure, and overcoming geographical constraints in the transition to hydrogen-based energy systems. The drive to achieve net-zero carbon emissions is contingent on accelerating clean hydrogen development, with blue and green hydrogen poised to complement traditional fuels. Public–private partnerships are emerging as catalysts for the commercialisation of hydrogen and fuel-cell technologies, fostering hydrogen demonstration projects worldwide. The anticipated integration of clean hydrogen into various sectors in the coming years signifies its importance as a complementary energy source, although specific applications across industries remain undefined. The paper provides a good reference on the gradual integration of hydrogen into the energy landscape, marking a significant step forward toward a cleaner, greener future.
Four-Dimensional History Matching Using ES-MDA and Flow-Based Distance-to-Front Measurement
Authors: Barrela Eduardo , Berthet Philippe , Trani Mario , Thual Olivier , Lapeyre Corentin
Published: December 2023

The use of 4D seismic data in history matching has been a topic of great interest in the hydrocarbon industry as it can provide important information regarding changes in subsurfaces caused by fluid substitution and other factors where well data is not available. However, the high dimensionality and uncertainty associated with seismic data make its integration into the history-matching process a challenging task. Methods for adequate data reduction have been proposed in the past, but most address 4D information mismatch from a purely mathematical or image distance-based standpoint. In this study, we propose a quantitative and flow-based approach for integrating 4D seismic data into the history-matching process. By introducing a novel distance parametrization technique for measuring front mismatch information using streamlines, we address the problem from a flow-based standpoint; at the same time, we maintain the amount of necessary front data at a reduced and manageable amount. The proposed method is tested, and its results are compared on a synthetic case against another traditional method based on the Hausdorff distance. The effectiveness of the method is also demonstrated on a semi-synthetic model based on a real-case scenario, where the standard Hausdorff methodology could not be applied due to high data dimensionality.
An Analysis of Energy Consumption in Railway Signal Boxes
Authors: Kampik Marian , Bodzek Krzysztof , Piaskowy Anna , Pilśniak Adam , Fice Marcin
Published: December 2023

This study assessed hourly electricity consumption profiles in railway signal boxes located in Poland. The analyses carried out consisted of assessing the correlation among the hourly demand profile, weather indicators, and calendar indicators, e.g., temperature, cloud cover, day of the week, and month. The analysis allowed us to assess which indicator impacts the energy consumption profile and would be useful when forecasting energy demand. In total, 15 railway signal boxes were selected for analysis and grouped according to three characteristic repeatability profiles. On this basis, six of the signal boxes and one that did not fit into any of the groups were selected for further analysis. Four correlation research methods were selected for analysis: Pearson’s method, Spearman’s method, scatter plots, and distance covariance. The possibility of forecasting electricity consumption based on previously aggregated profiles and determining correlations with indicators was presented. The given indicators vary depending on the facility. Analyses showed different dependencies of the electricity demand profile. The ambient temperature and time of day have the greatest impact on the profile. Regarding the correlation with temperature, the results of the Pearson’s and Spearman’s coefficients ranged from approximately −0.4 to more than −0.8. The highest correlation coefficients were obtained when comparing the demand profile with the previous day. In this case, the Pearson’s and Spearman’s coefficients for all analysed objects range from approximately 0.7 to over 0.9.
Hydrothermal Conversion of Microalgae Slurry in a Continuous Solar Collector with Static Mixer for Heat Transfer Enhancement
Authors: Chen Hao , Lou Fangfang , Zhang Xueyi , Shen Chengjun , Pan Weicheng , Wang Shuang
Published: December 2023

The continuous solar collector is a promising heater and reactor for the hydrothermal liquefaction (HTL) of microalgae biomass. To enhance the heat transfer and hydrothermal conversion of microalgae slurry in solar-driven reactors, a static mixer is inserted in the flow channel of the solar collector. A numerical model combining CFD and HTL reactions of microalgae biomass is proposed. Six composition equations of protein, carbohydrates, lipids, biocrude, aqueous phase and biogas were proposed, while corresponding HTL kinetics were utilized to simulate the conversion rate of the reactants and products. The effects of the twist ratio of the static mixer (3–10), flow rate (30–80 L/h) and solar intensity (650, 750, 850 W/m2) on the flow resistance, heat transfer and organics formation of microalgae slurry were investigated. The swirl flow caused by the static mixer with a twist ratio of three increased the convective heat transfer coefficient (97 W·m−2·K−1) by 2.06 times, while the production rate of biocrude (0.074 g·L−1·s−1) increased by 2.05 times at 50 L/h and 750 W/m2. This investigation gives guidance for utilizing static mixers in solar-driven reactors to optimize the heat transfer and HTL of microalgae biomass with solar heat sources.
Optimizing Forest-Biomass-Distribution Logistics from a Multi-Level Perspective—Review
Authors: Stanula Zygmunt , Wieruszewski Marek , Zydroń Adam , Adamowicz Krzysztof
Published: December 2023

Forest and wood biomass represent a sustainable reservoir of raw materials and energy, offering a viable alternative to fossil fuels. These resources find extensive use in producing bioproducts, including solid wood and wood materials. The judicious exploitation of forest and wood biomass can be pivotal in reducing carbon emissions and securing material and energy independence. The business viability of producing valuable goods from woody biomass hinges on ensuring its sustained availability. This necessitates access to high-quality biomass at a minimal cost, demanding the efficient design of wood-biomass-distribution logistics. Furthermore, it is imperative to give equal weight to social and ecological considerations in shaping the forest- and wood-biomass-distribution logistics, thereby ensuring the sustainable utilization of this renewable raw material source. This article presents research focused on the business optimization of distribution logistics for specific forms of forest biomass used in wood material production. While most studies have primarily concentrated on the business or ecological issues of biomass utilization, this article offers a comprehensive insight by addressing business, ecological, and social facets in assessing and optimizing wood-biomass-distribution logistics. Multi-stakeholder life-cycle-assessment optimization takes into account the reduction of greenhouse gases as an ecological metric, with production costs and capital expenditure forming the business metrics. At the same time, the generation of employment opportunities is commonly regarded as the pivotal social criterion. There remains a necessity for further exploration into the potential social impacts of forest biomass utilization. Additionally, developing enhanced methodologies and decision-support tools for scheduling wood-biomass-distribution logistics that holistically consider business, ecological, and social criteria is an essential ongoing task.
A Novel Wind Power Outlier Detection Method with Support Vector Machine Optimized by Improved Harris Hawk
Authors: Huang Jingtao , Qin Jin , Song Shuzhong
Published: December 2023

The accurate detection of wind power outliers plays a crucial role in wind power forecasting, while the inherited strong randomness and high fluctuations bring great challenges to this issue. This work investigates the way to improve the outlier detection accuracy based on support vector machine (SVM). Although SVM can achieve good results for outlier detection in theory, its performance is heavily dependent on the hyper-parameters. Parameter optimization is not an easy task due to its complex nonlinear multi-optimum nature; an improved Harris hawk optimization (IHHO) is proposed to optimize the parameters of SVM for more accurate outlier detection. HHO takes the cooperative behavior and chasing style of Harris’ hawks in nature called surprise pounce and can effectively search the optimal one in large parameter space, but it tends to fall into local optimum. To solve this issue, an improved Harris hawk optimization algorithm (IHHO) was proposed to obtain the optimal parameters of SVM. First, Hammersley sequence initialization is carried out to acquire good initial solutions. Then, a nonlinear factor control mode and an adaptive Gaussian–Cauchy mutation perturbation strategy are proposed to avoid getting trapped in local optima. In this way, a novel wind power outlier detection method named IHHO-SVM was constructed. The results on several wind power data with outliers show that IHHO-SVM outperforms SVM and HHO-SVM, which achieves the highest average F1 score of 96.63% and exhibits the smallest standard deviation. Compared to commonly used models for detecting outliers in wind power, such as isolation forest (IF), local outlier factor (LOF), SVM with grey wolf optimization (GWO-SVM), and SVM with particle swarm optimization (PSO-SVM), the proposed IHHO-SVM model shows the best overall performance with precision, recall, and F1 scores of 95.76%, 96.94%, and 96.35%, respectively.
From Patents to Progress: Genetic Algorithms in Harmonic Distortion Monitoring Technology
Authors: da Cruz Filho Pedro Gomes , Nunes Danielle Devequi Gomes , Malta Santos Hayna , Santos Alex Álisson Bandeira , Machado Bruna Aparecida Souza
Published: December 2023

Sustainable energy sources, such as wind energy, are pivotal in driving our energy landscape towards a more environmentally conscious and responsible future. Wind power, as an exemplar of clean and renewable energy solutions, adeptly harnesses the kinetic energy of the wind to generate electricity. While wind energy significantly contributes to our sustainability objectives, the quality of the energy it produces is equally essential. A critical challenge in this context is harmonic distortion, which manifests as unwanted fluctuations in the frequency and amplitude of electrical waveforms. Effectively mitigating these distortions within wind energy systems is vital to maintaining the stability and reliability of power grids, guaranteeing that the electricity supplied adheres to high-quality standards. The objective of this study was to conduct a technological prospection focused on the contemporary scenario of genetic algorithm applications in addressing harmonic variations. This investigation unearthed a total of 634 relevant documents. The findings suggest that the utilization of genetic algorithms for enhancing energy quality is a relatively recent but promising field. The State Grid Corp of China emerged as the principal contributor, with ten noteworthy inventors identified. Remarkably, both China and the United States lead in patent filings. The insights gleaned from these documents underscore the potential for further exploration and the synergistic application of these techniques. These collaborative efforts have the potential to yield processes and devices that offer significant economic and environmental advantages for the energy industry, solidifying our commitment to a cleaner and more sustainable energy future.
Wettability Alteration Mechanisms in Enhanced Oil Recovery with Surfactants and Nanofluids: A Review with Microfluidic Applications
Authors: Ratanpara Abhishek , Kim Myeongsub
Published: December 2023

Modifying reservoir surface wetting properties is an appealing topic to the upstream oil and gas industry for enhancing hydrocarbon recovery as the shifting of reservoir rock surface wetting from oil-wet to water-wet has enhanced the oil recovery by as much as 70–80%. In the last few decades, research has been conducted on core flooding experiments to reveal wettability alteration mechanisms associated with macroscopic fluid flow in reservoirs. In recent years, the microscopic wetting state and fluid distribution behavior have been studied using micromodel experimental techniques to promote the fundamental mechanisms of wettability alteration. To provide the concurrent knowledge and technology development, this comprehensive review focuses on micromodel investigations for wettability alteration in chemical-enhanced oil recovery using surfactants and/or nanofluids that reveal microscopic behaviors on the wetting state, fluid distribution, and their associated mechanisms. This comprehensive review focuses on micromodel investigations for wettability alteration in chemical-enhanced oil recovery using surfactants and/or nanofluids that reveal microscopic behaviors on the wetting state, fluid distribution, and their associated mechanisms. Wettability characteristics and measurement techniques are thoroughly assessed to understand the critical role of wettability for enhanced oil recovery. With the microfluidic-based studies, the effect of relative permeability along with the pore network and wetting order on oil recovery have been discussed. Later on, the new development in phase diagram related to viscus fingering and capillary fingering regime have been reviewed via various micromodels. Then, the wettability alteration mechanisms and governing parameters by surfactant and nanoparticles are summarized. Additionally, recent micromodel experiments on surfactants and nanofluid-assisted enhanced oil recovery are reviewed and listed, along with their fabrication methods.
A System Dynamics Approach to Technological Learning Impact for the Cost Estimation of Solar Photovoltaics
Authors: Wang Rong , Hasanefendic Sandra , Von Hauff Elizabeth , Bossink Bart
Published: December 2023

Technological learning curve models have been continuously used to estimate the cost development of solar photovoltaics (PV) for climate mitigation targets over time. They can integrate several technical sources that influence the learning process. Yet, the accurate and realistic learning curve that reflects the cost estimations of PV development is still challenging to determine. To address this question, we develop four hypothetical-alternative learning curve models by proposing different combinations of technological learning sources, including both local and global technological experience and knowledge stock. We specifically adopt the system dynamics approach to focus on the non-linear relationship and dynamic interaction between the cost development and technological learning source. By applying this approach to Chinese PV systems, the results reveal that the suitability and accuracy of learning curve models for cost estimation are dependent on the development stages of PV systems. At each stage, different models exhibit different levels of closure in cost estimation. Furthermore, our analysis underscores the critical role of incorporating global technical sources into learning curve models.
Lithium Battery State-of-Health Estimation Based on Sample Data Generation and Temporal Convolutional Neural Network
Authors: Guo Fang , Huang Guangshan , Zhang Wencan , Wen An , Li Taotao , He Hancheng , Huang Haolin , Zhu Shanshan
Published: December 2023

Accurate estimation of battery health is an effective means of improving the safety and reliability of electrical equipment. However, developing data-driven models to estimate battery state of health (SOH) is challenging when the amount of data is restricted. In this regard, this study proposes a method for estimating the SOH of lithium batteries based on sample data generation and a temporal convolutional neural network. First, we analyzed the charge/discharge curves of the batteries, from which we extracted features that were highly correlated with the SOH decay. Then, we used a Variational Auto-Encoder (VAE) to learn the features and distributions of the sample data to generate highly similar data and enrich the number of samples. Finally, a temporal convolutional neural network (TCN) was built to mine the nonlinear relationship between features and SOH by combining the source and extended domain data to realize SOH estimation. The experimental results show that the proposed method in this study has less than 2% error in SOH estimation, which improves the accuracy by 64.9% based on its baseline model. The feasibility of using data-driven models for battery health management in data-constrained application scenarios is demonstrated.
The Impact of Adjacent Road on Vascular Plant Species Composition in Herbaceous Layers of Peucedano-Pinetum and Tilio-Carpinetum Urban Forests in the City of Warsaw (Poland)
Authors: Fornal-Pieniak Beata , Kamionowski Filip , Ollik Marcin , Szumigała Paweł , Żarska Barbara , Szumigała Karolina
Published: December 2023

The research was conducted in two types of urban forests: Peucedano-Pinetum and Tilio-Carpinetum. The aim of the study was to determine the differences in plant species compositions in the herbaceous layer of urban forests with different habitat fertility adjacent to the road in the northern part of Warsaw (Poland). Seven transects were laid out in each type of forest, with 10 plots spread out from the edge zone (forest border) to the interior of the forests. The size of each plot was 100 m2. The other seven transects were located within the forest, 150 to 200 m away from the forest’s edge. The field research included phytosociological relevés carried out on the existing transects. The indoor studies included an analysis of forest community disturbance. Furthermore, an analysis of abiotic environmental conditions using ecological indicator numbers was carried out. Ecotones of Peucedano-Pinetum are more likely to be colonized by the species inconsistent with the habitat due to processes that increase habitat fertility. The Tilio-Carpinetum forest is more easily colonized by invasive plant species than the Peucedano-Pinetum. The range of road effects can be determined as an area located within 90 m inward of the forest in the case of the Peucedano-Pinetum community and 100 m in the case of the Tilio-Carpinetum community. The presented research is important for formulating directions about how to manage the forests and their surroundings in terms of shaping forests of more natural character, with species more consistent with a forest habitat.
Broadleaf Trees Increase Soil Aggregate Stability in Mixed Forest Stands of Southwest China
Authors: Zheng Yonglin , Wang Yunqi , Zhang Yuxuan , Zhang Jialiang , Wang Yujie , Zhu Junlin
Published: December 2023

In soils, high aggregate stability often represents higher quality and anti-erosion ability; however, few studies have systematically analyzed how different forest stands affect soil aggregate stability. We selected five typical mixed forest stands on Jinyun Mountain in Chongqing, China, as research sites to evaluate soil aggregate stability. Within these sites, we analyzed the factors influencing soil aggregate stability in different stands by measuring soil characteristics and root traits. Soil aggregation stability, plant root traits, and soil properties varied among the mixed forest stands. The broadleaf tree mixed forest improved soil aggregate stability by 57%–103% over that of the Pinus massoniana mixed forest. The soil organic carbon, cation exchange capacity, Fe-Al oxides, and fine root proportion were positively correlated with soil aggregate stability. The specific root length and very fine root proportion were negatively correlated with soil aggregate stability, whereas the fine root proportion was positively correlated with this property. Specifically, we found that arbuscular mycorrhizal fungi did not affect soil aggregate stability in acid rain areas. Structural equation modeling indicated that soil aggregate stability was closely related to soil physicochemical properties and plant root characteristics. Predictive factors accounted for 69% of the variation in mean weight diameter, and plant root traits influenced soil aggregate stability by affecting soil organic matter, texture, and Fe-Al oxides. This study elucidated the impact of soil physicochemical properties and plant root characteristics on soil aggregate stability in different forest stand types, which has crucial implications for optimizing the management of various forest types.
Turkey Oak (Quercus cerris L.) Resilience to Climate Change: Insights from Coppice Forests in Southern and Central Europe
Authors: Šimková Michaela , Vacek Stanislav , Šimůnek Václav , Vacek Zdeněk , Cukor Jan , Hájek Vojtěch , Bílek Lukáš , Prokůpková Anna , Štefančík Igor , Sitková Zuzana , Lukáčik Ivan
Published: December 2023

Turkey oak (Quercus cerris L.) is a thermophilic oak species that is gaining importance in the context of ongoing climate change because of its better resistance to climatic extremes and drier conditions. Therefore, this article focuses on Turkey oak’s role and growth properties in the coppice forests of Southern Europe (Italy, Bulgaria) compared to similar site conditions in Central Europe (Slovakia, Czechia). The aims are to evaluate the basic dendrometry indicators, stand biodiversity, growth dynamics, and the effect of climatic factors on tree-ring increment on specific site chronologies. We found that the tree density in coppices of 50–60 years varied between 475 and 775 trees ha−1, and the stand volume ranged from 141 to 407 m3 ha−1. The complex stand diversity of all plots ranged from a monotonous to uniform structure. The size of tree-ring growth was closely related to indicators of stand density. The lowest influence of climatic factors on tree-ring growth was found in sites in Italy and the highest in Slovakia. The primary limiting factor for growth was the lack of precipitation during the growing season, especially in June and July. In contrast, temperature had a marginal effect on radial growth compared to precipitation. The radial growth in research plots in Southern Europe goes through longer 6 to 8-year growth cycles, and in Central European sites, it goes through shorter cycles of 2.4 to 4.8 years, which confirms better growth conditions in this region. The studied coppice stands exhibit a stable reaction to climate change. Yet, regarding cyclical growth, the Central European stands benefit from an advantageous climate and grow better than in Southern Europe. As part of the changing environmental conditions, Turkey oak is becoming an important tree species that can achieve high production potential even in drier habitats due to its regeneration characteristic as coppice and may play a critical role in its northerly introduction in Europe.
Detection of Cherry Tree Crown Based on Improved LA-dpv3+ Algorithm
Authors: Cheng Zhenzhen , Cheng Yifan , Li Meng , Dong Xiangxiang , Gong Shoufu , Min Xiaoxiao
Published: December 2023

Accurate recognition of the canopy is a prerequisite for precision orchard yield estimation. This paper proposed an enhanced LA-dpv3+ approach for the recognition of cherry canopies based on UAV image data, with a focus on enhancing feature representation through the implementation of an attention mechanism. The attention mechanism module was introduced to the encoder stage of the DeepLabV3+ architecture, which improved the network’s detection accuracy and robustness. Specifically, we developed a diagonal discrete cosine transform feature strategy within the attention convolution module to extract finer details of canopy information from multiple frequency components. The proposed model was constructed based on a lightweight DeepLabv3+ network architecture that incorporates a MobileNetv2 backbone, effectively reducing computational costs. The results demonstrate that our proposed method achieved a balance between computational cost and the quality of results when compared to competing approaches. Our model’s accuracy exceeded 89% while maintaining a modest model size of only 46.8 MB. The overall performance indicated that with the help of a neural network, segmentation failures were notably reduced, particularly in high-density weed conditions, resulting in significant increases in accuracy (ACC), F1-score, and intersection over union (IOU), which were increased by 5.44, 3.39, and 8.62%, respectively. The method proposed in this paper may be applied to future image-based applications and contribute to automated orchard management.
Study of the Sustainability of a Forest Road Network Using GIS-MCE
Authors: Long Chen , Pang Yan , Wang Zhongwei
Published: December 2023

An environmentally friendly and economically efficient forest road network is the key to sustainable forest management, and it is essential to evaluate the sustainability of an established forest road network and to optimize the forest road network based on sustainability criteria. This study proposes a sustainability evaluation method for forest road networks. The evaluation index system was constructed by selecting 12 indices from the social, economic, and ecological dimensions. To improve the credibility of the weighting of the evaluation indicators, the evaluation indicators were assigned subjective and objective weights by the analytic hierarchy process and the entropy weight method, respectively, and the game theory idea was used to comprehensively assign the weights. The study area was chosen to be the Taohuajiang State-Owned Forest Farm, which is a national forest park and is also an ecological public welfare forest. The development of the forest road network in the social, economic, and environmental dimensions was calculated by adopting the evaluation method proposed in this study, and the sustainability of the forest road network in each subdivision of the study area was determined, to provide a detailed theoretical basis for the management to carry out the upgrading of the sustainability of the forest road network. The results derived from the evaluation methodology show that the sustainable development of the forest road network in the study area is “excellent”. At the same time, the level of sustainability of the forest road network in different areas of the study area was also evaluated. Finally, this paper puts forward policy recommendations for optimizing the sustainable development of forest road networks. The original sustainability evaluation method in this paper provides a scientific tool for assessing the degree of sustainable development of forest road networks, which contributes to optimizing the forest road networks.
Enabling Technologies for Next-Generation Smart Cities: A Comprehensive Review and Research Directions
Authors: Ali Shrouk A. , Elsaid Shaimaa Ahmed , Ateya Abdelhamied A. , ElAffendi Mohammed , El-Latif Ahmed A. Abd
Published: December 2023

The concept of smart cities, which aim to enhance the quality of urban life through innovative technologies and policies, has gained significant momentum in recent years. As we approach the era of next-generation smart cities, it becomes crucial to explore the key enabling technologies that will shape their development. This work reviews the leading technologies driving the future of smart cities. The work begins by introducing the main requirements of different smart city applications; then, the enabling technologies are presented. This work highlights the transformative potential of the Internet of things (IoT) to facilitate data collection and analysis to improve urban infrastructure and services. As a complementary technology, distributed edge computing brings computational power closer to devices, reducing the reliance on centralized data centers. Another key technology is virtualization, which optimizes resource utilization, enabling multiple virtual environments to run efficiently on shared hardware. Software-defined networking (SDN) emerges as a pivotal technology that brings flexibility and scalability to smart city networks, allowing for dynamic network management and resource allocation. Artificial intelligence (AI) is another approach for managing smart cities by enabling predictive analytics, automation, and smart decision making based on vast amounts of data. Lastly, the blockchain is introduced as a promising approach for smart cities to achieve the required security. The review concludes by identifying potential research directions to address the challenges and complexities brought about by integrating these key enabling technologies.
Addressing the Gaps of IoU Loss in 3D Object Detection with IIoU
Authors: Ravi Niranjan , El-Sharkawy Mohamed
Published: December 2023

Three-dimensional object detection involves estimating the dimensions, orientations, and locations of 3D bounding boxes. Intersection of Union (IoU) loss measures the overlap between predicted 3D box and ground truth 3D bounding boxes. The localization task uses smooth-L1 loss with IoU to estimate the object’s location, and the classification task identifies the object/class category inside each 3D bounding box. Localization suffers a performance gap in cases where the predicted and ground truth boxes overlap significantly less or do not overlap, indicating the boxes are far away, and in scenarios where the boxes are inclusive. Existing axis-aligned IoU losses suffer performance drop in cases of rotated 3D bounding boxes. This research addresses the shortcomings in bounding box regression problems of 3D object detection by introducing an Improved Intersection Over Union (IIoU) loss. The proposed loss function’s performance is experimented on LiDAR-based and Camera-LiDAR-based fusion methods using the KITTI dataset.
Potential Causal Association between Depression and Oral Diseases: A Mendelian Randomization Study
Authors: Zhang Xiaobin , Jiang Hehe , Zhang Linlin , Li Chunjing , Chen Chen , Xing Mengzhen , Ma Yuning , Ma Yuxia
Published: December 2023

Background: Globally, oral diseases are common, pose an economic burden, and significantly decline the quality of life of affected individuals. Recently, researchers have substantially highlighted the effect of depression on oral disease incidence and development. In this study, we elucidated the correlation between depression and oral diseases. Methods: Using two-sample Mendelian randomization (MR), the association between depression and the risk of 17 oral diseases was evaluated. Three methods were used to perform MR analysis: the inverse variance-weighted, weighted median, and MR-Egger methods. Furthermore, Cochran’s Q test, MR-Egger intercept test, MR Pleiotropy RESidual Sum and Outlier test, and leave-one-out analysis were performed to analyze sensitivity. Results: After implementing multiple test corrections, we observed that genetic susceptibility to depression was associated with an increased risk of mouth ulcers, toothache, loose teeth, bleeding gums, painful gums, chronic periodontitis, chronic tonsil and adenoid diseases, peritonsillar abscess, and excessive tooth attrition. However, a causal relationship between depression and other oral diseases was not observed. Sensitivity analysis confirmed the robustness of the results. Conclusions: We confirmed the causal relationship between depression and several oral diseases, thereby providing a novel viewpoint on the prevention and treatment of oral diseases. Our findings suggest the integration of depression control into routine clinical care to enhance the effectiveness of oral disease treatment.
Genomic Insights into Molecular Regulation Mechanisms of Intramuscular Fat Deposition in Chicken
Authors: Cao Yuzhu , Xing Yuxin , Guan Hongbo , Ma Chenglin , Jia Qihui , Tian Weihua , Li Guoxi , Tian Yadong , Kang Xiangtao , Liu Xiaojun , Li Hong
Published: December 2023

Intramuscular fat (IMF) plays an important role in the tenderness, water-holding capacity, and flavor of chicken meat, which directly affect meat quality. In recent years, regulatory mechanisms underlying IMF deposition and the development of effective molecular markers have been hot topics in poultry genetic breeding. Therefore, this review focuses on the current understanding of regulatory mechanisms underlying IMF deposition in chickens, which were identified by multiple genomic approaches, including genome-wide association studies, whole transcriptome sequencing, proteome sequencing, single-cell RNA sequencing (scRNA-seq), high-throughput chromosome conformation capture (HiC), DNA methylation sequencing, and m6A methylation sequencing. This review comprehensively and systematically describes genetic and epigenetic factors associated with IMF deposition, which provides a fundamental resource for biomarkers of IMF deposition and provides promising applications for genetic improvement of meat quality in chicken.
Genetic Origin and Introgression Pattern of Pingliang Red Cattle Revealed Using Genome-Wide SNP Analyses
Authors: Wang Yuanqing , Ma Jun , Wang Jing , Zhang Lupei , Hu Junwei , Ma Minghao , Xu Lingyang , Chen Yan , Zhu Bo , Wang Zezhao , Gao Huijiang , Li Junya , Gao Xue
Published: December 2023

The Pingliang red cattle, an outstanding indigenous resource in China, possesses an exceptional breeding value attributed to its tender meat and superior marbling quality. Currently, research efforts have predominantly concentrated on exploring its maternal origin and conducting conventional phenotypic studies. However, there remains a lack of comprehensive understanding regarding its genetic basis. To address this gap, we conducted a thorough whole-genome analysis to investigate the population structure, phylogenetic relationships, and gene flows of this breed using genomic SNP chip data from 17 bovine breeds. The results demonstrate that Pingliang red cattle have evolved distinct genetic characteristics unique to this breed, clearly distinguishing it from other breeds. Based on the analysis of the population structure and phylogenetic tree, it can be classified as a hybrid lineage between Bos taurus and Bos indicus. Furthermore, Pingliang red cattle display a more prominent B. taurus pedigree in comparison with Jinnan, Qinchuan, Zaosheng, Nanyang, and Luxi cattle. Moreover, this study also revealed closer genetic proximity within the Chinese indigenous cattle breed, particularly Qinchuan cattle, which shares the longest identical by descent (IBD) fragment with Pingliang red cattle. Gene introgression analysis shows that Pingliang red cattle have undergone gene exchange with South Devon and Red Angus cattle from Europe. Admixture analysis revealed that the proportions of East Asian taurine and Chinese indicine in the ancestry of Pingliang red cattle are approximately 52.44% and 21.00%, respectively, while Eurasian taurine, European taurine, and Indian indicine account for approximately 17.55%, 7.27%, and 1.74%. Our findings unveil distinct genetic characteristics in Pingliang red cattle and attribute their origin to B. taurus and B. indicus ancestry, as well as contributions from Qinchuan cattle, South Devon, and Red Angus.
Bictegravir/Tenofovir Alafenamide/Emtricitabine: A Real-Life Experience in People Living with HIV (PLWH)
Authors: Gidari Anna , Benedetti Sara , Tordi Sara , Zoffoli Anastasia , Altobelli Debora , Schiaroli Elisabetta , De Socio Giuseppe Vittorio , Francisci Daniela
Published: December 2023

Background: Bictegravir (BIC), a recently introduced integrase inhibitor, is available in a single tablet regimen with tenofovir alafenamide (TAF) and emtricitabine (FTC) (BIC-STR). This study aimed to describe a real-life experience with BIC-STR. Methods: We retrospectively analyzed the data of people living with HIV (PLWH) on antiretroviral therapy (ART) with BIC-STR followed by the Clinic of Infectious Diseases of Perugia (Perugia, Italy) from September 2019 to February 2023. Results: 270 PLWH were enrolled with a median follow-up time on BIC-STR of 2.2 years (IQR 1.2–2.7). In the overall population, in treatment-experienced (N = 242), in treatment-naïve (N = 28), and in population with age > 60 years old (N = 86), we observed that CD4 cell count improved in absolute number, percentage and CD4/CD8 ratio, under BIC-STR. Patients with viremia < 50 cp/mL increased in all groups. In the overall population, previous ART with TAF and nadir CD4 cell count favored immunological recovery. In the ART-experienced group, time in therapy with BIC-STR was associated with HIV-RNA undetectability. In the older group, previous opportunistic infection and advanced age were associated with lower CD4 count. Conclusions: BIC-STR was demonstrated, in real-life, to be a valid option for a switch, such as initial ART.
Exploring Personal Exposure to Airborne Microplastics across Various Work Environments in Pathum Thani Province, Thailand
Authors: Limsiriwong Kanokwan , Winijkul Ekbordin
Published: December 2023

This study used personal air samplers to assess the concentration of airborne microplastics exposed by different occupational groups during their working hours. The personal air sampler was placed in the “breathing zone” of the worker during working hours to collect microplastic exposure data. Occupations examined included housekeepers, laundromat staff, office workers, van drivers, street vendors, maintenance technicians in wastewater treatment plants, and waste segregation officers in the university and market. The level of airborne microplastic exposure was found to be influenced by the daily activities and environmental conditions of the workplace. Waste segregation officers in the university and market exhibited the highest levels of exposure to airborne microplastics, at 3964 ± 2575 microplastics per cubic meter (n/m3) and 3474 ± 678 n/m3, respectively. Further analysis focused on airborne microplastics less than 10 µm in size which can be taken in through inhalation. During the 8 h working period, the waste segregation officer in the university recorded the highest 10 µm airborne microplastic intake, at 5460 pieces, followed by the waste segregation officer in the market at 3301 pieces, housekeepers at 899 pieces, van drivers at 721 pieces, maintenance technicians in WWTPs at 668 pieces, laundromat staff at 454 pieces, street vendors at 249 pieces, and office workers at 131 pieces.
The Inverse Log-Rank Test: A Versatile Procedure for Late Separating Survival Curves
Authors: Efird Jimmy T.
Published: December 2023

Often in the planning phase of a clinical trial, a researcher will need to choose between a standard versus weighted log-rank test (LRT) for investigating right-censored survival data. While a standard LRT is optimal for analyzing evenly distributed but distinct survival events (proportional hazards), an appropriately weighted LRT test may be better suited for handling non-proportional, delayed treatment effects. The “a priori” misspecification of this alternative may result in a substantial loss of power when determining the effectiveness of an experimental drug. In this paper, the standard unweighted and inverse log-rank tests (iLRTs) are compared with the multiple weight, default Max-Combo procedure for analyzing differential late survival outcomes. Unlike combination LRTs that depend on the arbitrary selection of weights, the iLRT by definition is a single weight test and does not require implicit multiplicity correction. Empirically, both weighted methods have reasonable flexibility for assessing continuous survival curve differences from the onset of a study. However, the iLRT may be preferable for accommodating delayed separating survival curves, especially when one arm finishes first. Using standard large-sample methods, the power and sample size for the iLRT are easily estimated without resorting to complex and timely simulations.