bluuebunny/crossref_metadata_2025 huggingface dataset:
Dependent Competing Failure Processes in Reliability Systems
Authors: Dshalalow Jewgeni H. , Aljahani Hend , White Ryan T.
Published: May 2024

This paper deals with a reliability system hit by three types of shocks ranked as harmless, critical, or extreme, depending on their magnitudes, being below H1, between H1 and H2, and above H2, respectively. The system’s failure is caused by a single extreme shock or by a total of N critical shocks. In addition, the system fails under occurrences of M pairs of shocks with lags less than some δ (δ-shocks) in any order. Thus, the system fails when one of the three named cumulative damages occurs first. Thus, it fails due to the competition of the three associated shock processes. We obtain a closed-form joint distribution of the time-to-failure, shock count upon failure, δ-shock count, and cumulative damage to the system on failure, to name a few. In particular, the reliability function directly follows from the marginal distribution of the failure time. In a modified system, we restrict δ-shocks to those with small lags between consecutive harmful shocks. We treat the system as a generalized random walk process and use an embellished variant of discrete operational calculus developed in our earlier work. We demonstrate analytical tractability of our formulas which are also validated, through Monte Carlo simulation.
Multi-Server Multi-Function Distributed Computation
Authors: Malak Derya , Deylam Salehi Mohammad Reza , Serbetci Berksan , Elia Petros
Published: May 2024

The work here studies the communication cost for a multi-server multi-task distributed computation framework, as well as for a broad class of functions and data statistics. Considering the framework where a user seeks the computation of multiple complex (conceivably non-linear) tasks from a set of distributed servers, we establish the communication cost upper bounds for a variety of data statistics, function classes, and data placements across the servers. To do so, we proceed to apply, for the first time here, Körner’s characteristic graph approach—which is known to capture the structural properties of data and functions—to the promising framework of multi-server multi-task distributed computing. Going beyond the general expressions, and in order to offer clearer insight, we also consider the well-known scenario of cyclic dataset placement and linearly separable functions over the binary field, in which case, our approach exhibits considerable gains over the state of the art. Similar gains are identified for the case of multi-linear functions.
“The Statue of Venus with a Broken Arm Shows Us the Beauty of Incompleteness”: University Student Diversity and Experience in Chinese Well-Being and Mental Health Education Textbooks
Authors: Chen Ning , Dervin Fred
Published: May 2024

Research on introductory psychology textbooks used in higher education abounds around the world. Although most studies focus on textbooks designed for students majoring in psychology, this paper looks into textbooks used for a compulsory undergraduate course on mental health and well-being in mainland China. Like in many other countries, Chinese students face mental and well-being issues and this course aims to support all undergraduate students in dealing with their own psychology rather than specialising in the subject. Based on an in-depth discourse analysis of two widely used textbooks published in Chinese for this course, the authors examine how the textbooks construct the university experiences of Chinese students. At the same time, the paper also delves into the ways different kinds of Chinese students are portrayed and the types of changes that they are said to experience at university. The foci of invisibility and selectivity from the field of textbook analysis help highlight generalising, stereotyping, and recourse to biases in the textbooks. Recommendations as to how such textbooks could be used to boost university students’ mental health and well-being, while taking into account their diversity, are proposed at the end of the paper.
Beyond Trial and Error: Lane Keeping with Monte Carlo Tree Search-Driven Optimization of Reinforcement Learning
Authors: Kővári Bálint , Pelenczei Bálint , Knáb István Gellért , Bécsi Tamás
Published: May 2024

In recent years, Reinforcement Learning (RL) has excelled in the realm of autonomous vehicle control, which is distinguished by the absence of limitations, such as specific training data or the necessity for explicit mathematical model identification. Particularly in the context of lane keeping, a diverse set of rewarding strategies yields a spectrum of realizable policies. Nevertheless, the challenge lies in discerning the optimal behavior that maximizes performance. Traditional approaches entail exhaustive training through a trial-and-error strategy across conceivable reward functions, which is a process notorious for its time-consuming nature and substantial financial implications. Contrary to conventional methodologies, the Monte Carlo Tree Search (MCTS) enables the prediction of reward function quality through Monte Carlo simulations, thereby eliminating the need for exhaustive training on all available reward functions. The findings obtained from MCTS simulations can be effectively leveraged to selectively train only the most suitable RL models. This approach helps alleviate the resource-heavy nature of traditional RL processes through altering the training pipeline. This paper validates the theoretical framework concerning the unique property of the Monte Carlo Tree Search algorithm by emphasizing its generality through highlighting crossalgorithmic and crossenvironmental capabilities while also showcasing its potential to reduce training costs.
Self-Knowledge Distillation via Progressive Associative Learning
Authors: Zhao Haoran , Bi Yanxian , Tian Shuwen , Wang Jian , Zhang Peiying , Deng Zhaopeng , Liu Kai
Published: May 2024

As a specific form of knowledge distillation (KD), self-knowledge distillation enables a student network to progressively distill its own knowledge without relying on a pretrained, complex teacher network; however, recent studies of self-KD have discovered that additional dark knowledge captured by auxiliary architecture or data augmentation could create better soft targets for enhancing the network but at the cost of significantly more computations and/or parameters. Moreover, most existing self-KD methods extract the soft label as a supervisory signal from individual input samples, which overlooks the knowledge of relationships among categories. Inspired by human associative learning, we propose a simple yet effective self-KD method named associative learning for self-distillation (ALSD), which progressively distills richer knowledge regarding the relationships between categories across independent samples. Specifically, in the process of distillation, the propagation of knowledge is weighted based on the intersample relationship between associated samples generated in different minibatches, which are progressively estimated with the current network. In this way, our ALSD framework achieves knowledge ensembling progressively across multiple samples using a single network, resulting in minimal computational and memory overhead compared to existing ensembling methods. Extensive experiments demonstrate that our ALSD method consistently boosts the classification performance of various architectures on multiple datasets. Notably, ALSD pushes forward the self-KD performance to 80.10% on CIFAR-100, which exceeds the standard backpropagation by 4.81%. Furthermore, we observe that the proposed method shows comparable performance with the state-of-the-art knowledge distillation methods without the pretrained teacher network.
Adapted Diffusion for Energy-Efficient Routing in Wireless Sensor Networks
Authors: Hakim Gad , Braun Robin , Lipman Justin
Published: May 2024

We present a routing model called adapted diffusion for ad hoc wireless sensor networks that is more energy efficient than directed diffusion. Adapted diffusion is modeled using NetLogo with agent-based modeling. In this agent-based NetLogo model, we set the distance from a random source and the distance from the sink to optimize the routing protocols. By using this routing technique significant energy savings were achieved. We consider a three-dimensional communication network that can be used in a building or a stack of shipping containers. Our model can be extended to a 3D model.
Enhancing Code Readability through Automated Consistent Formatting
Authors: Kanoutas Thomas , Karanikiotis Thomas , Symeonidis Andreas L.
Published: May 2024

Code readability is critical to software development and has a significant impact on maintenance and collaboration in evolving technology landscapes. With the increasing complexity of projects and the diversity of developers’ coding styles, the need for automated tools to improve code readability has become more apparent. This paper presents an innovative automated system designed to improve code readability by modeling and enforcing consistent formatting standards. The approach uses techniques such as Long Short-Term Memory (LSTM) networks and N-gram models, allowing the system to adapt to different coding styles and preferences. The system works autonomously by analyzing code styling within a project, identifying deviations from established standards and providing actionable recommendations for consistent styling. To validate our approach, several evaluations were performed on a large dataset of Java files. The results demonstrate the system’s effectiveness in detecting and correcting formatting errors, identifying a formatting error within the first five predictions more than 90% of the time, while providing the correct fix nearly 96% of the time, regardless of formatting convention or programming language. By offering a solution tailored to the specific needs of different teams, our system represents a significant advance in automated code formatting and readability improvement.
Circularly Polarized 4 × 4 Array Antenna with a Wide Axial Ratio Bandwidth
Authors: Lee Tae-Hak , Jung Jinwoo , Pyo Seongmin
Published: May 2024

In this manuscript, we propose a circularly polarized (CP) antenna design that can achieve broadband axial ratio (AR) bandwidth in the X-band. A sequentially rotated serial feeding network is applied to achieve the broadband requirement. The proposed antenna consists of two substrates, one is for feeding elements and the other is for radiating elements. The 16 radiating elements located on an upper substrate are fed through cross-shaped slots located in a ground plane between two substrates. The feed network is based on a 1:4 power divider design and consists of a serial connection of the power dividers resulting in the proper power splitting to the 16 radiating elements. The proposed antenna is designed to exhibit an optimal radiation characteristic of around 8.2 GHz with a CP antenna gain greater than 16 dB and similarly wide impedance and AR bandwidths. Experiments with the fabricated CP antenna were performed to support the proposed CP antenna design theory and are in good agreement with the simulation results.
A Multiscale Grouped Convolution and Lightweight Adaptive Downsampling-Based Detection of Protective Equipment for Power Workers
Authors: Liu Xin , Li Yingna
Published: May 2024

Convolutional neural network-based detection models have been extensively applied in industrial production for monitoring the use of safety protection equipment, ensuring worker safety. This paper addresses scenarios in electrical operations where the safety protection requirements are more comprehensive and stringent. This paper proposes an improved detection model, ML-YOLOv8n-Light, based on YOLOv8n, targeting issues of low detection efficiency and large model size that make deployment challenging in current safety protection equipment-wearing detection models for electrical operations. Our model confronts the disparity in safety protection equipment sizes with a novel, lightweight multi-scale grouped convolution (MSGC) scheme integrated into the architecture. A lightweight adaptive weight downsampling (LAWD) mechanism is also fashioned to replace the traditional downsampling methods, optimizing resource consumption without sacrificing performance. Additionally, to enhance the detection fidelity of smaller items, such as insulated gloves, we added feature-rich shallow maps and a dedicated detection head for such objects. To enhance the detection efficiency of YOLOv8n, inspired by part convolution, we improved the spatial pyramid pooling fast (SPPF) and the detection heads. The experiments conducted on the custom dataset power safe attire dataset (PSAD) showed that compared to the original model, mAP50 increased by 2.1%, mAP50-95 by 3.1%, with a 29% reduction in parameters, an 18% reduction in computations, and a 23% compression of the model size. There are fewer detection omissions at long distances and under occlusion, fewer false positives, and computing resources are allocated more efficiently.
Pinicorn: Towards Automated Dynamic Analysis for Unpacking 32-Bit PE Malware
Authors: Lee Gwangyeol , Kim Minho , Yi Jeong Hyun , Cho Haehyun
Published: May 2024

Original Entry Point (OEP) and API obfuscation techniques greatly hinder the analysis of malware. Contemporary packers, employing these sophisticated obfuscation strategies, continue to pose unresolved challenges, despite extensive research efforts. Recent studies, like API-Xray, have mainly concentrated on rebuilding obfuscated import tables in malware, but research into OEP obfuscation is still limited. As a solution, we present Pinicorn, an automated dynamic de-obfuscation system designed to tackle these complexities. Pinicorn bypasses packers’ anti-analysis techniques and retrieves the original program from memory. It is specifically designed to detect and analyze trampoline codes within both OEP and the import table. Our evaluation shows that Pinicorn successfully deobfuscates programs hidden by three different packers, confirming its effectiveness through a comparative analysis with their original versions. Furthermore, we conducted experiments on malware obfuscated by Themida and VMProtect, analyzing the obfuscation techniques and successfully de-obfuscating them to validate the effectiveness of our approach.
EDUNet++: An Enhanced Denoising Unet++ for Ice-Covered Transmission Line Images
Authors: Zhang Yu , Dou Yinke , Zhao Liangliang , Jiao Yangyang , Guo Dongliang
Published: May 2024

New technology has made it possible to monitor and analyze the condition of ice-covered transmission lines based on images. However, the collected images are frequently accompanied by noise, which results in inaccurate monitoring. Therefore, this paper proposes an enhanced denoising Unet++ for ice-covered transmission line images (EDUNet++). This algorithm mainly comprises three modules: a feature encoding and decoding module (FEADM), a shared source feature fusion module (SSFFM), and an error correction module (ECM). In the FEADM, a residual attention module (RAM) and a multilevel feature attention module (MFAM) are proposed. The RAM incorporates the cascaded residual structure and hybrid attention mechanism, that effectively preserve the mapping of feature information. The MFAM uses dilated convolution to obtain features at different levels, and then uses feature attention for weighting. This module effectively combines local and global features, which can better capture the details and texture information in the image. In the SSFFM, the source features are fused to preserve low-frequency information like texture and edges in the image, hence enhancing the realism and clarity of the image. The ECM utilizes the discrepancy between the generated image and the original image to effectively capture all the potential information in the image, hence enhancing the realism of the generated image. We employ a novel piecewise joint loss. On the dataset of ice-covered transmission lines, PSNR (peak signal to noise ratio) and SSIM (structural similarity) achieved values of 29.765 dB and 0.968, respectively. Additionally, the visual effects exhibited more distinct detailed features. The proposed method exhibits superior noise suppression capabilities and robustness compared to alternative approaches.
Leading Point Multi-Regression Model for Detection of Anomalous Days in German Energy System
Authors: Karpio Krzysztof , Łukasiewicz Piotr , Ząbkowski Tomasz
Published: May 2024

In this article, the Leading Point Multi-Regression model was applied to identify days with anomalous energy consumption profiles. The data for the analysis come from the German energy system and they represent the hourly energy demand observed between 2006 and 2015. Days with abnormal daily profiles were identified based on the statistical analysis of the errors observed for the model. The accuracy of the model is very high and comparable with other models, e.g., LSTM, K-means, Recurrent NN, and tree-based ML methods. However, these methods rely on external factors (e.g., humidity, temperature, and sunshine) impacting energy consumption while our model uses only the energy consumption at specific fixed hours, regardless of external factors, thus being universal. Days with anomalous energy consumption profiles were identified as days related to celebration of New Year’s Eve and the New Year. Also, anomalies were identified for some other days, which were not that obvious, including Good Friday, National Day of Mourning, and, interestingly, the day of the Germany–Turkey match during the European Championship in 2008.
Identification of Both Distortion and Imbalance Sources in Electrical Installations: A Comparative Assessment
Authors: Sánchez-Herrera Reyes , Clavijo-Camacho Jesús , Gómez-Ruiz Gabriel , Vázquez Jesús R.
Published: May 2024

Electric power quality is becoming more critical with time because of the increasing use of electronic devices in all areas of society: in industry, in the household, etc. Many kinds of disturbances can be produced in the grid due to external or internal causes, such as, for example, switching operations. Among them, distortion and imbalance are long-term disturbances, which makes them very dangerous for electric equipment. In addition, the identification of the distortion and/or imbalance sources is quite difficult because they are instantaneously scattered by the grid. This paper presents a study of the algorithms published in the technical literature which identify distortion and imbalance sources in electrical installations and compares the results of applying them to a specific installation constituted by linear and non-linear, balanced and unbalanced loads, and a standard module to inject energy to the grid. Only two algorithms have been found in the review carried out and the results provided by both are presented and discussed. Neither of these algorithms is completely reliable, although one of them is more promising.
Equivalent Thermal Conductivity of Topology-Optimized Composite Structure for Three Typical Conductive Heat Transfer Models
Authors: Lu Biwang , He Jing
Published: May 2024

Composite materials and structural optimization are important research topics in heat transfer enhancement. The current evaluation parameter for the conductive heat transfer capability of composites is effective thermal conductivity (ETC); however, this parameter has not been studied or analyzed for its applicability to different heat transfer models and composite structures. In addition, the optimized composite structures of a specific object will vary when different optimization methods and criteria are employed. Therefore, it is necessary to investigate a suitable method and parameter for evaluating the heat transfer capability of optimized composites under different heat transfer models. Therefore, this study analyzes and summarizes three typical conductive heat transfer models: surface-to-surface (S-to-S), volume-to-surface (V-to-S), and volume-to-volume (V-to-V) models. The equivalent thermal conductivity (keq) is proposed to evaluate the conductive heat transfer capability of topology-optimized composite structures under the three models. A validated simulation method is used to obtain the key parameters for calculating keq. The influences of the interfacial thermal resistance and size effect on keq are considered. The results show that the composite structure optimized for the V-to-S and V-to-V models has a keq value of only 79.4 W m−1 K−1 under the S-to-S model. However, the keq values are 233.4 W m−1 K−1 and 240.3 W m−1 K−1 under the V-to-S and V-to-V models, respectively, which are approximately 41% greater than those of the in-parallel structure. It can be demonstrated that keq is more suitable than the ETC for evaluating the V-to-S and V-to-V heat transfer capabilities of composite structures. The proposed keq can serve as a characteristic parameter that is beneficial for heat transfer analysis and composite structural optimization.
An Improved WOA (Whale Optimization Algorithm)-Based CNN-BIGRU-CBAM Model and Its Application to Short-Term Power Load Forecasting
Authors: Dai Lei , Wang Haiying
Published: May 2024

The accuracy requirements for short-term power load forecasting have been increasing due to the rapid development of the electric power industry. Nevertheless, the short-term load exhibits both elasticity and instability characteristics, posing challenges for accurate load forecasting. Meanwhile, the traditional prediction model suffers from the issues of inadequate precision and inefficient training. In this work, a proposed model called IWOA-CNN-BIGRU-CBAM is introduced. To solve the problem of the Squeeze-and-Excitation (SE) attention mechanism’s inability to collect information in the spatial dimension effectively, the Convolutional Block Attention Module (CBAM) is firstly introduced as a replacement. This change aims to enhance the ability to capture location attributes. Subsequently, we propose an improved Whale Optimization Algorithm (IWOA) that addresses its limitations, such as heavy reliance on the initial solution and susceptibility to local optimum solutions. The proposed IWOA is also applied for the hyperparameter optimization of the Convolutional Neural Network–Bidirectional Gated Recurrent Unit–Convolutional Block Attention Module (CNN-BiGRU-CBAM) to improve the precision of predictions. Ultimately, applying the proposed model to forecast short-term power demand yields results that show that the CBAM effectively addresses the problem of the SE attention mechanism’s inability to capture spatial characteristics fully. The proposed IWOA exhibits a homogeneous dispersion of the initial population and an effective capability to identify the optimal solution. Compared to other models, the proposed model improves R2 by 0.00224, reduces the RMSE by 18.5781, and reduces MAE by 25.8940, and the model’s applicability and superiority are validated.
Examining the Spillover Effects of Renewable Energy Policies on China’s Traditional Energy Industries and Stock Markets
Authors: Zhao Haiwen , Yu Miao , Meng Juan , Jiang Yonghong
Published: May 2024

With the development and refinement of the carbon emissions trading market, the relationship between the carbon market and the stock market has grown increasingly intertwined. This has led to a surge in research investigating the interactions between the carbon market and related sectors. This study examines the intensity and direction of spillover effects among ten industries associated with carbon emissions, spanning traditional and emerging energy sectors. Through static analysis, we find that spillover effects between industries in the carbon and stock markets are bidirectional and asymmetric. Dynamic analysis reveals that the carbon market, acting as the primary recipient of spillover effects, is notably influenced by traditional energy industries such as coal and oil, followed by photovoltaics, new energy vehicles, and others. The magnitude of these spillover effects is subject to fluctuations influenced by energy crises and events like the COVID-19 pandemic, while policy interventions can alter the overall trends in net spillover effects across various industries.
Exploring the Role of Additives in Enhancing the Performance of Limestone-Based Thermochemical Energy Storage: A Review
Authors: Anwar Rehan , Sofianos M. Veronica
Published: May 2024

This review article explores the critical role of additives in enhancing the performance and durability of thermochemical energy storage (TCES) materials, particularly in limestone-based systems. It evaluates various strategies, including hydration and the use of fine particles, along with additives like Al2O3 and ZrO2, to address challenges like performance degradation and sintering over multiple cycles. Additionally, the review examines how multicyclic stability and material activity toward CO2 are related. It emphasizes the importance of selecting support materials that optimize both stability and reactivity. Furthermore, it highlights the need for systematic investigation into the selection, synthesis methods, and additive percentages to identify optimal formulations for improved multicyclic stability. Finally, it underscores the importance of understanding the mechanisms of interaction between additives and CaO/CaCO3 matrices to guide the design of effective additive-integrated systems. This comprehensive analysis provides valuable insights into current methodologies, emerging trends, and future directions for advancing sustainable energy storage technologies.
A Framework to Assess and Analyze Enhancement Options for Microgrid Resiliency against Extreme Wind
Authors: Karki Rajesh , Adhikari Binamra
Published: May 2024

The objective of a power system is to provide electricity to its customers as economically as possible with an acceptable level of reliability while safeguarding the environment. Power system reliability assessments are routinely performed to ensure adequate system resources and reliable operation using well-established methods, quantitative metrics, regulatory standards and compliance incentives in the jurisdictions of responsibilities. The alarming increase in the occurrence of extreme events, which are not included in routine reliability evaluation, has raised growing concerns due to the catastrophic impacts of these events on distribution systems. The potential economic losses due to prolonged and large-scale outages have motivated utility planners, operators and policy makers to acknowledge the importance of system resiliency against such events. Power system resiliency, however, lacks widely accepted modeling frameworks, standards, assessment methods and metrics. This paper presents a resilience assessment framework, along with quantifiable metrics to assess the resiliency of a distribution system against extreme winds, which are among the most common form of natural disasters affecting the North American region. The paper assesses the effectiveness of infrastructural and operational resilience enhancement strategies. The effectiveness of preventive and corrective strategies is also analyzed on a test distribution system.
Short-Term Forecast of Photovoltaic Solar Energy Production Using LSTM
Authors: Campos Filipe D. , Sousa Tiago C. , Barbosa Ramiro S.
Published: May 2024

In recent times, renewable energy sources have gained considerable vitality due to their inexhaustible resources and the detrimental effects of fossil fuels, such as the impact of greenhouse gases on the planet. This article aims to be a supportive tool for the development of research in the field of artificial intelligence (AI), as it presents a solution for predicting photovoltaic energy production. The basis of the AI models is provided from two data sets, one for generated electrical power and another for meteorological data, related to the year 2017, which are freely available on the Energias de Portugal (EDP) Open Project website. The implemented AI models rely on long short-term memory (LSTM) neural networks, providing a forecast value for electrical energy with a 60-min horizon based on meteorological variables. The performance of the models is evaluated using the performance indicators MAE, RMSE, and R2, for which favorable results were obtained, with particular emphasis on forecasts for the spring and summer seasons.
Experimental Investigation of Thermal Prediction and Heat Transfer Characteristics of Two-Phase RDE during Long-Duration Operation
Authors: Wang Jiaojiao , Song Feilong , Chen Qi , Kang Jinhui , Wu Yun
Published: May 2024

Accurately predicting the thermal characteristics and heat transfer distribution of the rotating detonation engine (RDE) and acquiring a clear understanding of the performance and mechanism of the rotating detonation are of great significance for achieving the safe and reliable long-duration operation of RDEs. Using RP-3 as fuel, a long-duration experimental study is performed on a 220 mm-diameter RDC to investigate the details with respect to the thermal environment. The heat flux at the typical location and the average heat flux of both the inner and outer cylinders are measured, respectively. Meanwhile, the peak pressure of the rotating detonation wave (RDW) and specific thrust are analyzed. When the ER is between 0.5 and 1 (oxidizer 2 kg/s), the stable rotating detonation mode is obtained, and the detonation duration is set as 40 s to accurately calculate the heat released by the detonation combustion. The heat flux in the upstream region of the RDW location ranges from 2.40 × 105 W/m2 to 3.17 × 105 W/m2, and the heat flux in the downstream area of the RDW location ranges from 1.05 × 106 W/m2 to 1.28 × 106 W/m2. The results demonstrate the important role of the detonation combustion zone, and the thrust performance of RDC can be improved by making the RDW move forward along the RDC axis, which is the optimal direction of detonation combustion. Through a comparison of average heat flux under different conditions, it is found that the heat released by the RDC is directly related to its thrust. In addition, the average heat flux of the inner cylinder is about three times that of the outer cylinder for the two-phase RDC with a Tesla valve intake structure, indicating that the high-temperature combustion product is closer to the inner wall. Therefore, more thermal protection should be allocated to the inner cylinder, and a more systematic analysis of the two-phase flow field distribution in the annular combustion chamber should be carried out to improve the thrust performance. In this paper, the average heat flux of the inner and outer cylinders of the RDC as well as the typical local heat flux of the outer cylinders is quantitatively measured by means of experiments, which not only deepens the understanding of RDC flow field distribution, but also provides quantitative boundary conditions for the thermal protection design of RDCs.
Experimental Study of Pollutant Emissions from Biomass Combustion and Modeling of PM Transportation
Authors: Delcourt François , Izerroukyene Abdelkader , Méresse Damien , Uystepruyst David , Beaubert François , Morin Céline
Published: May 2024

Experimental measurements and modeling have been performed in the chimney of a biomass boiler to study the gaseous and particulate matter (PM) emissions during the combustion of wood pellets. A 10 kW boiler with an underfeed burner is equipped with different sensors located in the chimney (anemometer, thermocouples). The PM emissions were measured in the chimney through the engine exhaust particle sizer (EEPS) technique. Moreover, the gaseous emissions (CO2, CO, total hydrocarbons THC, O2) were obtained through infrared (IR) spectroscopy and flame ionization detector (FID). The emissions were recorded during the steady phase of the boiler and averaged over several tests. Four locations were investigated in the chimney to evaluate the evolution of the particle size and the potential deposition on the surface. The experimental results were compared with a CFD model with particle transportation. The modeling of turbulent flow in the chimney is based on a Reynolds-averaged Navier–Stokes (RANS) approach with turbulent viscosity closure. To account for flow anisotropy, the v2¯−f turbulence model was selected for this study. The effect of turbulent fluctuations on the discrete phase is considered by the discrete random walk (DRW) turbulent dispersion model. The results obtained provide access to the topology of the carrier phase flow as well as the complete distribution of the particle field within the chimney enclosure. Advanced measurement of pollutant emissions and modeling of the PM transportation are developed for the first time in a domestic biomass boiler operating in real conditions. Experimental results demonstrate several relevant information. The CO and THC emissions show a similar evolution versus time. The PM granulometric distribution measured along the chimney highlights the particle agglomeration phenomena. Moreover, the CFD model and experimental results give similar results in terms of flow characteristics and PM granulometry.
A Systematic Review of Effects on ACL Injury of Soccer Shoe Outsoles, Soccer Playing Field Surfaces, and Outsole–Surface Interface
Authors: Cicchella Antonio , He Jierui
Published: May 2024

Soccer is a sport with a high incidence of injuries. The most common injury occurs when the anterior cruciate ligament of the knee has undergone a sprain, strain, or partial or total rupture. Besides fitness factors (e.g., proprioception, balance and strength capabilities), a principal cause of ACL injuries is sport shoes and playing surfaces. Especially with the emergence of artificial surfaces (rubbers, turfs, concrete, asphalt, red turf), the ACL injuries dramatically increase. The cost of ACL injuries is high both in terms of career termination and in social and economic costs. Thus, it is necessary to understand the impact of sport shoes and playing surfaces on the ACL and how it can be mitigated. The present literature review followed the PRISMA methodology to identify the major biomechanical factors influencing the behavior of surfaces and shoes in relation to the ACL damage. Fifty-eight papers were identified. After reviewing the mechanism of injuries, we identified several distinct factors: type of outsole and mechanical arrangement of the outsoles; shear forces; characteristics of artificial turf surfaces; effect of weather on artificial surface aging and change in mechanical characteristics; outsole/ground interface; and the frictional, tractional and rotational forces in the pathogenesis of ACL injuries in soccer.
The Evolutionary System of the Biosphere and the Metameric Concept of Its Evolution: From the Past to the Future
Authors: Protasov Alexander , Barinova Sophia
Published: May 2024

We offer a detailed description of our previously published new concept of the evolution of the biosphere as an integral system of its states over time, united by development trends. The structure of the biosphere is considered as a hierarchical fractal system, and the elementary unit of the biosphere is an ecosystem. The fractal structure of the biosphere corresponds to the emergent principle: each lower level is an element of a more complex system and has its own properties. The proposed concept of biosphere evolution is based on the general categories of dialectics: states and interstates, continuity and discreteness, reproducibility and uniqueness. The evolutionary history of the biosphere is a metameric picture of changing states and interstates. The most important feature of the biosphere organization in space–time is a complex system of continua. The development of an integral biospheric system occurs in a time continuum: in the biosphere, the differences between the early and subsequent states are quite significant and obvious. Moreover, these differences are associated with fundamental complications, development, which is, in fact, evolution. The states of the biosphere in certain periods are linked by trends that form an evolutionary system. Continuing states, when the system remains self-identical for a long period, are replaced by new states through interstates. Based on the principle of actualism, the problems of the biosphere’s future and evolutionary trends of the biosphere under anthropogenic impact are considered.
The Cultural Ecohydrogeology of Mediterranean-Climate Springs: A Global Review with Case Studies
Authors: Pascual Roger , Piana Lucia , Bhat Sami Ullah , Castro Pedro Fidel , Corbera Jordi , Cummings Dion , Delgado Cristina , Eades Eugene , Fensham Roderick J. , Fernández-Martínez Marcos , Ferreira Verónica , Filippini Maria , García Guillermo , Gargini Alessandro , Hopper Stephen D. , Knapp Lynette , Lewis Ian D. , Peñuelas Josep , Preece Catherine , Resh Vincent H. , Romero Estela , Samraoui Boudjéma , Samraoui Farrah , Segadelli Stefano , Skoulikidis Nikolaos Th. , Solak Cüneyt N. , Solé Jaume , Villholth Karen G. , Wani Huma Khurshid , Cantonati Marco , Stevens Lawrence E.
Published: May 2024

Cultures in Mediterranean climate zones (MCZs) around the world have long been reliant on groundwater and springs as freshwater sources. While their ecology and cultural sustainability are recognized as critically important, inter-relationships between springs and culture in MCZs have received less attention. Here we augmented a global literature review with case studies in MCZ cultural landscapes to examine the diversity and intensity of cultural and socio-economic relationships on spring ecohydrogeology. MCZs are often oriented on western and southern coasts in tectonically active landscapes which control aquifer structure, the prevalence of westerly winds, and aridity, and generally expose associated habitats and cultures to harsh afternoon sunlight. Cultural appreciation and appropriation of springs ranges widely, from their use as subsistence water supplies to their roles in profound traditions such as Greco-Roman nymphalea as well as Asian and Abrahamic spiritual cleansing and baptism. The abandonment of traditional ways of life, such as rural livestock production, for urban ones has shifted impacts on aquifers from local to regional groundwater exploitation. The commoditization of water resources for regional agricultural, industrial (e.g., mining, water bottling, geothermal resorts), and urban uses is placing ever-increasing unsustainable demands on aquifers and spring ecosystems. When the regional economic value of springs approaches or exceeds local cultural values, these irreplaceable aquatic ecosystems are often degraded, over-looked, and lost. Sustainable stewardship of springs and the aquifers that support them is a poorly recognized but central conservation challenge for modern Mediterranean societies as they face impending impacts of global climate change. Solutions to this crisis require education, societal dialogue, and improved policy and implementation.
Structural and Biochemical Characterization of the Nucleosome Containing Variants H3.3 and H2A.Z
Authors: Jung Harry , Sokolova Vladyslava , Lee Gahyun , Stevens Victoria Rose , Tan Dongyan
Published: May 2024

Variant H3.3, along with H2A.Z, is notably enriched at promoter regions and is commonly associated with transcriptional activation. However, the specific molecular mechanisms through which H3.3 influences chromatin dynamics at transcription start sites, and its role in gene regulation, remain elusive. Using a combination of biochemistry and cryo-electron microscopy (cryo-EM), we show that the inclusion of H3.3 alone does not induce discernible changes in nucleosome DNA dynamics. Conversely, the presence of both H3.3 and H2A.Z enhances DNA’s flexibility similarly to H2A.Z alone. Interestingly, our findings suggest that the presence of H3.3 in the H2A.Z nucleosome provides slightly increased protection to DNA at internal sites within the nucleosome. These results imply that while H2A.Z at active promoters promotes the formation of more accessible nucleosomes with increased DNA accessibility to facilitate transcription, the simultaneous presence of H3.3 offers an additional mechanism to fine-tune nucleosome accessibility and the chromatin environment.
Uptake and Transport of Selenium in a Soil–Tea Plant–Tea Infusion System: A Study of Typical Tea Plantations in a Selenium-Rich Area of China
Authors: Wu Haizhong , Zhang Dengxiao , Wu Xinmin , Tian Xiaosi , Hu Gang , Liu Shiliang , Jie Xiaolei , Wang Daichang
Published: May 2024

Selenium is an important indicator for the evaluation of tea quality. However, the relationship between selenium uptake by tea plants (Camellia sinensis) and soil properties, as well as selenium transport and distribution in a tea plantation soil–tea plant–tea infusion production system, remain unclear. In this study, 12 tea plantations situated in a typical selenium-rich area of China were selected, and the characteristics and crucial factors influencing the uptake and transport of selenium were analyzed using a plantation soil–tea plant–tea infusion production system. The soil total selenium content ranged from 1.12 to 6.67 mg kg−1, with an average of 2.57 mg kg−1. The average available selenium content was 53.56 µg kg−1, and the activation rate of soil selenium was 2.27%. Soil-available selenium was significantly positively correlated with total selenium, available potassium, and soil organic matter contents, and was significantly negatively correlated with soil pH. The selenium content in old leaves ranged from 0.29 to 2.73 mg kg−1, which met the standard for selenium-rich tea, whereas only 33% of young leaves met this standard. The selenium enrichment factor was highest in the fibrous root and lowest in the young leaves. The average selenium transport factors from fibrous roots to main roots, from main roots to main stems, from main stems to lateral stems, from lateral stems to young leaves, and from lateral stems to old leaves were 0.53, 0.92, 0.67, 0.97, and 2.30, respectively. The selenium concentration of tea infusion ranged from 1.88 to 12.49 μg L−1, and the average selenium dissolution rate was 22.62% after one brewing. This study identified critical factors that influence soil-selenium availability. The selenium content in tea plant organs is indicated to be strongly associated with the selenium content in the main roots.
Genome-Wide Identification, Characterization, and Expression Analysis of the CYP450 Family Associated with Triterpenoid Saponin in Soapberry (Sapindus mukorossi Gaertn.)
Authors: Zheng Chunyuan , Zhou Mingzhu , Fan Jialin , Gao Yuhan , Xu Yuanyuan , Jia Liming , An Xinmin , Chen Zhong , Wang Lianchun
Published: May 2024

Soapberry (Sapindus mukorossi Gaertn.) is a tree species of the family Sapindaceae, the pericarp of which is rich in triterpenoid saponins, which are important in chemical production, biomedicine, and other fields. Cytochrome P450 monooxygenase (CYP450) is involved in the modification of the skeletons of triterpenoid saponins and is linked to their diversity. We previously identified 323 CYP450 genes in the transcriptome of soapberry and screened 40 CYP450 genes related to the synthesis of triterpenoid saponins by gene annotation and conserved structural domain analysis. The genetic structure and phylogeny of the CYP450 genes were analyzed separately. Phylogenetic analysis categorized the CYP450 genes of soapberry into five subfamilies, the members of which had similar conserved cumulative sequences and intron structures. A cis-acting element analysis implicated several genes in the responses to environmental changes and hormones. The expression of several genes during eight periods of fruit development was analyzed by real-time quantitative qRT-PCR; most showed high expression during the first four periods of fruit development, and their expression decreased as the fruits matured. A co-expression network analysis of SmCYP450s and related genes in the triterpenoid saponin synthesis pathway was performed. Correlation analysis showed that 40 SmCYP450s may be involved in saponin synthesis in soapberry. The triterpenoid saponin synthesis-related candidate genes identified in this study provide insight into the synthesis and regulation of triterpenoid saponins in soapberry.
Bioprospecting and Selection of Indigenous Saccharomyces cerevisiae Yeasts from Pozo de Los Algarrobos (Argentina) for the Production of Genuine White Wines
Authors: Petrignani Diego Bernardo , Mestre María Victoria , Vargas Mercedes Fabiana , Chimeno Selva Valeria , Vazquez Fabio , Maturano Yolanda Paola
Published: May 2024

Yeasts play a crucial role in the winemaking process contributing to the typicity and originality of wines in a region. Therefore, the aim of the present study was to isolate, characterize, and select yeasts from the Geographical Indication “Pozo de Los Algarrobos”, San Juan, Argentina. Yeasts were directly isolated from grapes and at different stages of spontaneous fermentations of Vitis vinifera Viognier and Chardonnay varieties. Molecular and intraspecific identification of Saccharomyces cerevisiae yeasts was conducted using the D1/D2 domain and interdelta, respectively, observing 13 different yeast strains from Viognier and 12 from Chardonnay vinifications. Based on the enological traits assayed, two strains, V22 (Viognier) and C14 (Chardonnay), were selected for further studies. Microvinifications with these yeasts were carried out with Viognier and Chardonnay grape must in 2 L flasks, and the resulting wines were analytically and sensorially evaluated. Overall, strain V22 produced wines with positive and particular sensory properties, associated with fruity and floral aromas, color intensity, sweetness, aromatic persistence, and varietal typicity. Consequently, biomass propagation of V22 was conducted to inoculate pilot- (100 L) and industrial (12,000 L)-scale fermentations. V22 resulted in a correct wine fermentation performance obtaining a final product with distinctive and genuine properties.
Data Collection in Areas without Infrastructure Using LoRa Technology and a Quadrotor
Authors: Rojo-García Josué I. , Vera-Chavarría Sergio A. , Lozano-Hernández Yair , Sánchez-Meza Victor G. , González-Sierra Jaime , Oliva-Moreno Luz N.
Published: May 2024

The use of sensor networks in monitoring applications has increased; they are useful in security, environmental, and health applications, among others. These networks usually transmit data through short-range stations, which makes them attractive for incorporation into applications and devices for use in places without access to satellite or mobile signals, for example, forests, seas, and jungles. To this end, unmanned aerial vehicles (UAVs) have attractive characteristics for data collection and transmission in remote areas without infrastructure. Integrating systems based on wireless sensors and UAVs seems to be an economical and easy-to-use solution. However, the main difficulty is the amount of data sent, which affects the communication time and even the flight status of the UAV. Additionally, factors such as the UAV model and the hardware used for these tasks must be considered. Based on those difficulties mentioned, this paper proposes a system based on long-range (LoRa) technology. We present a low-cost wireless sensor network that is flexible, easy to deploy, and capable of collecting/sending data via LoRa transceivers. The readings obtained are packaged and sent to a UAV. The UAV performs predefined flights at a constant height of 30 m and with a direct line-of-sight (LoS) to the stations, during which it collects information from two data stations, concluding that it is possible to carry out a correct data transmission with a flight speed of 10 m/s and a transmission radius of 690 m for a group of three packages confirmed by 20 messages each. Thus, it is possible to collect data from routes of up to 8 km for each battery charge, considering the return of the UAV.