bluuebunny/crossref_metadata_2025 huggingface dataset:
A Review and Follow-Up of Uterine Smooth Muscle Tumours of Uncertain Malignant Potential (STUMP): A Case Series and Literature Review
Authors: Lapresa-Alcalde M. V. , Ruiz-Navarro M. J. , Sancho de Salas M. , Cubo A. M.
Published: July 2023

Objectives: to analyse the clinical–pathological characteristics, treatment, and evolution of uterine smooth muscle tumours with uncertain malignant potential (STUMP) diagnosed in the Salamanca University Hospital with the implementation of the 2014 WHO criteria. Materials and methods: a retrospective descriptive study of patients diagnosed with STUMP from January 2015 to March 2023 at the Salamanca University Hospital. Demographic data, preoperative clinical data, treatment, complications, therapeutic results, anatomopathological findings and recurrence time were obtained. Results: a total of four patients were identified and included in the study. The mean age at diagnosis was 48 years (range 36–67). The surgical indications were abnormal uterine bleeding, compressive symptoms, and the growth of a pelvic mass suspected to be a degenerated myoma from the residual cervix after a subtotal hysterectomy 6 years earlier. In all cases, a laparotomic procedure was performed. A total hysterectomy, sub-total hysterectomy, and the excision of the cervix with STUMP localization were accomplished in two, one, and one patient, respectively. The mean diameter of the tumour pieces was 13 cm (range 8–17 cm), with a mean volume of 816 cc (range 234–1467 cc). The mean follow-up was 47 months, with no recurrence to date. Conclusions: STUMPs are a heterogeneous group of tumours with a difficult-to-predict clinical evolution. In most cases, their diagnosis is histological after performing surgery for suspected leiomyoma. Due to their low incidence, there are no specific guidelines for their treatment and control. However, considering their potential risk of recurrence and metastasis, it is advisable to maintain six-monthly controls for 5 years and then annual controls for 5 years more.
The Relationship between Electrical Conductivity and Electromigration in Liquid Metals
Authors: Belashchenko David K.
Published: July 2023

The phenomena of electrical conductivity and electromigration in metallic systems are related, since in both cases the basic physical process is the scattering of conduction electrons by metal ions. Numerous searches have been made for equations connecting the conductivity with electromigration. In the case of a liquid metal, when using the Drude–Sommerfeld (DS) conductivity equation, it was not possible to obtain a quantitative relationship between these phenomena, which would be correct. Attempts to find such a relationship when taking into account the N. Mott correction (g-factor) in the DS equation were unsuccessful. This article proposes a different correction (b-factor) to the DS equation, which takes into account the possibility of varying the momentum transferred by the conduction electron to a metal ion during the scattering. This correction allows to establish a quantitative relationship between conductivity and electromigration as well as between electromigration in various binary systems with common components, in agreement with the experiment. The proposed theory describes well, in particular, two- and multi-component metal systems of any concentration (the consistency rule for triangles A–B, B–C, C–A). The value of the b-factor smoothly changes depending on the heat of vaporization of the metal, per unit volume.
Multi-Task Time Series Forecasting Based on Graph Neural Networks
Authors: Han Xiao , Huang Yongjie , Pan Zhisong , Li Wei , Hu Yahao , Lin Gengyou
Published: July 2023

Accurate time series forecasting is of great importance in real-world scenarios such as health care, transportation, and finance. Because of the tendency, temporal variations, and periodicity of the time series data, there are complex and dynamic dependencies among its underlying features. In time series forecasting tasks, the features learned by a specific task at the current time step (such as predicting mortality) are related to the features of historical timesteps and the features of adjacent timesteps of related tasks (such as predicting fever). Therefore, capturing dynamic dependencies in data is a challenging problem for learning accurate future prediction behavior. To address this challenge, we propose a cross-timestep feature-sharing multi-task time series forecasting model that can capture global and local dynamic dependencies in time series data. Initially, the global dynamic dependencies of features within each task are captured through a self-attention mechanism. Furthermore, an adaptive sparse graph structure is employed to capture the local dynamic dependencies inherent in the data, which can explicitly depict the correlation between features across timesteps and tasks. Lastly, the cross-timestep feature sharing between tasks is achieved through a graph attention mechanism, which strengthens the learning of shared features that are strongly correlated with a single task. It is beneficial for improving the generalization performance of the model. Our experimental results demonstrate that our method is significantly competitive compared to baseline methods.
Local Unitary Equivalence of Quantum States Based on the Tensor Decompositions of Unitary Matrices
Authors: Wang Jing , Liu Xiaoqi , Xu Li , Li Ming , Li Lei , Shen Shuqian
Published: July 2023

Since two quantum states that are local unitary (LU) equivalent have the same amount of entanglement, it is meaningful to find a practical method to determine the LU equivalence of given quantum states. In this paper, we present a valid process to find the unitary tensor product decomposition for an arbitrary unitary matrix. Then, by using this process, the conditions for determining the local unitary equivalence of quantum states are obtained. A numerical verification is carried out, which shows the practicability of our protocol. We also present a property of LU invariants by using the universality of quantum gates which can be used to construct the complete set of LU invariants.
A Model Selection Approach for Time Series Forecasting: Incorporating Google Trends Data in Australian Macro Indicators
Authors: Karim Ali Abdul , Pardede Eric , Mann Scott
Published: July 2023

This study examined whether the behaviour of Internet search users obtained from Google Trends contributes to the forecasting of two Australian macroeconomic indicators: monthly unemployment rate and monthly number of short-term visitors. We assessed the performance of traditional time series linear regression (SARIMA) against a widely used machine learning technique (support vector regression) and a deep learning technique (convolutional neural network) in forecasting both indicators across different data settings. Our study focused on the out-of-sample forecasting performance of the SARIMA, SVR, and CNN models and forecasting the two Australian indicators. We adopted a multi-step approach to compare the performance of the models built over different forecasting horizons and assessed the impact of incorporating Google Trends data in the modelling process. Our approach supports a data-driven framework, which reduces the number of features prior to selecting the best-performing model. The experiments showed that incorporating Internet search data in the forecasting models improved the forecasting accuracy and that the results were dependent on the forecasting horizon, as well as the technique. To the best of our knowledge, this study is the first to assess the usefulness of Google search data in the context of these two economic variables. An extensive comparison of the performance of traditional and machine learning techniques on different data settings was conducted to enable the selection of an efficient model, including the forecasting technique, horizon, and modelling features.
Estimation of Realized Asymmetric Stochastic Volatility Models Using Kalman Filter
Authors: Asai Manabu
Published: July 2023

Despite the growing interest in realized stochastic volatility models, their estimation techniques, such as simulated maximum likelihood (SML), are computationally intensive. Based on the realized volatility equation, this study demonstrates that, in a finite sample, the quasi-maximum likelihood estimator based on the Kalman filter is competitive with the two-step SML estimator, which is less efficient than the SML estimator. Regarding empirical results for the S&P 500 index, the quasi-likelihood ratio tests favored the two-factor realized asymmetric stochastic volatility model with the standardized t distribution among alternative specifications, and an analysis on out-of-sample forecasts prefers the realized stochastic volatility models, rejecting the model without the realized volatility measure. Furthermore, the forecasts of alternative RSV models are statistically equivalent for the data covering the global financial crisis.
Subject Student Teachers’ Perceptions of Key Environmental Problems and Their Own Role as Environmental Problem Solvers
Authors: Yli-Panula Eija , Jeronen Eila , Vesterkvist Sofia , Mulari Laura
Published: July 2023

Teachers play a crucial role in supporting the development of students’ environmental knowledge and skills for responding to environmental change, but previous research has not sufficiently explored teachers’ perceptions of this matter. The article reports on a survey aimed at studying subject student teachers’ (SSTs, n = 113) perceptions of environmental problems, their interest in sustainable development (SD) and their own role as environmental problem solvers. The material was gathered using a questionnaire and analysis by quantitative methods. Urban environment and infrastructure problems were the most frequently mentioned. Problems of ecosystems and indifference and a lack of information were also mentioned often, while the least noted were problems of human wellbeing. The majority of SSTs were interested in SD, but interest in SD was dependent on the discipline studied and gender. Two-thirds of women and one-third of men were very interested, and more than half of biology and geography SSTs were very interested. Over half of SSTs reported that environmental problems had to be solved by someone else; only about one-seventh perceived themselves to be solvers. The SSTs felt that they could influence the solutions to environmental problems more often on a local than on a regional or global level.
Architecture for Self-Evolution of 6G Core Network Based on Intelligent Decision Making
Authors: Lu Lu , Liu Chao , Zhang Chunhong , Hu Zheng , Lin Shangjing , Liu Zihao , Zhang Meng , Liu Xinshu , Chen Jinhao
Published: July 2023

The rapid progress of 6G mobile communication technologies has sparked a great deal research interests. The 6G core network architecture faces formidable challenges due to the escalating complexity of network service demands and diverse application scenarios. In response, our research endeavors to tackle these challenges by proposing a self-evolving architecture based on intelligent decision making. Inspired by the principles of biological morphological evolution, our architecture empowers the core network to dynamically adapt and reshape itself in order to effectively address the evolving communication environments. To facilitate this self-evolutionary process, we introduce a comprehensive framework encompassing mechanisms, architecture, agents, and algorithms that enable the network to autonomously generate and optimize its own structure, thereby ensuring adaptability to a wide range of application scenarios. By conducting concept proof simulation experiments, we have demonstrated the effectiveness of our self-evolution algorithm, which enables the 6G core network to make rational evolving decisions and exhibit remarkable adaptability to various application scenarios.
MoBiSea: A Binary Search Algorithm for Product Clustering in Industry 4.0
Authors: Herrero Angel C. , Sanguesa Julio A. , Garrido Piedad , Martinez Francisco J. , Calafate Carlos T.
Published: July 2023

Proprietary systems used to modernize Industry 4.0 usually involve high financial costs. Consequently, using low-cost devices with the same functionalities, capable of replacing these proprietary systems but at a lower cost, has become an incipient trend. However, these low-cost devices usually come with electromagnetic interference problems as they are encapsulated in electrical panels, sitting alongside electromechanical devices. In this article, we present Mode Binary Search, an algorithm specifically designed for use in a low-cost automated-industrial-productivity-data-collection system. Specifically, productivity data are obtained from the availability and sealing signals of the thermoplastic sealing machines in production lines belonging to the agri-food industry. Mode Binary Search was designed to cluster sealing signals, thus enabling us to identify which products have been made. Furthermore, the algorithm determines when the manufacturing of each product starts and ends, in other words, the exact moment a product change occurs and all this without the need for operator supervision or intervention. Finally, we compared our algorithm, based on binary search, with three clustering mechanisms: k-means, k-rms and x-means. Out of all the cases we analyzed, the maximum error committed by Mode Binary Search is limited to 2.69%, thereby outperforming all others.
Microwave-Based Dielectric Properties as an Electrophysiological Biomarker: Future Perspectives
Authors: Cherukuri Akhila Sai Sree , Modi Vaishnavi Kalpesh , Baraskar Bhavana , Sood Shubham , Reguram Reshma , Palvia Divyanshi , Gopalakrishnan Keerthy , Damani Devanshi N. , Gaddam Sunil , Samaddar Poulami , Katukuri Nishanth , Shivaram Suganti , Dey Shuvashis , Mitra Dipankar , Roy Sayan , Linden David R. , Beyder Arthur , Kulkarni Kanchan , Arunachalam Shivaram P.
Published: July 2023

Electrophysiology is the study of the electrical properties of biological tissues, which involves the movement of ions across cell membranes. The analysis of the movement of electrical charges through the body has a wide range of biomedical applications, such as diagnosing and planning treatment in cardiovascular, nervous systems, muscular, and gastrointestinal disorders. The dielectric properties of biological tissues change according to the water content in the tissue and are measured as permittivity and conductivity relative to the frequency of the electrical field. This principle has been applied in diagnostics and therapeutics using microwave energysuch as imaging and ablation, etc. This review article summarizes the potential use of measuring dielectric properties using microwave imaging and how it can augment electrophysiological studies in medicine.
Tunable Multiwavelength SOA-Based Fiber Laser
Authors: Kbashi Hani J. , Sharma Vishal , Sergeyev Sergey V.
Published: July 2023

Tunable multiwavelength fiber lasers based on semiconductor optical amplifiers (SOA) have received attracting interest due to their wide prospective applications in dense division multiplexing (DWDM) systems and optical sensing. Using an SOA in a nonlinear optical loop mirror (NOLM), we demonstrate up to 13 lasing peaks by controlling the pump current and the polarization controller. At maximum pump current (450 mA), the emitted multiwavelength is between 1550 nm and 1572 nm with a wavelength spacing of 1.87 nm and 3 dB output linewidth of 0.8 nm with an output power of −7 dBm and 27 dB optical signal-to-noise ratio (OSNR). The multiwavelength output power and multiwavelength peak stability are investigated, and it was found that the power fluctuation of each multiwavelength line is less than 0.2 dB. In addition, by adjusting the polarization controllers (PCs) and SOA temperature, we obtained a tunable multiwavelength emission. The proposed fiber laser offers advantages such as simple structure, low loss, and long-time stable and multiwavelength emission.
Towards Fake News Detection: A Multivocal Literature Review of Credibility Factors in Online News Stories and Analysis Using Analytical Hierarchical Process
Authors: Abrar Muhammad Faisal , Khan Muhammad Sohail , Khan Inayat , ElAffendi Mohammed , Ahmad Sadique
Published: July 2023

Information and communication technologies have grown globally in the past two decades, expanding the reach of news networks. However, the credibility of the information is now in question. Credibility refers to a person’s belief in the truth of a subject, and online readers consider various factors to determine whether a source is trustworthy. Credibility significantly impacts public behaviour, and less credible news spreads faster due to people’s interest in emotions like fear and disgust. This can have negative consequences for individuals and economies. To determine the credibility factors in digital news stories, a Multivocal Literature Review (MLR) was conducted to identify relevant studies in both white and grey literature. A total of 161 primary studies were identified from published (white) literature and 61 were identified from unpublished (grey) literature. As a result, 14 credibility factors were identified, including “number of views”, “reporter reputations”, “source information”, and “impartiality”. These factors were then analysed using statistical tests and the Analytic Hierarchy Process (AHP) for decision-making to determine their criticality and importance in different domains.
A 34.7 µW Speech Keyword Spotting IC Based on Subband Energy Feature Extraction
Authors: Wu Gexuan , Wei Jianlong , Wang Shuai , Wei Guangshun , Li Bing
Published: July 2023

In the era of the Internet of Things (IoT), voice control has enhanced human–machine interaction and the accuracy of keyword spotting (KWS) algorithms has reached 97%; however, the high power consumption of KWS algorithms caused by their huge computing and storage requirements has limited their application in Artificial Intelligence of Things (AIoT) devices. In this study, voice features are extracted by utilizing the fast discrete cosine transform (FDCT) for frequency-domain transformation and to shorten the process of calculating the logarithmic spectrum and cepstrum. The designed KWS system is a two-stage wake-up system, with a sound detection (SD) awakening KWS. The inference process of the KWS network is achieved using time-division computation, reducing the KWS clock to an ultra-low frequency of 24 kHz.At the same time, the implementation of a depthwise separable convolution neural network (DSCNN) greatly reduces the parameter quantity and computation. Under the GSMC 0.11 µm technology, post-layout simulation results show that the total synthesized area of the entire system circuit is 0.58 mm2, the power consumption is 34.7 µW, and the F1-score of the KWS is 0.89 with 10 dB noise, which makes it suitable as a KWS system in AIoT devices.
Agile Software Development and Reuse Approach with Scrum and Software Product Line Engineering
Authors: Lee Wen-Tin , Chen Chih-Hsien
Published: July 2023

Agile methods and software product line engineering (SPLE) are widely recognized as practical approaches for delivering high-quality software, adapting to evolving stakeholder needs, and tackling complex problems. This study proposes a hybrid agile software development and reuse approach called SPLE-Scrum based on the activities of software product line engineering and Scrum. Within the SPLE process, we incorporate requirement engineering and design practices to create a reference architecture with reusable components called core assets by introducing a product management meeting. The core assets are reused to build a series of applications with various product lines. The product increments are delivered in each Sprint with the review and retrospective meetings based on Scrum lifecycle and practices. We present a case study involving a blockchain online store to demonstrate the practical application of SPLE-Scrum, highlighting the benefits of integrating Scrum and software product line engineering. The research hypotheses of the proposed approach were validated through a study of structured interviews with 5 experts and 44 software practitioners, showing that the key factors of product management, project requirements, and product architecture in the SPLE-Scrum approach have a beneficial impact on project success. The SPLE-Scrum approach provides valuable insights and practical guidance for organizations seeking to optimize their software engineering practices while incorporating agile development and software reuse capabilities.
Deep-Learning-Based Natural Ventilation Rate Prediction with Auxiliary Data in Mismeasurement Sensing Environments
Authors: Yang Subhin , Kim Mintai , Lee Sungju
Published: July 2023

Predicting the amount of natural ventilation by utilizing environmental data such as differential pressure, wind, temperature, and humidity with IoT sensing is an important issue for optimal HVAC control to maintain comfortable air quality. Recently, some research has been conducted using deep learning to provide high accuracy in natural ventilation prediction. Therefore, high reliability of IoT sensing data is required to achieve predictions successfully. However, it is practically difficult to predict the accurate NVR in a mismeasurement sensing environment, since inaccurate IoT sensing data are collected, for example, due to sensor malfunction. Therefore, we need a way to provide high deep-learning-based NVR prediction accuracy in mismeasurement sensing environments. In this study, to overcome the degradation of accuracy due to mismeasurement, we use complementary auxiliary data generated by semi-supervised learning and selected by importance analysis. That is, the NVR prediction model is reliably trained by generating and selecting auxiliary data, and then the natural ventilation is predicted with the integration of mismeasurement and auxiliary by bagging-based ensemble approach. Based on the experimental results, we confirmed that the proposed method improved the natural ventilation rate prediction accuracy by 25% compared with the baseline approach. In the context of deep-learning-based natural ventilation prediction using various IoT sensing data, we address the issue of realistic mismeasurement by generating auxiliary data that utilize the rapidly changing or slowly changing characteristics of the sensing data, which can improve the reliability of observation data.
A Review of Federated Meta-Learning and Its Application in Cyberspace Security
Authors: Liu Fengchun , Li Meng , Liu Xiaoxiao , Xue Tao , Ren Jing , Zhang Chunying
Published: July 2023

In recent years, significant progress has been made in the application of federated learning (FL) in various aspects of cyberspace security, such as intrusion detection, privacy protection, and anomaly detection. However, the robustness of federated learning in the face of malicious attacks (such us adversarial attacks, backdoor attacks, and poisoning attacks) is weak, and the unfair allocation of resources leads to slow convergence and inefficient communication efficiency regarding FL models. Additionally, the scarcity of malicious samples during FL model training and the heterogeneity of data result in a lack of personalization in FL models. These challenges pose significant obstacles to the application of federated learning in the field of cyberspace security. To address these issues, the introduction of meta-learning into federated learning has been proposed, resulting in the development of federated meta-learning models. These models aim to train personalized models for each client, reducing performance discrepancies across different clients and enhancing model fairness. In order to advance research on federated meta-learning and its applications in the field of cyberspace security, this paper first introduces the algorithms of federated meta-learning. Based on different usage principles, these algorithms are categorized into client-level personalization algorithms, network algorithms, prediction algorithms, and recommendation algorithms, and are thoroughly presented and analyzed. Subsequently, the paper divides current cyberspace security issues in the network domain into three branches: information content security, network security, and information system security. For each branch, the application research methods and achievements of federated meta-learning are elucidated and compared, highlighting the advantages and disadvantages of federated meta-learning in addressing different cyberspace security issues. Finally, the paper concludes with an outlook on the deep application of federated meta-learning in the field of cyberspace security.
DB-YOLOv5: A UAV Object Detection Model Based on Dual Backbone Network for Security Surveillance
Authors: Liu Yuzhao , Li Wan , Tan Li , Huang Xiaokai , Zhang Hongtao , Jiang Xujie
Published: July 2023

Unmanned aerial vehicle (UAV) object detection technology is widely used in security surveillance applications, allowing for real-time collection and analysis of image data from camera equipment carried by a UAV to determine the category and location of all targets in the collected images. However, small-scale targets can be difficult to detect and can compromise the effectiveness of security surveillance. In this work, we propose a novel dual-backbone network detection method (DB-YOLOv5) that uses multiple composite backbone networks to enhance the extraction capability of small-scale targets’ features and improve the accuracy of the object detection model. We introduce a bi-directional feature pyramid network for multi-scale feature learning and a spatial pyramidal attention mechanism to enhance the network’s ability to detect small-scale targets during the object detection process. Experimental results on the challenging UAV aerial photography dataset VisDrone-DET demonstrate the effectiveness of our proposed method, with a 3% improvement over the benchmark model. Our approach can enhance security surveillance in UAV object detection, providing a valuable tool for monitoring and protecting critical infrastructure.
Multitask Coupling Network for Occlusion Relation Reasoning
Authors: Bao Shishui , Huang Yourui , Xu Jiachang , Xu Guangyu
Published: July 2023

Analysis of the occlusion relationships between different objects in an image is fundamental to computer vision, including both the accurate detection of multiple objects’ contours in an image and each pixel’s orientation on the contours of objects with occlusion relationships. However, the severe imbalance between the edge pixels of an object in an image and the background pixels complicates occlusion relationship reasoning. Although progress has been made using convolutional neural network (CNN)-based methods, the limited coupling relationship between the detection of object occlusion contours and the prediction of occlusion orientation has not yet been effectively used in a full network architecture. In addition, the prediction of occlusion orientations and the detection of occlusion edges are based on the accurate extraction of the local details of contours. Therefore, we propose an innovative multitask coupling network model (MTCN). To address the abovementioned issues, we also present different submodules. The results of extensive experiments show that the proposed method surpasses state-of-the-art methods by 2.1% and 2.5% in Boundary-AP and by 3.5% and 2.8% in Orientation AP on the PIOD and BSDS datasets, respectively, indicating that the proposed method is more advanced than comparable methods.
Demand-Side Management and Its Impact on the Growing Circular Debt of Pakistan’s Energy Sector
Authors: Hassan Muhammad Azhar , Khan Saad Ullah , Zia Muhammad Fahad , Sardar Azka , Mehmood Khawaja Khalid , Ahmad Fiaz
Published: July 2023

In this research, we propose an energy-management scheme for domestic users, which uses the load-shifting strategy of demand-side management (DSM). The research demonstrates that the energy sector’s circular debt problem from the viewpoint of a developing country can be solved by incorporating DSM. Circular debt is a chain reaction that arises when the balance between cost and energy supply collapses. Circular debt is an ongoing problem in Pakistan, where economic crises are continuously posing a threat to the energy sector. DSM is envisioned to address these concerns in a dynamic way thoroughly: introducing DSM can minimize circular debt, increase grid reliability, and smooth the supply–demand operation. Circular debt is directly linked with the subsidy offered by the government of Pakistan. As the cost of energy utilized by consumers increases, the subsidy also increases due to the direct link between the two entities. Therefore, the subsidy can be controlled by energy-consumption management with the adoption of DSM. This study addresses that by incorporating optimized cost solutions, circular debt can be regulated to improve the economy of the energy sector. A genetic algorithm is used as an optimization tool to manage demand and generate an optimal schedule under a dynamic electricity pricing signal. To support the utility, a solar system is used as a secondary energy source. Finally, the results show a curtailment in the payable costs at both the consumer and government ends, thus reducing the circular debt in the bigger picture. The reduction is 18% without and 41% with renewable energy support.
Release of Sulfur and Chlorine Gas Species during Combustion and Pyrolysis of Walnut Shells in an Entrained Flow Reactor
Authors: Yildiz Coskun , Richter Marcel , Ströhle Jochen , Epple Bernd
Published: July 2023

The release behavior of sulfur and chlorine compounds into the gas phase of walnut shell particles (WNS) is studied with an entrained flow reactor. Experiments are carried out in nitrogen (N2), carbon dioxide (CO2) atmosphere and under air and oxy-fuel conditions at different temperatures (T = 1000–1300 °C) and stoichiometries (λ = 0.8–1.1). A total of 98.7% of fuel-bound sulfur volatilizes as sulfur dioxide (SO2), carbonyl sulfide (COS) and hydrogen sulfide (H2S) in the gas phase in N2 atmosphere at 1000 °C. As hydrogen chloride (HCl), 37.0% of the chlorine is released at this temperature. In CO2 atmosphere, a similar total release of sulfur and chlorine is observed (1000 °C). With each temperature increment, the release of SO2, H2S and HCl in the gas phase decreases (N2 and CO2 atmosphere). SO2 forms the major sulfur component in both atmospheres. In CO2 atmosphere, higher concentrations of COS were detected than in N2 atmosphere. Air and oxy-fuel combustion conditions show significantly lower SO2, COS and HCl concentrations as in N2 and CO2 atmosphere. No H2S is detected in the gas phase during any of the combustion trials.
Proximal Policy Optimization for Energy Management of Electric Vehicles and PV Storage Units
Authors: Alonso Monica , Amaris Hortensia , Martin David , de la Escalera Arturo
Published: July 2023

Connected autonomous electric vehicles (CAEVs) are essential actors in the decarbonization process of the transport sector and a key aspect of home energy management systems (HEMSs) along with PV units, CAEVs and battery energy storage systems. However, there are associated uncertainties which present new challenges to HEMSs, such as aleatory EV arrival and departure times, unknown EV battery states of charge at the connection time, and stochastic PV production due to weather and passing cloud conditions. The proposed HEMS is based on proximal policy optimization (PPO), which is a deep reinforcement learning algorithm suitable for continuous complex environments. The optimal solution for HEMS is a tradeoff between CAEV driver’s range anxiety, batteries degradation, and energy consumption, which is solved by means of incentives/penalties in the reinforcement learning formulation. The proposed PPO algorithm was compared to conventional methods such as business-as-usual (BAU) and value iteration (VI) solutions based on dynamic programming. Simulation results indicate that the proposed PPO’s performance showed a daily energy cost reduction of 54% and 27% compared to BAU and VI, respectively. Finally, the developed PPO algorithm is suitable for real-time operations due to its fast execution and good convergence to the optimal solution.
Discounted Costs Range Criterion Modification for Controlled Asynchronous Electric Drives
Authors: Petrushin Victor , Plotkin Juriy , Almuratova Nurgul , Mustafin Marat , Zharkymbekova Makpal
Published: July 2023

A range criterion of discounted costs is proposed, which takes into account all costs for a speed-controlled asynchronous electric drive, including its price, costs of losses and maintenance, and amortization charges. It is also possible to take into account the cost and energy performance of matching transformers and gearboxes. Costs for reactive power compensation of both the first and second kind (due to the presence of harmonic components at the drive input) are taken into account. The latter requires determining the total harmonic distortion (THD) of the currents consumed by the drive. In the case of autonomous power systems, it is also necessary to determine the drive’s supplying voltage non-linear distortion coefficients. Representing the discounted costs as the sum of initial capital investments and annual costs made possible taking into account the presence of inflationary processes. The control characteristics of the active power consumed by the drive, efficiency, power factor, and phase shift factor are constructed, which are dependencies on the rotation frequency in a certain control range at a specific load. It is proposed to calculate the drive’s discounted costs criterion, depending on the operating mode of the load. According to the mode, the criterion is calculated as an average range in a certain range of speed control or is determined taking into account a given tachogram of speed changes. For the controlled asynchronous drive under consideration, the discounted costs are 4148 c.u. in the first case and 5139.3 c.u. in the second case. If inflationary processes are taken into account, the first case is 4219.4 c.u. and the second case is 5227.7 c.u., respectively.
A Simple Model for Wake-Induced Aerodynamic Interaction of Wind Turbines
Authors: Mahmoodi Esmail , Khezri Mohammad , Ebrahimi Arash , Ritschel Uwe , Chamorro Leonardo P. , Khanjari Ali
Published: July 2023

Wind turbine aerodynamic interactions within wind farms lead to significant energy losses. Optimizing the flow between turbines presents a promising solution to mitigate these losses. While analytical models offer a fundamental approach to understanding aerodynamic interactions, further development and refinement of these models are imperative. We propose a simplified analytical model that combines the Gaussian wake model and the cylindrical vortex induction model to evaluate the interaction between wake and induction zones in 3.5 MW wind turbines with 328 m spacing. The model’s validation is conducted using field data from a nacelle-mounted LiDAR system on the downstream turbine. The ‘Direction to Hub’ parameter facilitates a comparison between the model predictions and LiDAR measurements at distances ranging from 50 m to 300 m along the rotor axis. Overall, the results exhibit reasonable agreement in flow trends, albeit with discrepancies of up to 15° in predicting peak interactions. These deviations are attributed to the single-hat Gaussian shape of the wake model and the absence of wake expansion consideration, which can be revisited to improve model fidelity. The ‘Direction to Hub’ parameter proves valuable for model validation and LiDAR calibration, enabling a detailed flow analysis between turbines. This analytical modeling approach holds promise for enhancing wind farm efficiency by advancing our understanding of turbine interactions.
Maldistribution of a Thermal Fluid along the U-Tube with a Different Bending Radius—CFD and PIV Investigation
Authors: Ligus Grzegorz , Wasilewska Barbara
Published: July 2023

This paper investigates the effect of changing the bending radius of pipes on the maldistribution of velocity and turbulence of thermal fluid when flowing through a u-shaped tube bundle used in compact heat exchangers, among other applications. The study included three bending radii corresponding to successive rows of the actual tube bundle of a compact heat exchanger. Both liquid flow velocities recommended for compact heat exchangers and velocities elevated from the recommended ones were adopted. The results of the study were obtained by Computational Fluid Dynamics (CFD) and the performed experiment using the Particle Image Velocimetry (PIV) method. The limits of maldistribution were indicated by parameterizing this phenomenon with related geometric and flow values (turbulent flow intensity factor, flow velocity, pipe diameter, and bending radius). An increase in flow velocity above the recommended values did not result in a significant increase in turbulent flow intensity factor for u-tubes with large d/rg values. The shortest distance at which the return to steady-state flow conditions in a straight section of pipe downstream of an elbow took place was determined. This distance was 17d for geometry rg = 0.009 m, with velocity vp = 1.44 m/s. The localization of the areas of highest and lowest fluid velocity in the elbow element of the u-tube for extreme values of rg was opposite. This fact has an exploitable significance (non-uniform erosive effect of thermal fluid on pipes in different rows).
A Review for Green Energy Machine Learning and AI Services
Authors: Mehta Yukta , Xu Rui , Lim Benjamin , Wu Jane , Gao Jerry
Published: July 2023

There is a growing demand for Green AI (Artificial Intelligence) technologies in the market and society, as it emerges as a promising technology. Green AI technologies are used to create sustainable solutions and reduce the environmental impact of AI. This paper focuses on describing the services of Green AI and the challenges associated with it at the community level. This article also highlights the accuracy levels of machine learning algorithms for various time periods. The process of choosing the appropriate input parameters for weather, locations, and complexity is outlined in this paper to examine the ML algorithms. For correcting the algorithm performance parameters, metrics like RMSE (root mean square error), MSE (mean square error), MAE (mean absolute error), and MPE (mean percentage error) are considered. Considering the performance and results of this review, the LSTM (long short-term memory) performed well in most cases. This paper concludes that highly advanced techniques have dramatically improved forecasting accuracy. Finally, some guidelines are added for further studies, needs, and challenges. However, there is still a need for more solutions to the challenges, mainly in the area of electricity storage.
Is the Fine Root Tensile Strength Predictable from Structural and Morphological Traits across Mycorrhizal Types in Cool-Temperate Woody Species?
Authors: Zeng Ruiqi , Makoto Kobayashi
Published: July 2023

The mycorrhizal type affects the structure and functions of tree roots. Therefore, the mechanical traits of the roots of tree species with different types of mycorrhizal fungi may be linked to different root functional traits. Fine roots, in particular, are closely related to the root structure and are also important for slope protection. However, the relationship among the mycorrhizal types of trees, root mechanical traits, and root structure remains unclear. This study aims to investigate the link between fine root tensile strength (Tr) and root morphological and/or structural traits in temperate trees with different mycorrhizal types. We investigated the seedlings of 15 dominant tree species in the cool temperate forests of northern Japan. For each species, fine root Tr and other five common root morphological and structural traits were measured. There was a significant positive correlation between total fine root biomass and fine root Tr consistently, even in the analysis of mycorrhizal types (arbuscular mycorrhizal (AM), and ectomycorrhizae (EM)). Our findings indicate that the root structural trait is an important driver of fine root Tr, especially for AM and EM species, and suggest that including a plant-mycorrhizal framework in future work offers great potential to improve our understanding of forest restoration.
A Review on Acoustics of Wood as a Tool for Quality Assessment
Authors: Bucur Voichita
Published: July 2023

Acoustics is a field with significant application in wood science and technology for the classification and grading, through non-destructive tests, of a large variety of products from standing trees to building structural elements and musical instruments. In this review article the following aspects are treated: (1) The theoretical background related to acoustical characterization of wood as an orthotropic material. We refer to the wave propagation in anisotropic media, to the wood anatomic structure and propagation phenomena, to the velocity of ultrasonic waves and the elastic constants of an orthotropic solid. The acoustic methods for the determination of the elastic constants of wood range from the low frequency domain to the ultrasonic domain using direct contact techniques or ultrasonic spectroscopy. (2) The acoustic and ultrasonic methods for quality assessment of trees, logs, lumber and structural timber products. Scattering-based techniques and ultrasonic tomography are used for quality assessment of standing trees and green logs. The methods are based on scanning stress waves using dry-point-contact ultrasound or air-coupled ultrasound and are discussed for quality assessment of structural composite timber products and for delamination detection in wood-based composite boards. (3) The high-power ultrasound as a field with important potential for industrial applications such as wood drying and other applications. (4) The methods for the characterization of acoustical properties of the wood species used for musical instrument manufacturing, wood anisotropy, the quality of wood for musical instruments and the factors of influence related to the environmental conditions, the natural aging of wood and the effects of long-term loading by static or dynamic regimes on wood properties. Today, the acoustics of wood is a branch of wood science with huge applications in industry.
Sweetgum Leaf Spot Image Segmentation and Grading Detection Based on an Improved DeeplabV3+ Network
Authors: Wu Peng , Cai Maodong , Yi Xiaomei , Wang Guoying , Mo Lufeng , Chola Musenge , Kapapa Chilekwa
Published: July 2023

Leaf spot disease and brown spot disease are common diseases affecting maple leaves. Accurate and efficient detection of these diseases is crucial for maintaining the photosynthetic efficiency and growth quality of maple leaves. However, existing segmentation methods for plant diseases often fail to accurately and rapidly detect disease areas on plant leaves. This paper presents a novel solution to accurately and efficiently detect common diseases in maple leaves. We propose a deep learning approach based on an enhanced version of DeepLabV3+ specifically designed for detecting common diseases in maple leaves. To construct the maple leaf spot dataset, we employed image annotation and data enhancement techniques. Our method incorporates the CBAM-FF module to fuse gradual features and deep features, enhancing the detection performance. Furthermore, we leverage the SANet attention mechanism to improve the feature extraction capabilities of the MobileNetV2 backbone network for spot features. The utilization of the focal loss function further enhances the detection accuracy of the affected areas. Experimental results demonstrate the effectiveness of our improved algorithm, achieving a mean intersection over union (MIoU) of 90.23% and a mean pixel accuracy (MPA) of 94.75%. Notably, our method outperforms traditional semantic segmentation methods commonly used for plant diseases, such as DeeplabV3+, Unet, Segnet, and others. The proposed approach significantly enhances the segmentation performance for detecting diseased spots on Liquidambar formosana leaves. Additionally, based on pixel statistics, the segmented lesion image is graded for accurate detection.
Measurement Time in the Evaluation of Whole-Body Vibration: The Case of Mechanized Wood Extraction with Grapple Skidder
Authors: Lima Roldão Carlos Andrade , Minette Luciano José , Simões Danilo , Rocha Qüinny Soares , Miyajima Ricardo Hideaki , Fritz Gabriel Fratta , Schettino Stanley , Soranso Denise Ransolin , Nascimento Glícia Silvania Pedroso , Vieira Marlice Paes Leme , Schettini Bruno Leão Said , Silva Arthur Araújo
Published: July 2023

The grapple skidder is a self-propelled forestry machine that is used for the extraction of trees in wood harvesting—commonly used in full tree systems. Moving this machine can expose operators to occupational hazards of physical origin, among which whole body vibration stands out. However, the measurement of this risk agent does not have a standard measurement time, being performed for periods of approximately 30 min—disregarding the time of the daily workday. In view of this, it was analyzed whether occupational exposure to whole body vibration transmitted to grapple skidder operators using different measurement times complies with the guidelines for preventative purposes. Thus, measurements of whole-body vibration were carried out along three orthogonal axes over a period of one hour and over a daily workday of eight hours—by which were measured the daily (8 h) vibration exposure for the l-axis and the vibration value. The acceleration values in the three evaluated axes were higher for the daily working day, denoting the influence of the measurement time. In addition, the vibration dose value resulted in values above the action limit for both evaluations; however, the daily workload was highlighted—indicating the presence of higher vibration peaks over a longer measurement time. Thus, the assertiveness and influence of measurement times over the daily working day for whole-body vibration transmitted to grapple skidder operators is evidenced.
Impact of Root Rot Disease of Zanthoxylum armatum on Rhizosphere Soil Microbes and Screening of Antagonistic Bacteria
Authors: Han Shan , Zheng Zhenlei , Li Shujiang , Liu Yinggao , Li Shuying , Yang Chunlin , Qiao Tianmin , Lin Tiantian , Zhu Tianhui
Published: July 2023

Zanthoxylum armatum, a significant forest plant in southwestern China, is crucial for preserving soil and water resources. However, the presence of root rot disease has led to plant death, impacting the pepper sector. Effective control measures for this disease are still lacking. Rhizosphere microorganisms play a vital role in plant health by inhibiting plant pathogens and inducing plant resistance. This research aimed to isolate and characterize the pathogen responsible for root rot disease in Z. armatum. Comparative analysis of fungal and bacterial communities in the rhizosphere soil of healthy and diseased plants revealed Fusarium solani as the pathogenic fungus causing root rot disease. Diseased plants had a higher occurrence of Fusarium spp., while disease-free plants had a higher abundance of ecologically beneficial microbial communities that could potentially serve as biocontrol agents. Three bacterial strains (Bacillus subtilis, Bacillus amyloliquefaciens, and Bacillus siamensis) were identified as effective biocontrol agents, inhibiting the growth of the pathogenic fungus F. solani both in vivo and in vitro. This study deepens our understanding of the rhizosphere soil microbial community differences between diseased and healthy Z. armatum, providing potential biocontrol bacteria to enhance plant resistance against root rot disease.