bluuebunny/crossref_metadata_2025 huggingface dataset:
An Explainable Artificial Intelligence-Based Robustness Optimization Approach for Age-Related Macular Degeneration Detection Based on Medical IOT Systems
Authors: Wang Mini Han , Chong Kelvin Kam-lung , Lin Zhiyuan , Yu Xiangrong , Pan Yi
Published: June 2023

AI-based models have shown promising results in diagnosing eye diseases based on multi-sources of data collected from medical IOT systems. However, there are concerns regarding their generalization and robustness, as these methods are prone to overfitting specific datasets. The development of Explainable Artificial Intelligence (XAI) techniques has addressed the black-box problem of machine learning and deep learning models, which can enhance interpretability and trustworthiness and optimize their performance in the real world. Age-related macular degeneration (AMD) is currently the primary cause of vision loss among elderly individuals. In this study, XAI methods were applied to detect AMD using various ophthalmic imaging modalities collected from medical IOT systems, such as colorful fundus photography (CFP), optical coherence tomography (OCT), ultra-wide fundus (UWF) images, and fluorescein angiography fundus (FAF). An optimized deep learning (DL) model and novel AMD identification systems were proposed based on the insights extracted by XAI. The findings of this study demonstrate that XAI not only has the potential to improve the transparency, reliability, and trustworthiness of AI models for ophthalmic applications, but it also has significant advantages for enhancing the robustness performance of these models. XAI could play a crucial role in promoting intelligent ophthalmology and be one of the most important techniques for evaluating and enhancing ophthalmic AI systems.
MSWOA: A Mixed-Strategy-Based Improved Whale Optimization Algorithm for Multilevel Thresholding Image Segmentation
Authors: Wang Chunzhi , Tu Chengkun , Wei Siwei , Yan Lingyu , Wei Feifei
Published: June 2023

Multilevel thresholding image segmentation is one of the most widely used segmentation methods in the field of image segmentation. This paper proposes a multilevel thresholding image segmentation technique based on an improved whale optimization algorithm. The WOA has been applied to many complex optimization problems because of its excellent performance; however, it easily falls into local optimization. Therefore, firstly, a mixed-strategy-based improved whale optimization algorithm (MSWOA) is proposed using the k-point initialization algorithm, the nonlinear convergence factor, and the adaptive weight coefficient to improve the optimization ability of the algorithm. Then, the MSWOA is combined with the Otsu method and Kapur entropy to search for the optimal thresholds for multilevel thresholding gray image segmentation, respectively. The results of algorithm performance evaluation experiments on benchmark functions demonstrate that the MSWOA has higher search accuracy and faster convergence speed than other comparative algorithms and that it can effectively jump out of the local optimum. In addition, the image segmentation experimental results on benchmark images show that the MSWOA–Kapur image segmentation technique can effectively and accurately search multilevel thresholds.
Eye-Blink Event Detection Using a Neural-Network-Trained Frame Segment for Woman Drivers in Saudi Arabia
Authors: Al-Razgan Muna S. , Alruwaly Issema , Ali Yasser A.
Published: June 2023

Women have been allowed to drive in Saudi Arabia since 2018, revoking a 30-year ban that also adhered to the traffic rules provided in the country. Conventional drivers are often monitored for safe driving by monitoring their facial reactions, eye blinks, and expressions. As driving experience and vehicle handling features have been less exposed to novice women drivers in Saudi Arabia, technical assistance and physical observations are mandatory. Such observations are sensed as images/video frames for computer-based analyses. Precise computer vision processes are employed for detecting and classifying events using image processing. The identified events are unique to novice women drivers in Saudi Arabia, assisting with their vehicle usage. This article introduces the Event Detection using Segmented Frame (ED-SF) method to improve the abnormal Eye-Blink Detection (EBD) of women drivers. The eye region is segmented using variation pixel extraction in this process. The pixel extraction process requires textural variation identified from different frames. The condition is that the frames are to be continuous in the event detection. This method employs a convolution neural network with two hidden layer processes. In the first layer, continuous and discrete frame differentiations are identified. The second layer is responsible for segmenting the eye region, devouring the textural variation. The variations and discrete frames are used for training the neural network to prevent segment errors in the extraction process. Therefore, the frame segment changes are used for Identifying the expressions through different inputs across different texture luminosities. This method applies to less-experienced and road-safety-knowledge-lacking woman drivers who have initiated their driving journey in Saudi-Arabia-like countries. Thus the proposed method improves the EBD accuracy by 9.5% compared to Hybrid Convolutional Neural Networks (HCNN), Long Short-Term Neural Networks (HCNN + LSTM), Two-Stream Spatial-Temporal Graph Convolutional Networks (2S-STGCN), and the Customized Driving Fatigue Detection Method CDFDM.
Wideband Filtering Rat-Race Coupler with Shared Triple-Mode Resonator
Authors: Zhao Zimeng , Liu Hongmei , Ren Junhao , Wang Zhongbao , Fang Shaojun
Published: June 2023

In this paper, a filtering rat-race coupler (FRC) is presented, which features the merits of a wideband filtering response, good impedance matching at both input and output ports, good isolation, high-frequency selectivity, small in-band phase difference error, a wide stopband, and small size. It was composed of two leading triple-mode resonators, one core section, and two λ/2 open-circuited transmission lines (TLs). The core section consisted of three λ/4 TLs and one λ/4 short-circuited parallel coupled line (CL). The leading triple-mode resonator consisted of one λ/4 short-circuited stub and two λ/4 TLs. By loading the triple-mode resonator as the shared component and combining it with the core section, a wideband filtering response with four transmission poles could be achieved. In addition, two transmission zeros were generated by loading the λ/2 open-circuited TLs to the output ports of the FRC. The working principles of the proposed FRC are analyzed, and equations are derived with parametric analysis. For demonstration, a prototype operating at the center frequency of 1.4 GHz was designed, fabricated, and measured. Measurement results show that a fractional bandwidth of more than 60% was achieved under the criteria of 10 dB return loss and 20 dB isolation. In this bandwidth, the amplitude imbalances and phase difference errors were less than 1 dB and 10°, respectively. Additionally, it also exhibited high selectivity and a wide stopband.
Text Emotion Recognition Based on XLNet-BiGRU-Att
Authors: Han Tian , Zhang Zhu , Ren Mingyuan , Dong Changchun , Jiang Xiaolin , Zhuang Quansheng
Published: June 2023

Text emotion recognition (TER) is an important natural language processing (NLP) task which is widely used in human–computer interaction, public opinion analysis, mental health analysis, and social network analysis. In this paper, a deep learning model based on XLNet with bidirectional recurrent unit and attention mechanism (XLNet-BiGRU-Att) is proposed in order to improve TER performance. XLNet is used to build bidirectional language models which can learn contextual information simultaneously, while the bidirectional gated recurrent unit (BiGRU) helps to extract more effective features which can pay attention to current and previous states using hidden layers and the attention mechanism (Att) provides different weights to enhance the ’attention’ paid to important information, thereby improving the quality of word vectors and the accuracy of sentiment analysis model judgments. The proposed model composed of XLNet, BiGRU, and Att improves performance on the whole TER task. Experiments on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) database and the Chinese Academy of Sciences Institute of Automation (CASIA) dataset were carried out to compare XLNet-BiGRU-Att, XLNet, BERT, and BERT-BiLSTM, and the results show that the model proposed in this paper has superior performance compared to the others.
Selection of TCC Curve and Protection Cooperation Method of Distribution Line Using Linear Optimization
Authors: Kim Geonho , Kim Woo-Hyun , Lee Chun-Kwon
Published: June 2023

Distribution systems are mostly composed of radial structures, which are susceptible to an increased variability and complexity of system operation due to frequent line changes during operation. When multiple changes in distribution lines occur simultaneously, the relative positions of protective devices also change. The existing protection coordination method of distribution lines is configured by considering the operation characteristics and coordination time interval (CTI) of all protective devices in series from the substation to the terminal load. Therefore, the protection coordination algorithm needs to be redesigned whenever a line is changed or a protective device is added to the distribution line for which the existing protection coordination algorithm has been set. In addition, existing protection coordination methods require complex calculations and procedures, which are subject to human errors and are less feasible for responding in real-time to changes in the distribution system. In this paper, we propose the adaptive time–current curve (TCC) method by selecting the time dial setting (TDS) and minimum response time (MRT) of individual protective devices in accordance with the relative distance based on the linear optimization technique. Using PSCAD/EMTDC, a power system analysis program, the minimum operating current and the fault current of each protective device are obtained, and the proposed protection coordination algorithm is verified according to the series configuration relationship of the protective devices. Finally, the proposed method is applied to an actual distribution line to verify the improvement over the existing protection coordination.
Non-Iterative, Unique, and Logical Formula-Based Technique to Determine Maximum Load Multiplier and Practical Load Multiplier for Both Transmission and Distribution Systems
Authors: Nandi Sharmistha , Ghatak Sriparna Roy , Acharjee Parimal , Lopes Fernando
Published: June 2023

In recent days, due to the increasing number of electric vehicle charging stations (EVCSs) and additional power consumption by domestic, commercial, and industrial consumers, the overall power system performance suffers, which further degrades voltage profile, reduces stability, increases losses, and may also create a voltage collapse problem. Therefore, it is crucial to predetermine a maximum loadability limit for voltage collapse analysis and a practical allowable extra load for safe and secure operation, keeping the bus voltage within the security limits. To mitigate the problems, unique and innovative formulae such as the maximum load multiplier (MLM) and practical load multiplier (PLM) have been developed to consider line resistance. The determination of actual permissible extra load for a bus enables quick assessment of bus-wise suitable capacities and the number of EVs that can be charged simultaneously in the charging station. The planning engineers can easily settle on the extra load demand by domestic, commercial, and industrial consumers, while maintaining the voltage security constraint. The proposed technique is simple, non-iterative, computationally inexpensive, and applicable to both transmission and distribution systems. The proposed work is tested on a 57-bus transmission system and 69-bus radial distribution system, and the obtained results from the developed formulae are verified by comparing with conventional iterative methods.
Simulation-Driven Robust Optimization of the Design of Zero Emission Vessels
Authors: Nikolopoulos Lampros , Boulougouris Evangelos
Published: June 2023

The International Maritime Organization (IMO) Decarbonization Roadmap for curbing and eliminating Greenhouse Gas (GHG) emissions by 2030 and 2050, respectively, is a “herculean” task in its own respect. If it is now combined with fundamental changes in trade dynamics, volatile market conditions, tighter shipping financing platforms with sustainability-linked interest rates and international safety regulations setup, a completely new framework for commercial ship design characterized by strict and often contradicting requirements emerge In parallel, zero carbon fuels available (readily or in the future) require extensive technological modifications and technical leaps in the current arrangements ship propulsion plants (with little to no existing reference) characterized by elevated consumption figures due to low energy density leading to an overshoot in voyage expense costs and the Total Cost of Ownership (TCO), respectively. Considering such a tight design space, holistic approaches with lifecycle considerations aiming at robust designs are deemed necessary. Pursuant to this roadmap, the authors have developed a design methodology fully integrated within the CAE software CAESES™ that encompass all aspects of ship design (stability, strength, powering and propulsion, safety, economics) and has an inherent dynamic voyage simulation module, enabling the user to simulate the response in variations of the geometrical, design variables of the vessel under uncertainty. The methodology has been extended to model the design and propulsion plant of an Ammonia powered Large Bulk carrier and deployed in global ship design optimization studies and utility-based ranking and selection process.
Online Prediction of Electric Vehicle Battery Failure Using LSTM Network
Authors: Li Xuemei , Chang Hao , Wei Ruichao , Huang Shenshi , Chen Shaozhang , He Zhiwei , Ouyang Dongxu
Published: June 2023

The electric vehicle industry is developing rapidly as part of the global energy structure transformation, which has increased the importance of overcoming power battery safety issues. In this paper, first, we study the relationship between different types of vehicle faults and battery data based on the actual vehicle operation data in the big data supervisory platform of new energy vehicles. Second, we propose a method to realize the online prediction of electric vehicle battery faults, based on a Long Short-Term Memory (LSTM). Third, we carry out prediction research for two kinds of faults: low State of Charge (SOC) alarm and insulation alarm. Last, we show via experimental results that the model based on the LSTM network can effectively predict battery faults with an accuracy of more than 85%. Through this research, it is possible to complete online pre-processing of vehicle operation data and fault prediction of power batteries, improve vehicle monitoring capabilities and ensure the safety of electric vehicle use.
Optimal Control Method of Oil Well Production Based on Cropped Well Group Samples and Machine Learning
Authors: Wang Xiang , Ding Yangyang , Li Ding , He Yanfeng
Published: June 2023

Most traditional injection-production optimization methods that treat the entire oil reservoir as a whole require re-optimization when facing new reservoirs, which is not only time-consuming but also does not make full use of historical experience information. This study decomposes the reservoir into independent basic production units to increase sample size and diversity and utilizes image enhancement techniques to augment the number of samples. Two frameworks based on convolutional neural networks (CNNs) are employed to recommend optimal control strategies for inputted well groups. Framework 1 uses bottom hole pressure (BHP) as a control variable and trains a CNN with optimal BHP obtained by reinforcement learning algorithms as labels. Framework 2 saves BHP and corresponding oil well revenue (NPV) during reinforcement learning optimization and trains a CNN with well groups and BHP as features and NPV as labels. The CNN in this framework is capable of directly outputting the NPV according to control strategies. The particle swarm algorithm (PSO) is used to generate control strategies and call CNN to predict development effects until PSO converges to the optimal production strategy. The experimental results demonstrate that the CNN-based frameworks outperform the traditional PSO-based methods in terms of accuracy and computational efficiency. Framework 1 achieves an output accuracy of 87% for predicting the optimal BHP for new well groups, while Framework 2 achieves an accuracy of 78%. Both frameworks exhibit fast running times, with each iteration taking less than 1 s. This study provides a more effective and accurate method for optimizing oil well production in oil reservoirs by decomposing oil reservoirs into independent units and using CNN to construct an algorithm framework, which is of great significance for the real-time optimization and control of oil wells in oil fields.
Hyperparameter Bayesian Optimization of Gaussian Process Regression Applied in Speed-Sensorless Predictive Torque Control of an Autonomous Wind Energy Conversion System
Authors: Hamoudi Yanis , Amimeur Hocine , Aouzellag Djamal , Abdolrasol Maher G. M. , Ustun Taha Selim
Published: June 2023

This paper introduces a novel approach to speed-sensorless predictive torque control (PTC) in an autonomous wind energy conversion system, specifically utilizing an asymmetric double star induction generator (ADSIG). To achieve accurate estimation of non-linear quantities, the Gaussian Process Regression algorithm (GPR) is employed as a powerful machine learning tool for designing speed and flux estimators. To enhance the capabilities of the GPR, two improvements were implemented, (a) hyperparametric optimization through the Bayesian optimization (BO) algorithm and (b) curation of the input vector using the gray box concept, leveraging our existing knowledge of the ADSIG. Simulation results have demonstrated that the proposed GPR-PTC would remain robust and unaffected by the absence of a speed sensor, maintaining performance even under varying magnetizing inductance. This enables a reliable and cost-effective control solution.
Optimal Data-Driven Modelling of a Microbial Fuel Cell
Authors: Oyedeji Mojeed Opeyemi , Alharbi Abdullah , Aldhaifallah Mujahed , Rezk Hegazy
Published: June 2023

Microbial fuel cells (MFCs) are biocells that use microorganisms as biocatalysts to break down organic matter and convert chemical energy into electrical energy. Presently, the application of MFCs as alternative energy sources is limited by their low power attribute. Optimization of MFCs is very important to harness optimum energy. In this study, we develop optimal data-driven models for a typical MFC synthesized from polymethylmethacrylate and two graphite plates using machine learning algorithms including support vector regression (SVR), artificial neural networks (ANNs), Gaussian process regression (GPR), and ensemble learners. Power density and output voltage were modeled from two different datasets; the first dataset has current density and anolyte concentration as features, while the second dataset considers current density and chemical oxygen demand as features. Hyperparameter optimization was carried out on each of the considered machine learning-based models using Bayesian optimization, grid search, and random search to arrive at the best possible models for the MFC. A model was derived for power density and output voltage having 99% accuracy on testing set evaluations.
A Machine-Learning-Based Approach for Natural Gas Futures Curve Modeling
Authors: Castello Oleksandr , Resta Marina
Published: June 2023

This work studies the term structure dynamics in the natural gas futures market, focusing on the Dutch Title Transfer Facility (TTF) daily futures prices. At first, using the whole dataset, we compared the in-sample fitting performance of three models: the four-factor dynamic Nelson–Siegel–Svensson (4F-DNSS) model, the five-factor dynamic De Rezende–Ferreira (5F-DRF) model, and the B-spline model. Our findings suggest that B-spline is the method that achieves the best in-line fitting results. Then, we turned our attention to forecasting, using data from 20 January 2011 to 13 May 2022 as the training set and the remaining data, from 16 May to 13 June 2022, for day-ahead predictions. In this second part of the work we combined the above mentioned models (4F-DNSS, 5F-DRF and B-spline) with a Nonlinear Autoregressive Neural Network (NAR-NN), asking the NAR-NN to provide parameter tuning. All the models provided accurate out-of-sample prediction; nevertheless, based on extensive statistical tests, we conclude that, as in the previous case, B-spline (combined with an NAR-NN) ensured the best out-of-sample prediction.
Electric Water Heater Modeling for Large-Scale Distribution Power Systems Studies with Energy Storage CTA-2045 Based VPP and CVR
Authors: Alden Rosemary E. , Gong Huangjie , Rooney Tim , Branecky Brian , Ionel Dan M.
Published: June 2023

As the smart grid involves more new technologies such as electric vehicles (EVs) and distributed energy resources (DERs), more attention is needed in research to general energy storage (GES) based energy management systems (EMS) that account for all possible load shifting and control strategies, specifically with major appliances that are projected to continue electrification such as the electric water heater (EWH). In this work, a methodology for a modified single-node model of a resistive EWH is proposed with improved internal tank temperature for user comfort modeling and capabilities for conservation voltage reduction (CVR) simulations as well as Energy Star and Consumer Technology Association communications protocol (CTA-2045) compliant controls, including energy storage calculations for “energy take”. Daily and weekly simulations are performed on a representative IEEE test feeder distribution system with experimental load and hot water draw (HWD) profiles to consider user comfort. Sequential controls are developed to reduce power spikes from controls and lead to peak shavings. It is found that EWHs are suitable for virtual power plant (VPP) operation with sustainable tank temperatures, i.e., average water temperature is maintained at set-point or above at the end of the control period while shifting up to 78% of EWH energy out of shed windows per day and 75% over a week, which amounts to up to 23% of the total load shifted on the example power system. While CVR simulations reduced the peak power of individual EWHs, the aggregation effect at the distribution level negates this reduction in power for the community. The EWH is shown as an energy constant load without consistent benefit from CVR across the example community with low energy reductions of less than 0.1% and, in some cases, increased daily energy by 0.18%.
Evaluation of the Optimal Conditions for Oxygen-Rich and Oxygen-Lean Torrefaction of Forestry Byproduct as a Fuel
Authors: Park Sun Yong , Kim Seok Jun , Oh Kwang Cheol , Cho La Hoon , Jeon Young Kwang , Kim Dae Hyun
Published: June 2023

Wood biomass is an alternative to fossil fuels. However, biomass use has several limitations. Torrefaction, in which reduction conditions prevail to overcome these limitations, has been suggested. Here, torrefaction using different wood chips (Liriodendron tulipifera, Populus canadensis, Pinus rigida, and Pinus koraiensis) was conducted under oxygen-rich and oxygen-lean conditions to determine the effects of oxygen. Torrefaction was conducted at 230–310 °C for 1 h. A mass yield difference of 3.53–20.02% p (percentage point) was observed between oxygen-lean and oxygen-rich conditions. The calorific value increased by a maximum of 50.95% and 48.48% under oxygen-rich and oxygen-lean conditions, respectively. Decarbonization (DC), dehydrogenation (DH), and deoxygenation (DO) occurred in the following order because of dehydration and devolatilization during biomass torrefaction: DO > DH > DC. The calorific value of the torrefied biomass increased linearly with the extent of all three processes. The combustibility index and volatile ignitability were calculated based on proximate composition to suggest the optimal conditions for replacing anthracite and bituminous coal. This study provides suggestions for stable operation in a standard boiler design.
Ion Exchange Resin and Entrapped Candida rugosa Lipase for Biodiesel Synthesis in the Recirculating Packed-Bed Reactor: A Performance Comparison of Heterogeneous Catalysts
Authors: Hidayatullah Ibnu Maulana , Soetandar Frederick , Sudiyasa Pingkan Vanessa , Cognet Patrick , Hermansyah Heri
Published: June 2023

Ion exchange resins and immobilized lipase as heterogeneous catalysts are used to synthesize biodiesel for alternative fossil fuels. The use of ion exchange resins in the solid and activated phase can ease the separation process. Furthermore, resins can be reactivated and used repeatedly, reducing the need for catalysts. On the other hand, an immobilized enzyme is biodegradable and can catalyze the transesterification process to produce biodiesel with a lower alcohol-to-oil ratio, minimizing side reactions and impurities. Therefore, the catalysts used in this study are ion exchange resins, such as Lewatit MP-64, Amberlite IRA410Cl, and Diaion PK208LH, as well as immobilized Candida rugosa lipase. By using vegetable oil as a feedstock and methanol for the transesterification, biodiesel production was carried out in a packed bed reactor. The present study aims to investigate the optimum process parameters, including the concentration of resin and enzyme, resin activation time, resin types, flowrate, and stability of resin and enzyme on the biodiesel yield. The results showed that the optimum conditions for biodiesel production with ion exchange resin were 4 g of resin, activated for 3 h, and synthesized for 3 h; Lewatit obtained a biodiesel yield of 94.06%, Amberlite obtained 90.00%, and Diaion obtained 73.88%. Additionally, the stability test of the reactivated Lewatit resin showed that it still has the capability of producing biodiesel with a yield of more than 80% after three regeneration cycles. In contrast, Candida rugosa lipase as was immobilized by entrapment in sodium alginate before being used in the biodiesel production for 12 h. The results showed that lower flowrate in enzymatic biodiesel synthesis produced a higher amount of biodiesel, of up to 71.1%. Nonetheless, immobilized lipases can be used up to three times without a significant loss in biodiesel yield.
A Novel Method of Forecasting Chaotic and Random Wind Speed Regimes Based on Machine Learning with the Evolution and Prediction of Volterra Kernels
Authors: Abdul Majid Amir
Published: June 2023

This study aims to focus on using the Volterra series and machine learning for forecasting random and chaotic wind speed regimes, since calm weather is mostly noticed at the local site, making dataset selection difficult. A novel method is proposed to predict Volterra kernels up to the third order, using a forward–back propagation neural network with 12-month measurements at Fujairah site (United Arab Emirates). Both daily and monthly wind speed datasets are investigated for forecasting. The three dominant hourly and daily kernels are extracted for each day and each month. Predicted future Volterra kernels are estimated from past values using both statistical analysis and individual neuro networks for each of the Volterra kernel coefficients. Using the evolved Volterra kernels, the hourly and daily wind speeds are forecasted with similar patterns of the measured values. Due to the random nature of wind speed at the local site, a two-layer with four neurons per layer neuro network is used to locate the most variable and intense speed during 8 h in the day. Forecasted wind speed is determined with errors arising from different sources, such as the utilization of only third-order Volterra kernels and the difficulty of machine training of the employed shallow network. Nevertheless, this work depicts a useful algorithm to forecast chaotic and random wind speed regimes. Computational time is a trade of the complexity of Volterra mathematical analysis.
Multi-Dimensional Resource Allocation for Throughput Maximization in CRIoT with SWIPT
Authors: Fu Shuang , Jiang Dailin
Published: June 2023

To solve the power supply problem of battery-limited Internet of Things devices (IoDs) and the spectrum scarcity problem, simultaneous wireless information and power transfer (SWIPT) and cognitive radio (CR) technology were integrated into the Internet of Things (IoT) network to build a cognitive radio IoT (CRIoT) with SWIPT. In this network, secondary users (SUs) could adaptively switch between spectrum sensing, SWIPT, and information transmission to improve the total throughput. To solve the complicated multi-dimensional resource allocation problem in CRIoT with SWIPT, we propose a multi-dimensional resource allocation algorithm for maximizing the total throughput. Three-dimensional resources were jointly optimized, which are time resource (the duration of each process), power resource (the transmit power and the power splitting ratio of each node), and spectrum resource, under some constraints, such as maximum transmit power constraint and maximum permissible interference constraint. To solve this intractable mixed-integer nonlinear program (MINLP) problem, firstly, the sensing task assignment for cooperative spectrum sensing (CSS) was obtained by using a greedy sensing algorithm. Secondly, the original problem was transformed into a convex problem via some transformations with fixed-power splitting ratio and time switching. The Lagrange dual method and subgradient method were adopted to obtain the optimal power and channel allocation. Then, a one-dimensional search algorithm was used to obtain the optimal power splitting ratio and the time switching ratio. Finally, a heuristic algorithm was adopted to obtain the optimal sensing duration. The simulation results show that the proposed algorithm can achieve higher total system throughput than other benchmark algorithms, such as a greedy algorithm, an average algorithm, and the Kuhn–Munkres (KM) algorithm.
Adaptations of Potential Nitrogenase Activity and Microbiota with Long-Term Application of Manure Compost to Paddy Soil
Authors: Ao Zhalaga , Xia Juan , Seino Honoka , Inaba Katsuhiro , Takahashi Yukitsugu , Hayakawa Chie , Hirai Hideaki , Maeda Isamu
Published: June 2023

Biological nitrogen fixation complements nitrogen from fertilizers in crop plants under natural conditions. It also contributes to the reduction in chemical fertilizer (CF) utilization in cultivated lands, which fits the concept of sustainable agriculture. From this viewpoint, however, it is still unknown in paddy fields how soil bacterial nitrogenase and microbiota are affected by applied materials in the soil. Therefore, in this study, the effects of long-term material applications on potential nitrogenase activity and the microbiota of soil bacteria were investigated. The nitrogenase activity tended to be higher in manure compost (MC)-applied soils than in CF-applied soils in both summer and winter. Soil bacterial alpha diversity increased whereas soil ammonia availability decreased with the MC application. The dynamic response of soil bacterial microbiota was caused by the MC application. The abundance of Nitrospira, a class of ammonia and nitrite oxidation bacteria, was lower and the abundance of alpha-Proteobacteria was higher in the MC-applied soils than in the CF-applied soils. These results suggest that the alpha diversity increase and restricted availability of NH3-N might contribute to the increase in potential nitrogenase activity in the long-term MC-applied soils.
Effect of Freeze–Thaw Cycling on the Screw Direct Withdrawal Resistance of Beech, Ozigo, and Okoume Plywoods
Authors: Birinci Emre , Kaymakci Alperen
Published: June 2023

Wood has been used in the construction, furniture, and automotive industries since ancient times. In areas where wood material is used, it is combined with various fasteners. The durability of the products produced using wooden materials depends on the performance of the fasteners. Since wood is a hygroscopic and biodegradable material, various changes occur in its structure when exposed to external weather conditions. Wood materials used especially in the field of construction and urban furniture are exposed to effects such as extreme temperatures, freezing, moisture, or drying depending on the seasons. In this study, the effect of the freeze–thaw cycling (FTC) process on screw direct withdrawal resistance (SDWR) of plywood produced from beech, ozigo, and okoume species was investigated. In this context, the effects of screwing time (before or after), screw orientation (face or edge), the number of cycles (0 to 7) in the FTC process, and plywood type parameters on SDWR were investigated. As a result of the tests, when the mean SDWR values were examined according to the plywood type, the highest values were obtained in beech, ozigo, and okoume plywood, respectively. Considering the screwing time parameter, it was determined that there was no statistically significant difference between the mean SDWR values in other plywood types except beech plywood (p < 0.05). When the screw orientation parameter was examined, screwing in the face direction gave better results than screwing in the edge direction in all plywood types. There was a decrease in the mean SDWR values that was inversely proportional to the increase in the number of cycles in FTC-treated plywood.
The Potential of Paulownia fortunei L. for the Phytoremediation of Pb
Authors: Du Lu , Yang Hang , Xie Juan , Han Liangze , Liu Zhiyi , Liu Zhiming , Chen Yonghua , Su Rongkui
Published: June 2023

Pb endangers forest ecological health; phytoremediation is an effective Pb remediation technology. Woody plants with Pb tolerance provided a mechanism for the phytoremediation of Pb. Paulownia fortunei (L.), a fast-growing woody plant, has a good tolerance to Pb. However, its tolerance mechanism is unclear. The results in this study revealed that P. fortunei seedlings can withstand 400 mg·L−1 Pb stress. The quantification of Pb in different P. fortunei tissues showed an increasing trend of accumulation in root > leaf > stem; the transport coefficient and enrichment coefficient decreased with an increase in Pb concentration. The tolerance of P. fortunei to Pb may be related to cell partition and immobilization by the cell wall. Microstructural analysis performed using scanning electron microscopy showed that the absorbed Pb is mainly distributed in cell wall components, and when the concentration of Pb increases, it can be transferred to soluble parts and organelles. The Fourier transform infrared spectrometry results showed that excess hydroxyl groups occurred under Pb stress in the outer epidermis cell walls of roots and leaves adsorbing heavy metals. When the concentration of Pb was over 400 mg·L−1, the growth of P. fortunei was inhibited, the root cell wall was deformed, the plasmolysis occurred in the cauline cell, and the internal leaf capsule was ruptured. Furthermore, antioxidant enzyme activity was significantly reduced. Therefore, P. fortunei can transfer the underground part of Pb to the aboveground part up to the concentration of 400 mg·L−1. This study provides a theoretical basis and technical reference for fully utilizing woody plant resources to restore the ecological environment of forests.
Performance Influencing Factors of Convolutional Neural Network Models for Classifying Certain Softwood Species
Authors: Kim Jong-Ho , Purusatama Byantara Darsan , Savero Alvin Muhammad , Prasetia Denni , Yang Go-Un , Han Song-Yi , Lee Seung-Hwan , Kim Nam-Hun
Published: June 2023

This study aims to verify the wood classification performance of convolutional neural networks (CNNs), such as VGG16, ResNet50, GoogLeNet, and basic CNN architectures, and to investigate the factors affecting classification performance. A dataset from 10 softwood species consisted of 200 cross-sectional micrographs each from the total part, earlywood, and latewood of each species. We used 80% and 20% of each dataset for training and testing, respectively. To improve the performance of the architectures, the dataset was augmented, and the differences in classification performance before and after augmentation were compared. The four architectures showed a high classification accuracy of over 90% between species, and the accuracy increased with increasing epochs. However, the starting points of the accuracy, loss, and training speed increments differed according to the architecture. The latewood dataset showed the highest accuracy. The epochs and augmented datasets also positively affected accuracy, whereas the total part and non-augmented datasets had a negative effect on accuracy. Additionally, the augmented dataset tended to derive stable results and reached a convergence point earlier. In the present study, an augmented latewood dataset was the most important factor affecting classification performance and should be used for training CNNs.
Bio-Drying of Municipal Wastewater Sludge: Effects of High Temperature, Low Moisture Content and Volatile Compounds on the Microbial Community
Authors: Mironov Vladimir , Moldon Ivan , Shchelushkina Anna , Zhukov Vitaly , Zagustina Nataliya
Published: June 2023

This study examined microbiological processes during the bio-drying of municipal wastewater sludge (WS) from the waste treatment facilities of the Moscow region (Russia). In just 21 days of bio-drying, the moisture content of the mixture of WS and wood chips decreased by 19.7%. It was found that members of the genus Bacillus were the main organic matter destructors. In the period from 7 to 14 days, the rates of organic matter mineralization and moisture loss were the highest, and bacteria of the genus Bacillus dominated, accounting for 43.5 to 84.6% of the bacterial community with a total number of 1.20 (±0.09) × 106 to 6.70 (±0.44) × 105 gene copies µg−1. The maximum number of Amaricoccus was (15.7% of the total bacterial community) in the middle of bio-drying. There was an active accumulation of nitrate nitrogen due to the oxidation of nitrogen-containing substances during the same period of time. Bacteria of the genera Sphingobacterium, Brevundimonas, Brucella, Achromobacter and fungi of the genus Fusarium dominated in the biofilter, which removed volatile compounds from the waste air by 90%. The obtained results allow to model the further intensification of bio-drying, as well as its efficiency and safety.
Establishment of Flavonoid Fingerprint of TMR Diet and Optimization Factor Analysis Strategy and In Vitro Fermentation Parameters Based on Spectrum–Effect Relationship
Authors: Zhao Xiaobo , Xiong Anran , Yu Shiqiang , Wang Linwei , Wang Jing , Zhao Yuchao , Jiang Linshu
Published: June 2023

Nutricines, the nutritionally active substances in feed, play a vital role in enhancing immune function, antioxidant activity, and feed efficiency in dairy cows. Identifying nutricines in total mixed ration (TMR) provides insights into feed quality and their impact on dairy cow health. However, due to the structural diversity of nutricines, data mining using multivariate variable models faces challenges in exploring their relationships. To address this, this study established a hierarchical clustering and optimization factor strategy for 13 common flavonoid peaks detected using apparent data and HPLC-DAD. The establishment of the flavonoid fingerprint of TMR diet in dairy cows detected 13 common peaks, five of which were found using standard products: p-coumaric acid, sinapic acid, tricin, and diosmetin. In vitro fermentation results using different TMR samples in substrate fermentation indicated that the dry matter disappearance rate, NH3-N, acetate, propionate, butyrate, isovalerate, and valerate changes varied significantly (p < 0.05). In spectrum–activity relationship studies, P2, P6, P8, P9, P10, and P11 were all considered possible factors causing this effect. In the analysis of optimization factor strategy, the peak spectrum model of four fermentation parameters, i.e., pH, dry matter digestibility, NH3-N, and acetate, was constructed after optimization (p < 0.05), and the data model is listed in the main text. In structure–activity relationship studies, ferulic acid, isoferulic acid, methyl sinapic acid, methyl 4-hydroxycinnamate, and p-hydroxybenzalacetone may serve as candidate references for compound 10 and may play an important role in affecting the digestibility of dry matter in in vitro fermentation. These findings highlight the role of flavonoids in TMR feed as key factors in maintaining dairy cow health and differentiating nutritional value. This study proposes a novel method for future TMR diet formulation and quality evaluation, with potential implications for improving dairy cow health and performance. Further research is needed to validate these findings and elucidate the mechanisms underlying nutricine effects on dairy cow nutrition and health.
Effects of Phenyllactic Acid on Fermentation Parameters, Nitrogen Fractions and Bacterial Community of High-Moisture Stylo Silage
Authors: Dong Chenxi , Liu Ping , Wang Xianliu , Zhang Wei , He Liwen
Published: June 2023

To investigate the effect of phenyllactic acid (PLA) on the dynamic changes of high-moisture stylo silage, fresh stylo was ensiled with addition of PLA at the levels of 0, 1% and 2% using lab-level silage bags, where samples were collected on days 3, 7, 14 and 30 of ensiling fermentation to analyze fermentation parameters, nitrogen distribution and bacterial community. The results showed that PLA addition at ensiling led to the increase (p < 0.01) in dry matter content, lactic acid concentration and Flieg’s score of stylo silage as well as the decrease (p < 0.01) in dry matter loss, pH value and coliform bacteria population, with butyric acid only detected in the control group. It also resulted in the increase (p < 0.01) in true protein content and its proportion as well as the decrease (p < 0.01) in ammonia-N content and its proportion, almost with linearly dose effect. Sequencing analysis revealed that PLA addition led to the increase (p < 0.05) in Sobs, Shannon, Chao and Ace of bacterial community in the stylo silage, where the relative abundance of Enterobacter, Clostridium, and Kosakonia was decreased, and that of Lactobacillus, Enterococcus and Pantoea was increased. Furthermore, Kosakonia, Terriglobus, Sphingomonas and Sphingopyxis had an important role in the bacterial interaction network. It is suggested that PLA application at the level of 1–2% could improve silage quality of stylo silage via modifying bacterial community.
Experimental Study on Fire Suppression of the Outdoor Oil-Immersed Transformer by High-Pressure Water Mist System
Authors: Song Huaitao , Yao Haowei , Wei Xiaoge , Qin Hengjie , Li Youxin , Lv Kefeng , Chen Qianlong
Published: June 2023

Fire accidents due to oil-immersed transformers seriously threaten the safe operation of power systems. In this paper, the similarity principle was used to design a high-pressure water mist fire-extinguishing test platform for a small-scale transformer fire, and the design method achieved a good fire extinguishing effect. The results indicate that a deflagration phenomenon, lasting about 2–4 s, could be observed after activating the high-pressure water mist system; the flame temperature rose rapidly at first, then dropped sharply, and finally cooled to the indoor temperature. The nozzle’s flow rate in this system has a significant impact on the fire extinguishing time. Meanwhile, the adjustment of the upper nozzle height also influenced the fire suppression effectiveness of the system, where a height of 1800 mm achieved the best performance compared to the others. In addition, the ambient wind speed is a very unfavorable factor for transformer fire suppression, where the fire extinguishing efficiency decreases rapidly with the increase in wind speed. Therefore, under low wind speed conditions, the high-pressure water mist system has great advantages in the fire suppression of outdoor oil-immersed transformers, and the above research results can provide a reference for the optimization design of this system.
Differentiation of Spatial Units of Genus Euthynnus from the Eastern Atlantic and the Mediterranean Using Otolith Shape Analysis
Authors: Muñoz-Lechuga Rubén , Sow Fambaye Ngom , Constance Diaha N’Guessan , Angueko Davy , Macías David , Massa-Gallucci Alexia , da Silva Guelson Batista , Gonçalves Jorge M. S. , Lino Pedro G.
Published: June 2023

The shape of sagitta otoliths was used to compare individuals of little tunny (Euthynnus alleteratus) harvested on board commercial fishing vessels from the coastal areas along the Eastern Atlantic, including the Mediterranean Sea. Fish sampling and selection was designed to cover possible seasonal changes and tuna size. The research encompassed both morphometric and shape analyses of left sagittal otoliths extracted of 504 fish specimens. Four shape indices (Circularity, Roundness, Rectangularity, and Form-Factor) were significantly different between two groups, showing a statistical differentiation between two clear spatial units. The degree of divergence was even more pronounced along the rostrum, postrostrum, and excisura of the generated otolith outlines between these two groups. One group corresponds to the samples from the coastal areas in the Northeast Temperate Atlantic and Mediterranean Sea (NETAM Area) and a second group from the coastal areas off the Eastern Tropical Atlantic coast of Africa (ETA Area). This study is the first to use otolith shape to differentiate tunas from separate spatial units. These results could be used to re-classify previously collected samples and to correct time series of data collected.
West African Goliath Grouper: Where Are They between Senegal and Angola?
Authors: Barreiros João Pedro , Coleman Felicia C.
Published: June 2023

The Atlantic Goliath Grouper Epinephelus itajara (Lichtenstein, 1822) occurs on both sides of the Atlantic, from the Carolinas (USA) to Brazil in the western Atlantic and historically from North Senegal to North Angola off of West Africa in the eastern Atlantic. While there are relatively good data on their distribution in the western Atlantic, confirmed occurrences, population status, fishing reports, and trade data are almost non-existent for West Africa. Part of the problem is that tropical West African countries largely lack the wherewithal to fund the research needed to evaluate this species, nor do they have laws, management plans, or viable enforcement measures that might lead to effective population recovery or protection for essential juvenile mangrove habitats. Given the lack of published studies on this species in West Africa, the primary objectives of this paper are (1) to describe all known historical and current anecdotal information available on this species and (2) to encourage the increased monitoring of habitats where viable populations might still occur (i.e., mangroves, oil rigs, and oil platforms).