bluuebunny/crossref_metadata_2025 huggingface dataset:
Securing a Smart Home with a Transformer-Based IoT Intrusion Detection System
Authors: Wang Minxiao , Yang Ning , Weng Ning
Published: May 2023

Machine learning (ML)-based Network Intrusion Detection Systems (NIDSs) can classify each network’s flow behavior as benign or malicious by detecting heterogeneous features, including both categorical and numerical features. However, the present ML-based NIDSs are deemed insufficient in terms of their ability to generalize, particularly in changing network environments such as the Internet of Things (IoT)-based smart home. Although IoT devices add so much to home comforts, they also introduce potential risks and vulnerabilities. Recently, many NIDS studies on other IoT scenarios, such as the Internet of Vehicles (IoV) and smart cities, focus on utilizing the telemetry data of IoT devices for IoT intrusion detection. Because when IoT devices are under attack, their abnormal telemetry data values can reflect the anomaly state of those devices. Those telemetry data-based IoT NIDS methods detect intrusion events from a different view, focusing on the attack impact, from the traditional network traffic-based NIDS, which focuses on analyzing attack behavior. The telemetry data-based NIDS is more suitable for IoT devices without built-in security mechanisms. Considering the smart home IoT scenario, which has a smaller scope and a limited number of IoT devices compared to other IoT scenarios, both NIDS views can work independently. This motivated us to propose a novel ML-based NIDS to combine the network traffic-based and telemetry data-based NIDS together. In this paper, we propose a Transformer-based IoT NIDS method to learn the behaviors and effects of attacks from different types of data that are generated in the heterogeneous IoT environment. The proposed method utilizes a self-attention mechanism to learn contextual embeddings for input network features. Based on the contextual embeddings, our method can solve the feature set challenge, including both continuous and categorical features. Our method is the first to utilize both network traffic data and IoT sensors’ telemetry data at the same time for intrusion detection. Experiments reveal the effectiveness of our method on a realistic network traffic intrusion detection dataset named ToN_IoT, with an accuracy of 97.95% for binary classification and 95.78% for multiple classifications on pure network data. With the extra IoT information, the performance of our method has been improved to 98.39% and 97.06%, respectively. A comparative study with existing works shows that our method can achieve state-of-the-art performance on the ToN_IoT dataset.
A Skin Cancer Classification Method Based on Discrete Wavelet Down-Sampling Feature Reconstruction
Authors: Wu Qing-e , Yu Yao , Zhang Xinyang
Published: May 2023

Aiming at the problems of feature information loss during down-sampling, insufficient characterization ability and low utilization of channel information in skin cancer diagnosis of melanoma, a skin pathological mirror classification method based on discrete wavelet down-sampling feature reconstruction is proposed in this paper. The wavelet down-sampling method is given first, and the multichannel attention mechanism is introduced to realize the pathological feature reconstruction of high-frequency and low-frequency components, which reduces the loss of pathological feature information due to down-sampling and effectively utilizes the channel information. A skin cancer classification model is given, using a combination of depth-separable convolution and 3×3 standard convolution and wavelet down-sampling as the input backbone of the model to ensure the perceptual field while reducing the number of parameters; the residual module of the model is optimized using wavelet down-sampling and Hard-Swish activation function to enhance the feature representation capability of the model. The network weight parameters are initialized on ImageNet using transfer learning and then debugged on the augmentation HAM10000 dataset. The experimental results show that the accuracy of the proposed method for skin cancer pathological mirror classification is significantly improved, reaching 95.84%. Compared with the existing skin cancer classification methods, the proposed method not only has higher classification accuracy but also accelerates the classification speed and enhances the noise immunity. The method proposed in this paper provides a new classification method for skin cancer classification and has some practical value.
Multi-Objective Combinatorial Optimization Using the Cell Mapping Algorithm for Mobile Robots Trajectory Planning
Authors: Grisales-Ramírez Efraín , Osorio Gustavo
Published: May 2023

The use of optimal control theory for motion planning is a challenging task. Cell mapping offers a way to formulate combinatorial optimization problems, allowing the inclusion of complex cost functions as well as multi-objective optimization problems. This paper presents a suboptimal solution for a trajectory planning problem in a workspace with obstacles, for a differential drive mobile robot. This method relies on the use of any linearization technique that allows the regularization of the combinatorial optimization problem. We explore some classical problems in optimal control, i.e., distance, control effort, and navigation time), as well as the multi-objective optimization problem (MOP). We also performed a comparison with two classical path planning algorithms, namely A∗ and RRT∗, to validate the proposed method when the multi-objective optimization problem includes distance in the cost function, achieving a compromise of less than 2% for the worst-case scenario for our case study.
Research on Efficient Multi-Behavior Recommendation Method Fused with Graph Neural Network
Authors: Lu Huitong , Deng Xiaolong , Lu Junwen
Published: May 2023

Currently, most recommendation algorithms only use a single type of user behavior information to predict the target behavior. However, when browsing and selecting items, users generate other types of behavior information, which is important, but often not analyzed or modeled by traditional recommendation algorithms. This study aims to design a multi-behavior recommendation algorithm based on graph neural networks by analyzing multiple types of behavior information in users’ product purchasing process, to fully utilize multiple types of user behavior information. The algorithm models users, items, and user behavior in multiple dimensions by incorporating attention mechanisms and multi-behavior learning into graph neural networks, and solves the problem of imbalanced user behavior weights from the perspective of multi-task loss optimization. After experimental verification, we proposed that the multi-behavior graph attention network (MGAT) algorithm has better performance compared to four other classical recommendation algorithms on the Beibei and Taobao datasets. The results demonstrate that the multi-behavior recommendation algorithm based on graph neural networks has practicality in fully utilizing multiple types of user information, and can solve the problem of imbalanced user behavior weights to some extent.
Self-Paced Dual-Axis Attention Fusion Network for Retinal Vessel Segmentation
Authors: Shi Yueting , Wang Weijiang , Yuan Minzhi , Wang Xiaohua
Published: May 2023

The segmentation of retinal vessels plays an essential role in the early recognition of ophthalmic diseases in clinics. Increasingly, approaches based on deep learning have been pushing vessel segmentation performance, yet it is still a challenging problem due to the complex structure of retinal vessels and the lack of precisely labeled samples. In this paper, we propose a self-paced dual-axis attention fusion network (SPDAA-Net). Firstly, a self-paced learning mechanism using a query-by-committee algorithm is designed to guide the model to learn from easy to hard, which makes model training more intelligent. Secondly, during fusing of multi-scale features, a dual-axis attention mechanism composed of height and width attention is developed to perceive the object, which brings in long-range dependencies while reducing computation complexity. Furthermore, CutMix data augmentation is applied to increase the generalization of the model, enhance the recognition ability of global and local features, and ultimately boost accuracy. We implement comprehensive experiments validating that our SPDAA-Net obtains remarkable performance on both the public DRIVE and CHASE-DB1 datasets.
A Generate Adversarial Network with Structural Branch Assistance for Image Inpainting
Authors: Wang Jin , Jia Dongli , Zhang Heng
Published: May 2023

In digital image inpainting tasks, existing deep-learning-based image inpainting methods have achieved remarkable staged results by introducing structural prior information into the network. However, the corresponding relationship between texture and structure is not fully considered, and the inconsistency between texture and structure appears in the results of the current method. In this paper, we propose a dual-branch network with structural branch assistance, which decouples the inpainting of low-frequency and high-frequency information utilizing parallel branches. The feature fusion (FF) module is introduced to integrate the feature information from the two branches, which effectively ensures the consistency of structure and texture in the image. The feature attention (FA) module is introduced to extract long-distance feature information, which enhances the consistency between the local features of the image and the overall image and gives the image a more detailed texture. Experiments on the Paris StreetView and CelebA-HQ datasets prove the effectiveness and superiority of our method.
A Decade of Intelligent Software Testing Research: A Bibliometric Analysis
Authors: Boukhlif Mohamed , Hanine Mohamed , Kharmoum Nassim
Published: May 2023

It gets harder and harder to guarantee the quality of software systems due to their increasing complexity and fast development. Because it helps spot errors and gaps during the first phases of software development, software testing is one of the most crucial stages of software engineering. Software testing used to be done manually, which is a time-consuming, imprecise procedure that comes with errors and gaps and costs money, time, and effort. Currently, testing professionals routinely automate testing to obtain trustworthy results while saving time, cost, and labor. We’ve also moved the starting point of the software cycle to the developer, and made write tests before even writing code, or what’s known as TDD (Test Driven Development). The use of new artificial intelligence techniques will enable the generation of smart test cases to improve test quality and provide better coverage and accurate results. In this study, we used the Web of Science database to acquire bibliometric data about intelligent software testing papers which were conducted between 2012 and 2022, and we used Biblioshiny from the R bibliomerix package, alongside with VOSViewer in order to analyze the data and extract insights and answer research questions about the authors, articles, journals, organizations, and countries publishing in the field of intelligent software testing. The focus of this study is on scientific progress and collaborative trends in scholarly research, providing a blueprint for showcasing worldwide developments in the realm of intelligent software testing. By gaining a comprehensive understanding of the present state of research on the application of artificial intelligence in software testing, this study can offer valuable insights to software engineers, architects, and researchers in the field.
Radar Spectrum Image Classification Based on Deep Learning
Authors: Sun Zhongsen , Li Kaizhuang , Zheng Yu , Li Xi , Mao Yunlong
Published: May 2023

With the continuous development and progress of science and technology, the increasingly complex electromagnetic environment and the research and development of new radar systems have led to the emergence of various radar signals. Traditional methods of radar emitter identification cannot meet the needs of current practical applications. For the purpose of classification and recognition of radar emitter signals, this paper proposes an improved EfficientNetv2-s classification method based on deep learning for more precise classification and recognition of radar radiation source signals. Using 16 different types of radar signal parameters from the signal parameter setting table, the proposed method generates random data sets consisting of spectrum images with varying amplitude. The proposed method replaces two-dimensional convolution in EfficientNetV2 with one-dimensional convolution. Additionally, the channel attention mechanism of the EfficientNetv2-s is optimized and modified to obtain attention weights without dimensional reduction, resulting in superior accuracy. Compared with other deep-learning image-classification methods, the test results of this method have better classification accuracy on the test set: the top1 accuracy reaches 98.12%, which is 0.17~3.12% higher than other methods. Furthermore, the proposed method has lower complexity compared to most methods.
Iterative Design Algorithm for Robust Disturbance-Rejection Control
Authors: Li Jixuan , Yu Pan , Liu Nike , Zhao Henan , Liu Chunfang
Published: May 2023

An iterative design algorithm is developed for robust disturbance–rejection control of uncertain systems with time-varying parameter perturbations in this paper. For more design degrees of freedom, a generalized equivalent-input-disturbance estimator is adopted to approximate the effect of both disturbances and uncertainties. By the bound real lemma, the H∞ norm is used to evaluate the robust disturbance–rejection performance of the closed-loop uncertain system. To avoid the constraints introduced by the widely used commutative condition, the control gains are divided into two groups and calculated by steps. Further, two robust quadratic stability conditions are derived, and an iterative design algorithm is developed to optimize the robust H∞ disturbance–rejection performance. Finally, the effectiveness and advantages of the developed method are demonstrated by a case study of a suspension system of modern vehicles.
Feasibility Study of Scheme and Regenerator Parameters for Trinary Power Cycles
Authors: Kindra Vladimir , Maksimov Igor , Komarov Ivan , Xu Cheng , Xin Tuantuan
Published: May 2023

Natural gas-fired combined cycle plants are nowadays one of the most efficient and environmentally friendly energy complexes. High energy efficiency and low specific emissions are achieved primarily due to the high average integral temperature of heat supply in the Brayton–Rankine cycle. In this case, the main sources of energy losses are heat losses in the condenser of the steam turbine plant and heat losses with the exhaust gases of the waste heat boiler. This work is related to the analysis of the thermodynamic and economic effects in the transition from binary to trinary cycles, in which, in addition to the gas and steam–water cycles, there is an additional cycle with a low-boiling coolant. A method for the feasibility study of a waste heat recovery unit for trinary plants is proposed. The schematic and design solutions described will ensure the increased energy and economic performance of combined cycle power plants. Based on the results of the thermodynamic optimization of the structure and parameters of thermal schemes, it was found that the use of the organic Rankine cycle with R236ea freon for the utilization of the low-grade heat of a trinary plant’s exhaust gases operating from a GTE-160 gas turbine makes it possible to achieve a net electrical efficiency of 51.3%, which is a 0.4% higher efficiency for a double-circuit combined cycle gas turbine plant and a 2.1% higher efficiency for a single-circuit cycle with similar initial parameters. On the basis of the conducted feasibility study, the parameters and characteristics of the heat exchangers of the regenerative system of the waste heat recovery unit are substantiated. The use of plain fin-and-tube heat exchangers in the regenerative system of the utilization cycle is the most promising solution. It was found that the level of allowable pressure loss in the regenerator of 10 kPa and the degree of regeneration of 80% allow for maximum economic efficiency of the waste heat recovery unit.
Prospects of Controlled Auto-Ignition Based Thermal Propulsion Units for Modern Gasoline Vehicles
Authors: Bajwa Abdullah U. , Leach Felix C. P. , Davy Martin H.
Published: May 2023

Gasoline engines employing the spatially distributed auto-ignition combustion mode, known as controlled auto-ignition (CAI), are a prospective technology for significantly improving engine efficiency and reducing emissions. This review paper provides an overview of developments in various gasoline CAI technologies and discusses their attendant strengths and weaknesses. Hybrid propulsion systems powered by high-efficiency gasoline CAI engines can provide a low-carbon pathway for mobility sector decarbonisation. Therefore, this paper focuses on the challenges and opportunities of CAI implementation, especially for electrified powertrains. Different control actuators that can extend the CAI operating range are discussed, and opportunities for synergistic operation between thermal and electric components of hybridised powertrains are identified. Such synergies can remove impediments in the way of CAI system adoption and can, thus, support CAI adoption and maximise efficiency gains from its implementation. The prospects of supporting CAI combustion for different powertrain electrification levels, hybrid architectures, engine size, and energy management systems are discussed. Load levelling offered by electrified powertrains through CAI-favouring energy management strategies has the potential to substantially relax the operating point requirements for CAI-based thermal propulsion units and to remove the need for expensive actuators. The highly flexible spark-assisted partially premixed compression ignition hybrid mode (SACI-PPCI) emerges as a promising CAI strategy for conventional powertrains, and the moderately flexible spark-assisted compression ignition (SACI) configuration can be a cost-effective thermal propulsion mode for electrified powertrains.
A Comprehensive Evaluation of Different Power Quantities in DC Electric Arc Furnace Power Supplies
Authors: Hamedani Pegah , Garcia Cristian , Rodriguez Jose
Published: May 2023

Conventionally, DC arc furnaces are fed by thyristor rectifiers to control the level of current or power transferred to the load. With the advancement of high-power transistors, other structures such as diode rectifiers and DC choppers have been introduced and developed for the feeding system of DC electric arc furnaces. In this paper, the effect of different power supply systems on power quality indexes is discussed. In this regard, two types of feeding systems are considered, including a power supply system based on thyristor rectifiers and a power supply system based on diode rectifiers and DC choppers. In addition, different control methods, including constant current control (CCC) and constant power control (CPC), on DC electric arc furnaces are applied. For evaluating the power quality indexes, voltage and current harmonic distortion, unbalance of voltage and current, and power factor on the AC side are investigated for different power supply systems. Simulation results were performed with PSCAD/EMTDC software. It is shown that the power supply systems with diode rectifiers and DC choppers have superiority in comparison to the types based on thyristor rectifiers. According to the results, the average current THD is reduced from 16.55% in the thyristor rectifier to 8.40% in the chopper rectifiers with the CCC method and from 19.27% in the thyristor rectifier to 7.38% in the chopper rectifiers with the CPC method. Moreover, the average voltage THD is reduced from 5.67% in the thyristor rectifier to 3.02% in the chopper rectifiers with the CCC method. The result is similar to the CPC method. Furthermore, for a specific power supply system, the harmonic distortion is lower in the case of the CPC method than in the CCC method.
Long-Term Self-Discharge Measurements and Modelling for Various Cell Types and Cell Potentials
Authors: Azzam Mohamed , Ehrensberger Moritz , Scheuer Reinhard , Endisch Christian , Lewerenz Meinert
Published: May 2023

Self-discharge of lithium-ion cells leads to voltage decay over time. In this work, the self-discharge was measured at 30 ∘C for three cell types at various voltage levels for about 150 days in a constant voltage mode determining the current at a high precision (float current). All cells exhibit a transient part leading to a steady-state, which is no longer influenced by reversible effects. To study the effect of the check-ups on the steady-state float current, the cells, interrupted and not interrupted by check-ups, were compared. The results indicate that both the transient processes and steady-state currents are highly reproducible. In the first period of the float current, the polarization dominates the measured current, followed by the anode overhang effect dominating the process for a period of 5–30 days. After the decline of both processes, a mostly constant steady-state in the order of μA is observed. The check-up interruption generally shows no apparent effect on the reached steady-state and results only in an extended settling time. A model to simulate the transient process and steady-state of float currents was developed. The model shows a high accuracy in reproducing the results and identifying the time needed to reach the steady-state.
Energy Storage Systems for Photovoltaic and Wind Systems: A Review
Authors: Rekioua Djamila
Published: May 2023

The study provides a study on energy storage technologies for photovoltaic and wind systems in response to the growing demand for low-carbon transportation. Energy storage systems (ESSs) have become an emerging area of renewed interest as a critical factor in renewable energy systems. The technology choice depends essentially on system requirements, cost, and performance characteristics. Common types of ESSs for renewable energy sources include electrochemical energy storage (batteries, fuel cells for hydrogen storage, and flow batteries), mechanical energy storage (including pumped hydroelectric energy storage (PHES), gravity energy storage (GES), compressed air energy storage (CAES), and flywheel energy storage), electrical energy storage (such as supercapacitor energy storage (SES), superconducting magnetic energy storage (SMES), and thermal energy storage (TES)), and hybrid or multi-storage systems that combine two or more technologies, such as integrating batteries with pumped hydroelectric storage or using supercapacitors and thermal energy storage. These different categories of ESS enable the storage and release of excess energy from renewable sources to ensure a reliable and stable supply of renewable energy. The optimal storage technology for a specific application in photovoltaic and wind systems will depend on the specific requirements of the system. It is important to carefully evaluate these needs and consider factors, such as power and energy requirements, efficiency, cost, scalability, and durability when selecting an ESS technology.
Phase Change Material Composite Battery Module for Thermal Protection of Electric Vehicles: An Experimental Observation
Authors: Budiman Alexander C. , Azzopardi Brian , Sudirja  , Perdana Muhammad A. P. , Kaleg Sunarto , Hadiastuti Febriani S. , Hasyim Bagus A. , Amin  , Ristiana Rina , Muharam Aam , Hapid Abdul
Published: May 2023

A composite container for an electric vehicle (EV) battery module filled with a phase-change material (PCM) was experimentally tested at various discharge rates. The average cell temperatures at 1 C, 2 C, and 4 C discharge rates, respectively, might reach 38 °C, 50 °C, and 70 °C in the absence of any heat-absorbing material. The temperature was noticeably lower with PCM present than with a conventional battery module. For instance, at 4 C discharge rates, none of the battery cells inside the PCM-filled module were able to reach 70 °C. Unfortunately, the PCM addition also degraded the composite’s tensile qualities. Further investigations used Paraffin-20 and Caprylone since PCMs provide a notably different thermal performance due to their distinctive latent heat profiles. It was observed that a high melting temperature of the paraffin mixture, despite its slightly lower latent heat capacity compared to Caprylone, could lead to a more uniform temperature. Overall, both PCMs can be used as passive protection against any potential thermal abuses in EV battery modules, while in terms of mechanical strength, the use of a composite reinforcement material is strongly encouraged.
Power Transmission and Control in Microturbines’ Electronics: A Review
Authors: Abuhaiba Ahmad , Assadi Mohsen , Apostolopoulou Dimitra , Al-Zaili Jafar , Sayma Abdulnaser I.
Published: May 2023

When the shaft rotates in microturbines, the rotational movement is converted to electrical power. This is achieved through a permanent magnet synchronous machine (PMSM) housed on the shaft and the power electronics components. To the best of the authors’ knowledge, articles that comprehensively describe the power transmission and control in the electrical part of microturbines have yet to be introduced, namely, the PMSM and power electronics. This review paper presents a detailed review of power conversion in each component of the electrical part of microturbines. The paper also reviews the existing literature on microturbines’ electrical performance, noting areas where progress has already been made as well as those where more research is still needed. Furthermore, the paper explains the control system in the electrical part of microturbines, outlining the grid synchronisation control approach for grid-connected microturbines and reviews the possibility of employing control strategies that engage the PMSM and power electronics as controllers for certain variables in microturbines, such as the shaft rotational speed and torque. Such control methods are more crucial in externally fired microturbines since traditional control strategies used in internally fired microturbines, such as thermal input regulation, are no longer an option in externally fired microturbines for controlling the shaft speed. The significance of higher switching frequencies in power electronics is also discussed. The higher switching frequency, the faster response to load variations and, therefore, the more reliable the control system. A greater switching frequency allows for reduced power loss, cost, and unit size. In this context, it is recommended in this review paper that future research consider using silicon carbide switching devices rather than silicon ones, which is the current practice, to build up the microturbines converters’ topology. The recommendation was motivated by looking at the existing literature that compares the switching frequency, size, cost, thermal endurance, and power losses of silicon and silicon carbide components in applications other than microturbines since initiatives of using silicon carbide in microturbine power electronics have not been reported in the literature, as far as the authors are aware. The electrical components of microturbines account for a third of the entire size and cost of the unit. This means that reducing the size and cost of the electronics contributes effectively to reducing the total size and cost. In applications other than microturbines, silicon carbide exhibited promising results compared to silicon in terms of size and long-term cost. Investigating silicon carbide in microturbines is worthwhile to see if it provides such promising benefits to the microturbine unit.
From Hydrometeor Size Distribution Measurements to Projections of Wind Turbine Blade Leading-Edge Erosion
Authors: Letson Fred , Pryor Sara C.
Published: May 2023

Wind turbine blade leading-edge erosion (LEE) is a cause of increased operation and maintenance costs and decreased annual energy production. Thus, detailed, site-specific quantification of likely erosion conditions are critically needed to inform wind plant owner/operator decisions regarding mitigation strategies. Estimating the damage potential at a wind plant site requires accurate measurement of precipitation intensity, phase, droplet size distributions, wind speeds and their joint statistics. The current work quantifies the effect of disdrometer type on the characterization of LEE potential at a site in the US Southern Great Plains. using observations from three co-located disdrometers (an optical, an impact and a video disdrometer), along with hub-height wind-speed observations from a Doppler lidar and two LEE models: a kinetic energy model and the Springer model. Estimates of total kinetic energy of hydrometeor impacts over the four-year study period vary by as much as 38%, and coating lifetime derived from accumulated distance-to-failure estimates from the Springer model differ by an even greater amount, depending on disdrometer type. Damage potential at this site is concentrated in time, with 50% of impact kinetic energy occurring in 6–12 h per year, depending on which set of disdrometer observations is used. Rotor-speed curtailment during the most erosive 0.1–0.2% of 10 min periods is found to increase blade lifetimes and lead to the lowest levelized cost of energy.
Review of Hysteresis Models for Magnetic Materials
Authors: Mörée Gustav , Leijon Mats
Published: May 2023

There are several models for magnetic hysteresis. Their key purposes are to model magnetization curves with a history dependence to achieve hysteresis cycles without a frequency dependence. There are different approaches to handling history dependence. The two main categories are Duhem-type models and Preisach-type models. Duhem models handle it via a simple directional dependence on the flux rate, without a proper memory. While the Preisach type model handles it via memory of the point where the direction of the flux rate is changed. The most common Duhem model is the phenomenological Jiles–Atherton model, with examples of other models including the Coleman–Hodgdon model and the Tellinen model. Examples of Preisach type models are the classical Preisach model and the Prandtl–Ishlinskii model, although there are also many other models with adoptions of a similar history dependence. Hysteresis is by definition rate-independent, and thereby not dependent on the speed of the alternating flux density. An additional rate dependence is still important and often included in many dynamic hysteresis models. The Chua model is common for modeling non-linear dynamic magnetization curves; however, it does not define classical hysteresis. Other similar adoptions also exist that combine hysteresis modeling with eddy current modeling, similar to how frequency dependence is included in core loss modeling. Most models are made for scalar values of alternating fields, but there are also several models with vector generalizations that also consider three-dimensional directions.
Investigation into Pressure Appearances and Hydraulic Fracturing Roof-Cutting Technology in Mining Working Face under Residual Pillars: A Case Study
Authors: Wu Wenda , Feng Guorui , Yu Xiuxiu , Bai Jianbiao , Wang Xiangyu , Zhao Xiangzhuo
Published: May 2023

Strong mining pressure disasters are prone to happen when the mining working face is under residual pillars (MWFRPs). The purpose of this study was to experimentally investigate and evaluate pressure manifestations and hydraulic fracture roof-cutting technology in the development of a working face under residual pillars using a physical model and numerical modelling tools. A scheme for hydraulic fracturing cutting technology was proposed and carried out on-site at the 31106 working face. The results show that the instability of the overlying residual pillar causes the upper thick, hard strata (THS II) to rupture and form a “T-shaped structure”. The rotation and sinking movement of the structure leads to the transmission of the dynamic load downwards, causing shear failure in the lower thick, hard strata (THS I) along the boundary of the residual pillar. The smaller the length of the THS II fracture block, the smaller the shear damage of THS I, and the lesser the mining pressure in the working face. Field trials proved that hydraulic fracture roof cutting can effectively destroy the integrity of the thick hard strata and promote their collapse, which reduces the strong dynamic load borne by the hydraulic support. This research provides a reference for safe mining at a working face under similar conditions.
A Review to Update the Protected Areas in Ecuador and an Analysis of Their Main Impacts and Conservation Strategies
Authors: Mestanza-Ramón Carlos , Monar-Nuñez Joel , Guala-Alulema Paola , Montenegro-Zambrano Yuri , Herrera-Chávez Renato , Milanes Celene B. , Arguello-Guadalupe Carla , Buñay-Guisñan Pamela , Toledo-Villacís Marco
Published: May 2023

Establishing new protected areas (PAs) is one of the first steps needed to reduce habitat loss and fragmentation, protect ecosystems that are of vital importance to conserve biodiversity, and even protect traditional cultures. The correct management of a PA can be beneficial for the different forms of life found within it and can provide multiple benefits to humanity and to the continued functioning of productive ecosystems. Protected Areas act as buffers for life while serving as sanctuaries and strongholds for species in the face of climate change. Within these areas, genetic diversity is enabled to evolve in response to the pressures of natural selection. The causes of biodiversity loss include changes in land use due to agriculture and urbanization, invasive species, overexploitation, and pollution. As stipulated, the current study aims to update the National System of Protected Areas (SNAP) by applying a review of scientific and gray literature. This review presents updated information; Ecuador currently has 74 protected areas, with state, decentralized autonomous, community, and private subdivisions. The main social and environmental impacts found in the protected areas included in the SNAP are presented in a review of the existing literature. Finally, strategies are proposed to improve the management of the protected areas of the SNAP focused on strengthening the conservation of their different life forms and the responsible use of their ecosystem services through more efficient and productive spaces.
Tree Species Classification in a Complex Brazilian Tropical Forest Using Hyperspectral and LiDAR Data
Authors: Pereira Martins-Neto Rorai , Garcia Tommaselli Antonio Maria , Imai Nilton Nobuhiro , Honkavaara Eija , Miltiadou Milto , Saito Moriya Erika Akemi , David Hassan Camil
Published: May 2023

This study experiments with different combinations of UAV hyperspectral data and LiDAR metrics for classifying eight tree species found in a Brazilian Atlantic Forest remnant, the most degraded Brazilian biome with high fragmentation but with huge structural complexity. The selection of the species was done based on the number of tree samples, which exist in the plot data and in the fact the UAV imagery does not acquire information below the forest canopy. Due to the complexity of the forest, only species that exist in the upper canopy of the remnant were included in the classification. A combination of hyperspectral UAV images and LiDAR point clouds were in the experiment. The hyperspectral images were photogrammetric and radiometric processed to obtain orthomosaics with reflectance factor values. Raw spectra were extracted from the trees, and vegetation indices (VIs) were calculated. Regarding the LiDAR data, both the point cloud—referred to as Peak Returns (PR)—and the full-waveform (FWF) LiDAR were included in this study. The point clouds were processed to normalize the intensities and heights, and different metrics for each data type (PR and FWF) were extracted. Segmentation was preformed semi-automatically using the superpixel algorithm, followed with manual correction to ensure precise tree crown delineation before tree species classification. Thirteen different classification scenarios were tested. The scenarios included spectral features and LiDAR metrics either combined or not. The best result was obtained with all features transformed with principal component analysis with an accuracy of 76%, which did not differ significantly from the scenarios using the raw spectra or VIs with PR or FWF LiDAR metrics. The combination of spectral data with geometric information from LiDAR improved the classification of tree species in a complex tropical forest, and these results can serve to inform management and conservation practices of these forest remnants.
Response of Fine-Root Traits of Populus tomentosa to Drought in Shallow and Deep Soil
Authors: Tan Jianbiao , Yu Weichen , Liu Yang , Guo Youzheng , Liu Nan , Fu Haiman , Di Nan , Duan Jie , Li Ximeng , Xi Benye
Published: May 2023

Understanding the response characteristics of fine roots to soil drought of different degrees is essential for revealing the ecological adaptability of trees to different water environments and diverse plant resource absorption strategies. This study focused on a Chinese white poplar (Populus tomentosa) plantation stand, which gradually experienced the process of deep soil drying. In 2019 and 2021, by measuring the fine-root length density (FRLD), mean root diameter (MRD), specific root length (SRL), and root tissue density (RTD) of 1920 root samples and continuously monitoring the soil water content (SWC) in 0–600 cm soil layers, we explored the response characteristics of fine-root distributions and morphological traits relative to soil drought of different degrees. The results showed that P. tomentosa primarily changed the fine-root vertical distribution rather than the total amount of fine roots for coping with soil drought of different degrees. Shallow soil drought induced more fine-root distributions in the deep soil layer, while drought in both shallow and deep soil further aggravated this trend. Shallow soil drought restrained shallow fine-root growth, yet deep soil drought promoted deep fine-root growth. The very deep fine roots (400–600 cm) were more sensitive to soil drought than shallow fine roots. The shallow soil drought significantly increased the SRL of very deep fine roots; in contrast, when deep soil drought also occurred, the MRD and SRL significantly increased and decreased, respectively. In addition, fine-root morphological traits exhibited significant vertical spatial and temporal variation. MRD increased and then decreased, and the RTD gradually decreased with depth, while SRL had an increased trend in the very deep soil layer (400–600 cm). When the rainy season came, MRD and SRL increased and decreased, respectively. In conclusion, when facing gradual deep soil drying, P. tomentosa will use a large range of rooting patterns to meet the water demand of the canopy. These patterns range from “drought tolerant strategies” by distributing more fine roots in the deeper soil layer where water is abundant to “drought tolerant strategies” by changing very deep fine-root morphological traits to improve water-absorbing and transporting efficiencies. Our findings provide insight into the ecological adaption strategy of tree root systems relative to soil drought of different degrees in arid and semi-arid regions and provide crucial theoretical support for developing water management technologies to cope with deep soil drying under climate change.
Updating Ecology and Distribution of Wild Truffles in Morocco
Authors: Henkrar Fatima , Meyad Chaimae , Oikrim Marwa , Bouhaddou Nezha , Khabar Lahsen
Published: May 2023

The distribution and abundance of the various truffle species are influenced by the climate, soil, and vegetation conditions. The setting of these characteristics is necessary for the potential species’ cultivation. Here, we describe the ecological characteristics of Moroccan truffles and desert truffles and their associations with host plants. We also determine the climate and soil parameters relating to the geographic distribution and fructification of truffles and desert truffles. In contrast to truffles, which are found in sub-humid environments, desert truffles are found in semi-arid and arid regions of Morocco. The dissemination of desert truffles in the Mamora forest and oriental regions of Morocco is typically linked to the presence of Helianthemum sp., whereas the existence of truffles (Tuber spp.) in the Middle Atlas depends on the subsistence of Quercus ilex and Q. faginea. The truffles’ and desert truffles’ fructification depends mainly on the precipitation frequency. Terfezia arenaria and Tuber oligospermum, the two major desert truffles of Mamora forest, require an annual rainfall of 435 mm on average in slightly acidic soil. While the oriental and Highland desert truffles, namely Terfezia boudieri, T. claveryi, and Tirmania spp., require an annual precipitation average of 123 to 267 mm and a high CaCO3 content. Otherwise, there is Tuber aestivum, localized in humid regions with a rainfall rate of more than 650 mm, and found under calcareous soil rich in organic matter with the presence of potential host plants, such as oaks, cedars, and pines. Our findings open up the possibility of successful cultivation of truffles and desert truffles having an economic interest through understanding their ecological requirements in Morocco.
Predicting Football Team Performance with Explainable AI: Leveraging SHAP to Identify Key Team-Level Performance Metrics
Authors: Moustakidis Serafeim , Plakias Spyridon , Kokkotis Christos , Tsatalas Themistoklis , Tsaopoulos Dimitrios
Published: May 2023

Understanding the performance indicators that contribute to the final score of a football match is crucial for directing the training process towards specific goals. This paper presents a pipeline for identifying key team-level performance variables in football using explainable ML techniques. The input data includes various team-specific features such as ball possession and pass behaviors, with the target output being the average scoring performance of each team over a season. The pipeline includes data preprocessing, sequential forward feature selection, model training, prediction, and explainability using SHapley Additive exPlanations (SHAP). Results show that 14 variables have the greatest contribution to the outcome of a match, with 12 having a positive effect and 2 having a negative effect. The study also identified the importance of certain performance indicators, such as shots, chances, passing, and ball possession, to the final score. This pipeline provides valuable insights for coaches and sports analysts to understand which aspects of a team’s performance need improvement and enable targeted interventions to improve performance. The use of explainable ML techniques allows for a deeper understanding of the factors contributing to the predicted average team score performance.
Impact of Insoluble Dietary Fiber and CaCl2 on Structural Properties of Soybean Protein Isolate–Wheat Gluten Composite Gel
Authors: Lian Wentao , Hu Qinlin , Qu Min , Sun Bingyu , Liu Linlin , Zhu Ying , Xia Xiaoyu , Huang Yuyang , Zhu Xiuqing
Published: May 2023

The effect and mechanism of soybean insoluble dietary fiber (SIDF) (0~4%) and CaCl2 (0~0.005 M) on the properties of soybean protein isolate (SPI)–wheat gluten (WG) composite gel were studied. It was revealed that the addition of insoluble dietary fiber (1~2%) increased the strength and water-holding capacity (WHC) of the composite gel (p < 0.05) and enhanced the gel network structure compared with the control. WHC and LF-NMR showed that the water-binding ability of the gel system with only 2% SIDF was the strongest. The addition of excessive SIDF increased the distance between protein molecules, impeded the cross-linking of protein, and formed a three-dimensional network with low gel strength. The infrared spectrum and intermolecular force indicated that the interaction between SIDF and SPI were mainly physical, and the hydrophobic interaction and disulfide bond were the main forces in the gel system. The addition of CaCl2 can increase the critical content of gel texture destruction caused by SIDF, and the gel strength attained its peak at 3% SIDF, indicating that appropriate CaCl2 improved gel structure weakening caused by excessive SIDF. This study provides insights in enhancing the production of multi-component composite gel systems.
Application of Composite Flour from Indonesian Local Tubers in Gluten-Free Pancakes
Authors: Marta Herlina , Febiola Christine , Cahyana Yana , Arifin Heni Radiani , Fetriyuna Fetriyuna , Sondari Dewi
Published: May 2023

Pancakes are fast food snacks that are generally made with wheat flour as the basic ingredients, which is an imported commodity and detrimental for people who are allergic to gluten. To reduce the use of wheat, alternative raw materials derived from local commodities are used, such as modified cassava flour (mocaf), arrowroot flour, and suweg flour. The experiment was carried out by mixing mocaf flour, arrowroot flour, and suweg flour to produce composite flour with a ratio of 70:15:15 (CF1), 70:20:10 (CF2), and 70:20:5 (CF3). The result showed that the ratio of mocaf flour, arrowroot flour, and suweg flour had a significant effect on pasting temperature, peak viscosity, hold viscosity, breakdown viscosity, setback, L*, a*, hue, whiteness, ∆E, as well as swelling volume and solubility on the characteristics of the composite flour. There was also a significant effect on the texture characteristics of hardness, adhesiveness, chewiness, color characteristics L*, a*, whiteness, ∆E, and flavor preference for the gluten-free pancake products. The best formulation to produce pancakes that have characteristics similar to wheat flour-based pancakes was 70% mocaf flour, 15% arrowroot flour, and 15% suweg flour.
Glycosyltransferases Expression Changes in Leuconostoc mesenteroides subsp. mesenteroides ATCC 8293 Grown on Different Carbon Sources
Authors: Vallejo-García Luz Cristina , Sánchez-Olmos María del Carmen , Gutiérrez-Ríos Rosa María , López Munguía Agustín
Published: May 2023

Leuconostoc mesenteroides strains are common contributors in fermented foods producing a wide variety of polysaccharides from sucrose through glycosyltransferases (GTFs). These polymers have been proposed as protective barriers against acidity, dehydration, heat, and oxidative stress. Despite its presence in many traditional fermented products and their association with food functional properties, regulation of GTFs expression in Ln. mesenteroides is still poorly understood. The strain Ln. mesenteroides ATCC 8293 contains three glucansucrases genes not found in operons, and three fructansucrases genes arranged in two operons, levLX and levC-scrB, a Glycoside-hydrolase. We described the first differential gene expression analysis of this strain when cultivated in different carbon sources. We observed that while GTFs are expressed in the presence of most sugars, they are down-regulated in xylose. We ruled out the regulatory effect of CcpA over GTFs and did not find regulatory elements with a direct effect on glucansucrases in the condition assayed. Our findings suggest that only operon levLX is repressed in xylose by LexA and that both fructansucrases operons can be regulated by the VicK/VicR system and PerR. It is essential to further explore the effect of environmental conditions in Ln. mesenteroides bacteria to better understand GTFs regulation and polymer function.