bluuebunny/crossref_metadata_2025 huggingface dataset:
Functional Control Engineering of Coal Gangue Electrocatalyst with Amorphous SiCX/SiOX Active Layer Loading Enables Efficient Li-O2 Batteries
Authors: Sun Zhihui , Zhou Nan , Li Meng , Xu Jianfei , Feng Wenchang , Liu Shuo
Published: May 2023

The unreasonable accumulation of coal gangue in mining areas has caused serious resource waste and environmental pollution. The functional utilization of coal gangue with high added value has become the key to solving the previous problem. Coal gangue has inherent advantages such as large specific surface areas and rich active components, giving rise to an excellent precursor of electrode material in electrochemical energy storage devices. Herein, we, firstly, fabricated an amorphous SiCX/SiOX electrocatalyst with an abundant oxygen vacancy by acid–alkali activation derived from coal gangue for advanced Li-O2 batteries. The in-depth experimental results coupled with an in situ characterization analysis revealed that the amorphous SiCX/SiOX layer with abundant functional groups and oxygen vacancies on the surface of the activated gangue was conducive to promote structural stability and to improve the formation/decomposition efficiency of discharged products (Li2O2). Therefore, the LOBs based on the activated coal gangue electrocatalyst delivered a low overpotential of 1.12 V, high discharge capacity of 9156 mAh g−1, and an improved cyclic stability (more than 350 h). This work can provide a new approach for the development of new functions of coal gangue.
Implementation and Security Test of Zero-Knowledge Protocols on SSI Blockchain
Authors: Moya Cristina Vilchez , Bermejo Higuera Juan Ramón , Bermejo Higuera Javier , Sicilia Montalvo Juan Antonio
Published: May 2023

The problem of digital identity acquires more relevance every day in the eyes of a society that spends more and more time connected to the Internet. It has evolved throughout its history to reach a decentralized model known as Self-Sovereign Identity (SSI), which finds its natural tools in the blockchain technology and Zero-Knowledge Proofs (ZKPs). ZKPs, in this context, allow users to prove that their credentials are legitimate without revealing more information than is strictly necessary, and constitute one of the most promising areas of applied cryptography. In this work, an application is developed for the study of Zero-Knowledge Proof methods and, specifically, in their application for authentication in public-private key encryption systems. It focuses on the study of three ZKP protocols (Feige-Fiat-Shamir, Guillou-Quisquater, and Schnorr, which rely on the problems of large number factorizations and discrete logarithms for security) in the practical use-case where a prover wants to demonstrate knowledge of a private key for a public key without revealing the key itself. The application allows the user to modify the necessary parameters in each method to achieve a better understanding of their role in their safety and efficiency. Several types of attacks are carried out against the above-mentioned protocols to analyze their degree of security and what recommendations can be made to improve it.
Exploring Promising Biomarkers for Alzheimer’s Disease through the Computational Analysis of Peripheral Blood Single-Cell RNA Sequencing Data
Authors: Krokidis Marios G. , Vrahatis Aristidis G. , Lazaros Konstantinos , Vlamos Panagiotis
Published: May 2023

Alzheimer’s disease (AD) represents one of the most important healthcare challenges of the current century, characterized as an expanding, “silent pandemic”. Recent studies suggest that the peripheral immune system may participate in AD development; however, the molecular components of these cells in AD remain poorly understood. Although single-cell RNA sequencing (scRNA-seq) offers a sufficient exploration of various biological processes at the cellular level, the number of existing works is limited, and no comprehensive machine learning (ML) analysis has yet been conducted to identify effective biomarkers in AD. Herein, we introduced a computational workflow using both deep learning and ML processes examining scRNA-seq data obtained from the peripheral blood of both Alzheimer’s disease patients with an amyloid-positive status and healthy controls with an amyloid-negative status, totaling 36,849 cells. The output of our pipeline contained transcripts ranked by their level of significance, which could serve as reliable genetic signatures of AD pathophysiology. The comprehensive functional analysis of the most dominant genes in terms of biological relevance to AD demonstrates that the proposed methodology has great potential for discovering blood-based fingerprints of the disease. Furthermore, the present approach paves the way for the application of ML techniques to scRNA-seq data from complex disorders, providing new challenges to identify key biological processes from a molecular perspective.
Experimental Investigations on the Deformation and Breakup of Hundred-Micron Droplet Driven by Shock Wave
Authors: Zhang Yixin , Dong Ruolin , Shi Honghui , Liu Jinhong
Published: May 2023

This study examines the process of a 240 µm droplet breakup under a shock wave through experiments using a double-pulse laser holographic test technique on a shock tube. The technique allowed for high-resolution data to be obtained at the micron-nanosecond level, including the Weber number distribution of deformation and breakup modes for droplets of different sizes and loads. Results were compared with larger droplets at the same Weber number, revealing that higher Weber numbers result in more difficulty in droplet breakup, longer deformation times, and increased deformation behavior. At low Weber numbers within the critical range, changes in droplet diameter affect the Rayleigh–Taylor waves and alter the droplet’s characteristics. The study also investigates the laws and reasons behind windward displacement variation for hundred-micron droplets at different Weber numbers over time.
Development of an IoT-Based Precision Irrigation System for Tomato Production from Indoor Seedling Germination to Outdoor Field Production
Authors: Seyar Mohammad Hussain , Ahamed Tofael
Published: May 2023

Proper irrigation management, especially for tomatoes that are sensitive to water, is the key to ensuring sustainable tomato production. Using a low-cost sensor coupled with IoT technology could help to achieve precise control of the moisture content in the plant root-zone soil and apply water on demand with minimum human intervention. An IoT-based precision irrigation system was developed for growing Momotaro tomato seedlings inside a dark chamber. Four irrigation thresholds, 5%, 8%, 12%, and 15%, and two irrigation systems, surface and subsurface drip irrigation, were compared to assess which threshold and irrigation system referred the ideal tomato seedling growth. As a result, the 12% soil moisture threshold applied through the subsurface drip irrigation system significantly (p < 0.05) increased tomato seedling growth in soil composed of a main blend of peat moss, vermiculite, and perlite. Furthermore, in two repeated experiments, a subsurface drip irrigation system with 0.86 distribution uniformity used 10% less water than the surface drip irrigation system. The produced tomato seedlings were transplanted to open fields for further assessment. A low-power wide area networking Long Range Wide Area Network (LoRaWAN) protocol was developed with remote monitoring and controlling capability for irrigation management. Two irrigation systems, including surface and subsurface drip irrigations, were used to compare which system resulted in higher tomato yields. The results showed that the subsurface drip irrigation system with 0.74 distribution uniformity produced 1243 g/plant, while each plant produced 1061 g in the surface drip irrigation system treatment. The results also indicated that the LoRaWAN-based subsurface drip irrigation system was suitable under outdoor conditions with easy operation and robust controlling capability for tomato production.
Experimental Study on the Influence of Substrate Properties on Rainfall Infiltration and Runoff from Ecological Slopes
Authors: Tao Gaoliang , Cheng Yuzhe , Xiao Henglin , Huang Shaoping , Zhou Xinlong
Published: May 2023

Rain is an important factor influencing the instability of ecological slopes. There is little research on the inherent quantitative influence of substrate properties on slope runoff and water infiltration to support accurate ecological slope protection design. In this paper, the influence of substrate characteristics on slope runoff and water infiltration is quantitatively analyzed by constructing large physical models with different substrate characteristics for artificial rainfall simulations. The experimental results showed that the cumulative runoff volume and slope runoff rate were positively correlated with the cement content and substrate thickness in a 4 h, 60 mm/h artificially simulated rainfall. Specifically, the cumulative runoff volume increased by 2.06% for every 1% increase in cement content, and the cumulative runoff volume increased by 3.93% for every 1 cm increase in substrate thickness. The substrate inhibited the advance of the wetting front, and at different slope locations, the transport rate of the wetting front exhibited a mid-slope > upslope. Moreover, the transport rate of the wetting front showed a non-linear relationship with time as a power function V = a·tb, with the cement content showing a linear relationship with parameters a and b, and the substrate thickness showing a non-linear relationship with parameters a and b. The cumulative infiltration and infiltration rate were negatively correlated with cement content and substrate thickness, as shown by a 2.2% decrease in cumulative infiltration for every 1% increase in cement content and a 4.73% decrease in cumulative infiltration for every 1 cm increase in substrate thickness.
In Vitro Antibacterial Activity of Selected South African Plants against Drug-Resistant Staphylococci Isolated from Clinical Cases of Bovine Mastitis
Authors: Akinboye Ayodele O. , Famuyide Ibukun M. , Petzer Inge-Marie , McGaw Lyndy J.
Published: May 2023

Bovine mastitis in dairy livestock production is a serious economic problem causing milk production losses and increased management costs. Staphylococcus aureus and non-aureus staphylococcus (NAS) are important causes of bovine mastitis. Antimicrobial resistance can limit the control of mastitis pathogens. Antibacterial and cytotoxic activities of acetone and ethanol extracts of nine South African plants were determined against reference and drug-resistant staphylococci isolated from clinical cases of bovine mastitis. The nine plants, namely, Antidesma venosum, Elaeodendron croceum, Erythrina caffra, Indigofera frutescens, Pleurostylia capensis, Searsia lancea, Searsia leptodictya, Trichilia emetica, and Ziziphus mucronata, were chosen for research material. The antibacterial activity of extracts was determined using a serial microdilution method, while a tetrazolium-based assay was used to determine their cytotoxicity against Vero cells. The values of antibacterial minimum inhibitory concentration (MIC) obtained were promising, with MIC ranging between 0.01 and 1.41 mg/mL. Generally, acetone extracts of most plants had better activity than their ethanol counterparts. The S. aureus strains were more susceptible to all the extracts than the NAS strains. The plant extracts’ half-maximal toxicity (LC50) was higher than 0.02 mg/mL, which is the recommended cytotoxic cut-off concentration. The ethanol extract of E. caffra, which showed better MIC values than its acetone extract, had the highest mean selectivity index (SI) of 8.30. Among the plants tested, S. lancea exhibited the most potent antibacterial activity, suggesting its potential usefulness as a broad-spectrum antibacterial agent against staphylococci associated with bovine mastitis, at relatively non-cytotoxic concentrations. Therefore, it is recommended for further investigation.
Recent Advances in Bioconjugated Transition Metal Complexes for Cancer Therapy
Authors: Bortolamiol Enrica , Visentin Fabiano , Scattolin Thomas
Published: May 2023

The introduction of biologically relevant organic moieties in the coordination sphere of transition metal complexes has recently become a well-established strategy to increase the selectivity and biocompatibility of metallodrugs. In this review, the major advances achieved in this area of research in the last three years are described in detail. Particular attention is given to the metal complexes bearing the main biomolecules of life: carbohydrates, lipids, nucleotides, proteins and vitamins. Each paragraph summarizes the synthetic strategy employed to obtain the complexes of interest as well as the most interesting biological results obtained with these potential metallodrugs. Moreover, the structure–activity relationships observed by the different research groups are described and discussed, with the goal of suggesting to the reader the ligand/metal centre combinations that provide the most promising results in the fight against cancer. Some of the compounds examined in this review as well as other bioconjugated metal complexes published in recent decades exhibit interesting selectivity towards cancer cells over normal ones and a specific mode of action. These latter aspects are the basis of what is commonly known as anticancer target therapy.
Stability of a Compacted Sand Slope Model Subject to Crest Load
Authors: Djelabi Said , Karoui Hatem , Frikha Wissem , Dlala Mahmoud , Bouassida Mounir , Ninouh Tarek , El May Moufida
Published: May 2023

Studying the stability of slopes is of great interest since it is associated to various geotechnical applications, e.g., access embankments and landslide mitigation. This paper describes the research conducted to determine the failure load applied at the top of excavations in sandy soils during the construction of deep digs without the use of retaining systems. An experimental program was performed to measure the failure load of ten laboratory-compacted sand slope models that were constructed using different slope angle values and different locations for the applied loading, which consisted of an imposed uniform rate of vertical displacement at the top of the slope. Then, a three-dimensional (3D) numerical model of the laboratory tests was developed to simulate the observed behavior during the experiments by the Plaxis 3D code. The Mohr–Coulomb (MC) and hardening soil (HS) models were used to describe the behavior of the compacted sand. The results showed that the 3D numerical simulations based on the MC model were able to predict the measured failure load within a relative difference of less than 11% for nine tested slope models, while the HS model was better in predicting the measured failure load (a relative difference of 3.5%) for only one experimental setup when the slope angle was equal to 35°. Furthermore, analytical prediction of the failure load using the yield design theory (YDT) permitted the validation of the log-spiral curve describing the observed failure surface for the tested sand slope models.
Investigation on Pivot Rolling Motion Effect on the Behavior of Rocker Back Tilting Pad Journal Bearings
Authors: Betti Alberto , Forte Paola , Panara Daniele , Ciulli Enrico
Published: May 2023

The rolling motion of the pads with rocker back (RB) and ball and socket pivots is normally neglected in common software programs for the study of the rotor dynamic behavior of tilting pad journal bearings (TPJB). In other words, the theoretical contact point of the pivot is considered fixed. The aim of this work is to provide a novel way to implement in commercial software the effect of the variation of the circumferential coordinate of the theoretical contact point due to the pad rolling motion in RB TPJB. This is done by introducing an equivalent pivot rotational stiffness evaluated with an analytically derived formula, validated through finite element analysis. Such a stiffness is a function of the pad load and the radii of the contact pair, increasing with the load, the radii, and the degree of conformity of the contact. The static and dynamic characteristics of a five pad RB TPJB are then evaluated with a commercial software with and without the rotational stiffness contribution for two different pivot geometries. Non-negligible differences were found, particularly regarding the cross-coupled dynamic coefficients that show the higher sensitivity to the rotational stiffness. The inclusion of a pivot rotational stiffness among the data of commercial software for simulation of RB TPJB could contribute to fill the gap between numerical and experimental results.
Prediction Modeling of Ground Subsidence Risk Based on Machine Learning Using the Attribute Information of Underground Utilities in Urban Areas in Korea
Authors: Lee Sungyeol , Kang Jaemo , Kim Jinyoung
Published: May 2023

As ground subsidence accidents in urban areas that occur due to damage to underground utilities can cause great damage, it is necessary to predict and prepare for such accidents in order to minimize such damage. It has been reported that the main cause of ground subsidence in urban areas is cavities in the ground formed by damage to underground utilities. Thus, in this study, attribute information and historical ground subsidence information of six types of underground utility lines (water supply, sewage, power, gas, heating, and communication) were collected to develop a ground subsidence risk prediction model based on machine learning. To predict the risk of ground subsidence in the target area, it was divided into a grid with a square size of 500 m × 500 m, and attribute information of underground utility lines and historical information of ground subsidence included in the grid were extracted. Six types of underground utility lines were merged into single-type attribute information, and the risk of ground subsidence was categorized into three levels using the number of ground subsidence occurrences to develop a dataset. In addition, 12 datasets, which were developed based on the conditions of certain divided ranges of attribute information and risk levels, and 12 additional datasets, which were developed using the Synthetic Minority Oversampling Technique to resolve the imbalance of data, were built. Then, factors that represented significant correlations between input and output data were singled out and were then applied to the RandomForest, XGBoost, and LightGBM algorithms to select a model that produced the best performance. By classifying the ground subsidence risk levels through the selected model, it was found that density was the most important influencing factor used in the model. A risk map of ground subsidence in the target area was made through the model; the map showed the trend of well-predicted risk levels in the area where ground subsidence was concentrated.
Review of the Research Progress in Combat Simulation Software
Authors: Lu Fengshun , Hu Xingzhi , Zhao Bendong , Jiang Xiong , Liu Duoneng , Lai Jianqi , Wang Zhiren
Published: May 2023

To address the new functional requirements brought by the introduction of new weapons and new combat modes, a comprehensive survey of the research progress in the area of combat simulation software is performed from the perspective of software engineering. First, the top-level specification, simulation engine, and simulation framework of combat simulation software are reviewed. Then, several typical combat simulation software systems are demonstrated, and the relevant software frameworks are analyzed. Finally, combining the application prospect of artificial intelligence, metaverse, and other new technologies in combat simulation, the development trends of combat simulation software are presented, namely intellectualization, adaptation to an LVC (live, virtual, and constructive) system, and a more game-based experience. Based on a comprehensive comparison between the mentioned simulation frameworks, we believe that the AFSIM (Advanced framework for simulation, integration, and modeling) and the E-CARGO (Environments—classes, agents, roles, groups, and objects) are appropriate candidates for developing distributed combat simulation software.
Intelligent Materials for Labeling Dentures in Forensic Dental Identification—A Pilot Study
Authors: Ştefănescu Corina Laura , Neculai-Cândea Lavinia Simona , Popa Marius Florentin , Zaharia Agripina , Murineanu Rodica Maria , Pricop Ştefan , Sachelarie Liliana , Hurjui Loredana Liliana , Danila Vlad
Published: May 2023

(1) Background: the present study continues previous research on new marking techniques by using intelligent materials, NFC-tags (Near Field Communication tags) and Aerogel. They prevent the loss of information over time for cadavers of unknown identities in various stages of destruction or unknown living victims, as well as missing persons who have lost or are concealing their identity. (2) Methods: this study consisted of a technological and an experimental stage. In the technological stage, two different sizes were used: round (Ø 1 cm, 0.1 mm thickness, 0.1 g weight) and square (5/5/0.1 mm), both with a 140 byte memory and high-temperature resistance (at max. 200 °C), (by the classical producing technology). After loading the identification information on the NFC-tags, they were embedded (either alone or protected by Aerogel) in the sample dentures using a new “sandwich technique” method, before the polymerization process. In the experimental stage, the sample dentures with the new materials were exposed to various damaging environments such as liquid media (sea water, fresh water, alcohol 40%, and HCl 0.2%) in order to test the time resistance of the identification elements. The samples were monitored and tested over four years. (3) Results: the information stored on the NFC tags was retrieved unaltered at the end of the monitoring period, regardless of the damage caused by the liquid media to the sample denture material and will provide an innovative solution as compared to other labeling methods. (4) Conclusion: the use of intelligent materials for labeling acrylic dentures provides additional reliability by preserving the identification information over time.
Analysis and Predictive Evaluation of Mechanical Properties of Steel Anchor Box for High-Speed-Railway Cable-Stayed Bridge
Authors: Wang Haibo , Wu Hengxuan , Hu Lige , Zhang Chen
Published: May 2023

As the stress amplitude in the anchorage zone of cable-stayed bridges vary considerably, it is essential to study the fatigue-load-bearing capacity of the anchorage structures of cable-stayed-bridge girders. In this study, the mechanical properties and force-transmission mechanism of a built-in double-fixed steel anchor box, as well as the influence of the geometric design parameters of the main pressure plates and support plates on its stress performance, were studied. A PSO-BP built-in double-fixed steel anchor box mechanical-performance-prediction-and-evaluation system was established, with the geometric design parameters of the important plates of the main body of the anchor box as the input variables and the mechanical properties of the main stress-transmitting welds of the steel anchor box as the output-evaluation variables. The results were as follows: the cable force was mainly transmitted in the form of shear force through the welds between the support plate and the web of the main beam; the transmission ratio of the four main welds was generally maintained at about 23%, and the stress distribution of each plate was relatively uniform, with most of the stresses in the range of 10~50 MPa. The shear-stress-inhomogeneity coefficient of the transmission weld was sensitive to the changes in the thickness of the support plate and the pressure plate, and the transmission ratio of the main weld was sensitive to the changes in the thickness and length of the support plate. The PSO-BP-based mechanical-performance-prediction-and-evaluation system can be used to explore the intrinsic relationship between the designed cable force, important plate-geometry parameters, and the force performance of steel anchor boxes during the iterative process, and to generate more accurate prediction and evaluation values.
Advances in Permeation of Solutes into Hair: Influencing Factors and Theoretical Models
Authors: Li Lingyi , Qin Jiahao
Published: May 2023

The permeation and absorption of solutes into human hair are highly relevant to various applications, including the formulation of hair-care products, the development of water pollution control and remediation, and the risk assessment of environmental exposure. Based on a detailed introduction of the structure and composition of hair, the effects of the properties of hair (structure, composition, and charge properties), the physicochemical properties of solutes (molecular size, shape, and hydrophobicity), and the conditions of the surrounding medium (solvent composition, temperature, and pH) on the permeation and absorption were comprehensively analyzed. Several theoretical models were reviewed, including two-part/two-state, porous media diffusion, homogeneous medium diffusion, heterogeneous medium partition, and diffusion models. Finally, future research directions for the permeation and absorption of solutes in hair were proposed to provide a foundation for the further optimization and application of permeation models.
Characteristics and Causes of Coastal Water Chemistry in Qionghai City, China
Authors: Jiang Junyi , Fu Guowei , Feng Yu , Gu Xinchen , Jiang Pan , Shen Cheng , Chen Zongyi
Published: May 2023

The coastal zone area of Qionghai City is one of the important coastal zones in the South China Sea, and its water environment has been affected by human activities such as urbanization and industrialization. In order to protect the water resources and ecological environment of this area, the water chemistry characteristics of the main watersheds and their causes in the coastal zone area of eastern Hainan Island were investigated to provide a scientific basis for environmental protection and sustainable development. In this study, the characteristics and sources of water chemical ion components were analyzed using a Piper trilinear diagram, Gibbs diagram, and correlation analysis with the coastal zone area of Qionghai city as the research object. The results show the following: (1) the dominant cation of water chemistry in the coastal zone of Qionghai City is Na+ with a mean value of 35.001 mg·L−1, and the dominant anion is Cl− with a mean value of 30.69 mg·L−1; (2) the dominant cation content in the coastal zone of Qionghai City is Na+ > Ca2+ > Mg2+ > K+, and the dominant anion content is Cl− > SO42− > HCO3− > CO32−; (3) at the five collection sites in the study area, the ion concentrations showed different trends, with the highest ion concentration in the water samples collected from aquaculture ponds, and the main water chemistry type was Na-Cl; the lowest ion concentration was in the water samples collected from the rivers, and the main type of water chemistry was Ca·Mg-HCO3. The source of water chemistry ions in the study area mainly included seawater, rock weathering, atmospheric precipitation, and evaporation concentration. The results of this study can provide a scientific basis for the development, utilization, and management of local water resources and provide basic data for environmental protection and sustainable development.
Research Progress on the Influence of Thermo-Chemical Effects on the Swelling Pressure of Bentonite
Authors: Liu Jinjin , Yao Chuanqin , Su Wenbo , Zhao Yizhe
Published: May 2023

The swelling pressure of bentonite changes dramatically due to diffused nuclear radiation heat and underground osmosis, causing the failure of the buffer isolation layer in deep geological repositories for the disposal of high-level radioactive waste. A detailed overview of the relevant research results on the swelling pressure variation of bentonite under thermo-chemical effects is presented in this paper. The results showed that the values of the swelling pressure obtained by different test methods are dissimilar. The swelling pressure of bentonite decreased with the increasing pore solution concentration; nevertheless, the effect of temperature on the swelling pressure is still controversial. At the micro-level, crystal layer swelling and double- layer swelling are generally considered to be the main factors affecting the swelling pressure; the pore structure and water distribution of bentonite will change owing to thermo-chemical effects. At the macro-level, involving intergranular stress, a mechanical parameter was proposed to explain the mechanism of the changes in the swelling pressure of bentonite. Finally, future research directions for the study of the evolution of bentonite swelling properties under thermo-chemical effects are proposed, based on the current research results.
Saline Sediments as a Suitable Source for Halophilic Inoculums to Degrade Azo Dyes in Synthetic and Real Textile Wastewaters by Microbial Electrochemical Systems
Authors: Saadaoui Sirine , Erable Benjamin , Saidi Nesrine , Etcheverry Luc , Neifar Mohamed , Masmoudi Ahmed Salaheddine , Driouech Rim , Cherif Ameur , Chouchane Habib
Published: May 2023

The treatment of textile wastewater (TWW) loaded with recalcitrant azo dyes in bioelectrochemical systems (BES) rather than in physicochemical processes is a low-cost and environmentally friendly process. The main objective of this study is to investigate the potential of different saline sediments collected from extreme Tunisian environments for the formation of bioanodes capable ofsimultaneous azo dyes degradation and electric current generation in synthetic (STWW) and real textile wastewaters (RTWW) characterized by a varied composition of azo dyes and a high salinity. The obtained bioanodes and anolytes were studied comparatively by electrochemical, microscopic, analytical, and molecular tools.Based on the UV–visible spectra analysis, the breakdown of the azo bond was confirmed. With RTWW, the BES achieved a chemical oxygen demand (COD) abatement rate of 85%with a current density of 2.5 A/m2. Microbial community analysis indicated that a diverse community of bacteria was active for effluent treatment coupled with energy production. At the phylum level, the electrodes were primarily colonized by proteobacteria and firmicutes, which are the two phyla most involved in bioremediation. The analysis of the microbial community also showed the abundance of Marinobacter hydrocarbonoclasticus and Marinobacter sp. species characterized by their high metabolic capacity, tolerance to extremophilic conditions, and role in hydrocarbon degradation.
Impact of Port Clearance on Ships Safety, Energy Consumption and Emissions
Authors: Paulauskas Vytautas , Paulauskas Donatas , Paulauskas Vytas
Published: May 2023

The safety of shipping, energy consumption and environmental impact in ports and port channels is very critical. One of the most important elements in the provision of safe navigation, energy consumption and emissions generation is the depth of ports so that under all conditions the hull of a ship does not touch the bottom of the channels or the bottom of the basin, as well as optimizing energy consumption and minimizing the environmental impact. The very high depth reserves in ports make it possible to ensure the safety of shipping, but at the same time require huge investments in the dredging and maintenance of a port’s channels and basins, which can have a negative impact on a port’s economic results. Optimizing the depth of port channels and basins is very important from an economic, maritime safety, energy saving and environmental point of view, as vessels navigating port channels and basins must not only keep their hulls off the bottom of the channel or basin, but also have good controllability, use minimal energy consumption and minimize their environmental impact. With good maneuverability, the number of and need for auxiliary vehicles (tugs) can be minimized. This article analyses the relationship between ships’ draught and port channels and basins depths, which influences the aspects of a ship’s controllability, in order to optimize the depths of port channels and basins and, at the same time, minimize energy consumption and environmental impact while preserving the necessary navigational safety.
Assessment of Temperature, Precipitation, and Snow Cover at Different Altitudes of the Varzob River Basin in Tajikistan
Authors: Gulahmadov Nekruz , Chen Yaning , Gulakhmadov Manuchekhr , Satti Zulqarnain , Naveed Muhammad , Davlyatov Rashid , Ali Sikandar , Gulakhmadov Aminjon
Published: May 2023

The analysis of precipitation, snow cover, and temperature based on measured data is important for many applications in hydrology, meteorology, climatology, disaster management, and human activities. In this study, we used long-term historical datasets from the Varzob River Basin (VRB) in Tajikistan to evaluate the trend and magnitudinal changes in temperature, precipitation, and snow cover area in the Anzob (upstream), Maykhura (midstream), and Hushyori (downstream) regions of the VRB using the original Mann–Kendall test, modified Mann–Kendall test, Sen’s slope test, and Pettitt test. The results revealed a decreasing trend in the mean monthly air temperature at Anzob station in the upstream region for all months except January, February, and December between 1960 and 2018 and 1991 to 2018. In each of the three regions, the mean annual temperature indicated a clear upward trend. Seasonal precipitation indicated a large increasing trend in January and February at the Anzob station from 1960 to 2018, but a significant downward trend in April in the upstream, midstream, and downstream regions between 1960 and 1990 and from 1991 to and 2018. In the VRB, almost all stations exhibited a downward trend in annual precipitation across all periods, whereas the upstream region showed a non-significant upward trend between 1960 and 1990. The monthly analysis of snow cover in the VRB based on ground data showed that the maximum increase in snow cover occurs in April at the Anzob station (178 cm) and in March at Maykhura (138 cm) and Hushyori stations (54 cm). The Mann–Kendall test, based on MODIS data, revealed that the monthly snow cover in the VRB increased in April and July while a decrease was recorded in February, September, November, and December from 2001 to 2022. The trend’s stable pattern was observed in March, May, August, and October.
Modification of Genetic Algorithm Based on Extinction Events and Migration
Authors: Kieszek Rafał , Kachel Stanisław , Kozakiewicz Adam
Published: May 2023

This article presents a genetic algorithm modification inspired by events related to great extinctions. The main objective of the modification was to minimize the number of objective function solutions until the minimum for the function was established. It was assumed that, within each step, a population should be smaller than that recommended in the applicable literature, the number of iterations should be limited, the solution area should be variable, and a great extinction event should take place following several iterations. Calculations were performed for 10 individuals within a population, 10 iterations, two generations each, with a great extinction event happening once every three iterations. The developed algorithm was presented, capable of indicating the minimum number of Eggholder and Rastrigin functions, with a higher probability than the master algorithm (default “ga” in MATLAB) at the same number of objective function solutions. An algorithm was proposed focusing on minimizing the randomization of the objective function, which may be an alternative to the surrogate model. Typically, the emphasis is on achieving as much accuracy as possible. This article presents a method for minimizing the randomization of the objective function and obtaining the highest possible accuracy. A method is presented which minimizes the disadvantages of the largest computation time and the need to generate many samples for typical genetic algorithms (GAs). Optimization results for the classic GA, GEGA, WOA, SMA, and SSA algorithms for the Eggholder and Rastrigin functions were compared. A modification of the genetic algorithm was made to obtain a global extreme with satisfactory accuracy and a sufficiently high probability, while minimizing the number of samples calculated on the basis of the objective function. The developed methodology was used to fulfill the target function for the turbine disc.
LBFNet: A Tomato Leaf Disease Identification Model Based on Three-Channel Attention Mechanism and Quantitative Pruning
Authors: Chen Hailin , Wang Yi , Jiang Ping , Zhang Ruofan , Peng Jialiang
Published: May 2023

The current neural networks for tomato leaf disease recognition have problems such as large model parameters, long training time, and low model accuracy. To solve these problems, a lightweight convolutional neural network (LBFNet) is proposed in this paper. First, LBFNet is established as the base model. Secondly, a three-channel attention mechanism module is introduced to learn the disease features in tomato leaf disease images and reduce the interference of redundant features. Finally, a cascade module is introduced to increase the depth of the model, solve the gradient descent problem, and reduce the loss caused by increasing the depth of the model. The quantized pruning technique is also used to further compress the model parameters and optimize the model performance. The results show that the LBFNet model achieves 99.06% accuracy on the LBFtomato dataset, with a training time of 996 s and a single classification accuracy of over 94%. Further training using the saved weight file after quantized pruning enables the model accuracy to reach 97.66%. Compared with the base model, the model accuracy was improved by 28%, and the model parameters were reduced by 96.7% compared with the traditional Resnet50. It was found that LBFNet can quickly and accurately identify tomato leaf diseases in complex environments, providing effective assistance to agricultural producers.
Experimental Study on Particle Size Distribution Characteristics of Aerosol for Fire Detection
Authors: Dong Wen-Hui , Sheng Xue-Er , Wang Shu , Deng Tian
Published: May 2023

Current optical fire smoke detectors use scattering light intensity as an indicator of smoke concentration and trigger fire alarms when the intensity exceeds a threshold value. However, such detectors are prone to false alarms caused by non-fire aerosols since both fire smokes and non-fire aerosols scatter light. Thus, in order to reduce false alarms caused by non-fire aerosols such as dust and water vapor, fire detectors must be capable of distinguishing fire smoke from non-fire aerosols. Since the light scattering signals depend on the particle size information of aerosols, it is essential to study and characterize the particle size distribution of fire smoke and non-fire aerosols for differentiating them. In this paper, a comprehensive aerosol experimental platform is built to measure the particle size distribution of various typical fire smokes and non-fire aerosols. Through the conducted experiments, we note that there are significant differences in the particle size distributions of typical fire smokes and non-fire aerosols, with a boundary of about 1μm. The experimental results provide fundamental data support of the particle size distribution for developing a better fire detector that accurately identifies smoke.
Sampling by Difference as a Method of Applying the Sampling Kantorovich Model in Digital Image Processing
Authors: Seracini Marco , Vinti Gianluca
Published: May 2023

In this paper, the connections between the Sampling Kantorovich model and the sampling process are highlighted and exploited. Based on the theoretical framework of the Sampling Kantorovich operators, a sampling paradigm, here named Sampling Kantorovich by Difference (SKD), is introduced. In line of principle, SKD allows for overcoming the technical limitation due to the fact that the resolution of a signal/image is strictly connected with the size of the used sensors. We analyze the paradigm in the case of a simulated super resolution type problem. The same mathematical model, being extendable to other signal reconstruction procedures, suggests a theoretical way for new technical solutions in the sampling procedures.
Mechanical Properties of Disconnectable Coupling Joints for Steel Bracing under Eccentric Load
Authors: Xie Zhitian , Niu Xiaokai , Li Pengfei , Zhang Mingju , Liu Xiao
Published: May 2023

Disconnectable coupling (DC) joints of steel bracing in foundation pit engineering are inevitably subjected to eccentric load, but their mechanical properties under eccentric load have not been thoroughly investigated. Based on full-scale test results of DC joints under axial compression, a validated finite element model was established. The bearing capacity and flexural performance of DC joints under eccentric load were studied systematically through a series of numerical simulations. These parameters included the length, width and height of the steel wedge; eccentricity; steel tube wall thickness; channel steel thickness and middle-rib plate height. Based on the numerical results, a modified moment–rotation model was established. The results obtained show that the numerical models accurately reflect the failure mode and the load-displacement curves revealed by the full-scale test. The bearing capacity and flexural performance of DC joints decreases with eccentricity, middle-rib plate height, and steel wedge height. The effect of eccentricity is the most significant. By contrast, the bearing capacity and flexural performance of DC joints increases with steel wedge length, steel wedge width, channel steel thickness and steel tube wall thickness. The modified moment–rotation model can describe the flexural performance of DC joints accurately under eccentric load.
Recent Advancements in Anaerobic Digestion and Gasification Technology
Authors: Obileke KeChrist , Makaka Golden , Nwokolo Nwabunwanne
Published: May 2023

In recent times, there has been a growing demand for the use of biomass as an alternative energy due to its sustainable nature. At present, anaerobic digestion and gasification has been proven as a promising technology for exploiting this energy from biomass. This review aims to provide a comprehensive and detailed analysis of the combination of anaerobic and gasification technology as a hybrid system for sustainable waste-to-energy generation. This review reveals that both anaerobic digestion and biomass gasification have been successfully demonstrated as technologies for energy recovery. However, to improve the conversion efficiency in both technologies, the utilization of an intensifier, additive, and enhancer will be required. Moreover, temperature has been identified as a major factor affecting the technologies and should be considered. The bibliometric study conducted revealed that China is the leading country and has set the pace for other countries to follow suit. Subsequently, waste-to-energy research could be easily implemented on a global scale. This study recommends an experimental study of anaerobic digestion and gasification as a hybrid system.
Advanced Technologies for Geosite Visualization and Valorization: A Review
Authors: Pasquaré Mariotto Federico , Corti Noemi , Drymoni Kyriaki
Published: May 2023

This review attempts to summarize contributions by authors who, in the last decade, have dedicated their efforts to making geoheritage accessible to the public. Geoheritage is composed of geosites, which are, nowadays, real milestones on which field-based geological education can be conducted. However, the COVID-19 pandemic in particular has made it clear that a new paradigm is needed; a series of tools must be introduced and increasingly used to make it possible for potential users, be they academics, students, or the lay public, to experience geosites from locations that can be thousands of kilometers away. All these have been achieved over time by a wide range of evolving techniques and advanced technologies such as GIS tools, virtual reality applications and further innovative technologies such as WebGIS platforms accompanied by appropriate navigation tools (VR headsets and thumbsticks). The viewers, in this way, are provided with a complete view of a virtual geosite, which enables visualizing its characteristics at different scales. VR technologies, especially, have revealed a high degree of satisfaction, based on feedback collected from VR geosite visualization events, both by scientists, students and the general public, and could be the forefront of geosite visualization and valorization in the near future.
Aging of PA12 Powder in Powder Bed Fusion
Authors: Gazzerro Achille , Polini Wilma , Sorrentino Luca , Giuliano Gillo
Published: May 2023

Powder Bed Fusion (PBF) is a popular additive manufacturing technology due to its high build resolution and ability to produce microscale geometries without the use of additional support. Despite the many benefits of PBF, there are still some limitations associated with the materials to be built. A critical industrial limit is the aging of PA12 powder, which is the degradation of its physical and chemical properties due to high temperatures and long building cycles of the powder that is not directly fused into the final part but supports the part under construction. This powder is now being used to make another part in order to reduce manufacturing costs. The mechanical properties of the built parts are reduced due to the reused powder. The current study aims to characterize powder aging using experimental tests such as Differential Scanning Calorimetry, Dynamic Mechanical Analysis, and Thermogravimetric Analysis to define the physical and chemical parameters of the powder that will be used inside a simulation software to optimize the process.
Investigation of Machine Learning Model Flexibility for Automatic Application of Reverberation Effect on Audio Signal
Authors: Tamulionis Mantas , Sledevič Tomyslav , Serackis Artūras
Published: May 2023

This paper discusses an algorithm that attempts to automatically calculate the effect of room reverberation by training a mathematical model based on a recurrent neural network on anechoic and reverberant sound samples. Modelling the room impulse response (RIR) recorded at a 44.1 kHz sampling rate using a system identification-based approach in the time domain, even with deep learning models, is prohibitively complex and it is almost impossible to automatically learn the parameters of the model for a reverberation time longer than 1 s. Therefore, this paper presents a method to model a reverberated audio signal in the frequency domain. To reduce complexity, the spectrum is analyzed on a logarithmic scale, based on the subjective characteristics of human hearing, by calculating 10 octaves in the range 20–20,000 Hz and dividing each octave by 1/3 or 1/12 of the bandwidth. This maintains equal resolution at high, mid, and low frequencies. The study examines three different recurrent network structures: LSTM, BiLSTM, and GRU, comparing the different sizes of the two hidden layers. The experimental study was carried out to compare the modelling when each octave of the spectrum is divided into a different number of bands, as well as to assess the feasibility of using a single model to predict the spectrum of a reverberated audio in adjacent frequency bands. The paper also presents and describes in detail a new RIR dataset that, although synthetic, is calibrated with recorded impulses.