bluuebunny/crossref_metadata_2025 huggingface dataset:
Bacillus subtilis Effects on Growth Performance and Health Status of Totoaba macdonaldi Fed with High Levels of Soy Protein Concentrate
Authors: Olmos Jorge , López Lus M. , Gorriño Antonio , Galaviz Mario A. , Mercado Victor
Published: December 2022

T. macdonaldi is a carnivorous species endemic to the Gulf of California. Indiscriminate exploitation has put totoaba at risk, inducing the development of aquaculture procedures to grow it without affecting the wild population. However, aquafeeds increasing cost and low yields obtained with commercial feeds have motivated researchers to look for more nutritious and cheaper alternatives. Soybean (SB) is the most popular alternative to fishmeal (FM); however, antinutritional factors limit its use in carnivorous species. In this study, we analyzed B. subtilis 9b probiotic capacity to improve growth performance and health status of T. macdonaldi fed with formulations containing 30% and 60% substitution of fish meal with soy protein concentrate (SPC). In addition, we investigated its effect on internal organs condition, their capacity to modulate the intestinal microbiota, and to boost the immunological response of T. macdonaldi against V. harveyi infections. In this sense, we found that T. macdonaldi fed with SPC30Pro diet supplemented with B. subtilis 9b strain and 30% SPC produced better results than SPC30C control diet without B. subtilis and DCML commercial diet. Additionally, animals fed with SPC60Pro diet supplemented with B. subtilis 9b strain and 60% SPC doubled their weight and produced 20% more survival than SPC60C control diet without B. subtilis. Thus, B. subtilis 9b improved T. macdonaldi growth performance, health status, modulated intestinal microbiota, and increased animal’s resistance to V. harveyi infections, placing this bacterium as an excellent candidate to produce functional feeds with high levels of SPC.
Economic Improvement of Artisanal Fishing by Studying the Survival of Discarded Plectorhinchus mediterraneus
Authors: Ruiz-Jarabo Ignacio , Partida Blanca , Page María , Madera Diego , Saiz Nuria , Alonso-Gómez Aitana , Herrera-Castillo Lisbeth , Isorna Esther , Alonso-Gómez Ángel L. , Valenciano Ana I. , de Pedro Nuria , Saez Jorge , Delgado Maria J.
Published: December 2022

Europe calls for the end to fisheries discards, which means bringing all caught fish (subject to minimum sizes or quotas) to land. This decision is beneficial to the ecosystem, since it forces the selectivity of the fishing gears to improve. However, artisanal fishermen find themselves in a vulnerable situation where their subsistence depends on catches with small profit margins. An exemption to this landing obligation exists, as it is also ruled that those animals whose survival is scientifically guaranteed may be returned to the sea. Here we study the survival of Plectorhinchus mediterraneus captured by hookline and gillnet, as well as their physiological recovery. Survival exceeds 93% in both cases. The physiological assessment of primary (cortisol) and secondary (energy mobilization, acid-base and hydromineral balance, and immune system) stress responses indicates that surviving animals are able to recover after fishing. Thus, we propose the optimal size of capture of this species to achieve greater economic benefit. For this, we rely on the prices according to size in recent years, as well as on the growth curves of the species. In this way, by releasing fish of less than 1 kg, the current benefits could be multiplied between 2.3 and 9.6 times. This pilot study lays the groundwork for regulating artisanal fisheries through scientific data related to survival of discards along with information on the sale prices.
Zoonotic Disease Risks of Live Export of Cattle and Sheep, with a Focus on Australian Shipments to Asia and the Middle East
Authors: Phillips Clive J. C.
Published: December 2022

The growing human and livestock populations in the world today and increased international transport of livestock is increasing the risk of both emerging and endemic zoonotic diseases. This review focuses on the potential for the live export trade to transmit zoonotic diseases. Both cattle and sheep are exposed to major stresses during the transport process, which are described, together with the impact of these stresses on the immune function of transported animals. Heat stress, overcrowding, inanition, ship and vehicle motion and accumulation of noxious gases are analysed for their ability to potentiate infectious diseases. The major zoonoses are described: pustular dermatitis, pneumonia, salmonellosis, as well as some common conditions, such as conjunctivitis, with specific reference to stressors associated with each disorder. Historical precedents exist for restriction of the trade based on disease risks. Finally, the economic and regulatory frameworks are considered to evaluate ways in which the spread of zoonotic diseases can be controlled.
Production Efficiency and Utility Features of Broiler Ducks Fed with Feed Thinned with Wheat Grain
Authors: Biesek Jakub , Banaszak Mirosław , Grabowicz Małgorzata , Wlaźlak Sebastian , Adamski Marek
Published: December 2022

The aim of this study was to evaluate the production efficiency (economics), growth, and meat quality of ducks fed with feed partially replaced with wheat. A total of 200 ducks were reared for 49 days. Each group consisted of 50 ducks (5 pens with 10 birds). For slaughter, 10 birds per group were chosen. The control group (C) was provided with a complete feed. In the experimental groups, from 42 to 49 days, the feed was replaced with wheat grains at the level of 10% (W10), 20% (W20), or 40% (W40). In the W20 and W40 groups, the cost of feed was reduced. In the W40 group, the profit per 1 kg carcass was increased by PLN 3.34 (more than 24% higher than the C group profit). A higher percentage of pectoral muscles and intramuscular fat was observed in the W20 group, with lower water content. A lower water-holding capacity (WHC) was observed in the duck leg muscles in group W40. The muscles from the W20 group had higher protein, collagen, and water content, and the fat was highest in the W40 group. A lower toughness of cooked meat was observed in the W20 group, and lower shear force in the pectoral muscles of groups C and W40. Thinning feed with wheat grains could represent an alternative to conventional feeding of broiler ducks, owing to reduced feed costs, with no negative impact on utility features, including growth, except the share of pectoral muscle and water absorption traits.
Ameliorative Effect of Quercetin against Abamectin-Induced Hemato-Biochemical Alterations and Hepatorenal Oxidative Damage in Nile Tilapia, Oreochromis niloticus
Authors: Mansour Abdallah Tageldein , Mahboub Heba H. , Amen Rehab M. , El-Beltagy Marwa A. , Ramah Amany , Abdelfattah Abdelfattah M. , El-Beltagi Hossam S. , Shalaby Tarek A. , Ghazzawy Hesham S. , Ramadan Khaled M. A. , Alhajji Adnan H. M. , Hamed Heba S.
Published: December 2022

Abamectin (ABM) is a common agricultural pesticide and veterinary anthelmintic drug. It can discharge from the sites of application to aquatic systems via surface run-off or spray drift, causing harmful effects to aquatic organisms. The present study investigated the protective effect of dietary quercetin supplementation on hemato-biochemical parameters and hepato-renal oxidative stress biomarkers in Nile tilapia (Oreochromis niloticus) exposed to a sublethal dose of ABM. Fish were allocated into six equal groups. The first group was kept as a control group. The second and third groups (Q400, and Q800) were fed diets supplemented with two quercetin levels (400 and 800 mg/kg diet), respectively. The fourth group (ABM) was intoxicated with 20.73 µg/L of ABM. The fifth and sixth groups (ABM + Q400, and ABM + Q800) were fed diet supplemented with two quercetin levels (400 and 800 mg/kg diet) and simultaneously intoxicated with ABM for 60 days. The results showed that ABM significantly decreased RBCs, hemoglobin content, hematocrit, total protein, albumin levels, and acetylcholinesterase activity activities compared to the control. Meanwhile, ABM significantly increased white blood cells, glucose, total lipids, cholesterol, and alanine and aspartate aminotransferase activities. Liver and kidney levels of lipid peroxidation was significantly increased, while hepato-renal antioxidant biomarkers (reduced glutathione, super oxide dismutase, catalase, and total antioxidant capacity) were significantly decreased upon ABM exposure. On the other hand, quercetin dietary supplementation improved the hemato-biochemical alterations and alleviated oxidative stress induced by ABM exposure. Fish supplemented with quercetin at a level of 800 mg/kg diet showed better alleviating effects against ABM compared to 400 mg/kg diet. Based on these study findings, we suggest that quercetin dietary supplementation (800 mg/kg) offered direct protection against ABM-induced physiological disturbance and oxidative stress in Nile tilapia.
Histidine Deficiency Inhibits Intestinal Antioxidant Capacity and Induces Intestinal Endoplasmic-Reticulum Stress, Inflammatory Response, Apoptosis, and Necroptosis in Largemouth Bass (Micropterus salmoides)
Authors: Liang Hualiang , Xu Pao , Xu Gangchun , Zhang Lin , Huang Dongyu , Ren Mingchun , Zhang Lu
Published: December 2022

This 56-day study aimed to evaluate the effects of histidine levels on intestinal antioxidant capacity and endoplasmic-reticulum stress (ERS) in largemouth bass (Micropterus salmoides). The initial weights of the largemouth bass were (12.33 ± 0.01) g. They were fed six graded levels of histidine: 0.71% (deficient group), 0.89%, 1.08%, 1.26%, 1.48%, and 1.67%. The results showed that histidine deficiency significantly suppressed the intestinal antioxidant enzyme activities, including SOD, CAT, GPx, and intestinal level of GSH, which was supported by significantly higher levels of intestinal MDA. Moreover, histidine deficiency significantly lowered the mRNA level of nrf2 and upregulated the mRNA level of keap1, which further lowered the mRNA levels of the downstream genes sod, cat, and gpx. Additionally, histidine-deficiency-induced intestinal ERS, which was characterized by activating the PEPK-signalling pathway and IRE1-signalling pathway, including increased core gene expression of pepk, grp78, eif2α, atf4, chopα, ire1, xbp1, traf2, ask1, and jnk1. Dietary histidine deficiency also induced apoptosis and necroptosis in the intestine by upregulating the expressions of proapoptotic genes, including caspase 3, caspase 8, caspase 9, and bax, and necroptosis-related genes, including mlkl and ripk3, while also lowering the mRNA level of the antiapoptotic gene bcl-2. Furthermore, histidine deficiency activated the NF-κB-signalling pathway to induce an inflammatory response, improving the mRNA levels of the proinflammatory factors tnf-α, hepcidin 1, cox2, cd80, and cd83 and lowering the mRNA levels of the anti-inflammatory factors tgf-β1 and ikbα. Similarly, dietary histidine deficiency significantly lowered the intestinal levels of the anti-inflammatory factors TGF-β and IL-10 and upregulated the intestinal levels of the proinflammatory factor TNF-α, showing a trend similar to the gene expression of inflammatory factors. However, dietary histidine deficiency inhibited only the level of C3, and no significant effects were observed for IgM, IgG, HSP70, or IFN-γ. Based on the MDA and T-SOD results, the appropriate dietary histidine requirements of juvenile largemouth bass were 1.32% of the diet (2.81% dietary protein) and 1.47% of the diet (3.13% dietary protein), respectively, as determined by quadratic regression analysis.
Therapeutics of Alzheimer’s Disease: Recent Developments
Authors: Burns Scott , Selman Ashley , Sehar Ujala , Rawat Priyanka , Reddy Arubala P. , Reddy P. Hemachandra
Published: December 2022

With increasing aging, dementia is a growing public health concern globally. Patients with dementia have multiple psychological and behavioral changes, including depression, anxiety, inappropriate behavior, paranoia, agitation, and hallucinations. The major types of dementia are Alzheimer’s disease (AD), vascular dementia (VCID), Lewy body dementia (LBD), frontotemporal dementia (FTD), and mixed dementia (MiAD). Among these, AD is the most common form of dementia in the elderly population. In the last three decades, tremendous progress has been made in understanding AD’s biology and disease progression, particularly its molecular basis, biomarker development, and drug discovery. Multiple cellular changes have been implicated in the progression of AD, including amyloid beta, phosphorylated tau, synaptic damage, mitochondrial dysfunction, deregulated microRNAs, inflammatory changes, hormonal deregulation, and others; based on these changes, therapeutic strategies have been developed, which are currently being tested in animal models and human clinical trials. The purpose of our article is to highlight recent therapeutic strategies’ developments, critically discuss current strategies’ failures, and propose new strategies to combat this devasting mental illness.
Ordered-Bipartite Consensus of Multi-Agent Systems under Finite Time Control
Authors: Zhang Tiehui , Ma Xinru , Li Hengyu , Wang Zhaoyan , Xie Shaorong , Luo Jun
Published: December 2022

Since group consensus (GC) control accounts for one-half of the multi-robot coordination domain, as does complete consensus control, this paper will formulate a certain kind of GC and group bipartite consensus (GBC) in finite time for multi-agent systems (MAS). It should be noted that the key to this work is that information transfer under acyclic partition networks is zero-sum between groups and the cooperative–competitive interaction. The general GC will firstly be achieved under acyclic partition, which reflects the fact that such topology allows the MAS to reach agreement in an ordered and hierarchical process. Further, the information communication is zero-sum within each group under the acyclic partition signed digraph. Thus, the group–bipartite agreement for MAS is given under acyclic bipartite topology. These results display not only an ordered, hierarchical state, but also an intra-group symmetric state. In addition, simulation examples are presented in parallel with the theoretical results.
Study on the Erosion Morphology of Cohesive Soil under the Vertical Impact of a High-Pressure Moving Water Jet
Authors: Liu Jian , Lan Xiongdong , Wang Guanshi , Luo Sihai , Yang Zhipeng
Published: December 2022

In the in situ leaching process of ionic rare earth ore, high-pressure moving water jet technology has been applied to improve floor leakage problems. However, it has been found that the morphology of the erosion surface formed by a moving water jet cannot be accurately predicted. The range of the erosion surface has an important influence on the stability of liquid collection engineering and the liquid collection rate of rare earth resources, which is an important basis for the design of liquid collection engineering. To study this, soil-breaking tests using high-pressure moving water jets for a Pingnan mine were conducted. The influences of jet pressure and nozzle moving speed on soil-breaking morphology were analysed. The mechanism of erosion surface formation was revealed by erosion theory, and a method of predicting the erosion surface geometry based on jet pressure and nozzle speed is proposed. The results show that, in the jet pressure range of 5~10 MPa, with increases in jet pressure and decreases in nozzle speed, the geometric shape of the erosion surface tends to expand, and these parameters have no effect on the morphological characteristics of the erosion surface. In the soil-breaking process, the erosion speed decreases with increases in jet distance. As the nozzle speed becomes greater, the degree of the attenuation of erosion speed at a given jet pressure also becomes greater. Soil-breaking depth has a quadratic relationship with jet pressure and a 0.5 power function relationship with nozzle speed. The geometry of the erosion surface has self-similarity and can be divided into straight and curved sections. Accordingly, an empirical formula of the erosion surface geometric shape is proposed. The error of predicting the soil-breaking depth is less than 11%, which can provide a reference for engineering applications.
A Torpedo Target Recognition Method Based on the Correlation between Echo Broadening and Apparent Angle
Authors: Wang Zirui , Wu Jing , Wang Haitao , Hao Yukun , Wang Huiyuan
Published: December 2022

As acoustic decoys can simulate the scale of the target through orderly control of the echo delay, simulated acoustic decoys have scale characteristics similar to those of the scaled target. Consequently, simulated acoustic decoys make it difficult for active acoustic homing torpedoes to recognize acoustic decoys through traditional echo broadening or apparent angle. This will lead to a decrease in the anti-interference capability of torpedoes. In combat, acoustic decoys deceive torpedoes and deviate from the tracking course so that torpedoes cannot find the real target, or waste the range, eventually failing to strike the target and failing in combat. The accurate underwater target scale recognition of active acoustic homing torpedoes is considered a difficult technique. In this paper, we propose a target recognition method based on the correlation between target echo broadening and apparent angle. This specific simulation example shows that conventional target scale recognition methods cannot distinguish between suspended and homing acoustic decoys with virtual scale. By contrast, the target scale recognition method proposed in this paper can accurately distinguish between suspended and homing acoustic decoys with virtual scale at close range, under non-positive transverse port angle conditions. This method improves the anti-interference capability of torpedoes. In addition, it can improve the accuracy of active sonar recognition scale targets of ships, which guide active sonar target recognition.
The Effect of Beech Bark Extract in the Reduction of Discomfort from Acute Pericoronitis
Authors: Coșarcă Adina Simona , Golu Vlad , Temistocle Despina Luciana Bereczki , Frățilă Anca , Petrovan Cecilia , Cosarca Sanda , Coman Năstaca-Alina , Tanase Corneliu , Ormenișan Alina
Published: December 2022

Pericoronitis is characterized by inflammation of the tissues surrounding an erupting tooth and occurs most commonly in lower wisdom teeth. The extent of inflammatory phenomena can significantly affect the patient’s quality of life because of the pain, bleeding, and discomfort caused by it. The treatment of pericoronitis is carried out locally by irrigation and debridement in the early stages, but also generally by antibiotic therapy, when purulent secretion is present under the operculum and in the periodontal area. This study aims to evaluate the effect of beech bark extract (BBE) in the treatment of pericoronitis of the lower third molar. Parameters evaluated were pain, trismus, and bleeding index in a clinic study. Four groups of 20 patients were evaluated and grouped according to the irrigation solution used during the treatment: chlorhexidine 0.2% (CHX), BBE and chlorhexidine mixture solution 0.2% (1:1), and BBE and saline solution (control). The evaluation was carried out before and after the irrigation. Following the assessment of pain and trismus, a significant decrease in pain and a significant increase in mouth opening after 3 days was observed in patients treated with BBE, CHX, and BBE + CHX (p = 0.00). In the case of control patients, no significant decrease in pain (p = 0.83) was observed after 3 days, nor was there a significant increase in the opening of the oral cavity (p = 0.157). The evaluation of the inflammation index showed a significant decrease in gingival bleeding after 3 days for most patients treated with tested solutions (p < 0.05). It was concluded that irrigation with BBE was more effective in reducing pain, trismus, and bleeding compared to the other solutions (control, CHX, BBE + CHX), and this natural extract can be a choice in case of pericoronitis debut.
Applying BERT for Early-Stage Recognition of Persistence in Chat-Based Social Engineering Attacks
Authors: Tsinganos Nikolaos , Fouliras Panagiotis , Mavridis Ioannis
Published: December 2022

Chat-based social engineering (CSE) attacks are attracting increasing attention in the Small-Medium Enterprise (SME) environment, given the ease and potential impact of such an attack. During a CSE attack, malicious users will repeatedly use linguistic tricks to eventually deceive their victims. Thus, to protect SME users, it would be beneficial to have a cyber-defense mechanism able to detect persistent interlocutors who repeatedly bring up critical topics that could lead to sensitive data exposure. We build a natural language processing model, called CSE-PersistenceBERT, for paraphrase detection to recognize persistency as a social engineering attacker’s behavior during a chat-based dialogue. The CSE-PersistenceBERT model consists of a pre-trained BERT model fine-tuned using our handcrafted CSE-Persistence corpus; a corpus appropriately annotated for the specific downstream task of paraphrase recognition. The model identifies the linguistic relationship between the sentences uttered during the dialogue and exposes the malicious intent of the attacker. The results are satisfactory and prove the efficiency of CSE-PersistenceBERT as a recognition mechanism of a social engineer’s persistent behavior during a CSE attack.
Evaluation of Soil Nutrient Status Based on LightGBM Model: An Example of Tobacco Planting Soil in Debao County, Guangxi
Authors: Liang Zhipeng , Zou Tianxiang , Gong Jialin , Zhou Meng , Shen Wenjie , Zhang Jietang , Fan Dongsheng , Lu Yanhui
Published: December 2022

Soil nutrient status is the foundation of agricultural development. Exploring the features of soil nutrients and status evaluation can provide a reference for the development of modern agriculture. LightGBM is an optimization algorithm based on the boosting framework, which uses histograms to improve the accuracy of the model. Based on the construction of the LightGBM model, the main nutrient features and status of tobacco planting soil were analyzed in seven towns in Debao County, Guangxi Province, namely Yantong Town, Longguang Town, Najia Town, Zurong Town, Du’an Town, Dongling Town and Jingde Town. The confusion matrix results show the accuracy of the LightGBM model is 94.2%, and the eigenvalue analysis shows that the available potassium (K) contributes the most to the nutrient status. The pH value of soil ranging from 6.1 to 7.8 is favorable for tobacco growth, and the contents of soil organic matter, total nitrogen (N), available phosphorus (P), exchangeable calcium (Ca) and exchangeable magnesium (Mg) are at the appropriate level. Available potassium (K) and available zinc (Zn) are at a high level, but available boron (B) is slightly insufficient. The nutrient status of 10% of soil is at an extremely high level, and about 81.03% of soil is medium level or above. The LightGBM model has high reliability in the automatic evaluation of soil nutrient status, which not only can accurately monitor the soil nutrient status but also reflects the correlation and importance of nutrient factors. Therefore, the LightGBM model is significant for guiding soil cultivation and agricultural production.
Dual-Mode Control Scheme to Improve Light Load Efficiency for Dual Active Bridge DC-DC Converters Using Single-Phase-Shift Control
Authors: Tran Thanh Nhat Trung , Chang Wen-Yan , Wang Jian-Min
Published: December 2022

In vehicle-to-grid (V2G) applications, dual active bridge (DAB) converters are commonly used as the power interface because they offer high efficiency, galvanic isolation, and bidirectional power flow. For the DAB control strategy, phase-shift control is the mainstream, especially the single-phase-shift (SPS) method because of its ease of implementation. However, due to the phase shift, a DAB converter operated under this control method has relatively high backflow power, resulting in poor efficiency. The SPS control method has the drawback of high backflow power, especially at light loads. Thus, this paper proposes a new dual-mode control scheme to improve the light load efficiency of DAB converters by taking advantage of the pulse-width modulation (PWM) strategy in combination with the conventional SPS strategy for DAB converters based on load conditions. In other words, when the DAB converter operates under light load conditions, the PWM control strategy is used to avoid considerable backflow power. A prototype DAB converter with a power rating of 1 kW under a switching frequency of 100 kHz interfacing a DC bus (400 V) and a battery pack (50 V) is designed and implemented to verify the feasibility of this control strategy. A detailed analysis of the working principle and design parameters of the proposed converter is provided in this paper. Experimental results show that the highest efficiency of the proposed converter at light loads (10–200 W) was 96.2% for the forward power conversion and 97.3% for the backward power conversion.
Design and Optimization of Interdigital Capacitive Humidity Sensor with Highly Sensitive and Dynamic Response Time
Authors: Li Dapeng , Chen Aobei , Zheng Dezhi , Li Zhongxiang , Na Rui , Cui Fei , Yang Xiaojun
Published: December 2022

Humidity sensors are widely used in various fields of life. In meteorological detection, the sensor must have high sensitivity and fast dynamic response time due to extreme environmental interference. However, the sensitive mechanism of the humidity sensor determines that the dynamic response time will inevitably be increased while improving the sensitivity, which undoubtedly creates difficulties for sensor design. This article takes the interdigitated capacitive humidity sensor as the research object and proposes an optimal design scheme for the sensor that considers high dynamic response time and sensitivity. By constructing the sensor’s theoretical mathematical model, the influence of each structure is analyzed. The theoretical model has been verified by finite element simulation to have an accuracy higher than 95%. The article constructs the sensor optimization objective equation based on this model. Through analysis, within the range of structural parameters set in the article, to improve the sensitivity and reduce the dynamic response time of the sensor, the width and spacing of the interdigital electrodes should have a minimum value of 3 μm and a maximum value of 14 μm, respectively. The thickness of the electrode layer and the moisture-sensitive layer should be flexibly adjusted according to the application to ensure the lowest value of the optimization objective function. To further improve the sensor’s performance, the article optimizes the electrode structure and heating strategy of the sensor heating layer, which not only enhances the uniformity of heat transfer but also increases the optimal heat transfer area by 6% compared with the traditional scheme.
Joint Optimization Algorithm for Small Base Station States Control and User Association in Wireless Caching Networks
Authors: Wang Weipeng , Zhao Jihong , Qu Hua , Dai Huijun
Published: December 2022

The energy consumption management of small base stations (SBSs) in wireless caching networks with dense deployment of SBSs is an urgent problem to be solved. This paper jointly optimizes the SBS state control and user association problems in caching networks to reduce network energy consumption while taking into account the average service latency of the network to ensure user experience. First, a new definition of three-state SBSs in caching networks is proposed based on their ability to keep content cache updated. Then, a relaxed threshold setting method is designed and the SBS traffic prediction is used to obtain the initial state information of SBSs in the next period. In order to eliminate the impact on the accessing users when the switched-off base station (BS) wakes up, a SBS state asynchronous switching mechanism is proposed to ensure that the users who switch to the waking SBS can carry out communication services normally, and a user association strategy is constructed with the SBS load as the optimization target. Finally, a joint optimization model of user association and SBS state control (SSC-UA) is constructed to admit and correct the initial state of SBSs to maximize the system gain and obtain the final state strategy for each SBS in the next period. The simulation results show that the proposed SSC-UA algorithm can effectively improve the energy efficiency and reduce the network service delay at the same time.
Modeling the Submergence Depth of Oil Well States and Its Applications
Authors: Liu Tianshi , Tian Xue , Liu Liwen , Gu Xiaoyu , Zhao Yun , Zhang Liumei , Song Xinai
Published: December 2022

Obtaining the liquid storage state of oil wells in real time is very important for oilfield production. In this paper, under the premise of fully considering the transformation factors of full-pumping and nonfull-pumping states of oil wells, submergence depth models suitable for full- and nonfull-pumping wells are constructed. To reduce the application complexity of the models, parameter-reduction processing is performed to enhance the usability of the models. By analyzing the change trend of the submergence depth during the rising, maintaining, and falling of the oil well in the full-pumping state and nonfull-pumping state models, the judgment criteria for the transition of the oil well state are provided. On this basis, the application methods of nonlinear interpolation and least squares curve-fitting numerical solutions of submergence depth models are studied, and the unique existence of the solution of the corresponding one-variable nonlinear characteristic equation in the (0, 1) open interval is proven. Finally, the error estimation of the numerical solution is carried out, the calculation formula of the number of iterations for the numerical solution of the dichotomy is provided, and the error of the relevant numerical solution is verified.
MFO Tunned SVR Models for Analyzing Dimensional Characteristics of Cracks Developed on Steam Generator Tubes
Authors: William Mathias Vijay Albert , Ramesh Subramanian , Cep Robert , Kumar Mahalingam Siva , Elangovan Muniyandy
Published: December 2022

Accurate prediction of material defects from the given images will avoid the major cause in industrial applications. In this work, a Support Vector Regression (SVR) model has been developed from the given Gray Level Co-occurrence Matrix (GLCM) features extracted from Magnetic Flux Leakage (MFL) images wherein the length, depth, and width of the images are considered response values from the given features data set, and a percentage of data has been considered for testing the SVR model. Four parameters like Kernel function, solver type, and validation scheme, and its value and % of testing data that affect the SVR model’s performance are considered to select the best SVR model. Six different kernel functions, and three different kinds of solvers are considered as two validation schemes, and 10% to 30% of the testing data set of different levels of the above parameters. The prediction accuracy of the SVR model is considered by simultaneously minimizing prediction measures of both Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) and maximizing R2 values. The Moth Flame Optimization (MFO) algorithm has been implemented to select the best SVR model and its four parameters based on the above conflict three prediction measures by converting multi-objectives into a single object using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The performance of the MFO algorithm is compared statistically with the Dragon Fly Optimization Algorithm (DFO) and Particle Swarm Optimization Algorithm (PSO).
Analysis of All-Optical Generation of Graphene Surface Plasmons by a Frequency-Difference Process
Authors: Dias Rui , Viana Gomes José C. , Vasilevskiy Mikhail I.
Published: December 2022

The generation of graphene surface plasmons (SPs) by a frequency-difference nonlinear (NL) process caused by the interaction of two optical beams was experimentally demonstrated several years ago by measuring the differential reflectance of the probe beam. However, the understanding of these results requires much larger second-order optical conductivities of graphene than calculations performed so far can yield. In this work, we carefully calculate the relevant NL conductivities and show that, indeed, the experimental observations of the differential reflectance must have originated from physical processes beyond the coherent frequency-difference generation of SPs described by the density-matrix perturbation theory approach, presumably by hot-electron effects. We also suggest an alternative way of detecting optically generated SPs, which can be feasible at lower powers of the optical pulses. Such additional experiments are expected to help understand the remaining discrepancy between the theory and the existing experimental data.
A Review of Deep Reinforcement Learning Approaches for Smart Manufacturing in Industry 4.0 and 5.0 Framework
Authors: del Real Torres Alejandro , Andreiana Doru Stefan , Ojeda Roldán Álvaro , Hernández Bustos Alfonso , Acevedo Galicia Luis Enrique
Published: December 2022

In this review, the industry’s current issues regarding intelligent manufacture are presented. This work presents the status and the potential for the I4.0 and I5.0’s revolutionary technologies. AI and, in particular, the DRL algorithms, which are a perfect response to the unpredictability and volatility of modern demand, are studied in detail. Through the introduction of RL concepts and the development of those with ANNs towards DRL, the potential and variety of these kinds of algorithms are highlighted. Moreover, because these algorithms are data based, their modification to meet the requirements of industry operations is also included. In addition, this review covers the inclusion of new concepts, such as digital twins, in response to an absent environment model and how it can improve the performance and application of DRL algorithms even more. This work highlights that DRL applicability is demonstrated across all manufacturing industry operations, outperforming conventional methodologies and, most notably, enhancing the manufacturing process’s resilience and adaptability. It is stated that there is still considerable work to be carried out in both academia and industry to fully leverage the promise of these disruptive tools, begin their deployment in industry, and take a step closer to the I5.0 industrial revolution.
OMECDN: A Password-Generation Model Based on an Ordered Markov Enumerator and Critic Discriminant Network
Authors: Jiang Jihan , Zhou Anmin , Liu Liang , Zhang Lei
Published: December 2022

At present, static text passwords are still the most widely-used identity authentication method. Password-generation technology can generate large-scale password sets and then detect the defects in password-protection mechanisms, which is of great significance for evaluating password-guessing algorithms. However, the existing password-generation technology cannot ignore low-quality passwords in the generated password set, which will lead to low-efficiency password guessing. In this paper, a password-generation model based on an ordered Markov enumerator and critic discriminant network (OMECDN) is proposed, where passwords are generated via an ordered Markov enumerator (OMEN) and a discriminant network according to the probability of the combination of passwords. OMECDN optimizes the performance of password generation with a discriminative network based on the good statistical properties of OMEN. Moreover, the final password set is formed by the selected passwords with a higher score than the preset threshold, which guarantees the superiority of the hit rate of almost all ranges of combinations of passwords over the initial password set. Finally, the experiments show that OMECDN achieves a qualitative improvement in hit rate metrics. In particular, regarding the generation of 107 passwords on the RockYou dataset, the matching entries of the password set generated by the OMECDN model are 25.18% and 243.58% higher than those generated by the OMEN model and the PassGAN model, respectively.
Exploiting Stacked Autoencoders for Improved Sentiment Analysis
Authors: Ahmed Kanwal , Nadeem Muhammad Imran , Li Dun , Zheng Zhiyun , Ghadi Yazeed Yasin , Assam Muhammad , Mohamed Heba G.
Published: December 2022

Sentiment analysis is an ongoing research field within the discipline of data mining. The majority of academics employ deep learning models for sentiment analysis due to their ability to self-learn and process vast amounts of data. However, the performance of deep learning models depends on the values of the hyperparameters. Determining suitable values for hyperparameters is a cumbersome task. The goal of this study is to increase the accuracy of stacked autoencoders for sentiment analysis using a heuristic optimization approach. In this study, we propose a hybrid model GA(SAE)-SVM using a genetic algorithm (GA), stacked autoencoder (SAE), and support vector machine (SVM) for fine-grained sentiment analysis. Features are extracted using continuous bag-of-words (CBOW), and then input into the SAE. In the proposed GA(SAE)-SVM, the hyperparameters of the SAE algorithm are optimized using GA. The features extracted by SAE are input into the SVM for final classification. A comparison is performed with a random search and grid search for parameter optimization. GA optimization is faster than grid search, and selects more optimal values than random search, resulting in improved accuracy. We evaluate the performance of the proposed model on eight benchmark datasets. The proposed model outperformed when compared to the baseline and state-of-the-art techniques.
Software Reliability Growth Model with Dependent Failures and Uncertain Operating Environments
Authors: Lee Da Hye , Chang In Hong , Pham Hoang
Published: December 2022

Software is used in various industries, and its reliability has become an extremely important issue. For example, in the medical industry, software is used to provide medical services to underprivi-leged individuals. If a problem occurs with the software reliability, incorrect medical information may be provided. The software reliability is estimated using a software reliability growth model. However, most software reliability growth models assume that the failures are independent. In addition, it is assumed that the test and operating environments are the same. In this study, we propose a new software reliability growth model that assumes that software failures are dependent and uncertain operating environments. A comparison of the proposed model against existing NHPP SRMEs using actual datasets shows that the proposed model achieves the best fit.
Advances in Machine Learning for Sensing and Condition Monitoring
Authors: Ao Sio-Iong , Gelman Len , Karimi Hamid Reza , Tiboni Monica
Published: December 2022

In order to overcome the complexities encountered in sensing devices with data collection, transmission, storage and analysis toward condition monitoring, estimation and control system purposes, machine learning algorithms have gained popularity to analyze and interpret big sensory data in modern industry. This paper put forward a comprehensive survey on the advances in the technology of machine learning algorithms and their most recent applications in the sensing and condition monitoring fields. Current case studies of developing tailor-made data mining and deep learning algorithms from practical aspects are carefully selected and discussed. The characteristics and contributions of these algorithms to the sensing and monitoring fields are elaborated.
Vehicle Load Identification on Orthotropic Steel Box Beam Bridge Based on the Strain Response Area
Authors: Zhu Jun-He , Wang Chao , Qi Tian-Yu , Zhou Zhuo-Sheng
Published: December 2022

With the development of the economy and the rapid increase in traffic volume, an overload phenomenon often occurs. This paper studied a vehicle load identification technique based on orthotropic bridge deck stress monitoring data. The strain responses on the lower edge of multiple U-ribs were collected under vehicles crossed the deck. Firstly, an index based on the cross-correlation function of strain response between different measurement points on the same U-rib was used to evaluate vehicle speed. Secondly, a cosine similarity index was proposed to locate the transverse position of the vehicle. Finally, the unknown vehicle load was identified on the basis of a calibrated strain response area matrix. The effectiveness and anti-noise performance of the proposed method were verified using numerical simulation. An experimental model was designed and some strain gauges were installed to measure the strain response, and the test was carried out to further verify the algorithm’s performance. Numerical and experimental results show that the proposed method could effectively identify the vehicle load with good anti-noise performance. Moreover, a calibration space was provided to guide practical engineering applications. The proposed method does not damage bridge decks, does not affect traffic, and is economical.
Measuring Maritime Paint Thickness under Water Using THz Cross-Correlation Spectroscopy
Authors: Knarreborg Johan Østergaard , Hjortshøj-Nielsen Jonathan , Mølvig Bjørn Hübschmann , Bæk Thorsten , Jepsen Peter Uhd , Lange Simon Jappe
Published: December 2022

The shipping industry is a major contributor to global greenhouse gas (GHG) emissions, which is why it is important to optimize every aspect of the efficiency of ocean-going vessels. This includes the antifouling paint that ensures hydrodynamic efficiency. Measuring the thickness of the antifouling on top of all other paint layers using THz cross-correlation spectroscopy (THz-CCS) underwater could enable vessel operators to monitor the state of the paint on ship hulls and plan any vessel’s sailing route and maintenance optimally. However, due to the high absorption of water in the THz domain, measuring through any significant amount of water is impossible, making a water removal method necessary. This study shows how a THz-CCS system can be packaged for underwater measurements using a molded silicone contact seal. In combination with a spectroscopic model for data treatment, the thickness of a single paint layer is retrieved underwater. This paves the way for a more advanced system capable of measuring multilayer maritime paint underwater, which will enable shipping companies to continuously monitor the paint layers’ thickness.
Pulsed Electromagnetic Field Therapy and Direct Current Electric Field Modulation Promote the Migration of Fibroblast-like Synoviocytes to Accelerate Cartilage Repair In Vitro
Authors: Sakhrani Neeraj , Stefani Robert M. , Setti Stefania , Cadossi Ruggero , Ateshian Gerard A. , Hung Clark T.
Published: December 2022

Articular cartilage injuries are a common source of joint pain and dysfunction. As articular cartilage is avascular, it exhibits a poor intrinsic healing capacity for self-repair. Clinically, osteochondral grafts are used to surgically restore the articular surface following injury. A significant challenge remains with the repair properties at the graft-host tissue interface as proper integration is critical toward restoring normal load distribution across the joint. A key to addressing poor tissue integration may involve optimizing mobilization of fibroblast-like synoviocytes (FLS) that exhibit chondrogenic potential and are derived from the adjacent synovium, the specialized connective tissue membrane that envelops the diarthrodial joint. Synovium-derived cells have been directly implicated in the native repair response of articular cartilage. Electrotherapeutics hold potential as low-cost, low-risk, non-invasive adjunctive therapies for promoting cartilage healing via cell-mediated repair. Pulsed electromagnetic fields (PEMFs) and applied direct current (DC) electric fields (EFs) via galvanotaxis are two potential therapeutic strategies to promote cartilage repair by stimulating the migration of FLS within a wound or defect site. PEMF chambers were calibrated to recapitulate clinical standards (1.5 ± 0.2 mT, 75 Hz, 1.3 ms duration). PEMF stimulation promoted bovine FLS migration using a 2D in vitro scratch assay to assess the rate of wound closure following cruciform injury. Galvanotaxis DC EF stimulation assisted FLS migration within a collagen hydrogel matrix in order to promote cartilage repair. A novel tissue-scale bioreactor capable of applying DC EFs in sterile culture conditions to 3D constructs was designed in order to track the increased recruitment of synovial repair cells via galvanotaxis from intact bovine synovium explants to the site of a cartilage wound injury. PEMF stimulation further modulated FLS migration into the bovine cartilage defect region. Biochemical composition, histological analysis, and gene expression revealed elevated GAG and collagen levels following PEMF treatment, indicative of its pro-anabolic effect. Together, PEMF and galvanotaxis DC EF modulation are electrotherapeutic strategies with complementary repair properties. Both procedures may enable direct migration or selective homing of target cells to defect sites, thus augmenting natural repair processes for improving cartilage repair and healing.
Towards Improving Privacy and Security of Identity Management Systems Using Blockchain Technology: A Systematic Review
Authors: Alanzi Haifa , Alkhatib Mohammad
Published: December 2022

An identity management system (IDMS) manages and organizes identities and credentials information exchanged between users, identity providers (IDPs), and service providers (SPs) to ensure confidentiality and enhance privacy of users’ personal data. Traditional or centralized IDMS rely on a third party to store a user’s personal information, authenticate the user, and organize the entire process. This clearly constitutes threats to the privacy of the user, in addition to other issues, such as single point of failure (SPOF), user tracking, and data availability issues. Blockchain technology has many useful features that can contribute to solving traditional IDMS issues, such as decentralization, immutability, and anonymity. Blockchain represents an attractive solution for many issues related to traditional IDMS, including privacy, third-party control, data leakage, and SPOF, supported by Distributed Ledger Technology (DLT) security features and powerful smart contracts technology. The current study presents a systematic literature review and analysis for recently proposed solutions that adopt the traditional centralized approach, as well as solutions based on blockchain technology. The study also aims to provide a deep understanding of proposed IDMS solutions and best practices, and highlight the research gaps and open issues related to IDMSs and users’ privacy. In particular, the current research focuses on analyzing the blockchain-based solutions and illustrating their strengths and weaknesses, as well as highlighting the promising blockchain technology framework that can be utilized to enhance privacy and solve security issues in a centralized IDMS. Such a study is an important step towards developing efficient solutions that address the pressing needs in the field.