bluuebunny/crossref_metadata_2025 huggingface dataset:
Predation Rate on Olive Riley Sea Turtle (Lepidochelys olivacea) Nests with Solitary Nesting Activity from 2008 to 2021 at Corozalito, Costa Rica
Authors: Espinoza-Rodríguez Nínive , Rojas-Cañizales Daniela , Mejías-Balsalobre Carmen , Naranjo Isabel , Arauz Randall
Published: February 2023

In Corozalito beach, Costa Rica, Olive Ridley turtles (Lepidochelys olivacea) nest both solitarily and in arribadas. The predation of solitary nests was monitored from 2008 to 2021, recording date, time, sector of the beach, zone, status of nest (predated or partially predated) and predator when possible. We recorded 4450 predated nests in total (N = 30,148 nesting events); predation rates showed a fluctuating trend, with recent percentages reaching up to 30%, with four distinctive dips in 2010, 2014, 2016 and 2017. The spatial distribution of predated nests along the beach showed significant differences among the sectors regardless of the seasons (Friedman test, chi-squared = 14.778, df = 2, p-value = 0.000), with most predated nests (47.62%) occurring in the northern sectors of the beach. Predators were identified by their tracks and/or direct observations (N = 896, 24.08%). The most conspicuous predators identified were raccoons (55.69%) and black vultures (22.77%). As seen in Corozalito, predation rates have increased in recent years despite established conservation efforts. A comprehensive assessment of all threats towards the overall hatching success for clutches is needed, considering predation during mass nesting events, poaching and beach erosion, among other factors, to fully understand the nesting dynamics occurring in this beach.
Use of Organic Acid Mixtures Containing 2-Hydroxy-4-(Methylthio) Butanoic Acid (HMTBa) to Mitigate Salmonella enterica, Shiga Toxin-Producing Escherichia coli (STEC) and Aspergillus flavus in Pet Food Kibbles
Authors: Deliephan Aiswariya , Dhakal Janak , Subramanyam Bhadriraju , Aldrich Charles G.
Published: February 2023

Post-processing operations of extruded pet food kibbles involve coating the product with fats and flavorings. These processes increase the risk for cross-contamination with food-borne pathogens such as Salmonella and Shiga toxin-producing Escherichia coli (STEC), and mycotoxin-producing molds such as Aspergillus spp. after the thermal kill step. In this study, the antimicrobial effects of two types of organic acid mixtures containing 2-hydroxy-4-(methylthio) butanoic acid (HMTBa), Activate DA™ and Activate US WD-MAX™, against Salmonella enterica, STEC and Aspergillus flavus when used as a coating on pet food kibbles were evaluated. Using canola oil and dry dog digest as fat and flavor coatings, the efficacy of Activate DA (HMTBa + fumaric acid + benzoic acid) at 0%, 1% and 2%, and Activate US WD-MAX (HMTBa + lactic acid + phosphoric acid) at 0%, 0.5% and 1% was tested on kibbles inoculated with a cocktail of S. enterica serovars (Enteritidis, Heidelberg and Typhimurium) or Shiga toxin-producing E. coli (STEC) serovars (O121, and O26) at 37 °C for 0, 12, 24, 48, 72 h, 30 and 60 days. Similarly, their efficacy was tested against A. flavus at 25 °C for 0, 3, 7, 14, 21, 28 and 35 days. Activate DA at 2% and Activate US WD-MAX at 1% reduced Salmonella counts by ~3 logs after 12 h and 4–4.6 logs after 24 h. Similarly, STEC counts were reduced by ~2 logs and 3 logs after 12 h and 24 h, respectively. Levels of A. flavus did not vary up to 7 days, and afterwards started to decline by >2 logs in 14 days, and up to 3.8-log reduction in 28 days for Activate DA and Activate US WD-MAX at 2% and 1%, respectively. The results suggest that the use of these organic acid mixtures containing HMTBa during kibble coating may mitigate post-processing enteric pathogen and mold contamination in pet food kibbles, with Activate US WD-MAX being effective at a lower concentration (0.5–1%) compared to Activate DA.
Evidence of the Autophagic Process during the Fish Immune Response of Skeletal Muscle Cells against Piscirickettsia salmonis
Authors: Valenzuela Cristián A. , Azúa Marco , Álvarez Claudio A. , Schmitt Paulina , Ojeda Nicolás , Mercado Luis
Published: February 2023

Autophagy is a fundamental cellular process implicated in the health of the cell, acting as a cytoplasmatic quality control machinery by self-eating unfunctional organelles and protein aggregates. In mammals, autophagy can participate in the clearance of intracellular pathogens from the cell, and the activity of the toll-like receptors mediates its activation. However, in fish, the modulation of autophagy by these receptors in the muscle is unknown. This study describes and characterizes autophagic modulation during the immune response of fish muscle cells after a challenge with intracellular pathogen Piscirickettsia salmonis. For this, primary cultures of muscle cells were challenged with P. salmonis, and the expressions of immune markers il-1β, tnfα, il-8, hepcidin, tlr3, tlr9, mhc-I and mhc-II were analyzed through RT-qPCR. The expressions of several genes involved in autophagy (becn1, atg9, atg5, atg12, lc3, gabarap and atg4) were also evaluated with RT-qPCR to understand the autophagic modulation during an immune response. In addition, LC3-II protein content was measured via Western blot. The challenge of trout muscle cells with P. salmonis triggered a concomitant immune response to the activation of the autophagic process, suggesting a close relationship between these two processes.
Establishment of a Real-Time PCR Assay for the Detection of Devriesea agamarum in Lizards
Authors: Brockmann Maria , Leineweber Christoph , Hellebuyck Tom , Martel An , Pasmans Frank , Gentil Michaela , Müller Elisabeth , Marschang Rachel E.
Published: February 2023

(1) Background: Devriesea (D.) agamarum is a potential cause of dermatitis and cheilitis in lizards. The aim of this study was to establish a real-time PCR assay for the detection of D. agamarum. (2) Methods: Primers and probe were selected targeting the 16S rRNA gene, using sequences of 16S rRNA genes of D. agamarum as well as of other bacterial species derived from GenBank. The PCR assay was tested with 14 positive controls of different D. agamarum cultures as well as with 34 negative controls of various non-D. agamarum bacterial cultures. Additionally, samples of 38 lizards, mostly Uromastyx spp. and Pogona spp., submitted to a commercial veterinary laboratory were tested for the presence of D. agamarum using the established protocol. (3) Results: Concentrations of as low as 2 × 104 colonies per mL were detectable using dilutions of bacterial cell culture (corresponding to approximately 200 CFU per PCR). The assay resulted in an intraassay percent of coefficient of variation (CV) of 1.31% and an interassay CV of 1.80%. (4) Conclusions: The presented assay is able to detect D. agamarum in clinical samples, decreasing laboratory turn-around time in comparison to conventional culture-based detection methods.
Effects of Environmental Factors on Bird Communities in Different Urbanization Grades: An Empirical Study in Lishui, a Mountainous Area of Eastern China
Authors: Zhang Wenwen , Zhou Ying , Fang Xuelan , Zhao Shengjun , Wu Yi , Zhang Haonan , Cui Liangwei , Cui Peng
Published: February 2023

The rapid development of urbanization has changed landscape patterns and biological habitats severely and, therefore, affected biodiversity. In this study, we selected 75 townships in Lishui, a mountainous area in eastern China, to conduct bird surveys for two years. We analyzed the birds’ composition characters in townships with different levels of development in order to identify the effects on bird diversity of the urban development level, land cover pattern, landscape pattern, and other factors. In total, 296 bird species from 18 orders and 67 families were recorded between December 2019 and January 2021. A total of 166 species of birds belonged to Passeriformes (56.08%). The seventy-five townships were divided into three grades by K-means cluster analysis. The average number of bird species, richness index, and diversity index were higher in G-H (highest urban development level) compared with the other grades. At the township level, landscape diversity and landscape fragmentation were the key factors that positively affected the bird species number, diversity index, and richness index. Landscape diversity had a greater effect than landscape fragmentation, particularly on the Shannon–Weiner diversity index. The diversity and heterogeneity of urban landscapes could be improved by constructing biological habitats in future urban development planning to maintain and increase biodiversity. The results obtained in this study provide a theoretical basis for urban planning in mountainous areas, and a reference for policymakers to formulate biodiversity conservation strategies, construct reasonable biodiversity patterns, and solve practical biodiversity conservation problems.
Research Progress on Lycopene in Swine and Poultry Nutrition: An Update
Authors: Chen Jun , Cao Xuehai , Huang Zhouyin , Chen Xingping , Zou Tiande , You Jinming
Published: February 2023

Oxidative stress and in-feed antibiotics restrictions have accelerated the development of natural, green, safe feed additives for swine and poultry diets. Lycopene has the greatest antioxidant potential among the carotenoids, due to its specific chemical structure. In the past decade, increasing attention has been paid to lycopene as a functional additive for swine and poultry feed. In this review, we systematically summarized the latest research progress on lycopene in swine and poultry nutrition during the past ten years (2013–2022). We primarily focused on the effects of lycopene on productivity, meat and egg quality, antioxidant function, immune function, lipid metabolism, and intestinal physiological functions. The output of this review highlights the crucial foundation of lycopene as a functional feed supplement for animal nutrition.
Preparation and Evaluation of Nanoemulsion of Citronella Essential Oil with Improved Antimicrobial and Anti-Cancer Properties
Authors: Jawaid Talha , Alaseem Ali Mohammed , Khan Mohammed Moizuddin , Mukhtar Beenish , Kamal Mehnaz , Anwer Razique , Ahmed Saif , Alam Aftab
Published: February 2023

The development of new pharmaceutical solutions for treating various diseases results from a growing understanding of the benefits of using essential oils. One of the most often used volatile materials among essential oils is the oil of the citronella plant, termed citronella essential oil (CITEO), which has potential for use in food and medicine. Its wide use is limited due to lipophilicity, high volatility and poor physicochemical stability. With this background, the present study aims to evaluate the properties of CITEO-nanoemulsion (CITEO-NE) by analyzing its antimicrobial activities against Staphylococcus aureus (S. aureus) and Candida albicans (C. albicans) and its anticancer activity against, human skin adenocarcinoma cell line (A431). The CITEO-NE was prepared and evaluated for the size range of 130 ± 5 nm, polydispersity index (PDI) of 0.127 and zeta potential −12.6 mV. The percentage % of entrapment efficiency (%EE) of nanoemulsions loaded with CIT was very high at the beginning of the study, at 95.5 ± 4.775%. The MIC was observed to be 500 µg/mL for CITEO and 250 µg/mL for CITEO-NE against S. aureus and 250 µg/mL for CITEO and 125 µg/mL for CITEO-NE against C. albicans. The time-kill assay also suggests the effectiveness of CITEO-NE against the test pathogens as a novel alternative therapy. The IC50 values of CITEO and CITEO-NE exhibited significant cytotoxic properties against the A431 cell line, with 41.20 μg/mL and 37.71 μg/mL, respectively. Hence, our findings revealed that encapsulation of CITEO increased the pharmacological properties.
Swietenine Alleviates Nonalcoholic Fatty Liver Disease in Diabetic Mice via Lipogenesis Inhibition and Antioxidant Mechanisms
Authors: Mak Kit-Kay , Zhang Shiming , Chellian Jestin , Mohd Zulkefeli , Epemolu Ola , Dinkova-Kostova Albena T. , Balijepalli Madhu Katyayani , Pichika Mallikarjuna Rao
Published: February 2023

Our previous studies have reported the effect of swietenine (a major bioactive component of Swietenia macrophylla seeds) in reversing and potentiating the effect of metformin in hyperglycemia and hyperlipidaemia in diabetic rats. Moreover, we reported that the anti-inflammatory effect of swietenine is mediated via the activation of nuclear factor erythroid 2-related factor 2 (Nrf2). This study evaluated the effect of swietenine and its mechanisms in nonalcoholic fatty liver disease (NAFLD) in high-fat diet/streptozotocin-induced diabetic mice. The effect was assessed by determining blood biochemical parameters (glucose, cholesterol, triglycerides, alanine transaminase (ALT), asparate transaminase (AST), alkaline phosphatase (ALP), glutathione (GSH), total antioxidant capacity (TAC), and malondialdehyde (MDA)) and liver biochemical parameters (liver index, cholesterol, and triglycerides). Hepatic lipid accumulation (initial causative factor in NAFLD) was determined by oil-O-red staining. Gene expression (qPCR) and immunohistochemical studies were performed to elucidate the mechanism of swietenine’s effect in NAFLD. The critical regulators (genes and proteins) involved in lipogenesis (ACLY, ACC1, FASN, SREBP1c, and ChREBPβ) and oxidative stress (Nrf2, NQO-1 and HO-1) pathways were determined. In mice fed with a high-fat diet followed by streptozotocin injection, the liver cholesterol, triglycerides, and lipids were elevated. These increases were reversed by the oral administration of swietenine, 80 mg/kg body weight, on alternate days for eight weeks. Gene expression and immunohistochemical studies showed that swietenine reversed the elevated levels of crucial enzymes of lipogenesis (ACLY, ACC1 and FASN) and their master transcription factors (SREBP1c and ChREBPβ). Furthermore, swietenine activated the Nrf2 antioxidant defense mechanism, as evidenced by the upregulated levels of Nrf2, NQO-1, and HO-1. It is concluded that swietenine shows beneficial effects in diabetes-induced NAFLD via inhibiting lipogenesis and activating the Nrf2 pathway.
Antibacterial and Phytochemical Screening of Artemisia Species
Authors: Bordean Maria-Evelina , Ungur Rodica Ana , Toc Dan Alexandru , Borda Ileana Monica , Marțiș Georgiana Smaranda , Pop Carmen Rodica , Filip Miuța , Vlassa Mihaela , Nasui Bogdana Adriana , Pop Anamaria , Cinteză Delia , Popa Florina Ligia , Marian Sabina , Szanto Lidia Gizella , Muste Sevastița
Published: February 2023

Taking into account the increasing number of antibiotic-resistant bacteria, actual research focused on plant extracts is vital. The aim of our study was to investigate leaf and stem ethanolic extracts of Artemisia absinthium L. and Artemisia annua L. in order to explore their antioxidant and antibacterial activities. Total phenolic content (TPC) was evaluated spectrophotometrically. Antioxidant activity was evaluated by DPPH and ABTS. The antibacterial activity of wormwood extracts was assessed by minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) in Escherichia coli, Staphylococcus aureus, Listeria monocytogenes, and Salmonella enteritidis cultures, and by zone of inhibition in Klebsiella carbapenem-resistant enterobacteriaceae (CRE) and Escherichia coli extended-spectrum β-lactamases cultures (ESBL). The Artemisia annua L. leaf extract (AnL) exhibited the highest TPC (518.09 mg/mL) and the highest expression of sinapic acid (285.69 ± 0.002 µg/mL). Nevertheless, the highest antioxidant capacity (1360.51 ± 0.04 µM Trolox/g DW by ABTS and 735.77 ± 0.02 µM Trolox/g DW by DPPH) was found in Artemisia absinthium L. leaf from the second year of vegetation (AbL2). AnL extract exhibited the lowest MIC and MBC for all tested bacteria and the maximal zone of inhibition for Klebsiella CRE and Escherichia coli ESBL. Our study revealed that AbL2 exhibited the best antioxidant potential, while AnL extract had the strongest antibacterial effect.
Water Level Simulation in River Network by Data Assimilation Using Ensemble Kalman Filter
Authors: Chen Yifan , Cao Feifeng , Meng Xiangyong , Cheng Weiping
Published: February 2023

Water level simulation for complex water river networks is complex, and existing forecasting models are mainly used for single-channel rivers. In this paper, we present a new data assimilation model based on the ensemble Kalman filter (EnKF) for accurate water level simulation in complex river networks. The EnKF-based data model was tested on simulated water level data from a river network hydrodynamic model and optimized through parameter analysis. It was then applied to a real mountainous single-channel river and plain river network and compared with a data assimilation model based on the extended Kalman filter (EKF). The results showed that the EnKF-based model, with a medium ensemble sample size of 100–150, normal observation noise of 0.0001–0.01 m, and a high standard deviation of 0.01–0.1 m, outperformed the EKF-based model, with a 49% reduction in simulation errors, a 45% reduction in calculation cost, and a 43% reduction in filtering time. Furthermore, the EnKF-based data assimilation model predicted the water level in the plain river network better than the mountainous single-channel river. Around 5 to 8 h were required for data assimilation; afterwards, the model could make accurate predictions covering 20 to 30 h. The EnKF-based data assimilation model offers a potential solution for water level predictions in river networks.
Early Forest Fire Detection Using a Protocol for Energy-Efficient Clustering with Weighted-Based Optimization in Wireless Sensor Networks
Authors: Kaur Puneet , Kaur Kiranbir , Singh Kuldeep , Kim SeongKi
Published: February 2023

Wireless sensor networks (WSNs) have proven to be incredibly useful for forest applications that rely on sensing technologies for event detection and monitoring. This radical sensing technology has revolutionized data gathering, analysis, and application. Despite the many advantages of this technology, one key drawback is the rapid drain on sensor batteries caused by their intensive processing activities and communication processes. The effectiveness of sensor nodes is strongly influenced by two factors: the amount of energy they consume and the length of their coverage lifetimes. Using our proposed method, we can find fire zones in a forest, detect and monitor battlefield surveillance, combat monitoring and intruder detection, and then wirelessly send all the information to a central station. So, extending the life of WSNs is essential to ensure that Sensor Nodes (SN) will always be available. Our proposed EEWBP (energy-efficient weighted-based protocol) technique uses a composite weighted metric that includes system elements such as the node degree, residual energy, the number of neighbors’ nodes, average flying speed, and trust value, which are evaluated separately and then added together to help in cluster-building and node-scheduling processes. Our proposed protocol makes it easy to set up many clusters of SNs, each with their own cluster head (CH). This way, data can be sent between clusters in a way that uses the least amount of energy and makes coverage last longer. After putting our cluster-based routing strategy in place, we tested how it worked and evaluated it with different network parameters. The simulation results show that EEWBP consumes less energy and maintains a higher level of consistency in the CH than coverage preserving clustering protocol (CPCP), coverage clustering protocol (CACP), coverage aware unequal clustering algorithm (CUCA), and low-energy adaptive clustering hierarchy (LEACH). EEWBP also shows a better packet delivery rate and an improvement in first-node death.
Data-Intensive Inventory Forecasting with Artificial Intelligence Models for Cross-Border E-Commerce Service Automation
Authors: Tang Yuk Ming , Chau Ka Yin , Lau Yui-yip , Zheng Zehang
Published: February 2023

Building an adaptative, flexible, resilient, and reliable inventory management system provides a reliable supply of cross-border e-commerce commodities, enhances supply chain members with a flow of products, fulfills ever-changing customer requirements, and enables e-commerce service automation. This study uses an e-commerce company as a case study to collect intensive inventory data. The key process of the AI approach for an intensive data forecasting framework is constructed. The study shows that the AI model’s optimization process needs to be combined with the problems of specific companies and information for analysis and optimization. The study provides optimization suggestions and highlights the key processes of the AI-predicting inventory model. The XGBoost method demonstrates the best performance in terms of accuracy (RMSE = 46.64%) and reasonable computation time (9 min 13 s). This research can be generalized and used as a useful basis for further implementing algorithms in other e-commerce enterprises. In doing so, this study highlights the current trend of logistics 4.0 solutions via the adoption of robust data-intensive inventory forecasting with artificial intelligence models for cross-border e-commerce service automation. As expected, the research findings improve the alleviation of the bullwhip impact and sustainable supply chain development. E-commerce enterprises may provide a better plan for their inventory management so as to minimize excess inventory or stock-outs, and improve their sales strategies and promotional and marketing activities.
Signal Control Study of Oversaturated Heterogeneous Traffic Flow Based on a Variable Virtual Waiting Zone in Dedicated CAV Lanes
Authors: Yu Haiyang , Wang Jixiang , Ren Yilong , Chen Siqi , Dong Chenglin
Published: February 2023

To meet the demand for cooperative signal control at oversaturated heterogeneous traffic flow intersections containing CAVs and HVs, cooperative control including dedicated CAV lanes has been explored to improve intersection safety capacity and reduce vehicle delays while avoiding uncertain HV driving behaviour. However, this approach does not fully exploit CAV network connectivity advantages and intersection spatial and temporal resources. Here, an oversaturated heterogeneous traffic flow signal control model based on a variable virtual waiting zone with a dedicated CAV lane is proposed. Within the model, CAVs going straight or left share a dedicated CAV lane, a CAV variable virtual waiting zone is within the intersection ahead of the dedicated CAV lane, and CAVs and HVs share the straight-through lane. The model framework has three layers. The upper layer optimizes the barrier time using a rolling time domain scheme. The middle layer optimizes the phase duration and variable virtual waiting zone switching time based on the fixed phase sequence, returning the vehicle delay to the upper optimization model. The lower layer performs CAV grouping and trajectory planning in the dedicated CAV lane based on signal timing and variable virtual waiting zone duration, returning the CAV delays to the middle level.
Hydrological Drought Frequency Analysis in Water Management Using Univariate Distributions
Authors: Anghel Cristian Gabriel , Ilinca Cornel
Published: February 2023

The study of extreme phenomena in hydrology generally involves frequency analysis and a time series analysis. In this article we provide enough mathematics to enable hydrology researchers to apply a wide range of probability distributions in frequency analyses of hydrological drought. The article presents a hydrological drought frequency analysis methodology for the determination of minimum annual flows, annual drought durations and annual deficit volumes for exceedance probabilities common in water management. Eight statistical distributions from different families and with different numbers of parameters are used for the frequency analysis. The method of ordinary moments and the method of linear moments are used to estimate the parameters of the distributions. All the mathematical characteristics necessary for the application of the eight analyzed distributions, for the method of ordinary moments and the method of linear moments, are presented. The performance of the analyzed distributions is evaluated using relative mean error and relative absolute error. For the frequency analysis of the annual minimum flows, only distributions that have a lower bound close to the annual minimum value should be used, a defining characteristic having the asymptotic distributions at this value. A case study of hydrological drought frequency analysis is presented for the Prigor River. We believe that the use of software without the knowledge of the mathematics behind it is not beneficial for researchers in the field of technical hydrology; thus, the dissemination of mathematical methods and models is necessary. All the research was conducted within the Faculty of Hydrotechnics.
The Use of Artificial Intelligence (AI) in Online Learning and Distance Education Processes: A Systematic Review of Empirical Studies
Authors: Dogan Murat Ertan , Goru Dogan Tulay , Bozkurt Aras
Published: February 2023

Artificial intelligence (AI) technologies are used in many dimensions of our lives, including education. Motivated by the increasing use of AI technologies and the current state of the art, this study examines research on AI from the perspective of online distance education. Following a systematic review protocol and using data mining and analytics approaches, the study examines a total of 276 publications. Accordingly, time trend analysis increases steadily with a peak in recent years, and China, India, and the United States are the leading countries in research on AI in online learning and distance education. Computer science and engineering are the research areas that make the most of the contribution, followed by social sciences. t-SNE analysis reveals three dominant clusters showing thematic tendencies, which are as follows: (1) how AI technologies are used in online teaching and learning processes, (2) how algorithms are used for the recognition, identification, and prediction of students’ behaviors, and (3) adaptive and personalized learning empowered through artificial intelligence technologies. Additionally, the text mining and social network analysis identified three broad research themes, which are (1) educational data mining, learning analytics, and artificial intelligence for adaptive and personalized learning; (2) algorithmic online educational spaces, ethics, and human agency; and (3) online learning through detection, identification, recognition, and prediction.
Mercury Levels in Sediment, Water and Selected Organisms Collected in a Coastal Contaminated Environment: The Marano and Grado Lagoon (Northern Adriatic Sea, Italy)
Authors: Bettoso Nicola , Pittaluga Federico , Predonzani Sergio , Zanello Antonella , Acquavita Alessandro
Published: February 2023

Mercury (Hg) is a global pollutant capable of bioaccumulates/biomagnifies along the trophic chain and posing concerns for organisms and humans. The historical mining in Idrija (NW Slovenia) and the more recent activity of a chlor-alkali plant (CAP) sited in Torviscosa (NE Italy) causes diffuse Hg contamination in the Marano and Grado Lagoon (MGL, northern Adriatic Sea, Italy). Despite the importance of fishing and aquaculture for local inhabitants, knowledge of the Hg content of MGL fish is still scarce and fragmentary. This paper reports the results obtained from the collection of sediments, water, and biota during the implementation of the WFD/2000/60/CE. The solid phase is characterised by high Hg concentrations (up to 7.4 mg kg−1) with a net positive gradient moving eastward, but chemical speciation suggests the prevalence of cinnabar (not mobile) species. The scarce mobility of Hg is attested to by the low concentrations found in surface waters. Hg in fish often exceeds the limit set for commercialization (0.5 mg kg−1 ww), especially in the Grado basin, but its content is variable depending on the size and habits of species. Although there was a significant linear relationship between THg content in sediment and tissues of Chelon auratus, the values of the biota sediment accumulation factor (BSAF), which were always less than one, suggest that the Hg bioavailable for transfer from sediment to biota is low. Additionally, the Target Hazard Quotient (THQ) calculated on C. auratus’s daily consumption showed that adverse effects on human health are out of the question at least for the Marano basin.
Spherical Atomic Norm-Inspired Approach for Direction-of-Arrival Estimation of EM Waves Impinging on Spherical Antenna Array with Undefined Mutual Coupling
Authors: Famoriji Oluwole John , Shongwe Thokozani
Published: February 2023

A spherical antenna array (SAA) is an array-designed arrangement capable of scanning in almost all the radiation sphere with constant directivity. It finds recent applications in aerospace, spacecraft, vehicular and satellite communications. Therefore, estimation of the direction-of-arrival (DoA) of electromagnetic (EM) waves that impinge on an SAA with unknown mutual coupling called for research attention. This paper proposed a spherical harmonic atomic norm minimization (SHANM) for DoA estimation using an SAA configuration. The gridless sparse signal recovery problem is considered in the spherical harmonic (SH) domain in conjunction with the atomic norm minimization (ANM). Because of the unavailability of the Vandermonde structure in the SH domain, the theorem of Vandermonde decomposition that is the mathematical basis of the traditional ANM methods finds no application in SH. Addressing this challenge, a low-dimensional semidefinite programming (SDP) approach implementing the SHANM method is developed. This approach is independent of Vandermonde decomposition, and directly recovers the atomic decomposition in SH. The numerical experimental results show the superior performance of the proposed method against the previous methods. In addition, accounting for the impacts of mutual coupling, an experimental measured data, which is the generally accepted ground of testing any method, is employed to illustrate the efficacy and robustness of the proposed methods. Finally, for achieving DoA estimation with sufficient localization accuracy using a SAA, the proposed SHANM-based method is a better option.
Adaptive Gaussian Mixture Model for Uncertainty Propagation Using Virtual Sample Generation
Authors: Xu Tianlai , Zhang Zhe , Han Hongwei
Published: February 2023

Orbit uncertainty propagation plays an important role in the analysis of a space mission. The accuracy and computation expense are two critical essences of uncertainty propagation. Repeated evaluations of the objective model are required to improve the preciseness of prediction, especially for long-term propagation. To balance the computational complexity and accuracy, an adaptive Gaussian mixture model using virtual sample generation (AGMM-VSG) is proposed. First, an unscented transformation and Cubature rule (UT-CR) based splitting method is employed to adaptive update the weights of Gaussian components for nonlinear dynamics. The Gaussian mixture model (GMM) approximation is applied to better approximate the original probability density function. Second, instead of the pure expensive evaluations by conventional GMM methods, virtual samples are generated using a new active-sampling-based Kriging (AS-KRG) method to improve the propagation efficiency. Three cases of uncertain orbital dynamical systems are used to verify the accuracy and efficiency of the proposed manuscript. The likelihood agreement measure (LAM) criterion and the number of expense evaluations prove the performance.
Differential Expression Analysis of Blood MicroRNA in Identifying Potential Genes Relevant to Alzheimer’s Disease Pathogenesis, Using an Integrated Bioinformatics and Machine Learning Approach
Authors: Tan Mei Sze , Cheah Phaik-Leng , Chin Ai-Vyrn , Looi Lai-Meng , Chang Siow-Wee
Published: February 2023

Alzheimer’s disease (AD) is a neurodegenerative disease characterized by cognitive and functional impairment. Recent research has focused on the deregulation of microRNAs (miRNAs) in blood as the potential biomarkers for AD. As such, a differential expression analysis of miRNAs was conducted in this study using an integrated framework that utilized the advantages of statistical and machine learning approaches. Three miRNA candidates that showed the strongest significance and correlation with each other, namely hsa-miR-6501-5p, hsa-miR-4433b-5p, and hsa-miR-143-3p, were identified. The roles and functions of the identified differentiated miRNA candidates with AD development were verified by predicting their target mRNAs, and their networks of interaction in AD pathogenesis were investigated. Pathway analysis showed that the pathways involved in contributing to the development of AD included oxidative phosphorylation, mitochondrial dysfunction, and calcium-mediated signalling. This study supports evidence that the miRNA expression changes in AD and indicates the need for further study in this area.
Understanding Customers’ Insights Using Attribution Theory: A Text Mining and Rule-Based Machine Learning Two-Step Multifaceted Method
Authors: Sann Raksmey , Lai Pei-Chun , Liaw Shu-Yi
Published: February 2023

By looking at complaints made by guests of different star-rated hotels, this study attempts to detect associations between complaint attributions and specific consequences. A multifaceted approach is applied. First, a content analysis is conducted to transform textual complaints into categorically structured data. Furthermore, a web graph analysis and rule-based machine learning method are applied to discover potential relationships among complaint antecedents and consequences. These are validated using a qualitative projective technique. Using an Apriori rule-based machine learning algorithm, optimal priority rules for this study were determined for the respective complaining attributions for both the antecedents and consequences. Based on attribution theory, we found that Customer Service, Room Space, and Miscellaneous Issues received more attention from guests staying at higher star-rated hotels. Conversely, cleanliness was a consideration more prevalent amongst guests staying at lower star-rated hotels. Qualitative research was conducted to corroborate the findings. Other machine learning techniques (i.e., Decision Tree) build rules based on only a single conclusion, while association rules attempt to determine many rules, each of which may lead to a different conclusion. The main contributions of this study lie in the fact that this is one of the first attempts to detect correlations within the online complaining behaviors of guests of different star-rated hotels by utilizing rule-based machine learning.
Long-Term Pre-Treatment of Municipal Sewage Sludge with Solidified Carbon Dioxide (SCO2)—Effect on Anaerobic Digestion Efficiency
Authors: Kazimierowicz Joanna , Dębowski Marcin , Zieliński Marcin
Published: February 2023

Studies on harnessing solidified carbon dioxide (SCO2) for municipal sewage sludge (MSS) pre-treatment have been conducted exclusively in batch reactors. This makes it difficult to accurately assess how long-term SCO2 treatment affects anaerobic digestion (AD) conditions and performance. The aim of our study was to evaluate the effect of long-term MSS pre-treatment with SCO2 on AD conditions, anaerobic bacterial community, and biogas composition and yields. The presented experiments are the first studies on the effect of pre-treatment with SCO2 on the efficiency of AD of MSS in continuous reactors. So far, the impact of the organic load rate (OLR) on the efficiency of MSS methane fermentation has not been assessed, which is also a novelty of the conducted research. The AD process was conducted in continuous-stirred, continuous-flow anaerobic with an active volume of 20 dm3. The digestion process was run at 38 ± 1 °C. The experiment was divided into two stages. Raw (non-pretreated) MSS was used in stage 1, whereas the MSS used in stage 2 was pre-treated with SCO2. The SCO2/MSS ratio was 1:3. Each stage was sub-divided into four variants, with different levels of the OLR ranging from 2.0 to 5.0 gCOD/dm3·day. Pre-treatment with SCO2 was found to improve AD performance at an OLR of 3.0–4.0 gVS/dm3·day. The 3.0 gVS/dm3·day variant offered the best biogas production performance—both daily (29 ± 1.3 dm3/day) and per VS added (0.49 ± 0.02 dm3/gVS)—as well as the highest CH4 content in the biogas (70.1 ± 1.0%). In this variant, the highest energy output effect of 187.07 ± 1.5 Wh/day was obtained. The SCO2 pre-treatment was not found to change the pH, FOS/TAC, or the anaerobic bacterial community composition. Instead, these variables were mainly affected by the OLR. Our study shows that MSS pre-treatment with SCO2 at a SCO2/MSS ratio of 0.3 (by volume) significantly improves AD performance in terms of methane production and feedstock mineralization. The pre-treatment was found to have no negative effect on the long-term continuous operation of the reactor.
An Efficient Digital Channelized Receiver for Low SNR and Wideband Chirp Signals Detection
Authors: Cheng Wenhai , Zhang Qunying , Lu Wei , Wang Haiying , Liu Xiaojun
Published: February 2023

Synthetic aperture radar (SAR) is essential for obtaining intelligence in modern information warfare. Wideband chirp signals with a low signal-to-noise ratio (SNR) are widely used in SAR. Intercepting low-SNR wideband chirp signals is of great significance for anti-SAR reconnaissance. Digital channelization technology is an effective means to intercept wideband signals. The existing digital channelization methods have the following problems: the contradiction of reception blind zone and signal spectrum aliasing, high computational complexity, and low estimating accuracy for chirp signals with a low SNR. This paper proposes a non-critical sampling digital channelized receiver architecture to intercept chirp signals. The receiver architecture has no blind zone in channel division and no aliasing of signal spectrum in the channel, which can provide reliable instantaneous frequency measurements. An adaptive threshold generation algorithm is proposed to detect signals without prior information. In addition, an improved instantaneous frequency measurement (IFM) algorithm is proposed, improving low SNR chirp signals’ frequency estimation accuracy. Moreover, a simple channel arbitration logic is proposed to complete the cross-channel combination of wideband signals. Simulations show that the proposed receiver architecture is reliable and robust for low SNR and wideband chirp signal detection. When the input SNR is 0 dB, the absolute frequency root-mean-square error (RMSE) of bandwidth and the center frequency is 0.57 MHz and 1.05 MHz, respectively. This frequency accuracy is great for radio frequency (RF) wideband systems.
Explainable Artificial Intelligence Enabled Intrusion Detection Technique for Secure Cyber-Physical Systems
Authors: Almuqren Latifah , Maashi Mashael S. , Alamgeer Mohammad , Mohsen Heba , Hamza Manar Ahmed , Abdelmageed Amgad Atta
Published: February 2023

A cyber-physical system (CPS) can be referred to as a network of cyber and physical components that communicate with each other in a feedback manner. A CPS is essential for daily activities and approves critical infrastructure as it provides the base for innovative smart devices. The recent advances in the field of explainable artificial intelligence have contributed to the development of robust intrusion detection modes for CPS environments. This study develops an Explainable Artificial Intelligence Enabled Intrusion Detection Technique for Secure Cyber-Physical Systems (XAIID-SCPS). The proposed XAIID-SCPS technique mainly concentrates on the detection and classification of intrusions in the CPS platform. In the XAIID-SCPS technique, a Hybrid Enhanced Glowworm Swarm Optimization (HEGSO) algorithm is applied for feature selection purposes. For intrusion detection, the Improved Elman Neural Network (IENN) model was utilized with an Enhanced Fruitfly Optimization (EFFO) algorithm for parameter optimization. Moreover, the XAIID-SCPS technique integrates the XAI approach LIME for better understanding and explainability of the black-box method for accurate classification of intrusions. The simulation values demonstrate the promising performance of the XAIID-SCPS technique over other approaches with maximum accuracy of 98.87%.
Kinematics Model Optimization Algorithm for Six Degrees of Freedom Parallel Platform
Authors: Liu Mingzhe , Gu Qiuxiang , Yang Bo , Yin Zhengtong , Liu Shan , Yin Lirong , Zheng Wenfeng
Published: February 2023

The attitude closed-loop control of the parallel platform in the working space needs to determine the relationship between the pose of the top moving platform and the length of each mechanical arm, that is, the kinematics problem of the parallel platform. In this study, the kinematics model of the six-degree-of-freedom parallel platform was established. The kinematics forward solution algorithm based on Newton–Raphson iteration was studied. The kinematics forward solution method usually adopts a numerical solution, which often needs multiple iterations, and the algorithm has a poor real-time performance. In order to improve the real-time performance of the parallel platform control system, a multivariate polynomial regression kinematics forward solution algorithm is proposed in this paper. Moreover, by combining the multivariate polynomial regression with the Newton iterative method, we obtained an efficient solution algorithm with controllable solution accuracy. The effectiveness of the proposed method was verified by simulation tests and physical tests.
Applicability of Deep Reinforcement Learning for Efficient Federated Learning in Massive IoT Communications
Authors: Tam Prohim , Corrado Riccardo , Eang Chanthol , Kim Seokhoon
Published: February 2023

To build intelligent model learning in conventional architecture, the local data are required to be transmitted toward the cloud server, which causes heavy backhaul congestion, leakage of personalization, and insufficient use of network resources. To address these issues, federated learning (FL) is introduced by offering a systematical framework that converges the distributed modeling process between local participants and the parameter server. However, the challenging issues of insufficient participant scheduling, aggregation policies, model offloading, and resource management still remain within conventional FL architecture. In this survey article, the state-of-the-art solutions for optimizing the orchestration in FL communications are presented, primarily querying the deep reinforcement learning (DRL)-based autonomy approaches. The correlations between the DRL and FL mechanisms are described within the optimized system architectures of selected literature approaches. The observable states, configurable actions, and target rewards are inquired into to illustrate the applicability of DRL-assisted control toward self-organizing FL systems. Various deployment strategies for Internet of Things applications are discussed. Furthermore, this article offers a review of the challenges and future research perspectives for advancing practical performances. Advanced solutions in these aspects will drive the applicability of converged DRL and FL for future autonomous communication-efficient and privacy-aware learning.
Nash Equilibrium and Stackelberg Approach for Traffic Flow Optimization in Road Transportation Networks—A Case Study of Warsaw
Authors: Oszczypała Mateusz , Ziółkowski Jarosław , Małachowski Jerzy , Lęgas Aleksandra
Published: February 2023

The article discusses the issue of modelling traffic flows and the transport network. Faced with an increase in the number of vehicles in road networks, the problem of congestion and the need to optimise traffic and adapt the transport infrastructure to changing demand are growing, especially in large cities. With this in mind, the authors of this publication developed a model of the road network in the north-eastern part of the Warsaw agglomeration based on the proposed algorithm. Two methods were used to optimise the distribution of traffic flows: the Nash equilibrium and the Stackelberg approach. The Nash equilibrium assumes the aim of achieving equal average times on all roads for each origin–destination (O-D) pair. This describes the state pursued by a decentralised system guided by the individual benefits of the traffic users. On the contrary, the Stackelberg approach aims to achieve optimal travel times for the entire system. The study was carried out for three scenarios that differed in the assumed traffic demand on the road network. The basic scenario assumed the average hourly traffic demand during the morning peak hour based on traffic measurements. On the other hand, the two alternative scenarios were developed as a 10% variation in traffic volumes from the baseline scenario. On the basis of the results, it was concluded that an increase in traffic volumes for all O-D pairs could result in a decrease in traffic volumes on some links of the road network. This means that the transport network is a complex system and any change in parameters can cause significant and difficult to predict changes. Therefore, the proposed approach is useful in terms of traffic forecasting for road networks under conditions of changing traffic flow volumes. Additionally, the total travel time for the entire system differed for each scenario by a percentage difference of 0.67–1.07% between the optimal solution according to the Nash equilibrium and the Stackelberg approach.
Optimization of the 3D Point Cloud Registration Algorithm Based on FPFH Features
Authors: Sun Ruiyang , Zhang Enzhong , Mu Deqiang , Ji Shijun , Zhang Ziqiang , Liu Hongwei , Fu Zheng
Published: February 2023

In order to solve the problem of the traditional iterative closest point algorithm (ICPA), which requires a high initial position of point cloud and improves the speed and accuracy of point cloud registration, a new registration method is proposed in this paper. Firstly, the rough registration method is optimized. As for the extraction of the feature points, a new method of feature point extraction is adopted, which can better keep the features of the original point cloud. At the same time, the traditional point cloud filtering method is improved, and a voxel idea is introduced to filter the point cloud. The edge length data of the voxels is determined by the density, and the experimentally verified noise removal rates for the 3D cloud data are 95.3%, 98.6%, and 93.5%, respectively. Secondly, a precise registration method that combines the curvature feature and fast point feature histogram (FPFH) is proposed in the precise registration stage, and the algorithm is analyzed experimentally. Finally, the two point cloud data sets Stanford bunny and free-form surface are analyzed and verified, and it is concluded that this method can reduce the error by about 40.16% and 36.27%, respectively, and improve the iteration times by about 42.9% and 37.14%, respectively.