bluuebunny/crossref_metadata_2025 huggingface dataset:
Spatiotemporal Cleaning of PIR Sensor Data for Elderly Movement Monitoring
Authors: Utsumi Tomihiro , Arikawa Masatoshi
Published: November 2024

This study presents a robust framework designed to address the limitations of passive infrared (PIR) sensors in home-based elderly monitoring, particularly focusing on false detections and sensor blind times, which compromise data accuracy. While PIR sensors are low-cost and privacy-preserving, their inherent inaccuracies hinder their use in reliable monitoring systems. To overcome these challenges, we propose a novel spatiotemporal data cleaning framework that integrates non-deterministic tracking (NDT) and late-binding adjustment (LBA). This framework enhances the quality and accuracy of sensor data by filtering out false positives and omissions through analysis of walking speed and sensor connectivity. Simulations demonstrated significant improvements in movement tracking accuracy, and real-world experiments involving three elderly participants further validated the framework’s practicality. The experiments confirmed that the proposed method can remove errors such as false positives and false negatives from PIR sensors. It can achieve 90% accuracy in tracking the movements of elderly people, highlighting the potential for this framework to be applied in the real world. The key scientific contribution of this research lies in the development of a scalable, non-wearable indoor tracking solution that reduces the need for dense sensor arrays, making it cost-effective and adaptable to existing infrastructure with minimal modifications. This framework contributes to advancing the field of indoor localization and offers a reliable solution for sensor-based monitoring systems, especially in elderly care, addressing the urgent needs of an aging global population.
Anti-Sway Adaptive Fast Terminal Sliding Mode Control Based on the Finite-Time State Observer for the Overhead Crane System
Authors: Wang Xin , He Zhenxin , Liu Chuntong , Du Wenzheng
Published: November 2024

This work proposes an adaptive rapid terminal SMC (sliding mode control) approach based on the FFTSO (fast finite-time state observer) for overhead crane trajectory tracking and anti-swing control in the presence of external disturbances and parameter uncertainty. First, the system state observation under the constraint of unknown system parameters is accomplished by designing the FFTSO based on finite-time theory. Next, a parameter-adaptive fast terminal SMC is created for an overhead crane based on the model transformation. This technique can still monitor the intended trajectory and reduce payload swing even in cases when the payload mass and wire rope length are uncertain. Next, the Lyapunov theorem is used to demonstrate the stability of the overhead crane system’s positioning and anti-swing angle control mechanism. Lastly, the platform experiments confirm that the suggested closed-loop system control technique is successful.
CFF-Net: Cross-Hierarchy Feature Fusion Network Based on Composite Dual-Channel Encoder for Surface Defect Segmentation
Authors: Qian Ke’er , Ding Xiaokang , Jiang Xiaoliang , Ji Yingyu , Dong Ling
Published: November 2024

In industries spanning manufacturing to software development, defect segmentation is essential for maintaining high standards of product quality and reliability. However, traditional segmentation methods often struggle to accurately identify defects due to challenges like noise interference, occlusion, and feature overlap. To solve these problems, we propose a cross-hierarchy feature fusion network based on a composite dual-channel encoder for surface defect segmentation, called CFF-Net. Specifically, in the encoder of CFF-Net, we design a composite dual-channel module (CDCM), which combines standard convolution with dilated convolution and adopts a dual-path parallel structure to enhance the model’s capability in feature extraction. Then, a dilated residual pyramid module (DRPM) is integrated at the junction of the encoder and decoder, which utilizes the expansion convolution of different expansion rates to effectively capture multi-scale context information. In the final output phase, we introduce a cross-hierarchy feature fusion strategy (CFFS) that combines outputs from different layers or stages, thereby improving the robustness and generalization of the network. Finally, we conducted comparative experiments to evaluate CFF-Net against several mainstream segmentation networks across three distinct datasets: a publicly available Crack500 dataset, a self-built Bearing dataset, and another publicly available SD-saliency-900 dataset. The results demonstrated that CFF-Net consistently outperformed competing methods in segmentation tasks. Specifically, in the Crack500 dataset, CFF-Net achieved notable performance metrics, including an Mcc of 73.36%, Dice coefficient of 74.34%, and Jaccard index of 59.53%. For the Bearing dataset, it recorded an Mcc of 76.97%, Dice coefficient of 77.04%, and Jaccard index of 63.28%. Similarly, in the SD-saliency-900 dataset, CFF-Net achieved an Mcc of 84.08%, Dice coefficient of 85.82%, and Jaccard index of 75.67%. These results underscore CFF-Net’s effectiveness and reliability in handling diverse segmentation challenges across different datasets.
A Multi-Sensor Fusion Autonomous Driving Localization System for Mining Environments
Authors: Wang Yi , Own Chungming , Zhang Haitao , Luo Minzhou
Published: November 2024

We propose a multi-sensor fusion localization framework for autonomous heavy-duty trucks suitable for mining scenarios, which enables high-precision, real-time trajectory generation, and map construction. The motion estimated through pre-integration of the inertial measurement unit (IMU) can eliminate distortions in the point cloud and provide an initial guess for LiDAR odometry optimization. The point cloud information obtained from LiDAR can assist in recovering depth information from image features extracted by the monocular camera. To ensure real-time performance, we introduce an iKD-tree to organize the point cloud data. To address issues arising from bumpy road segments and long-distance driving in practical mining scenarios, we can incorporate a large number of relative and absolute measurements from different sources, such as GPS information and AprilTag-assisted localization data, including loop closure, as factors in the system. The proposed method has been extensively evaluated on public datasets and self-collected datasets from mining sites.
A Smart Platform for Monitoring and Managing Energy Harvesting in Household Systems
Authors: Sanislav Teodora , Mois George D. , Zeadally Sherali , Folea Silviu , Hedesiu Horia
Published: November 2024

To address global warming challenges, industry, transportation, residential, and other sectors must adapt to reduce the greenhouse effect. One promising solution is the use of renewable energy and energy-saving mechanisms. This paper analyzes several renewable energy sources and storage systems, taking into consideration the possibility of integrating them with smart homes. The integration process requires the development of smart home energy management systems coupled with renewable energy and energy storage elements. Furthermore, a real-life solar energy power plant composed of programmable components was designed and mounted on the roof of a single-family residential building. Based on a long-term analysis of its operation, the main advantages and disadvantages of the proposed implementation solution are highlighted, exemplifying the concepts presented in the paper. Being composed of programmable components, which allow the implementation of custom algorithms and monitoring applications to optimize its operation, the system will be used as a prototyping platform in future research. The evaluation of the developed system over a period of one year showed that, even when using a basic implementation such as the one in this paper, significant savings regarding a household’s energy consumption can be achieved (36% of the energy bought from the supplier, meaning EUR 545 from a total of EUR 1497). Finally, based on the analysis of the developed prototype system, the main technical challenges that must be addressed in the future to efficiently manage renewable energy storage and use in today’s smart homes were identified.
Overvoltage Suppression Strategy for VSG-Based DFIGs Under Commutation Failures of HVDC Transmission Systems
Authors: Wang Shuyi , Wang Qicai , Zeng Zhijie , Jiang Wei , Chen Jinyu , Wang Zhijun
Published: November 2024

Virtual synchronous generator (VSG) control, which can provide inertia output, damp power oscillations, and offer frequency and voltage support to power grids, has become a growing trend in the control field of wind power generation. As a new technology, there are still challenges that VSG control has not solved well, such as transient overvoltage suppression. A kind of transient overvoltage, which often occurs during the commutation failures of HVDC transmission systems, will trigger a mass of wind turbine generators (WTGs) disconnecting from grids. To reduce the grid-disconnection risk of the virtual synchronous generator control-based doubly fed induction generators (VSG-DFIGs), this paper first analyzes the mechanism of the automatic voltage regulation (AVR) control usually employed by VSG-DFIGs, then proposes measures to suppress the transient overvoltage. To solve the problem of the reactive power response lag issued by VSG-DFIGs, which will further aggravate the transient overvoltage in continuous low and high voltage faults, the time constant of the AVR control is switched. To fully exploit the potential of the DFIGs’ reactive power support, the droop coefficient of the AVR control is switched during the abnormal voltage stages. The switched droop coefficient will change the rotor excitation current magnitude, thus adjusting the internal potential of a DFIG, finally better supporting or suppressing the terminal voltage during the low or high voltage periods. Simulation results based on the DIgSILENT/PowerFactory platform demonstrate that the proposed method can effectively suppress the transient overvoltage that occurs in continuous low and high voltage events caused by the commutation failures of HVDC transmission systems, reducing the number of WTGs disconnecting from the grids.
Electrification Planning for Off-Grid Communities in Sub-Saharan Africa: Advancing Energy Access
Authors: Lwakatare Bertha , Vyavahare Priyanka , Mehta Kedar , Zörner Wilfried
Published: November 2024

Sub-Saharan Africa, especially its rural areas, faces significant challenges in achieving universal electrification despite its abundant renewable energy resources. The region has the highest population without access to electricity, largely due to economic, infrastructural, and geographical barriers. Energy poverty is a critical issue that hinders sustainable development and exacerbates inequalities. Namibia’s sustainable energy policy aligns with the global Sustainable Development Goals (SDGs), particularly SDG 7, which aims to provide affordable and reliable modern energy access for all. The policy emphasizes mini-grids and decentralized power systems as key strategies for rural electrification. However, despite increased deployment of mini-grids, these solutions often struggle with long-term sustainability. This research explores cost-effective electrification strategies through scenario-based modeling to reduce energy poverty and expand energy access in Namibia’s rural communities, focusing on the existing mini-grids in Tsumkwe and Gam. Using a comprehensive methodology that incorporates HOMER Pro for mini-grid capacity expansion and MS Excel for evaluating main-grid extensions, this study aims to identify the most feasible and economical electrification solutions. The analysis compares electricity supply, total net present cost, and the levelized cost of electricity across these systems. The findings will offer insights into addressing energy poverty in Namibia and provide recommendations for sustainable and scalable rural electrification across Sub-Saharan Africa.
Machine Learning-Assisted Prediction of Ambient-Processed Perovskite Solar Cells’ Performances
Authors: Pyun Dowon , Lee Seungtae , Lee Solhee , Jeong Seok-Hyun , Hwang Jae-Keun , Kim Kyunghwan , Kim Youngmin , Nam Jiyeon , Cho Sujin , Hwang Ji-Seong , Lee Wonkyu , Lee Sangwon , Lee Hae-Seok , Kim Donghwan , Kang Yoonmook
Published: November 2024

As we move towards the commercialization and upscaling of perovskite solar cells, it is essential to fabricate them in ambient environment rather than in the conventional glove box environment. The efficiency of ambient-processed perovskite solar cells lags behind those fabricated in controlled environments, primarily owing to external environmental factors such as humidity and temperature. In the case of device fabrication in ambient environments, relying solely on a single parameter, such as temperature or humidity, is insufficient for accurately characterizing environmental conditions. Therefore, the dew point is introduced as a parameter which accounts for both temperature and humidity. In this study, a machine learning model was developed to predict the efficiency of ambient-processed perovskite solar cells based on meteorological data, particularly the dew point. A total of 238 perovskite solar cells were fabricated, and their photovoltaic parameters and dew points were collected from March to December 2023. The collected data were used to train various tree-based machine learning models, with the random forest model achieving the highest accuracy. The efficiencies of the perovskite solar cells fabricated in January and February 2024 were predicted with a MAPE of 4.44%. An additional Shapley Additive exPlanations analysis confirmed the significance of the dew point in the performance of perovskite solar cells.
Logistic Decisions in the Installation of Offshore Wind Farms: A Conceptual Framework
Authors: González Mario O. A. , Nascimento Gabriela , Jones Dylan , Akbari Negar , Santiso Andressa , Melo David , Vasconcelos Rafael , Godeiro Monalisa , Nogueira Luana , Almeida Mariana , Oprime Pedro
Published: November 2024

Offshore wind energy has achieved significant reductions in its levelized cost of energy (LCoE) in the past decade, but still needs efficiency improvements. Approximately 18% of the LCoE is related to logistical costs, underscoring the need for optimization in this area. Despite its importance, logistical decisions during offshore wind farm installations remain underexplored in the literature. This article aims to identify and structure the relationships of logistic decisions to optimize total installation costs. A conceptual framework is proposed, detailing logistical decisions and their influencing factors. The results are based on a literature review and survey research for validation with specialists in logistics and offshore wind farms. The findings include the key decisions: port installation selection; vessel fleet selection; installation strategy selection; turbine pre-assembly method selection; aggregate planning approach; installation schedule coverage; storage strategy of components; and the degree of sharing information. The framework reveals the importance of coordinating the value chain in the installation process, mainly due to the influence of weather factors; the logistic decisions, when considered in a systemic view, can contribute to a global efficiency gain in the installation process.
Research on the Resource-Allocation-Optimization Strategy for Offshore Wind Power Construction Considering Complex Influencing Factors
Authors: Wu Ning , He Rongrong , Jin Chunwei , Xu Yuan , Pan Guobing , Qi Lianzhen
Published: November 2024

The construction process of offshore wind farms involves multiple complexities, which is very complex to be scheduled manually, and the coordinating and optimized scheduling not only decreases project construction costs but also increases the construction speed. The impact of meteorological conditions on offshore wind power construction has been considered, and optimizing resource-allocation strategies under complex influencing factors has been analyzed. Then, a comprehensive strategy optimization index system is developed, which includes key indicators, such as the minimum working hours, resource-allocation-optimization rate, window period utilization rate, and cost–benefit ratio. Additionally, an offshore wind power resource-allocation-optimization model is formulated based on discrete event simulation (DES). A statistical analysis of each optimization index was performed using this model to assess the impact of resource-allocation strategies. The simulation results demonstrate that the model can not only simulate the construction process of offshore wind farms and monitor the state of wind turbines, personnel, and meteorological conditions in real time but also accurately calculate key indicators, such as the minimum working hours, resource-allocation-optimization rate, window period utilization rate, and cost–benefit ratio. This strategy effectively enhances resource-allocation efficiency during the wind farm installation phase and improves the overall construction process efficiency.
Short-Term Effects of Thinning on Vegetation Carbon Dynamics and Soil Respiration in Secondary Broadleaf Forests
Authors: Dai Wenzhe , Chen Jiahua , Xu Ruizhi , Shi Haitao , Chen Yangen , Li Dabiao , Zheng Suyun , Miao Guojun , Qiu Weiqing , Shi Yongjun
Published: November 2024

As an important means of near-natural management in secondary forests, thinning effectively enhances the growth status of forest trees, and increases the overall forest yield and carbon sequestration capacity of the forest floor through rational stand regulation. This study focused on subtropical secondary broadleaf forests in the mountainous regions of western Zhejiang, China, and involved three treatment groups: a high-intensity thinning group (HT) (retaining trees with a diameter greater than 8 cm and removing shrub-layer vegetation), a low-intensity thinning group (LT) (retaining trees with a diameter greater than 5 cm and removing shrub-layer vegetation), and a control plot (no thinning treatment) (CK). The study aimed to investigate the effects of different thinning intensities on forest carbon stocks, soil respiration, and soil environmental factors, as well as to assess how thinning modifications impact the carbon sequestration capacity of the forest floor. The results illustrate the following: (1) The biomass growth and carbon sequestration capacity of individual trees under HT were significantly greater compared to other treatment groups. Conversely, carbon stock growth in the arboreal layer was higher under LT than under other treatments. (2) Thinning operations increased soil carbon stocks and improved soil fertility. (3) High-intensity (HT) and low-intensity thinning (LT) increased the soil moisture in both the surface and subsurface layers, with HT showing the highest levels. (4) One year after thinning, the soil organic matter (SOM) increased significantly, rising by 30.86% in HT and 21.84% in LT compared to CK. (5) In comparison to LT and CK, HT showed a significant increase in soil respiration rates, which led to higher CO2 and N2O emissions from the soil while also suppressing CH4 uptake. The study concludes that LT can enhance carbon sequestration in both the tree and soil layers, improve soil fertility, and reduce greenhouse gas emissions. This supports the sustainable development of forest ecosystems and effective carbon management.
Integrating Ward’s Clustering Stratification and Spatially Correlated Poisson Disk Sampling to Enhance the Accuracy of Forest Aboveground Carbon Stock Estimation
Authors: Xu Mingrui , Han Xuelian , Zhang Jialong , Huang Kai , Peng Min , Qiu Bo , Yang Kun
Published: November 2024

In forest resource surveys, using sampling methods to estimate aboveground carbon stock (ACS) can significantly reduce survey costs. This study improves the accuracy of ACS estimation by optimizing the stratified sampling design. The sampling process was divided into two stages: stratification and intra-stratum sampling. For stratification, remote sensing features were used as stratification variables, and a spatial clustering stratification method was introduced. For intra-stratum sampling, a composite method, Spatially Correlated Poisson Disk Sampling (SCPDS), was proposed. Using Random Forest (RF) and the sample points selected by SCPDS, the ACS was estimated and compared with traditional sampling methods for Pinus densata in Shangri-La, Yunnan, China. The results showed that (1) by selecting effective stratification variables (e.g., texture features), the required sample size was reduced by up to 19.35% compared to that of simple random sampling; (2) the Ward clustering method greatly improved stratification heterogeneity; (3) for intra-stratum sampling, the SCPDS method ensured spatial independence within strata, particularly at low sampling rates (1%–5%), where its error was significantly lower than that of other methods, indicating greater stability and improved accuracy; (4) the SCPDS-based model achieved the best fitting accuracy, with R2 = 0.886. The total carbon stock of Pinus densata using RF was 7,872,787.5 t, closely matching forest management inventory (FMI) data. Through sampling, even with a relatively small sample size, the representative plots can still accurately reflect ACS estimates that are consistent with those derived from large-scale plot surveys. Thus, the optimized stratified sampling method effectively reduced sampling costs while significantly enhancing the stability and accuracy of the results.
Material Flow Analysis of the Wood-Based Value Chains in a Rapidly Changing Bioeconomy: A Literature Review
Authors: Khan Muhammad Tayyab , Pettenella Davide , Masiero Mauro
Published: November 2024

Material Flow Analysis (MFA) is a key tool in the circular bioeconomy, providing insight into the flow of materials within a system. Its use in the wood-based value chain is increasingly recognized and provides valuable information for policy making. However, to the best of our knowledge, this topic has never been systematically reviewed. To fill this gap, this study developed a systematic literature review of MFA research in the wood-based value chain. Peer-reviewed articles published between 2000 and 2024 were identified via databases such as Scopus and Google Scholar and analyzed in detail to identify and deepen different approaches to MFA with reference to its conceptualization, scope, and methodological implementation. Based on our review we categorized various MFA models based on their scale and scope, revealing significant diversity in methodological terms and data requirements. The results emphasize the existing MFA approaches often face limitations due to inconsistent data quality and lack of detailed product-level analyses. This research provides practical insights on improving data collection methods, such as standardizing input datasets and incorporating economic and social indicators, to enhance the reliability of MFA studies. It also provides guidelines for implementing MFA models aligned with circular economy principles, integrating both traditional and emerging wood products streams. These insights offer valuable directions for future research aimed at more accurately capturing the complexities of wood flows, promoting better resource management, and supporting policy formulation in the bioeconomy sector. The findings of this review underscore the importance of adopting holistic and integrated methodologies that incorporate new bio-based materials and circular economy principles, ensuring that MFA continues to be an effective tool for advancing sustainable resource management in the forest sector.
Efficient Two-Stage Meso- and Thermophilic Anaerobic Digestion of Food Waste from a Microbial Perspective
Authors: Ohdoi Katsuaki , Okamoto Yoshihiro , Koga Tomonori , Takahashi Haruka , Oshiro Mugihito , Morimitsu Toshihito , Muraoka Hideki , Tashiro Yukihiro , Sakai Kenji
Published: November 2024

Two-stage meso- and thermophilic anaerobic digestion (TSMTAD) of food waste was examined and its microbiological structure was investigated. The first stage was designed for the primary storage of perishable food waste and the second stage for central biogas production. Mesophilic storage with initial neutralization and inoculation of lactic acid bacteria (LAB) resulted in an accumulation of lactic acid (21–23 g/L) with a decreased pH, in which bacterial members in facultative hetero-fermentation-type LAB dominated. Repeated fed-batch storage showed stable accumulation of lactic acid, retaining 89.3% (av.) carbon and preventing the growth of exogenous food pathogens. When the second stage of TSMTAD was compared with direct single-stage anaerobic digestion (SSAD) at 55 °C, the amount of methane accumulated was 1.48-fold higher (896 NmL/g-vs.). The methane yield of the original food refuse was 6.9% higher in the case of TSMTAD. The microbial community structures of both cases were similar, consisting of a sole thermophilic hydrogen-assimilating methanogen, Methanothermobacter thermautotrophicus. However, the abundance of bacteria belonging to two functional groups, H2 CO2 and acetic acid producer, and syntrophic acetate-oxidizing bacteria increased in TSMTAD. This may change the metabolic pathway, contributing to the stimulation of methane productivity.
Electricity-Caused Wildland Fires: Costs, Social Fairness, and Proposed Solution
Authors: Babrauskas Vytenis
Published: November 2024

If electric power is distributed by an overhead network of cables, the ignition of wildland fires is unavoidable, although prudent management efforts can reduce the losses. The economic aspects of these fires are driven by tort litigation, which tends to create serious problems of social fairness. The present system does not contain adequate incentives towards minimizing these costs, nor appropriate measures to ensure that costs are allocated in a fair manner. The problems are universal, but a specific focus on the wildfire situation in California is assumed. The electric utilities there are regulated by the California Public Utilities Commission, and the regulations are almost wholly prescriptive. Striking is the absence of any benefit/cost considerations in the regulation of utilities. Regulations have also created distortions in the insurance market, notably subsidizing fire-unsafe behaviors. The legal and regulatory system should be changed to promote fire-safe behaviors. It should also be changed to more fairly apportion the cost burdens. A strategy which should be adopted more widely is that of Public Safety Power Shutoffs. The optimal solution is to enact legislation to create a no-fault system, similar to Workers’ Compensation. In such a system, tort-based liability litigation is replaced by a system which provides basic compensation to wildfire victims, irrespective of the cause and associated fault. However, reform of insurance regulation and zoning are also required.
Revolutionizing Wildfire Detection Through UAV-Driven Fire Monitoring with a Transformer-Based Approach
Authors: Muksimova Shakhnoza , Umirzakova Sabina , Mardieva Sevara , Abdullaev Mirjamol , Cho Young Im
Published: November 2024

The rapid detection and accurate localization of wildfires are critical for effective disaster management and response. This study proposes an innovative Unmanned aerial vehicles (UAVs)-based fire detection system leveraging a modified Miti-DETR model tailored to meet the computational constraints of drones. The enhanced architecture incorporates a redesigned AlexNet backbone with residual depthwise separable convolution blocks, significantly reducing computational load while improving feature extraction and accuracy. Furthermore, a novel residual self-attention mechanism addresses convergence issues in transformer networks, ensuring robust feature representation for complex aerial imagery. The model, which was trained on the FLAME dataset encompassing diverse fire scenarios, demonstrates superior performance in terms of Mean Average Precision (mAP) and Intersection over Union (IoU) metrics compared to existing systems. Its capability to detect and localize fires across varied backgrounds highlights its practical application in real-world scenarios. This advancement represents a pivotal step forward in applying deep learning for real-time wildfire detection, with implications for broader emergency management applications.
Comprehensive Transcriptome Sequencing and Analysis of Euspira gilva: Insights into Aquaculture and Conservation
Authors: Su Zhixing , Xu Jiayuan , Lv Xiaokang , Song Xuefeng , Sui Yanming , Wang Benjian , Wang Xiaoshan , Zhang Bianbian , Tang Baojun , Yang Liguo
Published: November 2024

Euspira gilva, a member of the family Naticidae, is predominantly found in intertidal soft mud, sandy soil, and sandy seabeds along the coast of China, where it is valued for its nutritional richness and significant economic value. This study presents a comprehensive transcriptome sequencing and analysis of E. gilva specimens from the Lianyungang area, yielding 3385 high-quality isoform sequences and 3310 non-redundant transcripts. Annotation against various databases, including NR, Swiss-Prot, KEGG, KOG, eggNOG, GO, and Pfam, successfully annotated a significant number of transcripts. A total of 7929 simple sequence repeat (SSR) loci were identified, with single nucleotide repeats predominating at 85.0%. Predictive analysis of coding DNA sequences (CDS) resulted in 1340 BLAST comparisons, while ESTScan predicted 840. Further, 530 long non-coding RNAs (lncRNAs) were identified through the application of the CPC2, CNCI, Pfam, and PLEK algorithms. The highest overall sequence similarity in the NR database was observed with Pomacea canaliculata, a freshwater species, but with a similarity of only 36.6%, indicating a unique genetic makeup of E. gilva. The KEGG database annotation revealed a predominance of signal transduction pathways, particularly the PI3K-Akt signaling pathway, with 29 non-redundant transcripts encoding key genes such as IGH (immunoglobulin heavy chain), PCK (phosphoenolpyruvate carboxykinase), COL2A (collagen, type II, alpha), ITGB1 (integrin beta 1), and GNG7 (guanine nucleotide-binding protein G(I)/G(S)/G(O) subunit gamma-7). These genes play crucial roles in cellular processes, including cell growth, transcription, translation, proliferation, movement, and glycogen metabolism. The findings of this research elucidate the full-length transcriptome profile of E. gilva, thereby establishing a foundational dataset and providing valuable insights for the species’ aquaculture, health management, conservation efforts, and future molecular biological investigations.
Experimental Investigation of Anisotropic Invariants in Streams with Rigid Vegetation and 3D Bedforms
Authors: Nosrati Kourosh , Rahm Rahimpour Ali , Afzalimehr Hossein , Nazari-Sharabian Mohammad , Karakouzian Moses
Published: November 2024

The presence of vegetation in submerged conditions and bedforms are a reality in coarse-bed streams. However, this reality has not been well investigated in the literature, despite being a major challenge for natural stream restoration. In order to control many unknown factors affecting prototype scale, this experimental study has been conducted in a laboratory flume, considering 3D bedforms. The results of this study show that 3D bedforms with submerged vegetation elements may change all estimations from 3D to 2D forms near the bed due to the change in roughness. This will change the classic determinations of resistance to flow and sediment transport via Reynolds stress and turbulent flow and may lead to more-affordable complex hydraulic process modeling.
A Quantitative Risk Assessment Model for Listeria monocytogenes in Ready-to-Eat Smoked and Gravad Fish
Authors: Gonzales-Barron Ursula , Pouillot Régis , Skjerdal Taran , Carrasco Elena , Teixeira Paula , Stasiewicz Matthew J. , Hasegawa Akio , De Oliveira Mota Juliana , Guillier Laurent , Cadavez Vasco , Sanaa Moez
Published: November 2024

This study introduces a quantitative risk assessment (QRA) model aimed at evaluating the risk of invasive listeriosis linked to the consumption of ready-to-eat (RTE) smoked and gravad fish. The QRA model, based on published data, simulates the production process from fish harvest through to consumer intake, specifically focusing on smoked brine-injected, smoked dry-salted, and gravad fish. In a reference scenario, model predictions reveal substantial probabilities of lot and pack contamination at the end of processing (38.7% and 8.14% for smoked brined fish, 34.4% and 6.49% for smoked dry-salted fish, and 52.2% and 11.1% for gravad fish), although the concentrations of L. monocytogenes are very low, with virtually no packs exceeding 10 CFU/g at the point of sale. The risk of listeriosis for an elderly consumer per serving is also quantified. The lot-level mean risk of listeriosis per serving in the elderly population was 9.751 × 10−8 for smoked brined fish, 9.634 × 10−8 for smoked dry-salted fish, and 2.086 × 10−7 for gravad fish. Risk reduction strategies were then analyzed, indicating that the application of protective cultures and maintaining lower cold storage temperatures significantly mitigate listeriosis risk compared to reducing incoming fish lot contamination. The model also addresses the effectiveness of control measures during processing, such as minimizing cross-contamination. The comprehensive QRA model has been made available as a fully documented qraLm R package. This facilitates its adaptation for risk assessment of other RTE seafood, making it a valuable tool for public health officials to evaluate and manage food safety risks more effectively.
Almond Hull Extract Valorization: From Waste to Food Recovery to Counteract Staphylococcus aureus and Escherichia coli in Formation and Mature Biofilm
Authors: D’Arcangelo Sara , Santonocito Debora , Messina Luciano , Greco Valentina , Giuffrida Alessandro , Puglia Carmelo , Di Giulio Mara , Inturri Rosanna , Vaccaro Susanna
Published: November 2024

The increase in food waste accumulation needs innovative valorization strategies that not only reduce environmental impacts but also provide functional applications. This study investigates the potential of almond hulls, an abundant agricultural by-product, as a source of bioactive compounds. For the first time, almond hull extract (AHE), was evaluated in terms of anti-adhesive and anti-biofilm activity against Staphylococcus aureus ATCC 29213 and Escherichia coli ATCC 9637. The extract was obtained by an optimized eco-friendly green technique using ultrasound-assisted extraction (UAE), and it was characterized for its main compounds by high-performance liquid chromatography–mass spectrometry (HPLC-MS) and nuclear magnetic resonance (NMR) analysis. Antimicrobial activity was evaluated on planktonic cells by minimum inhibitory/bactericidal concentration (MIC/MBC) and by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assays. Afterward, AHE activity was evaluated against the bacterial sessile phase, both against in-formation and mature biofilm. Finally, the toxicity of the extract was tested on normal human adult cells (HDFa) by an MTT test. The principal active compounds present in AHE belong to the polyphenol group, in particular, the phenolic acid (Hydroxycinnammic sub-class) and, more significantly, the flavonoid class. The results showed that the extract has a relevant antimicrobial activity against the planktonic cells of both tested strains. Moreover, it significantly inhibited bacterial adhesion and promoted biofilm removal, highlighting its potential as a sustainable antimicrobial agent. The MTT test on human fibroblasts showed that the extract is not toxic for normal human cells. This research highlights how food waste valorization could have a high potential in the antimicrobial field.
Optimization of Bioactive Compound Extraction from Iranian Brown Macroalgae Nizimuddinia zanardini with Ultrasound and Microwave Methods Using Fuzzy Logic
Authors: Taherkhani Atefe , Sharifi Akram , Koubaa Mohamed
Published: November 2024

In this study, three extraction methods of bioactive compounds from the brown algae Nizimuddinia zanardini were ranked using the fuzzy weighting system in two stages, ranking between different conditions and choosing the optimal conditions for each extraction method separately. The inputs included extraction yield (EY), antioxidant activity (DPPH), total flavonoid content (TFC), total phenolic content (TPC), total phlorotannin content (TPhC), time, temperature, power, and cost. The top ranks of the first phase output included: Maceration Extraction (ME) with a score of 52.67, Ultrasound-Assisted Extraction (UAE) with a score of 54.31, and Microwave-Assisted Extraction (MAE) with a score of 73.09. The results of the second stage indicated that the lowest and highest extraction yields were obtained using UAE and MAE, respectively. The TFC in the UAE was determined as 103.29 mg QE (Quercetin Equivalent)/g as the lowest value and, in the ME, 140.95 mg QE/g was the highest value. The highest and lowest TPC and TPhC were observed with MAE and UAE, respectively. DPPH decreased in UAE, MAE, and ME, respectively. According to the fuzzy weighted results and considering the purpose of extraction, MAE can be introduced as the optimal method for extracting bioactive compounds from N. zanardini. The findings on extraction methods underscore the potential to reduce costs and improve the yields of bioactive compounds, such as antioxidants and polyphenols, thereby enhancing the economic viability of functional foods and nutraceuticals.
Volatile Compounds of Sucuk, a Dry Fermented Sausage: The Effects of Ripening Rate, Autochthonous Starter Cultures and Fat Type
Authors: Kaya Mükerrem , Kaban Güzin
Published: November 2024

The aim of this study was to determine the effects of ripening rate (slow or fast), usage autochthonous starter cultures (control—spontaneous fermentation, Lactiplantibacillus plantarum GM77, Staphylococcus xylosus GM92 or L. plantarum GM77 + S. xylosus GM92) and type of fat (beef fat-BF, sheep tail fat-STF and BF+STF) on the volatile compounds of sucuk (a Turkish dry fermented sausage). A total of 74 volatile compounds were identified, including groups of aliphatic hydrocarbons, aldehydes, ketones, alcohols, sulfide compounds, esters, aromatic hydrocarbons, nitrogenous compounds, acids and terpenes in sucuk. Slow ripening resulted in significant increases in the abundance of ethanol, acetic acid, ethyl acetate, acetoin and diacetyl. A similar situation was determined for a mixed culture (L. plantarum + S. xylosus). Correlation analysis showed that the effects of slow ripening and mixed culture use were more pronounced in terms of volatile compound content. Although the effect of fat type on volatile compounds was quite limited compared to other factors, correlation analysis showed that STF had a different volatile compound profile.
Anti-Bacterial and Anti-Biofilm Activities of Essential Oil from Citrus reticulata Blanco cv. Tankan Peel Against Listeria monocytogenes
Authors: Peng Jinming , Chen Guangwei , Guo Shaoxin , Lin Ziyuan , Zeng Yue , Ren Jie , Wang Qin , Yang Wenhua , Liang Yongqian , Li Jun
Published: November 2024

In recent years, plant essential oils have been confirmed as natural inhibitors of foodborne pathogens. Citrus reticulata Blanco cv. Tankan peel essential oil (CPEO) showed anti-Listeria monocytogenes (LM) activities, and this study investigated the associated mechanisms by using high-resolution electron microscope, fluorescence spectrometer, flow cytometer, potentiometer, and transcriptome sequencing. The results showed that CPEO restrained LM growth at a minimum inhibitory concentration of 2% (v/v). The anti-LM abilities of CPEO were achieved by disrupting the permeability of the cell wall, damaging the permeability, fluidity, and integrity of the cell membrane, disturbing the membrane hydrophobic core, and destroying the membrane protein conformation. Moreover, CPEO could significantly inhibit the LM aggregation from forming biofilm by reducing the extracellular polymeric substances’ (protein, polysaccharide, and eDNA) production and bacterial surface charge numbers. The RNA sequencing data indicated that LM genes involved in cell wall and membrane biosynthesis, DNA replication and repair, quorum sensing and two-component systems were expressed differently after CPEO treatment. These results suggested that CPEO could be used as a novel anti-LM agent and green preservative in the food sector. Further studies are needed to verify the anti-LM activities of CPEO in real food.
Effect of Using Oleogel on the Physicochemical Properties, Sensory Characteristics, and Fatty Acid Composition of Meat Patties
Authors: Igenbayev Aidyn , Kakimov Mukhtarbek , Mursalykova Maigul , Wieczorek Bartosz , Gajdzik Bożena , Wolniak Radosław , Dzienniak Damian , Bembenek Michał
Published: November 2024

This study investigated the physicochemical properties of meat patties, comparing a control sample and an experimental sample with the addition of 10% oleogel. The experimental sample showed a reduction in protein content (19.47%) and fat (18.37%) compared with the control sample (20.47% and 19.95%, respectively), accompanied by an increase in carbohydrates (2.56% vs. 1.65%). The fatty acid composition analysis revealed that the inclusion of oleogel significantly increased the content of polyunsaturated fatty acids (PUFAs) from 12.458% to 18.94%. Saturated fatty acids (SFAs), such as capric, lauric, myristic, and stearic acids, were markedly reduced, while the level of linoleic acid increased, indicating an improved and balanced fatty acid profile in the experimental patties. The moisture-binding capacity of the experimental sample was determined to be 75.54%, a 2.53% improvement over the control one. Microstructural analysis found no significant differences between the control and experimental samples, with no large oleogel particles visible. Overall, the substitution of pork fat with oleogel did not adversely affect key physicochemical properties, such as pH and moisture-binding capacity, or the structural integrity of the beef patties. These findings suggest that oleogel can be used effectively as a fat substitute in meat products, enhancing their nutritional profile without compromising quality.
Novel Injectable Collagen/Glycerol/Pullulan Gel Promotes Osteogenic Differentiation of Mesenchymal Stem Cells and the Repair of Rat Cranial Defects
Authors: Wang Xin , Komasa Satoshi , Tahara Yoshiro , Inui Shihoko , Matsumoto Michiaki , Maekawa Kenji
Published: November 2024

Bone tissue engineering is a technique that simulates the bone tissue microenvironment by utilizing cells, tissue scaffolds, and growth factors. The collagen hydrogel is a three-dimensional network bionic material that has properties and structures comparable to those of the extracellular matrix (ECM), making it an ideal scaffold and drug delivery system for tissue engineering. The clinical applications of this material are restricted due to its low mechanical strength. In this investigation, a collagen-based gel (atelocollagen/glycerol/pullulan [Col/Gly/Pul] gel) that is moldable and injectable with high adhesive qualities was created by employing a straightforward technique that involved the introduction of Gly and Pul. This study aimed to characterize the internal morphology and chemical composition of the Col/Gly/Pul gel, as well as to verify its osteogenic properties through in vivo and in vitro experiments. When compared to a standard pure Col hydrogel, this material is more adaptable to the complexity of the local environment of bone defects and the apposition of irregularly shaped flaws due to its greater mechanical strength, injectability, and moldability. Overall, the Col/Gly/Pul gel is an implant that shows great potential for the treatment of complex bone defects and the enhancement of bone regeneration.
Visual-Spatial Search in Neglect Syndrome as a Function of the Number of Stimuli in the Hemifields
Authors: Varako Nataliya , Kovyazina Maria , Yurina Daria , Propustina Victoria , Stepanov Georgiy , Vasilyeva Svetlana , Daminov Vadim , Skvortsov Anatoliy , Baulina Maria , Zinchenko Yuri
Published: November 2024

Background: Neglect syndrome is a serious condition that often affects the ability to perform visual-spatial search tasks, interfering with the ability to detect stimuli on the left side of space. A number of factors can affect the success of visual search in patients with neglect syndrome, including visual field load. The purpose of this study is to investigate how the number of stimuli in the right visual hemifield influences the efficiency of visual search in the left (neglected) hemifield, hypothesizing that an increased object load on the right side may impair search performance on the left. Methods: The sample comprised 30 patients with neglect syndrome as the target group and 20 patients with right hemisphere damage but no signs of hemispatial neglect as the control group. This study employed several neuropsychological tests, including neuropsychological examination according to the scheme of A.R. Luria. The SPSS 23.0 software was used for statistical analysis of the data. Results: The Red Shapes test revealed a significant decrease in the number of stimuli detected in both the right and left visual hemifields across successive series (p < 0.001) in patients within the target group. No significant differences were observed in the participants of the control group. This study’s results may be explained by the phenomenon of SSM (subsequent search misses) and the theory of attentional resource depletion during visual search tasks. These results indicate the need for further research into the features of visual search under various conditions, including the load and structuredness of the visual field. Conclusions: This study confirmed that the number of elements in the right visual hemifield influences the ability to detect elements in the left hemifield in patients with left-sided neglect, as demonstrated using the Red Shapes test.
Analysis on Unveiling the Natural Dynamics of Parthenocarpy and Self-Compatibility in Apple Trees
Authors: Wu Rongmei , Chen Xiaoying , Xia Bin , Yang Yujia , Molloy Claire , Wang Ruiling , Ireland Hilary S. , Schaffer Robert J. , Kumar Satish , Yao Jia-Long
Published: November 2024

Apple (Malus domestica) is self-incompatible and typically requires cross-pollination for seed and fruit development. Parthenocarpy (fruit development without fertilization) and self-compatibility (fruit set without external pollen) are highly desirable traits in apple breeding, as they ensure consistent fruit production and quality without cross-pollination. However, apple parthenocarpic and self-compatible accessions have not been available for practical breeding. To identify these accessions, we analysed 436 accessions of Malus domestica and 84 accessions of wild Malus species by assessing fruit production. Flowers were bagged before opening to prevent cross-pollination. If fruit developed from the bagged flowers, it indicated the presence of self-compatibility or parthenocarpy, depending on whether the fruit contained seeds. We observed and scored a range of phenotypic expressions among accessions, from weak to strong in both parthenocarpy and potential self-compatibility. Strong parthenocarpy was observed in 5.95% of wild Malus species accessions and 3.44% of M. domestica accessions. Similarly, strong self-compatibility was exhibited in 5.95% of wild Malus species accessions and 2.75% of M. domestica accessions. Although bagged flowers showed lower fruit set rates than open-pollinated (OP) flowers, fruit size, weight, firmness, and soluble sugar and starch content showed no significant differences between fruits produced from bagged and OP flowers. Furthermore, a genome-wide association study (GWAS) was conducted with a high-throughput SNP array. This analysis identified several genes potentially associated with these traits. This research provides parthenocarpic and self-compatible apple accessions for breeding, which can generate novel cultivars that eliminate the need for cross-pollination or produce seedless fruit without pollination.