bluuebunny/crossref_metadata_2025 huggingface dataset:
Research Impact and Sustainability in Education: A Conceptual Literature Review
Authors: Torres Ricardo , Simões Ana Raquel , Pinto Susana
Published: January 2024

Research sustainability and research impact are closely related concepts, both referring to the importance of extending research projects’ outcomes, changes or benefits beyond their period of implementation. However, the prominence assigned to these two concepts has been provided in an individualized way. This study aims to understand how research sustainability and impact are portrayed and problematized in the literature, in the field of Education, and how they (inter)connect, identifying reference authors and frameworks and describing factors that influence research sustainability and its impact in Education. A conceptual literature review was conducted, and 31 articles were analyzed under the scope of an interpretative paradigm according to the content analysis methodology, using webQDA 3.0 software as support. First, categories were established to identify reference authors and frameworks. Afterward, an inductive codification was carried out, and 15 categories emerged to research sustainability and its impact on Education. This analysis highlights the interconnectedness of both concepts at the conceptualization level and the factors that influence them. The results allow us to identify and discuss action axes, that need more attention in order to assure research sustainability and to evidence research impact in the field of Education.
Level of Satisfaction with the Application of the Collaborative Model of the Flipped Classroom in the Sport of Sailing
Authors: Caraballo Vidal Israel , Pezelj Luka , Ramos-Álvarez Juan José , Guillen-Gamez Francisco David
Published: January 2024

The aim of this study was to assess student satisfaction levels when implementing the collaborative flipped classroom learning model in sailing-sports-related content. The sample consisted of eighty second-year students (21.9 ± 4 years old) pursuing a degree in Physical Activity and Sports Sciences. To evaluate student satisfaction levels, a validated self-assessment questionnaire was administered following completion of the various workshops. The language used was formal, objective, and clear, without biased or ornamental language. The survey comprised six questions, three of which were in the Likert format and three in the dichotomous format. These questions explored students’ viewpoints on participation, usefulness, and satisfaction regarding the implementation of this pedagogical approach. The findings indicate a significant degree of satisfaction and usefulness associated with the employment of this model for teaching sailing sports content. Moreover, students demonstrated a strong sense of engagement with their peers. The implementation of the flipped classroom teaching model among higher education students enhances their satisfaction levels while acquiring knowledge on sailing sports content.
Predictive Role of Physical Activity and Health-Related Quality of Life in Police Officers’ Work Assessment
Authors: Faria Paula Alexandrina , Santos Vanessa , Massuça Luís Miguel
Published: January 2024

Police officers (POs) frequently encounter high stress and burnout risks in their demanding professional environment. This study delves into the relationship between physical activity (PA), health-related quality of life (HRQoL), and job performance among POs. A cross-sectional survey was conducted involving 1175 POs, with 691 providing complete responses. The survey included questions on biosocial and professional characteristics; the International Physical Activity Questionnaire—short form; the Short Form Health Survey version 2.0; and a qualitative job performance evaluation. The key findings highlight that vigorous PA significantly enhances job performance. About 46.2% of POs engage in vigorous PA, with a notable 73.7% participating in some form of PA weekly. This study also found that age and gender considerably impact the HRQoL, especially in mental health aspects like vitality and social functioning. Vigorous PA is linked to higher job performance ratings, especially when practised consistently. In conclusion, this research underscores the importance of vigorous PA in improving job performance among POs. We suggest that institutions prioritise facilitating environments that encourage regular PA, recognising its substantial benefits in both professional effectiveness and the overall health of POs. This study contributes to understanding the critical role of physical fitness in enhancing the occupational well-being of law enforcement personnel.
Development of a Human–Robot Interface for Cobot Trajectory Planning Using Mixed Reality
Authors: Calderón-Sesmero Raúl , Duque-Domingo Jaime , Gómez-García-Bermejo Jaime , Zalama Eduardo
Published: January 2024

The growing demand for projects with collaborative robots, known as “cobots”, underlines the need to efficiently address the execution of tasks with speed and flexibility, without neglecting safety in human–robot interaction. In general terms, this practice requires knowledge of robotics programming and skill in the use of hardware. The proposed solution consists of a mixed reality (MR) application integrated into a mixed reality head-mounted device (HMD) that accelerates the process of programming the complex manoeuvres of a cobot. This advancement is achieved through voice and gesture recognition, in addition to the use of digital panels. This allows any user, regardless of his or her robotics experience, to work more efficiently. The Robot Operating System (ROS) platform monitors the cobot and manages the transfer of data between the two. The system uses QR (Quick Response) codes to establish a precise frame of reference. This solution has proven its applicability in industrial processes, by automating manoeuvres and receiving positive feedback from users who have evaluated its performance. This solution promises to revolutionize the programming and operation of cobots, and pave the way for efficient and accessible collaborative robotics.
A Comparison of Backbone and Mesh Clustering Strategies for Collaborative Management of 6G Vehicle-to-Vehicle Exchanges
Authors: Devred Thomas , Wahl Martine , Sondi Patrick
Published: January 2024

Sixth-generation (6G) announcements promise the best performance not only for latency but also for the number of connected objects. These characteristics particularly suit intelligent transport system (ITS) applications involving a large number of moving vehicles with stringent latency constraints. Moreover, in the 6G era, these applications will often operate while relying on direct cooperation and exchanges between vehicles, in addition to centralized services through a telecommunication infrastructure. Therefore, addressing collaborative intelligence for ad hoc routing protocols that ensure efficient management of multihop vehicle-to-vehicle communications is mandatory. Among the numerous organization models proposed in the literature, the chain–branch–leaf (CBL), a virtual backbone-like model, has demonstrated best performance regarding latency against the state-of-the-art approaches. However, its structure, which lacks redundancy, may lead to higher data loss in the case of the failure of one of the relaying branch nodes. This study investigated how the multipoint relay (MPR) technique—which is intrinsically redundant—used in the optimized link state routing (OLSR) protocol can be efficiently adapted to the road traffic context, especially by restricting MPR selection to a single traffic flow direction (TFD-OLSR). The simulation results confirmed that CBL-OLSR obtains the least end-to-end delay for various types of application traffic due to its efficient reduction in the number of relays and the amount of routing traffic. However, despite higher routing traffic, TFD-OLSR improves the delivery rate, especially for more than two-hop communications, thus demonstrating the benefits of its redundancy property.
Trends in LPWAN Technologies for LEO Satellite Constellations in the NewSpace Context
Authors: Ledesma Oscar , Lamo Paula , Fraire Juan A.
Published: January 2024

This study applies satellite constellations in Internet of Things (IoT) communications, specifically within low-power wide-area network (LPWAN) technologies in the NewSpace context. It comprehensively categorizes and describes the functionality and typology of low Earth orbits (LEOs), examines the societal impacts of these technologies, and provides an in-depth analysis of IoT communication architectures and protocols utilizing satellites. Additionally, the study identifies and addresses the challenges faced in this domain while highlighting future trends and developments. By collating and synthesizing pertinent information, this research offers a thorough overview of the opportunities and challenges in this evolving field of study.
Incremental Scene Classification Using Dual Knowledge Distillation and Classifier Discrepancy on Natural and Remote Sensing Images
Authors: Yu Chih-Chang , Chen Tzu-Ying , Hsu Chun-Wei , Cheng Hsu-Yung
Published: January 2024

Conventional deep neural networks face challenges in handling the increasing amount of information in real-world scenarios where it is impractical to gather all the training data at once. Incremental learning, also known as continual learning, provides a solution for lightweight and sustainable learning with neural networks. However, incremental learning encounters issues such as “catastrophic forgetting” and the “stability–plasticity dilemma”. To address these challenges, this study proposes a two-stage training method. In the first stage, dual knowledge distillation is introduced, including feature map-based and response-based knowledge distillation. This approach prevents the model from excessively favoring new tasks during training, thus addressing catastrophic forgetting. In the second stage, an out-of-distribution dataset is incorporated to calculate the discrepancy loss between multiple classifiers. By maximizing the discrepancy loss and minimizing the cross-entropy loss, the model improves the classification accuracy of new tasks. The proposed method is evaluated using the CIFAR100 and RESISC45 benchmark datasets, comparing it to existing approaches. Experimental results demonstrate an overall accuracy improvement of 6.9% and a reduction of 5.1% in the forgetting rate after adding nine consecutive tasks. These findings indicate that the proposed method effectively mitigates catastrophic forgetting and provides a viable solution for image classification in natural and remote sensing images.
Evolution of Siamese Visual Tracking with Slot Attention
Authors: Wang Jian , Ye Xiangzhou , Wu Dongjie , Gong Jinfu , Tang Xinyi , Li Zheng
Published: January 2024

Siamese network object tracking is a widely employed tracking method due to its simplicity and effectiveness. It first employs a two-stream network to independently extract template and search region features. Subsequently, these features are then combined through feature association to yield object information within the visual scene. However, the conventional approach faces limitations when it leverages the template features as a convolution kernel to convolve the search image features, which restricts the ability to capture complex and nonlinear feature transformations of objects, thereby restricting its discriminative capabilities. To overcome this challenge, we propose replacing traditional convolutional correlation with Slot Attention for feature association. This novel approach enables the effective extraction of nonlinear features within the scene, while augmenting the discriminative capacity. Furthermore, to increase the inference efficiency and reduce the parameter occupation, we suggest deploying a single Slot Attention module for multiple associations. Our tracking algorithm, SiamSlot, was evaluated on diverse benchmarks, including VOT2019, GOT-10k, UAV123, and Nfs. The experiments show a remarkable improvement in performance relative to previous methods under the same network size.
Battery State-of-Health Estimation: A Step towards Battery Digital Twins
Authors: Safavi Vahid , Bazmohammadi Najmeh , Vasquez Juan C. , Guerrero Josep M.
Published: January 2024

For a lithium-ion (Li-ion) battery to operate safely and reliably, an accurate state of health (SOH) estimation is crucial. Data-driven models with manual feature extraction are commonly used for battery SOH estimation, requiring extensive expert knowledge to extract features. In this regard, a novel data pre-processing model is proposed in this paper to extract health-related features automatically from battery-discharging data for SOH estimation. In the proposed method, one-dimensional (1D) voltage data are converted to two-dimensional (2D) data, and a new data set is created using a 2D sliding window. Then, features are automatically extracted in the machine learning (ML) training process. Finally, the estimation of the SOH is achieved by forecasting the battery voltage in the subsequent cycle. The performance of the proposed technique is evaluated on the NASA public data set for a Li-ion battery degradation analysis in four different scenarios. The simulation results show a considerable reduction in the RMSE of battery SOH estimation. The proposed method eliminates the need for the manual extraction and evaluation of features, which is an important step toward automating the SOH estimation process and developing battery digital twins.
Speech Emotion Recognition Using RA-Gmlp Model on Time–Frequency Domain Features Extracted by TFCM
Authors: Sha Mo , Yang Wenzhong , Wei Fuyuan , Lu Zhifeng , Chen Mingliang , Ma Chengji , Zhang Linlu , Shi Houwang
Published: January 2024

Speech emotion recognition (SER) is a key branch in the field of artificial intelligence, focusing on the analysis and understanding of emotional content in human speech. It involves a multidisciplinary knowledge of acoustics, phonetics, linguistics, pattern recognition, and neurobiology, aiming to establish a connection between human speech and emotional expression. This technology has shown broad application prospects in the medical, educational, and customer service fields. With the evolution of deep learning and neural network technologies, SER research has shifted from relying on manually designed low-level descriptors (LLDs) to utilizing complex neural network models for extracting high-dimensional features. A perennial challenge for researchers has been how to comprehensively capture the rich emotional features. Given that emotional information is present in both time and frequency domains, our study introduces a novel time–frequency domain convolution module (TFCM) based on Mel-frequency cepstral coefficient (MFCC) features to deeply mine the time–frequency information of MFCCs. In the deep feature extraction phase, for the first time, we have introduced hybrid dilated convolution (HDC) into the SER field, significantly expanding the receptive field of neurons, thereby enhancing feature richness and diversity. Furthermore, we innovatively propose the residual attention-gated multilayer perceptron (RA-GMLP) structure, which combines the global feature recognition ability of GMLP with the concentrated weighting function of the multihead attention mechanism, effectively focusing on the key emotional information within the speech sequence. Through extensive experimental validation, we have demonstrated that TFCM, HDC, and RA-GMLP surpass existing advanced technologies in enhancing the accuracy of SER tasks, fully showcasing the powerful advantages of the modules we proposed.
A Study on Fractional Power-Law Applications and Approximations
Authors: Emad Salma , Hassanein Ahmed M. , AbdelAty Amr M. , Madian Ahmed H. , Radwan Ahmed G. , Said Lobna A.
Published: January 2024

The frequency response of the fractional-order power-law filter can be approximated by different techniques, which eventually affect the expected performance. Fractional-order control systems introduce many benefits for applications like compensators to achieve robust frequency and additional degrees of freedom in the tuning process. This paper is a comparative study of five of these approximation techniques. The comparison focuses on their magnitude error, phase error, and implementation complexity. The techniques under study are the Carlson, continued fraction expansion (CFE), Padé, Charef, and MATLAB curve-fitting tool approximations. Based on this comparison, the recommended approximation techniques are the curve-fitting MATLAB tool and the continued fraction expansion (CFE). As an application, a low-pass power-law filter is realized on a field-programmable analog array (FPAA) using two techniques, namely the curve-fitting tool and the CFE. The experiment aligns with and validates the numerical results.
Robustness and Transferability of Adversarial Attacks on Different Image Classification Neural Networks
Authors: Smagulova Kamilya , Bacha Lina , Fouda Mohammed E. , Kanj Rouwaida , Eltawil Ahmed
Published: January 2024

Recent works demonstrated that imperceptible perturbations to input data, known as adversarial examples, can mislead neural networks’ output. Moreover, the same adversarial sample can be transferable and used to fool different neural models. Such vulnerabilities impede the use of neural networks in mission-critical tasks. To the best of our knowledge, this is the first paper that evaluates the robustness of emerging CNN- and transformer-inspired image classifier models such as SpinalNet and Compact Convolutional Transformer (CCT) against popular white- and black-box adversarial attacks imported from the Adversarial Robustness Toolbox (ART). In addition, the adversarial transferability of the generated samples across given models was studied. The tests were carried out on the CIFAR-10 dataset, and the obtained results show that the level of susceptibility of SpinalNet against the same attacks is similar to that of the traditional VGG model, whereas CCT demonstrates better generalization and robustness. The results of this work can be used as a reference for further studies, such as the development of new attacks and defense mechanisms.
The Effect of Diluted N2O Annealing Time on Gate Dielectric Reliability of SiC Metal-Oxide Semiconductor Capacitors and Characterization of Performance on SiC Metal-Oxide Semiconductor Field Effect Transistor
Authors: Dong Zhihua , Jiang Leifeng , Su Manqi , Zeng Chunhong , Liu Hui , Li Botong , Sun Yuhua , Cui Qi , Zeng Zhongming , Zhang Baoshun
Published: January 2024

We performed dry oxidation on n-type silicon carbide (SiC), followed by annealing in diluted N2O, and subsequently fabricated n-type MOS structures. The study aimed to investigate the impact of different annealing times on the trap charges near the SiC/SiO2 interface and the reliability of the gate dielectric. Capacitance-voltage (C-V) and current-voltage (I-V) measurements of the n-type MOS revealed that increasing the annealing time with N2O effectively reduces the density of electron traps near the SiC/SiO2 interface, mitigates the drift in flat-band voltage and enhances the oxide breakdown field strength. However, excessive annealing time leads to an increase in the flat-band voltage drift of the MOS, resulting in premature oxide breakdown. Using the optimized annealing conditions, we fabricated n-type LDMOSFETs and obtained the threshold voltage (Vth), field-effect mobility (μFE) and specific on-resistance (Ron-sp) from the transfer curve (Id-Vg) and output curve (Id-Vd) measurements. The research findings provide valuable insights for the gate oxidation process of SiC.
Hydrodynamic Insights on Floating Bubbling Fluidized Beds: Dynamic Solutions for Mitigating Gas Maldistribution
Authors: Sarbanha Ali Akbar , Larachi Faïçal , Taghavi Seyed Mohammad
Published: January 2024

This study examined bubbling fluidized beds as an alternative to fixed-bed dry scrubbers on ships for reducing pollutants from marine fuels. It focused on overcoming the challenges of gas maldistribution/slug formation, especially under rough sea conditions. This research departed from traditional methods by introducing mobile internal elements into the bed emulsion phase and investigating their effectiveness in various settings, such as vertical, inclined, and rolling beds. A specialized hexapod-driven bubbling fluidized bed was developed to mimic marine operating conditions and to study the behavior of shipboard fluidized beds. Techniques such as digital image analysis (DIA) and particle image velocimetry (PIV) were used to observe bubble dynamics and granular phases, measuring local void fractions and particle velocities. A key finding is the effectiveness of moving internals in preventing bubble coalescence, which is critical for avoiding wall slugs, at different inclinations. Three types of packing were used as mobile internals: Super Raschig, Pall, and square rings. Super Raschig rings, which are characterized by high porosity, were the most efficient in reducing bubble coalescence, making them a preferred choice for offshore fluidized bed applications. This research contributes to the advancement of fluidized bed technology in marine applications and provides insight for future improvements.
Optimizing the Neural Network Loss Function in Electrical Tomography to Increase Energy Efficiency in Industrial Reactors
Authors: Kulisz Monika , Kłosowski Grzegorz , Rymarczyk Tomasz , Słoniec Jolanta , Gauda Konrad , Cwynar Wiktor
Published: January 2024

This paper presents innovative machine-learning solutions to enhance energy efficiency in electrical tomography for industrial reactors. Addressing the key challenge of optimizing the neural model’s loss function, a classifier tailored to precisely recommend optimal loss functions based on the measurement data is designed. This classifier recommends which model, equipped with given loss functions, should be used to ensure the best reconstruction quality. The novelty of this study lies in the optimal adjustment of the loss function to a specific measurement vector, which allows for better reconstructions than that by traditional models trained based on a constant loss function. This study presents a methodology enabling the development of an optimal loss function classifier to determine the optimal model and loss function for specific datasets. The approach eliminates the randomness inherent in traditional methods, leading to more accurate and reliable reconstructions. In order to achieve the set goal, four models based on a simple LSTM network structure were first trained, each connected with various loss functions: HMSE (half mean squared error), Huber, l1loss (L1 loss for regression tasks—mean absolute error), and l2loss (L2 loss for regression tasks—mean squared error). The best classifier training results were obtained for support vector machines. The quality of the obtained reconstructions was evaluated using three image quality indicators: PSNR, ICC, and MSE. When applied to simulated cases and real measurements from the Netrix S.A. laboratory, the classifier demonstrated effective performance, consistently recommending models that produced reconstructions that closely resembled the real objects. Such a classifier can significantly optimize the use of EIT in industrial reactors by increasing the accuracy and efficiency of imaging, resulting in improved energy management and efficiency.
Valorization of Coffee Residue from Convenience Store and Retail Mass-Selling Store for Producing Highly Porous Carbon Materials and Taiwan Perspectives
Authors: Lee Yu-Ru , Huang Hsiang-Lan , Tsai Chi-Hung , Tsai Wen-Tien
Published: January 2024

In Taiwan, a considerable amount of coffee residue is produced from commercial activities without valuable utilization. To evaluate high-value valorization in the production of highly porous carbon materials, this study investigated the thermochemical properties of coffee residues and further pyrolysis for producing highly porous biochar products at an elevated temperature (i.e., 850 °C) and a moderate residence time of 30 min. Our findings indicate that this biomass has a relatively high calorific value (about 27 MJ/kg, dry basis) due to its low ash and high lignocellulose content. It can be also concluded that the non-activated biochar products are highly porous carbon materials with excellent pore properties (i.e., a BET surface area of about 800 m2/g and a total pore volume of 0.4 cm3/g), which are slightly lower than those of commercial activated carbon products. Based on the above-mentioned results and the high-value circular bio-economy promoted by regulatory policy in Taiwan, the prospects for the possible valorization of coffee residue from commercial shops are addressed here, focusing both on the reuse of plant-based residue (or agricultural waste) as a high-value bioresource in the production of biomass-based fuels and on carbon materials. The former includes solid recovered fuel (SRF) and biomass-to-biogas power. By contrast, the latter aims at the production of plant-based carbon as natural, edible colorants in accordance with the regulation of food safety and sanitation in Taiwan.
Modeling of a Biomass-Based Energy Production Case Study Using Flexible Inputs with the P-Graph Framework
Authors: Éles András , Heckl István , Cabezas Heriberto
Published: January 2024

In this work, a modeling technique utilizing the P-Graph framework was used for a case study involving biomass-based local energy production. In recent years, distributed energy systems gained attention. These systems aim to satisfy energy supply demands, support the local economy, decrease transportation needs and dependence on imports, and, in general, obtain a more sustainable energy production process. Designing such systems is a challenge, for which novel optimization approaches were developed to help decision making. Previous work used the P-Graph framework to optimize energy production in a small rural area, involving manure, intercrops, grass, and corn silage as inputs and fermenters. Biogas is produced in fermenters, and Combined Heat and Power (CHP) plants provide heat and electricity. A more recent result introduced the concept of operations with flexible inputs in the P-Graph framework. In this work, the concept of flexible inputs was applied to model fermenters in the original case study. A new implementation of the original decision problem was made both as a Mixed-Integer Linear Programming (MILP) model and as a purely P-Graph model by using the flexible input technique. Both approaches provided the same optimal solution, with a 31% larger profit than the fixed input model.
Application of the Typology Approach for Energy Renovation Planning of Public Buildings’ Stocks at the Local Level: A Case Study in Greece
Authors: Stavrakakis George M. , Bakirtzis Dimitris , Drakaki Korina-Konstantina , Yfanti Sofia , Katsaprakakis Dimitris Al. , Braimakis Konstantinos , Langouranis Panagiotis , Terzis Konstantinos , Zervas Panagiotis L.
Published: January 2024

According to the latest energy efficiency European directive (EED 2023/1791/EU), the expected energy renovation rate of at least 3% of the buildings’ floor area each year towards nearly zero-energy buildings (nZEBs) is extended to include public buildings not only of the central government (as per the first EED 2012/27/EU) but also of regional and local authorities. This poses a great challenge, especially for Municipalities that often manage large building stocks with high energy demands. In response to this challenge, this paper presents the application of the so-called “typology approach” for conducting public buildings’ energy renovation plans at the local level. A computational survey is initially introduced to decide the optimal set of building-stock clustering criteria among all possible combinations, involving the minimization of the RMSE index regarding the primary energy consumption of each building. For a representative building from each identified typology, the key performance indicators (KPIs) are computed for alternative energy-upgrading scenarios. Exploiting the IMPULSE Interreg-MED project tools, the KPIs from each representative building are at first extrapolated to all buildings of the examined stock and, finally, a gradual energy renovation plan is automatically produced based on user-defined decision parameters including the required annual renovation rate. The methodology is applied for the case of the Municipality of Hersonissos in Greece. For the specific 44-buildings’ stock it was found that the optimal clustering set included four criteria, building use, construction year, heating, and a cooling system, leading to 15 building typologies. Finally, assuming a 7% renovation rate per year, a 12-year gradual renovation (nZEB transformation) plan is obtained foreseeing an 85% CO2 emissions’ reduction.
Bayesian Entropy Methodology: A Novel Approach to Setting Anti-Islanding Protections with Enhanced Stability and Sensibility
Authors: Seguin Batadi Eduardo Marcelo , Martínez Maximiliano , Molina Marcelo Gustavo
Published: January 2024

The risk of unintentional islanding creation in distributed energy systems poses a significant security concern since unintentional islanding formation could lead to a supply of energy outside of the optimal quality limits. This constitutes a risk for users, maintenance personnel, infrastructure, and devices. To mitigate this problem, anti-islanding protections are widely used to prevent the distributed generator from feeding a portion of the radial distribution grid when a protection device trips upstream. However, the effectiveness of these protections heavily relies on properly tuning protection setting thresholds (such as time delay and pickup). This work proposes a novel approach that utilizes entropy as a model and metric of the uncertainty associated with a particular protection setting. By minimizing entropy, the proposed method aims to improve stability and sensitivity, consequently improving the overall performance of anti-islanding protection. Simulation results demonstrate that the Bayesian entropy methodology (BEM) approach achieves enhanced stability in various scenarios, including frequency transients, and demonstrates a notable reduction in the size of the dataset and computational burden, ranging between 91% and 98%, when compared to related works, with an improvement of the uncertainty achieved. The findings of this study contribute to the development of more robust and reliable anti-islanding protections.
Predicting Sub-Forest Type Transition Characteristics Using Canopy Density: An Analysis of the Ganjiang River Basin Case Study
Authors: Zhou Yuchen , Hu Juhua , Liu Mu , Xie Guanhong
Published: January 2024

In the process of societal development, forest land categories often conflict with other land use types, leading to impacts on the ecological environment. Therefore, research on changes in forest land categories has increasingly become a globally focused topic. To anticipate potential forest ecological security issues under urbanization trends, studies on regional land use simulation become more important. This paper, based on land use data from the Ganjiang River basin, analyzes the distribution characteristics and changing trends of land use types from 2000 to 2020. Using the CA-Markov model, it predicts the land use pattern of the basin in 2040 and analyzes the transfer characteristics of forest land categories. The conclusions indicate that, between 2000 and 2020, the most significant trend in land use evolution was the transfer between various subcategories of forest land, especially frequent in the high-altitude mountainous areas in the southern and western parts of the basin. The land use pattern prediction model constructed in this paper has a kappa index of 0.92, indicating high accuracy and reliability of the predictions. In 2040, the most significant land evolution phenomenon would be from forest land to arable land to construction land, particularly pronounced around large cities. Over the next 20 years, the focus of land use evolution may shift from the southern part of the basin to the central and northern parts, with urban expansion possibly becoming the main driving force of land use changes during this period. Forest land restoration work is an effective method to compensate for the loss of forest land area in the Ganjiang River basin, with key areas for such work including Longnan, Yudu, Xingguo, Ningdu, Lianhua, and Yongxin counties.
Probiotic Evaluation of Lactiplantibacillus pentosus 68-1, a Rutin Conversion Strain Isolated from Jiangshui, by Genomic Analysis and Tests In Vitro
Authors: Xue Wenjiao , Liu Chen , Liu Yao , Ding Hao , An Chao , Zhang Shizhe , Ma Saijian , Zhang Qiwen
Published: January 2024

To assess the probiotic potential of strain 68-1 with rutin conversion capabilities, isolated from Chinese traditional Jiangshui, a complete genomic analysis and in vitro tests were conducted. The Oxford Nanopore Technologies (ONT, Oxford, UK)–Illumina (San Diego, CA, USA) hybrid sequencing platform was used for whole genome sequencing and the results showed that strain 68-1 had a chromosome sequence of 3,482,151 bp, with 46.53% GC content and five plasmids with a sequence length ranging from 2009 bp to 48,711 bp. Strain 68-1 was identified as Lactiplantibacillus pentosus based on the whole genome sequence. A total of 133 genes encoding for carbohydrate-active enzymes (CAZymes) were identified and genes that may be involved in rutin conversion were found in the L. pentosus 68-1 genome. L. pentosus 68-1 showed excellent tolerance to gastrointestinal juice and adhesion properties, and corresponding genes were identified. In addition, L. pentosus 68-1 exhibited strong antibacterial and antifungal activity, where organic acids may play a crucial role in its antagonistic ability. Moreover, the gene cluster for plantaricin_EF production was detected. No high virulence factor was found in the L. pentosus 68-1 genome and no hemolytic effect was observed. In addition, L. pentosus 68-1 showed resistance to ampicillin, gentamycin, and kanamycin, and the genomic analysis indicated that horizontal ARG transfer should not be possible. The results show that L. pentosus 68-1 could be developed as a novel probiotic candidate to improve rutin bioavailability in the food and feed industry.
Novel Placenta-Derived Liquid Product Suitable for Cosmetic Application Produced by Fermentation and Digestion of Porcine or Equine Placenta Using Lactic Acid Bacterium Enterococcus faecalis PR31
Authors: Matsunaga Kanako , Komatsu Yasuhiko
Published: January 2024

Since ancient times, the placenta has been used to produce cosmetic and health food products, whereas fermentation is a technology that has been used to produce foods and cosmetics. For application in cosmetics, traditional placental extracts produced solely by proteolysis have not had enough moisturizing properties or the ability to stimulate the proliferation of epidermal keratinocytes. We combined these two traditional approaches to produce raw materials without such drawbacks that are suitable for cosmetic applications. Using a unique lactic acid bacterial strain, Enterococcus faecalis PR31, to directly ferment and digest both porcine and equine placentas, we produced the following liquid products: placenta ferment filtrates. The ferment filtrates stimulated the proliferation of not only normal human dermal fibroblasts but also epidermal keratinocytes. The ferments had higher equilibrium water content properties than traditional placental extracts, and the ferment derived from the porcine placenta maintained high stratum corneum water content levels for up to 6 h after its application on the skin. Metabolome analysis revealed various molecules that were increased by fermentation, among which lactic acid was assumed to play a central role in the high moisturizing properties. To conclude, the placenta ferment filtrates developed in this study are beneficial for cosmetic applications.
A New Dynamic Game-Based Pricing Model for Cloud Environment
Authors: Saadatfar Hamid , Gholampour Ahangar Hamid , Hassannataj Joloudari Javad
Published: January 2024

Resource pricing in cloud computing has become one of the main challenges for cloud providers. The challenge is determining a fair and appropriate price to satisfy users and resource providers. To establish a justifiable price, it is imperative to take into account the circumstances and requirements of both the provider and the user. This research tries to provide a pricing mechanism for cloud computing based on game theory. The suggested approach considers three aspects: the likelihood of faults, the interplay among virtual machines, and the amount of energy used, in order to determine a justifiable price. In the game that is being proposed, the provider is responsible for determining the price of the virtual machine that can be made available to the user on each physical machine. The user, on the other hand, has the authority to choose between the virtual machines that are offered in order to run their application. The whole game is implemented as a function of the resource broker component. The proposed mechanism is simulated and evaluated using the CloudSim simulator. Its performance is compared with several previous recent mechanisms. The results indicate that the suggested mechanism has successfully identified a more rational price for both the user and the provider, consequently enhancing the overall profitability of the cloud system.
The Investigation of Changes in Bacterial Community of Pasteurized Milk during Cold Storage
Authors: Lan Xinyi , Wu Shuyan , Du Qijing , Min Li
Published: January 2024

The quality of pasteurized milk is commonly assessed through microbiological analysis, with variations in storage conditions significantly impacting the suppression of bacterial growth throughout the milk’s shelf life. This study investigated the dynamics of total bacterial counts (TBCs) and bacterial community shifts in milk that underwent pasteurization at 80 °C for 15 s. The milk was subsequently stored at 4 °C for varying intervals of 1, 4, 7, 10, 13, and 16 days. Culture-based testing revealed a significant TBC increase during the storage period spanning 1 to 16 days (up to −log10 4.2 CFU/mL at day 16). The TBC in pasteurized milk exhibited accelerated microbial growth from day 13 onwards, ultimately peaking on day 16. Bacillus was detected through 16S rRNA identification. Principal component analysis demonstrated a significant impact of storage time on bacterial communities in pasteurized milk. Analysis of bacterial diversity revealed a negative correlation between the Shannon index and the duration of pasteurized milk storage. Using high-throughput sequencing, Streptococcus and Acinetobacter were detected as prevalent bacterial genera, with Streptococcus dysgalactiae and Streptococcus uberis showing as dominant taxa. The presence of Streptococcus dysgalactiae and Streptococcus uberis in pasteurized milk might be attributed to the initial contamination from raw milk with mastitis. This study offers new evidence of the prevalence of bacterial community in pasteurized milk, thereby adding value to the enhancement of quality control and the development of strategies for reducing microbial risks.
Photodynamic Inactivation of Foodborne Bacteria: Screening of 32 Potential Photosensitizers
Authors: Prasad Amritha , Wynands Erin , Roche Steven M. , Romo-Bernal Cristina , Allan Nicholas , Olson Merle , Levengood Sheeny , Andersen Roger , Loebel Nicolas , Sabino Caetano P. , Ross Joseph A.
Published: January 2024

The development of novel antimicrobial technologies for the food industry represents an important strategy to improve food safety. Antimicrobial photodynamic disinfection (aPDD) is a method that can inactivate microbes without the use of harsh chemicals. aPDD involves the administration of a non-toxic, light-sensitive substance, known as a photosensitizer, followed by exposure to visible light at a specific wavelength. The objective of this study was to screen the antimicrobial photodynamic efficacy of 32 food-safe pigments tested as candidate photosensitizers (PSs) against pathogenic and food-spoilage bacterial suspensions as well as biofilms grown on relevant food contact surfaces. This screening evaluated the minimum bactericidal concentration (MBC), minimum biofilm eradication concentration (MBEC), and colony forming unit (CFU) reduction against Salmonella enterica, methicillin-resistant Staphylococcus aureus (MRSA), Pseudomonas fragi, and Brochothrix thermosphacta. Based on multiple characteristics, including solubility and the ability to reduce the biofilms by at least 3 log10 CFU/sample, 4 out of the 32 PSs were selected for further optimization against S. enterica and MRSA, including sunset yellow, curcumin, riboflavin-5′-phosphate (R-5-P), and erythrosin B. Optimized factors included the PS concentration, irradiance, and time of light exposure. Finally, 0.1% w/v R-5-P, irradiated with a 445 nm LED at 55.5 J/cm2, yielded a “max kill” (upwards of 3 to 7 log10 CFU/sample) against S. enterica and MRSA biofilms grown on metallic food contact surfaces, proving its potential for industrial applications. Overall, the aPDD method shows substantial promise as an alternative to existing disinfection technologies used in the food processing industry.
“The Noise of Our Living”: Richard Wright and Chicago Blues
Authors: Wimble Jeff
Published: January 2024

Historicizing the musical genre known as “Chicago blues,” I further complicate Richard Wright’s already complicated attitudes toward “the folk” and modernity. Utilizing close readings of 12 Million Black Voices, I show how Wright’s apparent denigration of the blues as an outmoded, pre-modern artistic form is dependent on his historical situation writing before the advent of a new electrified form of blues that developed in Chicago shortly after the book’s publication. Utilizing biographical details of the life of Muddy Waters, I show how his work as a musician in Mississippi, then in Chicago, and his development of an electrified blues style, parallels and personifies the shift from an African American perspective rooted in an agrarian, pre-modern south to an industrial, modern north documented so effectively by Wright. Furthermore, the Chicago blues musicians’ transmogrification of the rural Delta blues into an electrified, urban expression manifests the vernacular-modernist artistic conception which Wright seems to be envisioning and pointing toward in 12 Million Black Voices.
Siliceous Sandstones Used in Local Khmer Temples in Battambang, Ta Keo, and Kampong Cham Provinces, Cambodia
Authors: Uchida Etsuo , Kobayashi Yoshimitsu
Published: January 2024

Siliceous sandstones used in Khmer temples such as the Prasat Basset, Wat Ek Phnom, and Phnom Banan temples in Battambang Province, Phnom Chisor and Ta Prohm temples in Ta Keo Province, and Wat Nokor Bachey temple in Kampong Cham Province in Cambodia were investigated in this study to determine the supply source of the siliceous sandstones used in these temples. Chemical composition analyses and magnetic susceptibility measurements were conducted for the sandstone blocks used in these temples. Siliceous sandstone is the primary construction material in the investigated temples, except for the Phnon Chisor (bricks). Two types of siliceous sandstone, pale brown and red, were found. Pale brown siliceous sandstone was used for the construction of all the investigated temples. In contrast, red siliceous sandstone was found only in the Wat Ek Phnom, Prasat Basset, and Phnom Banan temples. It is suggested that the pale brown siliceous sandstone originated from the Phra Wihan Formation within the Khorat Group. Based on the chemical composition analysis, the pale brown siliceous sandstone in the Ta Prohm temple at Tonle Bati and the Wat Nokor Bachey temple may have been sourced from the lower level of the Phra Wihan Formation. In contrast, the pale brown siliceous sandstone used in the Prasat Basset, Wat Ek Phnom, Phnom Banan, and Phnom Chisor temples was likely sourced from the upper level. The origin of the red siliceous sandstone is proposed to be from the Sao Khua Formation, which is situated above the Phra Wihan Formation.
The Role of Aerial Prospection for Monitoring and Preserving Cultural Heritage in Northeastern Africa
Authors: Laguna-Palma David , Toscano Maurizio , Rodríguez-Rellán Carlos
Published: January 2024

Anthropogenic and environmental processes present unique challenges for preserving cultural heritage in North Africa. Large parts of this region are characterised by unfavourable arid and semi-arid conditions and rapid changes to the landscapes caused by heightened regional development (e.g., urban expansion, road building, agricultural intensification, and socio-political conflicts). As a result, we are facing a fast-paced disappearance of heritage sites in regions that are still poorly understood. Following this, the utilisation of Earth observation data through aerial photographs and satellite imagery has emerged as an unmatched tool in the exploration of endangered archaeological heritage. Drawing on this context, this paper underscores the critical significance of incorporating digital research methods, such as remote sensing, GIS, or cartographic analysis, to ensure the evaluation and (digital) preservation of the historical sites along these vulnerable areas. Furthermore, our study seeks to provide new insights into data management and dissemination, fostering open research practices within North African archaeological research.
Changes in Secondary Metabolite Production in Response to Salt Stress in Alcea rosea L.
Authors: Sadeghi Arezoo , Razmjoo Jamshid , Karimmojeni Hassan , Baldwin Timothy C. , Mastinu Andrea
Published: January 2024

The effect of three levels of salinity on physio-biochemical traits in 10 Alcea rosea (hollyhock) varieties were evaluated. It was observed that salt stress increased both the total phenolic content (TPC) and total flavonoid content (TFC) in some varieties and decreased them in others. The greatest increases in both TPC and TFC were recorded in the Saman variety (104% and 62%, respectively) when cultivated under severe salt stress, indicating that this is the most salt-tolerant variety amongst those tested. The most abundant phenolic compound recorded was ellagic acid, and the phenolic compounds that showed the greatest increases in concentration due to salt stress were p-coumaric acid (87% in the Isfahan variety) and chlorogenic acid (142% in the Mahallat variety). Salt stress was also shown to decrease the production of diphenyl-2-picrylhydrazyl (DPPH) in all varieties. The highest concentration of DPPH (133%) was recorded in the Shiraz 1 variety, grown under conditions of severe salt stress. Salt stress also increased the mucilage content present in the petals, leaves, and seeds of some of the selected varieties. These data suggest that the selection of salt-tolerant varieties of hollyhock for direct cultivation or for use in future breeding programs is feasible.