bluuebunny/crossref_metadata_2025 huggingface dataset:
Quality Evaluation Model of Vocational Education in China: A Qualitative Study Based on Grounded Theory
Authors: Lu Yuzheng , Wang Tian
Published: August 2023

Enhancing quality is the focus of vocational education reform, and building a quality evaluation system that is adapted to the needs of modern vocational education is an important task of vocational education reform. However, international research on quality evaluation models of vocational education has still not been explored in depth. In order to reveal the influencing factors and the relationships between them in the quality evaluation system of vocational education in China, this study establishes a theoretical model of quality evaluation of vocational education in China and tests the model. Based on in-depth interviews, this study reached the following conclusions: (1) Educational orientation is the starting and ending point of vocational education quality evaluation. The quality evaluation of vocational education in China is directly reflected through professional settings and talent training objectives; (2) the quality evaluation of vocational education in China focuses on factors external to the education system, which is an important means through which the quality of vocational education can be recognized by the public, enterprises, and industries; (3) there are internal factors influencing the quality evaluation of vocational education, namely institutional resources and evaluation system construction. In addition, strategies for vocational institutions are proposed based on the study results, suggesting that other countries should strictly regulate the settings of majors based on the real needs of industries; promote the development of technical knowledge, teaching materials, and teacher training; and explore the function of international exchange and cooperation, industry–education integration, and school–enterprise cooperation in educating people. This study provides a theoretical model for the quality evaluation of vocational education; in terms of practical value, the constructed model extends the scope of basic theory.
Climate Change in School Geography Textbooks (Spain) in the Era of the ICTs: Perception and Teaching According to Teachers
Authors: Morote Álvaro-Francisco , Hernández-Hernández María
Published: August 2023

Climate change has become one of the major themes in the academic world, as it constitutes a socio-environmental challenge faced by society in the twenty-first century. The objectives of this study, based on the opinions of geography teachers (secondary education and baccalaureate, Spain), were to analyze the frequency of use of school textbooks in geography classes, to examine how climate change is addressed in these resources, to study how teachers impart these contents, and to review the importance of teaching global warming according to the opinions of the teachers. Based on a sample of 96 teachers surveyed between 2019 and 2022, the results show a disparity of opinions regarding textbook use, a not-altogether-adequate treatment of this phenomenon with these resources (the sum of the responses with values of 1 and 2 amounted to 44.8%), and a predominance of an explanation of climate change by teachers through everyday cases in the students’ environment (35.4%) and talks and workshops given in class by experts (27.1%). Knowing the use made by teachers of the traditional textbook in the era of the ICTs, their perception of the adequacy of the contents on climate change in these resources, and how this phenomenon is taught is highly relevant to raising awareness and educating the youngest cohorts about one of the main challenges faced by society today.
Distributed ItemCF Recommendation Algorithm Based on the Combination of MapReduce and Hive
Authors: Feng Yijia , Wang Lei
Published: August 2023

The ItemCF algorithm is currently the most widely used recommendation algorithm in commercial applications. In the early days of recommender systems, most recommendation algorithms were run on a single machine rather than in parallel. This approach, coupled with the rapid growth of massive user behavior data in the current big data era, has led to a bottleneck in improving the execution efficiency of recommender systems. With the vigorous development of distributed technology, distributed ItemCF algorithms have become a research hotspot. Hadoop is a very popular distributed system infrastructure. MapReduce, which provides massive data computing, and Hive, a data warehousing tool, are the two core components of Hadoop, each with its own advantages and applicable scenarios. Scholars have already utilized MapReduce and Hive for the parallelization of the ItemCF algorithm. However, these pieces of literature make use of either MapReduce or Hive alone without fully leveraging the strengths of both. As a result, it has been difficult for parallel ItemCF recommendation algorithms to feature both simple and efficient implementation and high running efficiency. To address this issue, we proposed a distributed ItemCF recommendation algorithm based on the combination of MapReduce and Hive and named it HiMRItemCF. This algorithm divided ItemCF into six steps: deduplication, obtaining the preference matrixes of all users, obtaining the co-occurrence matrixes of all items, multiplying the two matrices to generate a three-dimensional matrix, aggregating the data of the three-dimensional matrix to obtain the recommendation scores of all users for all items, and sorting the scores in descending order, with Hive being used to carry out steps 1 and 6, and MapReduce for the other four steps involving more complex calculations and operations. The Hive jobs and MapReduce jobs are linked through Hive’s external tables. After implementing the proposed algorithm using Java and running the program on three publicly available user shopping behavior datasets, we found that compared to algorithms that only use MapReduce jobs, the program implementing the proposed algorithm has fewer lines of source code, lower cyclomatic complexity and Halstead complexity, and can achieve a higher speedup ratio and parallel computing efficiency when processing all datasets. These experimental results indicate that the parallel and distributed ItemCF algorithm proposed in this paper, which combines MapReduce and Hive, has both the advantages of concise and easy-to-understand code as well as high time efficiency.
Improved Parallel Implementation of 1D Discrete Wavelet Transform Using CPU-GPU
Authors: Rodriguez-Martinez Eduardo , Benavides-Alvarez Cesar , Aviles-Cruz Carlos , Lopez-Saca Fidel , Ferreyra-Ramirez Andres
Published: August 2023

This work describes a data-level parallelization strategy to accelerate the discrete wavelet transform (DWT) which was implemented and compared in two multi-threaded architectures, both with shared memory. The first considered architecture was a multi-core server and the second one was a graphics processing unit (GPU). The main goal of the research is to improve the computation times for popular DWT algorithms for representative modern GPU architectures. Comparisons were based on performance metrics (i.e., execution time, speedup, efficiency, and cost) for five decomposition levels of the DWT Daubechies db6 over random arrays of lengths 103, 104, 105, 106, 107, 108, and 109. The execution times in our proposed GPU strategy were around 1.2×10−5 s, compared to 3501×10−5 s for the sequential implementation. On the other hand, the maximum achievable speedup and efficiency were reached by our proposed multi-core strategy for a number of assigned threads equal to 32.
Image Authentication and Restoration Using Block-Wise Variational Automatic Encoding and Generative Adversarial Networks
Authors: Lee Chin-Feng , Yeh Chin-Ting , Shen Jau-Ji , Shon Taeshik
Published: August 2023

The Internet is a conduit for vast quantities of digital data, with the transmission of images being especially prevalent due to the widespread use of social media. However, this popularity has led to an increase in security concerns such as image tampering and forgery. As a result, image authentication has become a critical technology that cannot be overlooked. Recently, numerous researchers have focused on developing image authentication techniques using deep learning to combat various image tampering attacks. Nevertheless, image authentication techniques based on deep learning typically classify only specific types of tampering attacks and are unable to accurately detect tampered images or indicate the precise location of tampered areas. The paper introduces a novel image authentication framework that utilizes block-wise encoding through Variational Autoencoder and Generative Adversarial Network models. Additionally, the framework includes a classification mechanism to develop separate authentication models for different images. In the training phase, the image is first divided into blocks of the same size as training data. The goal is to enable the model to judge the authenticity of the image by blocks and to generate blocks similar to the original image blocks. In the verification phase, the input image can detect the authenticity of the image through the trained model, locate the exact position of the image tampering, and reconstruct the image to ensure the ownership.
Extensive Analysis of a Reinvigorated Solar Water Heating System Using Low-Density Polyethylene Glazing
Authors: Duraivel Balamurali , Muthuswamy Natarajan , Shaik Saboor , Cuce Erdem , Owolabi Abdulhameed , Li Hong , Kavgic Miroslava
Published: August 2023

Solar energy is one of the most promising forms of alternative energy because it has no adverse effects on the environment and is entirely free. Converting solar energy into thermal energy is the most common and straightforward method; the efficiency of solar thermal conversion is approximately 70 percent. The intermittent nature of solar energy availability affects the performance of solar water heaters (SWH), which lowers the usefulness of solar energy in residential and commercial settings, particularly for water heating. Even at low temperatures, the performance of a collector can be improved by using low-density polyethylene (LDPE) glazing instead of traditional glass because it is less expensive and lighter than glass. Using a comprehensive experimental-simulative study, the Glass Solar water heater (glass SWH) and the low-density polyethylene solar water heater (LDPE SWH) are analyzed, examined, and compared in this work. These solar water heaters have galvanized iron (GI) as their absorber material. The SWHs were operated in a closed loop at a constant mass flow rate of 0.013 kg/s, and a 4E analysis (which stands for energy, exergy, economics, and efficiency recovery ratio) was carried out. This analysis included a look at the dynamic time, uncertainty, weight reduction, carbon footprint, and series connection. An LDPE SWH has an energy efficiency that is 5.57% and an exergy efficiency that is 3.2% higher than a glass SWH. The weight of the LDPE SWH is 32.56% lower than that of the glass SWH. Compared to the price of a conventional geyser, installing our SWH results in a cost savings of 40.9%, and monthly energy costs are reduced by an average of 25.5%. Compared to October, September has the quickest dynamic time to reach the desired temperature, while October has the most significant dynamic time. The efficiency recovery ratio (ERR) of a glass SWH is 0.0239% lower than that of an LDPE SWH. LDPE SWHs had a carbon credit worth INR 294.44 more than glass SWHs. The findings of these tests demonstrate that the LDPE SWH is a practical replacement for traditional means of heating water, such as SWHs and geysers.
Overview of Intelligent Inverters and Associated Cybersecurity Issues for a Grid-Connected Solar Photovoltaic System
Authors: Vodapally Sai , Ali Mohd
Published: August 2023

The major problem associated with the grid-connected solar photovoltaic (PV) system is the integration of the generated DC power into the AC grid and maintaining the stability of the system. With advancements in research on these PV inverters, artificial intelligence (AI)-based control models are replacing the existing linear methods. These smart PV systems are prone to a variety of attacks, ranging from physical attacks on the PV plants to data integrity attacks and communication-based attacks. This paper provides an overview of the cybersecurity issues with smart PV inverters, their impacts on the grids, and control methods that exist to detect and identify cyber-attacks on a smart PV grid system. An extensive bibliography is provided on grid-forming and grid-following inverters with a variety of control techniques like Proportional–Integral–Derivative (PID) control, fuzzy-based control, and their performances under different fault situations. Multi-level inverter design approaches with various intelligent control techniques to overcome switching failures and other system faults are reviewed. Moreover, some recommendations for future research on intelligent inverters for grid-connected PV systems are proposed. This work will benefit researchers, scientists, and engineers who are working in the field of intelligent inverters for the grid-connected PV system.
Optimizing Distributed Generation Placement and Sizing in Distribution Systems: A Multi-Objective Analysis of Power Losses, Reliability, and Operational Constraints
Authors: Salam Izhar Us , Yousif Muhammad , Numan Muhammad , Zeb Kamran , Billah Moatasim
Published: August 2023

The integration of distributed generation (DG) into distribution networks introduces uncertainties that can substantially affect network reliability. It is crucial to implement appropriate measures to maintain reliability parameters within acceptable limits and ensure a stable power supply for consumers. This paper aims to optimize the location, size, and number of DG units to minimize active power losses and improve distribution System (DS) reliability while considering system operational constraints. To achieve this objective, multiple tests are conducted, and the particle swarm optimization (PSO) technique is implemented. The simulation studies are performed using the ETAP software 19.0.1 version, while the PSO algorithm is implemented in MATLAB R2018a. ETAP enables a comprehensive evaluation of the DG system’s performance, providing valuable insights into its effectiveness in reducing power losses and enhancing system reliability. The PSO algorithm in MATLAB ensures accurate optimization, facilitating the identification of the optimal DG unit location and size. This study uses a modified IEEE-13 bus unbalanced radial DS as the test system, assessing the effects of photovoltaic (PV) and wind DG units under various scenarios and penetration levels. The results demonstrate that the optimal DG unit location and size of either a single PV or wind DG unit significantly reduce power losses, improve DS reliability, and enable effective load sharing with the substation. Moreover, this study analyzes the impact of DG unit uncertainty on system performance. The findings underscore the potential of optimized DG integration to enhance DS efficiency and reliability in the presence of renewable energy sources.
Digital Architecture for Monitoring and Operational Analytics of Multi-Vector Microgrids Utilizing Cloud Computing, Advanced Virtualization Techniques, and Data Analytics Methods
Authors: Patsidis Angelos , Dyśko Adam , Booth Campbell , Rousis Anastasios Oulis , Kalliga Polyxeni , Tzelepis Dimitrios
Published: August 2023

Microgrids are considered a viable solution for achieving net-zero targets and increasing renewable energy integration. However, there is a lack of conceptual work focusing on practical data analytics deployment schemes and case-specific insights. This paper presents a scalable and flexible physical and digital architecture for extracting data-driven insights from microgrids, with a real-world microgrid utilized as a test-bed. The proposed architecture includes edge monitoring and intelligence, data-processing mechanisms, and edge–cloud communication. Cloud-hosted data analytics have been developed in AWS, considering market arrangements between the microgrid and the utility. The analysis involves time-series data processing, followed by the exploration of statistical relationships utilizing cloud-hosted tools. Insights from one year of operation highlight the potential for significant operational cost reduction through the real-time optimization and control of microgrid assets. By addressing the real-world applicability, end-to-end architectures, and extraction of case-specific insights, this work contributes to advancing microgrid design, operation, and adoption.
Limits on the Range and Rate of Change in Power Take-Off Load in Ocean Wave Energy Conversion: A Study Using Model Predictive Control
Authors: Simmons Jeremy W. , Van de Ven James D.
Published: August 2023

Previous work comparing power take-off (PTO) architectures for ocean wave-powered reverse osmosis suggests that variable displacement in the wave energy converter (WEC)-driven pump does not offer a significant performance advantage. A limitation of that study is that the WEC was subject to a constant load within a given sea state (“Coulomb damping”) and did not account for controlled, moment-to-moment variation of the PTO load enabled by a variable displacement pump. This study explores the potential performance advantage of a variable PTO load over Coulomb damping. Model predictive control is used to provide optimal load control with constraints on the PTO load. The constraints include minimum and maximum loads and a limit on the rate of load adjustment. Parameter studies on these constraints enable conclusions about PTO design requirements in addition to providing an estimated performance advantage over Coulomb damping. Numerical simulation of the Oyster 1 WEC is carried out with performance weighted by historical sea state data from Humboldt Bay, CA. The results show a performance advantage of up to 20% higher yearly-average power absorption over Coulomb damping. Additionally, the parameter studies suggest that the PTO load should be adjustable down to at least 25% of the maximum load and should be adjustable between the minimum and maximum loads within a few seconds.
Energy Price Decoupling and the Split Market Issue
Authors: Weyman-Jones Thomas
Published: August 2023

Load scheduling and dispatch by merit order on electricity generation networks has used a wholesale market electricity system operator model focused on system marginal pricing, in which the spot price of electricity at any point in time is equal to the system marginal cost given by the higher value of the price, which rations demand to capacity or the operating cost of the most expensive plant on the system, which is usually a fossil fuel price. This idea has come under challenge because renewable technologies such as wind power farms or solar power farms are treated as having close to zero operating costs. The challenges, under the general heading of energy price decoupling, include suggestions for operating split markets possibly overseen by a regulator, and the prediction that marginal cost pricing should be abandoned. This review evaluates these in terms of their economic impact, relating them to the policy debates on electricity market reform.
Optimization with Neural Network Feasibility Surrogates: Formulations and Application to Security-Constrained Optimal Power Flow
Authors: Kilwein Zachary , Jalving Jordan , Eydenberg Michael , Blakely Logan , Skolfield Kyle , Laird Carl , Boukouvala Fani
Published: August 2023

In many areas of constrained optimization, representing all possible constraints that give rise to an accurate feasible region can be difficult and computationally prohibitive for online use. Satisfying feasibility constraints becomes more challenging in high-dimensional, non-convex regimes which are common in engineering applications. A prominent example that is explored in the manuscript is the security-constrained optimal power flow (SCOPF) problem, which minimizes power generation costs, while enforcing system feasibility under contingency failures in the transmission network. In its full form, this problem has been modeled as a nonlinear two-stage stochastic programming problem. In this work, we propose a hybrid structure that incorporates and takes advantage of both a high-fidelity physical model and fast machine learning surrogates. Neural network (NN) models have been shown to classify highly non-linear functions and can be trained offline but require large training sets. In this work, we present how model-guided sampling can efficiently create datasets that are highly informative to a NN classifier for non-convex functions. We show how the resultant NN surrogates can be integrated into a non-linear program as smooth, continuous functions to simultaneously optimize the objective function and enforce feasibility using existing non-linear solvers. Overall, this allows us to optimize instances of the SCOPF problem with an order of magnitude CPU improvement over existing methods.
Clustering Combined Multi-Objective Optimal Operation of Transmission Systems Considering Voltage Stability
Authors: Park Kyeongseon , Lee Dongyeong , Jang Gilsoo
Published: August 2023

In recent years, power systems have undergone major changes called energy transitions during which synchronous generators have been replaced with power electronics-based generation. Therefore, the voltage stability of power systems has become a major concern owing to the absence of synchronous generators. This study proposes multi-objective optimization using the non-dominated sorting genetic algorithm III to achieve optimal reactive power reserve procurement and improve the voltage stability of the overall system. These systematic approaches require high computational power and are unsuitable for the operational frameworks currently used for large-scale power systems. Previous works have rarely considered the local characteristics of reactive power or generation de-commitment with sufficient re-dispatch owing to greater renewable energy integration. We propose a framework for achieving systematic optimization by considering various objective functions while utilizing the regional aspect of reactive power via spectral clustering-based voltage control area (VCA) identification. The proposed method comprises systematic and regional approaches to optimizing systems for voltage stability improvement based on VCAs. The results demonstrate that the proposed method shows satisfactory performance. These results will be helpful for decision making for power system operations in harsher environments with more renewable energy.
Layout Optimization Algorithms for the Offshore Wind Farm with Different Densities Using a Full-Field Wake Model
Authors: Liang Zhichang , Liu Haixiao
Published: August 2023

To decrease the power deficit of a wind farm caused by wake effects, the layout optimization is a feasible way for the wind farm design stage. A suitable optimization algorithm can significantly improve the quality and efficiency of the optimization process. For exploring the high-performance algorithms under different layout densities, a comparison is conducted by optimizing the layout of a real offshore wind farm with five algorithms, namely two population-based algorithms and three single-point algorithms. Wake effects are considered by a full-field wake model. A penalty function is proposed for the population-based algorithms to handle the constraint violations. Different iterations and constraints of the layout density are applied in the optimization. The random search has the best optimization results in all the cases and the control of the feasibility check is necessary for this algorithm. More iterations can advance the optimization results. The density constraint greatly affects the computational cost of the random search, which is significantly increased under the strict constraint. Except under the strict constraint, the random search has the best performance of optimization efficiency. A combination of the pattern search and random search is recommended when the strict constraint is applied in the layout optimization.
Scenario Analysis of the Development of the Polish Power System towards Achieving Climate Neutrality in 2050
Authors: Pluta Marcin , Wyrwa Artur , Zyśk Janusz , Suwała Wojciech , Raczyński Maciej
Published: August 2023

The Polish power system has been reducing its impact on the environment for a long time, mainly through modernization and investments in new generation capacities, including renewable energy technologies. However, its starting point is still more difficult compared to other less coal-dependent power systems in the European Union. The study was conducted in the direction of optimizing the Polish energy mix of electricity generation from the perspective of 2050. Two energy scenarios for the possible transformation of the Polish power system towards achieving at least a 95% reduction in CO2 emissions by 2050 with an increasing share of renewables were analyzed. The study was carried out with the use of the TIMES-PL model, which minimizes the total system cost over the analyzed time horizon. The model was calibrated according to data from 2018. The two scenarios show relatively similar pathways for CO2 emission reductions by 2050. In the case of no investment in nuclear power plants, power plants equipped with CO2 capture and storage systems are an alternative solution for achieving climate neutrality and increasing national energy security.
Conceptual Design of a Floating Modular Energy Island for Energy Independency: A Case Study in Crete
Authors: Kurniawati Ika , Beaumont Beatriz , Varghese Ramon , Kostadinović Danka , Sokol Ivan , Hemida Hassan , Alevras Panagiotis , Baniotopoulos Charalampos
Published: August 2023

This paper aims to investigate the development of a floating artificial sustainable energy island at a conceptual design level that would enhance the energy independence of islands focusing on a case study on the island of Crete. This paper provides a baseline assessment showing the immense potential of wind and solar energy in and around Crete integrating the third significant renewable energy source (RES) of ocean waves into the energy island. The selection of the best location for the floating offshore platforms that compose the energy island is addressed through exploiting the great potential of the above-mentioned RES, taking into consideration criteria with regard to several significant human activities. To this end, the concept of an innovative floating modular energy island (FMEI) that integrates different renewable energy resources is proposed; in addition, a case study that focuses on the energy independency of a big island illustrates the concept referring to the substitution of the local thermal power plants that are currently in operation in Crete with sustainable energy power. Although focused on the renewable energy resources around Crete, the work of this paper provides a basis for a systematic offshore renewable energy assessment as it proposes a new methodology that could be used anywhere around the globe.
Impact of the Limited Heat Source Capacity on Indoor Temperature and Energy Consumption in Serial nZEB Residential Buildings across the Baltic Region
Authors: Staveckis Arturs , Zemitis Jurgis
Published: August 2023

This paper is dedicated to research of the impact of the limited heat source capacity on indoor temperature and energy consumption in serial nZEB residential buildings. This is an innovative aspect as it explores the potential design simplification for different locations, allowing for cost optimization and quicker construction timelines. The objective of this paper is to examine the impact of limited heat source capacity by utilizing thermal mass and optimizing the ventilation operation. Numerical results demonstrate that incorporating thermal mass increases heating energy consumption by up to 1%. The study addresses the impact of limited heating capacity on indoor temperatures and the need to manage ventilation’s impact during peak temperatures using simulation software IDA ICE. The study reveals that a limited heating capacity reduces energy consumption up to 2.6%, but may result in lower indoor temperatures. By optimizing ventilation strategies, energy consumption can be reduced from 2.4% to 4.4% compared to the suboptimal solution, and from 2.8% to 6.5% compared to the initial case. Parametric analysis reveals optimal ventilation operation change-over point at an outdoor temperature of −17 °C during winter. The research provides practical recommendations for adjusting heating schedules, selecting appropriate heating capacities and implementing optimal ventilation strategies.
Research Progress of Carbon-Neutral Design for Buildings
Authors: Liang Rui , Zheng Xichuan , Wang Po-Hsun , Liang Jia , Hu Linhui
Published: August 2023

The construction industry has become one of the main drivers of the increase in carbon emissions and subsequent climate change. In this study, we focused on building carbon neutrality design and used CiteSpace V.6.2.R2 to conduct bibliometric analysis of published papers (2008–2023). After the initial screening, 280 pieces of relevant literature were obtained, including reviews, research papers, and case studies. Following further screening and excluding duplicate literature articles, 50 pieces of literature were ultimately selected as references for this paper, covering various aspects of key scientific issues, implementation approaches, and emerging research frontiers in carbon-neutral design for buildings. The research results show that significant progress has been made in energy conservation, materials, structures, systems, and operations in the research on carbon-neutral design for buildings. However, there are still issues, such as unclear implementation paths for carbon-neutral design, incomplete lifecycle assessment of carbon-neutral design, and high cost of carbon neutrality technologies in current research. Therefore, further research on the overall concept of carbon-neutral design, the progress and implementation of carbon neutrality technologies, and the integration of carbon-neutral design with sustainable development concepts are necessary. To sum up, this paper presents a thorough overview of the advancements in carbon-neutral design for buildings, examines the existing research challenges, and suggests potential avenues for future research. This paper’s findings can provide guidance for researchers, policymakers, and practitioners to promote the development and application of carbon-neutral design for buildings and to achieve sustainable development goals.
Approaches to Forest Site Classification as an Indicator of Teak Volume Production
Authors: Santos Mario Lima dos , Miguel Eder Pereira , Nappo Mauro Eloi , Souza Hallefy Junio de , Santos Cassio Rafael Costa dos , Silva José Natalino Macedo , Matricardi Eraldo Aparecido Trondoli
Published: August 2023

We conducted a study on the dominant height growth of clonal teak (Tectona grandis Linn F.) plantations in the Brazilian Amazon to assess their potential and its agreement with volumetric production. We employed two approaches, ADA (algebraic difference) and GADA (generalized algebraic difference), and analyzed data from 58 permanent plots collected over a 10 year period. To classify the sites, we developed equations and evaluated their accuracy using various criteria, including correlation coefficient, mean square of residual, Akaike’s criterion, distribution of residuals, and validation through equivalence testing (TOST). We also assessed the biological realism of the constructed curves. We used cluster evaluation and dendrogram comparison to assess the agreement between site index and volumetric production for each approach. The Lundqvist–Korf baseline models (M1–ADA and M4–GADA) proved to be accurate and realistic in estimating dominant height in both approaches. Our findings indicate that the approaches utilizing dynamic equations and generating polymorphic curves effectively represent the sites and indicate the volumetric production of the plantations, with 98.3% of agreement rate. Based on our results, we recommend the use of ADA and GADA approaches for estimating the dominant height of clonal teak plantations in the Eastern Brazilian Amazon.
Distribution and Abundance of the East Asian Finless Porpoise in the Coastal Waters of Shandong Peninsula, Yellow Sea, China
Authors: Li Yongtao , Cheng Zhaolong , Zuo Tao , Niu Mingxiang , Chen Ruisheng , Wang Jun
Published: August 2023

The baseline data pertaining to the population of the East Asian finless porpoise (Neophocaena asiaeorientalis sunameri) in Chinese waters are significantly deficient. Boat-based visual line transect surveys were conducted from 2018 to 2020 to evaluate the distribution and abundance of the East Asian finless porpoise in the coastal waters (depth < 30 m) of the Yellow Sea, with a specific focus on the Shandong Peninsula. A total of 50 transects covering 2705 km were conducted, encompassing an area of 23,604 km2 between 119°30′ E–123°15′ E and 35°25′ N–38° N. Throughout the surveys, a total of 117 East Asian finless porpoise groups were detected, with over 90% of these groups occurring within 15 km of the coast or adjacent islands. The estimated abundance of the East Asian finless porpoise in the study area was determined to be 3978 individuals (CV = 22.39%, 95%CI = 2561–6177), reflecting a low density of 0.169 individuals/km2 (CV = 22.39%, 95%CI = 0.109–0.262) within the coastal waters of the Yellow Sea in the Shandong Peninsula. The findings highlight the need for conservation measures to address the threats faced by this species, including bycatch, habitat degradation, and pollution. The establishment of marine protected areas and the implementation of bycatch mitigation measures are crucial for the long-term survival of the East Asian finless porpoise population in the study area. By providing valuable data on the distribution and abundance of the East Asian finless porpoise, this study contributes to our understanding of the population dynamics and conservation status of this threatened species in the Yellow Sea of the Shandong Peninsula.
Xylooligosaccharides: A Bibliometric Analysis and Current Advances of This Bioactive Food Chemical as a Potential Product in Biorefineries’ Portfolios
Authors: Manicardi Tainá , Baioni e Silva Gabriel , Longati Andreza A. , Paiva Thiago D. , Souza João P. M. , Pádua Thiago F. , Furlan Felipe F. , Giordano Raquel L. C. , Giordano Roberto C. , Milessi Thais S.
Published: August 2023

Xylooligosaccharides (XOS) are nondigestible compounds of great interest for food and pharmaceutical industries due to their beneficial prebiotic, antibacterial, antioxidant, and antitumor properties. The market size of XOS is increasing significantly, which makes its production from lignocellulosic biomass an interesting approach to the valorization of the hemicellulose fraction of biomass, which is currently underused. This review comprehensively discusses XOS production from lignocellulosic biomass, aiming at its application in integrated biorefineries. A bibliometric analysis is carried out highlighting the main players in the field. XOS production yields after different biomass pretreatment methods are critically discussed using Microsoft PowerBI® (2.92.706.0) software, which involves screening important trends for decision-making. Enzymatic hydrolysis and the major XOS purification strategies are also explored. Finally, the integration of XOS production into biorefineries, with special attention to economic and environmental aspects, is assessed, providing important information for the implementation of biorefineries containing XOS in their portfolio.
Profiling the Major Aroma-Active Compounds of Microwave-Dried Jujube Slices through Molecular Sensory Science Approaches
Authors: Yan Xinhuan , Pan Shaoxiang , Liu Xuemei , Tan Mengnan , Zheng Xiaodong , Du Wenyu , Wu Maoyu , Song Ye
Published: August 2023

To discriminate the aroma-active compounds in dried jujube slices through microwave-dried treatments and understand their sensory attributes, odor activity value (OAV) and detection frequency analysis (DFA) combined with sensory analysis and analyzed through partial least squares regression analysis (PLSR) were used collaboratively. A total of 21 major aromatic active compounds were identified, among which 4-hexanolide, 4-cyclopentene-1,3-dione, 5-methyl-2(5H)-furanone, 4-hydroxy-2,5-dimethyl-3(2H)furanone, 3,5-dihydroxy-2-methyl-4-pyrone were first confirmed as aromatic compounds of jujube. Sensory evaluation revealed that the major characteristic aromas of dried jujube slices were caramel flavor, roasted sweet flavor, and bitter and burnt flavors. The PLSR results showed that certain compounds were related to specific taste attributes. 2,3-butanedione and acetoin had a significant positive correlation with the roasted sweet attribute. On the other hand, γ-butyrolactone, 4-cyclopentene-1,3-dione, and 4-hydroxy-2,5-dimethyl-3(2H)furanone had a significant positive impact on the caramel attributes. For the bitter attribute, 2-acetylfuran and 5-methyl-2(5H)-furanone were positively correlated. Regarding the burnt flavor, 5-methyl-2-furancarboxaldehyde and 3,5-dihydroxy-2-methyl-4-pyrone were the most influential odor-active compounds. Finally, 2-furanmethanol and 2,3-dihydro-3,5-dihydroxy-6-methyl-4H-pyran-4-one were identified as the primary sources of the burnt and bitter flavors. Importantly, this work could provide a theoretical basis for aroma control during dried jujube slices processing.
Finite Time Stability Results for Neural Networks Described by Variable-Order Fractional Difference Equations
Authors: Hamadneh Tareq , Hioual Amel , Alsayyed Omar , Al-Khassawneh Yazan Alaya , Al-Husban Abdallah , Ouannas Adel
Published: August 2023

Variable-order fractional discrete calculus is a new and unexplored part of calculus that provides extraordinary capabilities for simulating multidisciplinary processes. Recognizing this incredible potential, the scientific community has been researching variable-order fractional discrete calculus applications to the modeling of engineering and physical systems. This research makes a contribution to the topic by describing and establishing the first generalized discrete fractional variable order Gronwall inequality that we employ to examine the finite time stability of nonlinear Nabla fractional variable-order discrete neural networks. This is followed by a specific version of a generalized variable-order fractional discrete Gronwall inequality described using discrete Mittag–Leffler functions. A specific version of a generalized variable-order fractional discrete Gronwall inequality represented using discrete Mittag–Leffler functions is shown. As an application, utilizing the contracting mapping principle and inequality approaches, sufficient conditions are developed to assure the existence, uniqueness, and finite-time stability of the equilibrium point of the suggested neural networks. Numerical examples, as well as simulations, are provided to show how the key findings can be applied.
Morphotectonic Features in the Middle Biferno River Valley: The Case of Ponte Liscione Dam (Central Italy)
Authors: Mancinelli Vania , Paglia Giorgio , Discenza Marco Emanuele , Minnillo Mariacarmela , Cerrone Francesco , Rizzo Marzia , Miccadei Enrico
Published: August 2023

This paper presents a morphometric and structural-geomorphological approach to identifying morphotectonic features across an area underlain by lithologies that do not easily record tectonic deformations but are widely affected by seismic activity. The middle Biferno River Valley (Central Italy) was chosen as a study area. It was investigated through analyses performed from the drainage basin scale (Biferno River) to the local scale (Ponte Liscione Dam). This methodological approach was applied to investigate the impact of morphotectonic processes in the study area, providing a geomorphology-based contribution to landscape evolution. The aim of the work was to discriminate the main morphotectonic elements acting in the complex tectonic framework of the study area by means of a combination of morphometric, morphotectonic, and structural-geomorphological analyses. The resulting data allowed us to identify three main systems of tectonic elements (F1, F2, and F3), which are attributable to compressive and extensional kinematics, as already reported in previous thematic studies. The tectonic setting reflects the geodynamic framework of the Periadriatic region, as highlighted by the distribution of seismogenic sources and the historical to recent seismicity. Finally, the present work could act as a valuable scientific tool for any geomorphological studies aimed at better defining the impact of morphotectonic processes in similar tectonically active regions hosting important and strategic artificial dams.
Understanding Hearing Health: A Cross-Sectional Study of Determinants in a Metropolitan Area
Authors: Pennino Francesca , Di Lillo Maddalena , Sorrentino Michele , Fiorilla Claudio , Parisi Antonio , Mirizzi Pasquale Domenico , De Simone Bruna , Montuori Paolo , Triassi Maria , Nardone Antonio
Published: August 2023

Hearing health prevention has emerged as a significant public health concern worldwide. With nearly two and a half billion people experiencing some degree of hearing loss, and around seven hundred million requiring medical intervention, the impact on global health is substantial. The economic burden is equally substantial, with estimated health costs reaching 980 billion dollars in the United States alone. To shed light on this issue, we conducted a survey-based cross-sectional study involving 1150 individuals. Utilizing multiple linear regression across three models, we aimed to explore the association between demographic variables and knowledge, attitude, and behaviors related to hearing health. In Model I, we observed a correlation between knowledge and several factors, including age, smoking habits, marital status, and education. In Model II, attitudes were found to associate with non-smoking habits, education, and knowledge. Model III revealed a statistically significant correlation between behaviors and age, gender, parenthood, knowledge, and attitudes. These findings emphasize the importance of targeted public health programs aimed at improving behaviors among the general population. Such interventions can be both effective and relatively inexpensive. By addressing these determinants, we can enhance overall hearing health in the community. Our study contributes valuable information about the knowledge, attitudes, and behaviors related to hearing health in the general population. Understanding these factors is crucial in developing evidence-based strategies to promote hearing health and prevent hearing loss effectively. As we continue to work towards better hearing health, the findings from this study can serve as a cornerstone for informed decision-making and successful intervention implementation.
Using the Multi-Theory Model (MTM) of Health Behavior Change to Explain the Seeking of Stool-Based Tests for Colorectal Cancer Screening
Authors: Sharma Manoj , Johansen Christopher , Batra Kavita , Dai Chia-Liang , Batra Ravi , Hayes Traci , Singh Aditi
Published: August 2023

Colorectal cancer is the third most common cancer worldwide and is the second leading cause of cancer-associated deaths. While colorectal cancer is on the decline in the United States (US), disparities still exist, despite the non-invasive screening modalities, such as stool-based tests have shown themselves to be effective in the detection of colorectal cancer. Many of the existing stool-based test interventions lack the use of a contemporary theory-based approach. Given the paucity of theory-based interventions intended to promote stool-based tests, this cross-sectional study utilizes the multi-theory model (MTM) of health behavior change to explain the seeking of stool-based tests for colorectal cancer (CRC) screening. An online 57-item questionnaire with an established psychometric validity was used to collect responses from the US-based sample (n = 640) of adults aged 45–75 years old. The data were analyzed using bivariate and multivariate statistical methods. Structural equation modeling (SEM) was conducted to test the construct validity of the survey instrument. In this nationwide sample, 39.2% (n = 251) of participants reported having received some form of a stool-based test. Among the participants who did not undergo stool-based CRC screening, the MTM subscales, including “participatory dialogue”, “behavioral confidence”, and “changes in the social environment”, were significant predictors of initiating screening behavior and explained 48% of the variance in the initiation among this group (R2 = 0.579, F = 5.916, p < 0.001; adjusted R2 = 0.481). The MTM may be a useful framework with which to design educational, mass media, social media, and clinical interventions for the promotion of stool-based CRC screening among adults aged 45–75 years old.
The Dual Role of Necroptosis in Pancreatic Ductal Adenocarcinoma
Authors: Giansante Valentina , Stati Gianmarco , Sancilio Silvia , Guerra Emanuela , Alberti Saverio , Di Pietro Roberta
Published: August 2023

Pancreatic cancer (PC) is the seventh leading cause of cancer-related death. PC incidence has continued to increase by about 1% each year in both men and women. Although the 5-year relative survival rate of PC has increased from 3% to 12%, it is still the lowest among cancers. Hence, novel therapeutic strategies are urgently needed. Challenges in PC-targeted therapeutic strategies stem from the high PC heterogeneity and from the poorly understood interplay between cancer cells and the surrounding microenvironment. Signaling pathways that drive PC cell growth have been the subject of intense scrutiny and interest has been attracted by necroptosis, a distinct type of programmed cell death. In this review, we provide a historical background on necroptosis and a detailed analysis of the ongoing debate on the role of necroptosis in PC malignant progression.
Immune System and Brain/Intestinal Barrier Functions in Psychiatric Diseases: Is Sphingosine-1-Phosphate at the Helm?
Authors: Martín-Hernández David , Muñoz-López Marina , Tendilla-Beltrán Hiram , Caso Javier R. , García-Bueno Borja , Menchén Luis , Leza Juan C.
Published: August 2023

Over the past few decades, extensive research has shed light on immune alterations and the significance of dysfunctional biological barriers in psychiatric disorders. The leaky gut phenomenon, intimately linked to the integrity of both brain and intestinal barriers, may play a crucial role in the origin of peripheral and central inflammation in these pathologies. Sphingosine-1-phosphate (S1P) is a bioactive lipid that regulates both the immune response and the permeability of biological barriers. Notably, S1P-based drugs, such as fingolimod and ozanimod, have received approval for treating multiple sclerosis, an autoimmune disease of the central nervous system (CNS), and ulcerative colitis, an inflammatory condition of the colon, respectively. Although the precise mechanisms of action are still under investigation, the effectiveness of S1P-based drugs in treating these pathologies sparks a debate on extending their use in psychiatry. This comprehensive review aims to delve into the molecular mechanisms through which S1P modulates the immune system and brain/intestinal barrier functions. Furthermore, it will specifically focus on psychiatric diseases, with the primary objective of uncovering the potential of innovative therapies based on S1P signaling.