bluuebunny/crossref_metadata_2025 huggingface dataset:
AMSformer: A Transformer for Grain Storage Temperature Prediction Using Adaptive Multi-Scale Feature Fusion
Authors: Zhang Qinghui , Zhang Weixiang , Huang Quanzhen , Wan Chenxia , Li Zhihui
Published: December 2024

Grain storage temperature prediction is crucial for silo safety and can effectively prevent mold and mildew caused by increasing grain temperature and condensation due to decreasing grain temperature. However, current prediction methods lead to information redundancy when capturing temporal and spatial dependencies, which diminishes prediction accuracy. To tackle this issue, this paper introduces an adaptive multi-scale feature fusion transformer model (AMSformer). Firstly, the model utilizes the adaptive channel attention (ACA) mechanism to adjust the weights of different channels according to the input data characteristics and suppress irrelevant or redundant channels. Secondly, AMSformer employs the multi-scale attention mechanism (MSA) to more accurately capture dependencies at different time scales. Finally, the ACA and MSA layers are integrated by a hierarchical encoder (HED) to efficiently utilize adaptive multi-scale information, enhancing prediction accuracy. In this study, actual grain temperature data and six publicly available datasets are used for validation and performance comparison with nine existing models. The results demonstrate that AMSformer outperforms in 36 out of the 58 test cases, highlighting its significant advantages in prediction accuracy and efficiency.
Ammonium Content Determination by Different Analytical Methods in the Manure with Different Additives and Its Change During Storage
Authors: Parašotas Irmantas , Juškienė Violeta , Šlepetienė Alvyra , Kadžienė Gitana
Published: December 2024

Ammonium (NH4+) content is one of the most important parameters in manure assessment. The accurate and rapid determination of this inorganic form of nitrogen is therefore important not only in agronomy, when calculating fertilizer application rates, but also in scientific studies, for example, in the study of greenhouse gas emissions from stored manure. There is not enough research to assess which analytical method is the most appropriate for the determination of (NH4+) in manure with additives such as perlite, vermiculite, or peat. We compared three analytical methods for NH4+ determination: distillation, ionometry, and spectrophotometry. The results showed that the distillation method had the lowest average coefficient of variation (Cv) between the two laboratory replicates, with a Cv = 0.77%, while the ionometry and spectrophotometry methods had average Cv values of 1.83% and 3.97%, respectively. A lower coefficient of variation indicates that the analytical method is less sensitive to various interferences, resulting in more reliable data. Experimental data also show that storing manure for 40 days reduces the NH4+ content from about 21,000 ppm to about 7000 ppm and that the use of additives such as perlite, vermiculite, or peat did not significantly affect NH4+ retention compared with control samples (without additives). Based on the results of our study, we recommend using the distillation method for the determination of NH4+ in the manure with additives in agrochemical and scientific laboratories.
Strigolactone and Karrikin Signaling Influence the Recruitment of Wild Tobacco’s Root Microbiome in the Desert
Authors: Cheng Jie , Luo Shuai , Baldwin Gundega , Cheng Xu , Baldwin Ian T. , Li Suhua
Published: December 2024

Survival in desert ecosystems poses significant challenges for plants due to harsh conditions. Plant microbiomes are thought to promote resilience; however, whether plant hormones, specifically strigolactones (SLs) and karrikins (KARs), shape plant microbiomes remains unknown. The recruitment of root-associated microbiomes in Nicotiana attenuata, a model desert plant, silenced in specific genes associated with SL biosynthesis (CCD7) and perception (D14), karrikin perception (KAI2), and in the shared receptor (MAX2), required for both pathways, was studied. SL and KAR signaling, with MAX2 as a co-regulator, fine-tuned the assembly of root-associated microbiomes, with unique and shared regulatory functions on bacterial microbiome recruitment, particularly in taproot. Significant variation among the different plant genotypes in bacterial diversity and composition in taproot and lateral roots provides a foundation for future research to explore how microbiomes function in plant resilience in these harsh environments.
Effects of Dietary Resveratrol and Black Soldier Fly (Hermetia illucens) Larvae Meal Supplements on Quail Egg Production, Quality, and Consumer Acceptance
Authors: Razmaitė Violeta , Šiukščius Artūras , Pileckas Vidmantas , Bliznikas Saulius
Published: December 2024

This study aimed to examine the effects of supplementing a basal diet with resveratrol and black soldier fly (Hermetiaillucens) larvae meal on Manchurian Golden quail egg production and quality as well as consumer attitudes towards the quail eggs and their acceptability. Quail were allotted three treatments for a laying period of 3 months. The dietary treatment groups were those of a basal diet, a basal diet with 250 mg/kg resveratrol pliusACE, and a diet supplemented with 10% black soldier fly larvae meal (BSF). The BSF larvae meal supplement increased the live weight of the quail, but the differences in the mean egg production and egg external parameters between the experimental groups were insignificant. The egg yolk from the quail fed with resveratrol and BSF supplementation demonstrated lower (p < 0.001 and p < 0.01) pH compared with the quail fed a basal diet. Egg yolk colour showed lower (p < 0.05) lightness and lower (p < 0.01) yellowness and colour saturation in the group of quail fed with resveratrol supplement compared with BSF supplement. Resveratrol inclusion in the basal diet resulted in increased (p < 0.001) protein content in the yolk. BSF inclusion in the diet showed the effect by increasing (p < 0.001) dry matter in albumen. The total monounsaturated fatty acids, polyunsaturated fatty acids, and cholesterol content were not affected by the dietary treatment. The inclusion of BSF larvae meal in the diet increased the proportion of total saturated fatty acids and lowered the proportion of total trans fatty acids in yolk lipids and demonstrated less favourable ratios of fatty acids and nutritional indices, except for the peroxidisability index, and showed higher oxidative status (p < 0.05), flavour score, and overall egg acceptance compared with the quail fed on a basal diet.
A Review of Therapies for Clostridioides difficile Infection
Authors: Morado Faiza , Nanda Neha
Published: December 2024

Clostridioides difficile is an urgent public health threat that affects approximately half a million patients annually in the United States. Despite concerted efforts aimed at the prevention of Clostridioides difficile infection (CDI), it remains a leading cause of healthcare-associated infections. CDI is associated with significant clinical, social, and economic burdens. Therefore, it is imperative to provide optimal and timely therapy for CDI. We conducted a systematic literature review and offer treatment recommendations based on available evidence for the treatment and prevention of CDI.
Green Extraction of Phenolic Compounds from Aronia melanocarpa Using Deep Eutectic Solvents and Antioxidant Activity Investigation
Authors: Molnar Maja , Kovač Martina Jakovljević , Jakobek Lidija , Mihajlović Lovro , Pavić Valentina
Published: December 2024

This study explores the green extraction of phenolic antioxidants from Aronia melanocarpa fruit using choline-chloride-based deep eutectic solvents (DESs) as an eco-friendly alternative to conventional solvents. Sixteen DESs, prepared by combining choline chloride with various hydrogen bond donors, were characterized for their physical properties, including viscosity, polarity, and pH, and applied to extract phenolics from Aronia melanocarpa. High-performance liquid chromatography (HPLC) quantified key phenolic compounds, including neochlorogenic and chlorogenic acid, quercetin derivatives, and cyanidin derivatives, as well as total phenolic acids, flavanols, and anthocyanins. The results revealed that DES composition and physical properties significantly influenced extraction efficiency and antioxidant activity. Additionally, the intrinsic antioxidant activity of DESs contributed substantially to the overall activity of the extracts, particularly in DESs containing organic acids or thiourea. Choline chloride/tartaric acid DES demonstrated the highest total phenolic content, attributed to its high viscosity and strongly acidic pH, while choline chloride/thiourea DES, with low viscosity and slightly acidic pH, exhibited the greatest antioxidant activity. This study highlights how tailoring DES formulations can optimize the extraction of target compounds while accounting for the solvent’s intrinsic properties. The findings support the potential application of DESs as environmentally friendly solvents in the food, pharmaceutical, and cosmetic industries.
Exploiting Extrinsic Information for Serial MAP Detection by Utilizing Estimator in Holographic Data Storage Systems
Authors: Nguyen Thien An , Lee Jaejin
Published: December 2024

In the big data era, data are created in huge volume. This leads to the development of storage devices. Many technologies are proposed for the next generation of storage fields. However, among them, holographic data storage (HDS) has attracted much attention and has been introduced as the promising candidate to meet the increasing demand for capacity and speed. For signal processing, HDS faces two major challenges: inter-page interference (IPI) and two-dimensional (2D) interference. To access the IPI problem, we can use balanced coding, which converts user data into an intensity level with uniformly distributed values for each page. For 2D interference, we can use the equalizer and detection to mitigate the 2D interference. However, the often-used equalizer and detection are methods in wireless communication and only handle the one-dimensional (1D) signal. Thus, we can combine the equalizer, detection, and estimator to reduce 2D interference into 1D interference. In this paper, we proposed a combined model using serial maximum a posteriori (MAP) detection and estimator to improve the detection of HDS systems. In our proposed model, instead of using an estimator with the Viterbi algorithm to predict the upper–lower interference (UPI) or left–right interference (LRI) and converting the received signal into 1D ISI, we used the estimator to predict the extrinsic information for serial MAP detection. This preserves the 2D information in the received signal in serial MAP detection and improves the detection of serial MAP detection by extrinsic information. The simulation results demonstrate that our proposed model significantly improves the bit-error rate (BER) performance compared to previous studies.
Influence of Adding Conductive Materials and Integrating Bio Electrochemical Systems on the Efficiency of Anaerobic Digestion
Authors: Mur-Gorgas Alberto , García-Triviño Antonio V. , Mateos Raúl , Escapa Adrián , Morán Antonio
Published: December 2024

This study explores the impact of incorporating conductive materials and bioelectrochemical systems (BES) on the efficiency of anaerobic digestion (AD) of sewage sludge. The research consists of two phases: biodegradability tests using 3D-printed polylactic acid-based conductive fillers (PLA/Carbon Black and PLA/Graphene) and semi-continuous assays integrating an external BES into the AD process. Results from biodegradability tests indicate that conductive fillers enhance around 50% methane production during the start-up phase, with microbial communities adapting over time to reduce variability in methane yields. Moreover, as the experiment progressed, the methane yields of the digesters with and without fillers became equal. Semi-continuous experiments demonstrate that BES integration improves process stability and methane production by achieving a 5–10% improvement in the amount of methane in the biogas throughout the entire operation, even under high organic loads, by facilitating diverse electron transfer pathways. The challenges of BES operation highlight the need for optimized designs to ensure scalability. Microbial analyses reveal that BES application shifts methanogenic pathways, favouring acetoclastic methanogenesis. Overall, the findings underscore the potential of conductive materials and BES to improve biogas quality and production, contributing to sustainable wastewater management and renewable energy generation.
Circular Economy Alternative Repurposing Textile Solid Discarded Materials from the Leather Goods Sector in Ubrique (Cádiz), Southern Spain
Authors: Suffo Miguel , García-Morales José Luis , Abelleira-Pereira José María , D’Agostino Pierpaolo
Published: December 2024

The leather industry is experiencing a significant rise in production, resulting in an increase in discarded materials–often classified as urban waste—that end up in landfills or are incinerated. Given the geographical proximity of the leather goods sector in Ubrique to the cork stopper manufacturing industry in El Puerto de Santa María/Jerez and to the viticulture sector in Jerez/Sanlúcar/Chiclana, it is essential to explore synergies that can address environmental challenges by contemplating a circular economy approach. This study focuses on the existing potential of repurposing discarded materials, such as reinforcements and linings, by meticulously recording, classifying, and characterizing them. The aim is to valorize these by-products in line with the circular economy principles set out by the European Union. Specifically, the research investigates the feasibility of using these materials as raw inputs, in combination with resins, to produce bottle stoppers for the high-end spirits industry. Findings indicate that a single company generates over six tons of textile waste per month, which equates to approximately EUR 25,000 annually in landfill disposal costs. By implementing a circular alternative that uses just 8.75% of these materials, an economic saving of around EUR 750 per year was reached. In addition, a low-cost vacuum molding method was tested for producing stoppers, yielding aesthetically pleasing and durable prototypes. The application of a materials circularity index enabled the identification of optimal remnants for reintroduction into the production cycle. This re-integration not only reduces the reliance on virgin plastic materials, but also has the potential to significantly reduce the overall environmental impact across interconnected sectors. Ultimately, this study highlights the viability of adopting circular economy practices within the leather goods sector to promote sustainability and economic efficiency.
Permanent Canine Morphometrics in the Saudi Arabian Population: A Sex-Based Comparison Using Mesiodistal and Cervicoincisal Widths
Authors: Aldhuwayhi Sami , Deeban Yahya , Joseph Angel , Mustafa Mohammed , Thakare Amar , Alghamdi Sara , Mallineni Sreekanth
Published: December 2024

The present study aimed to evaluate the sex dimorphism in morphometrics of permanent canines using mesiodistal and cervicoincisal widths in the Saudi Arabian population. The study was performed using dental casts of patients attending the prosthodontics department, College of Dentistry, Majmaah University, Saudi Arabia. The inclusion criteria involved the presence of all four permanent canines (maxillary left and right (13 and 23) and mandibular left and right (33 and 43)) without any form of developmental anomalies or dental caries with a complete eruption. Mesiodistal width and cervicoincisal width were measured using a digital caliper, ensuring precision and consistency. Statistical tests included t-tests and correlation analyses using IBM Statistics (version 21) with p ≤ 0.05 significance level. A total of 94 sets of dental casts of patients with a mean age of 24.6 ± 4.06 were available in the study for evaluation. Tooth 13 was found to have a higher mesiodistal width (8.12 ± 0.57 mm), while tooth 43 was observed to have a higher cervicoincisal width (9.3 ± 0.9 mm). The study observed gender-based differences in mean scores, with females generally having slightly lower mean scores in mesiodistal and where males (p < 0.05) observed with slightly low cervicoincisal widths compared to females. Positive correlations were found between age and mesiodistal and cervicoincisal scores across various dimensions (p < 0.005). There was evidence of difference in mesiodistal and cervicoincisal widths among antimeres. The percentage of sex dimorphism varied across dimensions, with some showing more pronounced gender-related differences. The study establishes the mesiodistal and cervicoincisal widths of all permanent canines can be used to identify sex dimorphism in the Saudi Arabian population.
Design and Implementation of a Compiled Declarative Language for Game AI Control
Authors: Cromer Christopher , Araneda Martin , Rubio-Manzano Clemente
Published: December 2024

Video games have become one of the most popular forms of entertainment around the world. Currently, agents (bots or non-player characters) are predominantly programmed using procedural and deterministic imperative techniques, which pose significant drawbacks in terms of cost and time efficiency. An interesting and alternative line of work is to develop declarative scripting languages which align the programming task closer to human logic. This allows programmers to intuitively implement agents’ behaviors using straightforward rules. In this regard, most of these languages are interpreted, which may impact performance. Hence, this article presents the design and implementation of a new declarative and compiled scripting language called Obelysk for controlling agents. To test and evaluate the language, a video game was created using the Godot game engine, which allowed us to demonstrate the correct functionality of our scripting language to program the AIs participating in the video game. Finally, an analytics platform was also developed to evaluate the correct behavior of the programmed agents.
Thrust and Pressure Control in a Solid Propulsion System via Reinforcement Learning
Authors: Hua Zuohao , Fu Zhuang , Niu Lu
Published: December 2024

A reinforcement learning control method for a solid attitude and divert propulsion system is proposed. The system in this study includes four divert thrust nozzles, six attitude thrust nozzles, and a common combustion chamber. To achieve the required thrust, the pressure in the combustion chamber is first adjusted by controlling the total opening of the nozzles to generate the gas source. Next, by controlling the opening of nozzles at different positions, the required thrust is produced in the five-axis direction. Finally, the motor speed is regulated to drive the valve core to the specified position, completing the closed-loop control of the nozzle opening. The control algorithm used is the Proximal Policy Optimization (PPO) reinforcement learning algorithm. Through system identification and numerical modeling, the training environment for the intelligent agent is created. To accommodate different training objectives, multiple reward functions are implemented. Ultimately, through training, a multi-layer intelligent agent architecture for pressure, thrust, and nozzle opening is established, achieving effective system pressure and thrust control.
Evaluating the Functionality of a Field-Based Test Battery for the Identification of Risk for Anterior Cruciate Ligament Injury: An Exploratory Factor Analysis
Authors: Tsarbou Charis , Liveris Nikolaos I. , Xergia Sofia A. , Papageorgiou George , Sideris Vasileios , Giakas Giannis , Tsepis Elias
Published: December 2024

(1) Background: A parsimonious test battery is deemed necessary to efficiently assess the functional performance of athletes avoiding redundant measurements. This study investigates the interrelationships between elements of an experimental field-based test battery during pre-season assessment (PA), with the purpose of enhancing comprehension of the underlying structure of the assessed variables and suggesting guidelines for the tests incorporated in a PA. (2) Methods: Sixty-two professional football athletes performed a PA, including isometric muscle strength, triple hop and core stability tests, the LESS, and evaluation of landing performance through kinetic and electromyographic data. (3) Results: For the dominant lower limb, the factor analysis resulted in six factors, explaining 79.04% of the variance including core stability, ground reaction forces, dynamic balance, hamstrings strength, quadriceps–hamstring EMG ratio, and quadriceps performance. For the non-dominant lower limb, factor analysis resulted in five factors, explaining 76.60% of the variance including core stability, dynamic balance, ground reaction force, quadriceps–hamstring EMG ratio, and quadriceps–abductors strength. The LESS was loaded with various factors. (4) Conclusions: Given the need for efficient field-based assessments that can be repeated throughout the season without sacrificing data quality, we suggest incorporating the LESS, the prone bridge test, and force-plate-based landing performance evaluation as key elements of the PA.
Software Integration of Power System Measurement Devices with AI Capabilities
Authors: Arenas-Ramos Victoria , Cuesta Federico , Pallares-Lopez Victor , Santiago Isabel
Published: December 2024

The latest changes on the distribution network due to the presence of distributed energy resources (DERs) and electric vehicles make it necessary to monitor the grid using a real-time high-precision system. The present work centers on the development of an open-source software platform that allows for the joint management of, at least, power quality monitors (PQMs), phasor measurement units (PMUs), and smart meters (SMs), which are three of the most widespread devices on distribution networks. This framework could work remotely while allowing access to the measurements in a comfortable way for grid analysis, prediction, or control tasks. The platform must meet the requirements of synchronism and scalability needed when working with electrical monitoring devices while considering the large volumes of data that these devices generate. The framework has been experimentally validated in laboratory and field tests in two photovoltaic plants. Moreover, real-time Artificial Intelligence capabilities have been validated by implementing three Machine Learning classifiers (Neural Network, Decision Tree, and Random Forest) to distinguish between three different loads in real time.
Experimental Evaluation of a Lignocellulosic Biomass Downdraft Gasifier on a Small-Scale Basis: A Thermodynamic Approach
Authors: Montuori Lina , Alcázar-Ortega Manuel , Vargas-Salgado Carlos , Adinolfi Ennio Andrea
Published: December 2024

This research study explores the technology of biomass syngas production by using an experimental downdraft fixed-bed gasifier coupled to a two-cylinder engine, designed and implemented at the Polytechnic University of Valencia, Spain. Furthermore, it deals with the study of the experimental and analytical relations between the driving thermodynamic parameters that control the gasification process, in order to contribute to the development of a theoretical model for the design of a small-scale gasification facility. Different experiments have been performed to investigate the variations in parameters such as low heating values, the air–syngas ratio, the reduction and combustion temperature, efficiency, and electrical power generation during the continuous functioning of the gasification power production facility. The results obtained show that the low heating value is directly related to the inlet air flow rate, so that it increases when the air flow increases, while the increase in the inlet air flow of the gasifier makes both the reduction and the combustion temperature increase. Moreover, the efficiency of the motor–generator reaches a maximum value of 0.204 at the maximum power (around 5 kW), being characterized by an excellent operating range for the air–fuel ratio of a gasification facility.
Evaluation of the Fit of Additively and Subtractively Produced Resin-Based Crowns for Primary Teeth Using a Triple-Scan Protocol
Authors: Aktaş Nagehan , Bal Cenkhan , İnal Ceyda Başak , Kaynak Öztürk Esra , Bankoğlu Güngör Merve
Published: December 2024

Background: Additively and subtractively manufactured resin-based crowns have emerged as a new option for pediatric patients. In this study, it was aimed to test the effect of the production technique on the marginal and internal gap values of resin-based crowns for primary teeth. Methods: A total of 30 crowns were produced with additive (3D printing; DLP and SLA) and subtractive (milling) technologies (n = 10). The crowns were cemented on the resin dies. Three STL files were obtained from the scans of the die, crown, and crown cemented on the die. STL data of the die and crown were superimposed on the STL data of the crown cemented on the die, and gap volume calculations in the marginal and internal areas were performed in the software. The results were statistically analyzed with one-way ANOVA and Tukey HSD tests (α = 0.05). Results: The results showed that the production technique was significantly effective on the marginal and internal gap values. The highest marginal and internal gap value was observed in the milling group, followed by the SLA and DLP groups. Conclusion: The production technique affected the fit of the resin-based crowns for primary teeth. Compared to subtractive techniques, lower marginal and internal gap volumes were found with additive production techniques.
Alternating Generalized Projection Method for Satellite Reflectarray Synthesis with Sequential Stages of Different Illuminations
Authors: Florencio Rafael , Escalante René
Published: December 2024

Satellite reflectarray synthesis using the intersection approach by means of algorithms of the alternating generalized projection (AGP) method to avoid trap points with sequential stages of different illuminations is presented. In this scenario, we examine two distinct sets: the set of radiation patterns producible by the reflectarray and the set of radiation patterns which meet the mission’s criteria. These sets are generally non-convex. Therefore, it is expected that conventional algorithms of the method of alternating projections (MAP) converge to trap points (i.e., local minima of the distance between the involved sets). Thus, the AGP method, which takes into account the trap points, has been considered. When large reflectarrays are considered for satellite applications, several trap points can appear, producing oscillatory behavior between several trap points. A mathematical formalism which supports the idea that a reduction in the edge illumination of an antenna involves fewer trap points is described. Thus, the oscillatory behavior can be avoided by using sequential stages in the synthesis process with different edge illumination levels in each stage. In this work, we demonstrate this synthesis technique with sequential stages, using the proposed algorithm to avoid trap points.
Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals
Authors: Saleem Faisal , Ahmad Zahoor , Kim Jong-Myon
Published: December 2024

This study introduces an advanced deep-learning framework for the real-time detection of pipeline leaks in smart city infrastructure. The methodology transforms acoustic emission (AE) signals from the time domain into scalogram images using continuous wavelet transform (CWT) to enhance leak-related features. A Gaussian filter minimizes background noise and clarifies these features further. The core of the framework combines convolutional neural networks (CNNs) with long short-term memory (LSTM), ensuring a comprehensive examination of both spatial and temporal features of AE signals. A genetic algorithm (GA) optimizes the neural network by isolating the most important features for leak detection. The final classification stage uses a fully connected neural network to categorize pipeline health conditions as either ‘leak’ or ‘non-leak’. Experimental validation on real-world pipeline data demonstrated the framework’s efficacy, achieving accuracy rates of 99.69%. This approach significantly advances smart city capabilities in pipeline monitoring and maintenance, offering a durable and scalable solution for proactive infrastructure management.
Spatiotemporal Evolution and PLUS Simulation Prediction of Ecosystem Service Value in the Southern Jiangsu Urban Agglomeration Based on Land Use Changes
Authors: Ma Qingqing , He Shi , Jing Haitao , Wang Wensheng , Zhang Shiye , Liang Xiujuan
Published: December 2024

In recent years, rapid urbanization in the Southern Jiangsu urban area has significantly altered land use patterns, impacting local ecosystem service value (ESV). This study examines land use changes (LUCs) from 2000 to 2020 and assesses ESV changes with the equivalent factor method. The PLUS model is also used to project future LUCs in line with the “Land and Space Planning of Jiangsu Province (2021–2035)”. The simulation considers scenarios for natural development (NDS), ecological protection (EPS), farmland protection (FPS), and comprehensive development (CDS) for the year 2035. The research findings show that over the past two decades, urbanized areas have persistently grown, whereas there has been a steady decline in the extents of farmland, forested areas, and grasslands. The total ESV has shown a decreasing trend, with the spatial distribution of ESV exhibiting higher values in the eastern regions and lower values in the west. The relationship between land use and ESV is negative, as evidenced by both the comprehensive land use (LA) and the Moran’s I index. Scenario predictions reveal that farmland, forest land, and grassland show a decreasing trend under the NDS, while it is the opposite under the EPS. The FPS is predominant in farmland area, and all land types in the CDS comply with national land planning requirements. Overall, the research findings reveal and predict the influence of LUCs on ESV in the study area under four scenarios, which is beneficial optimizing regional resource planning schemes and formulating ecological compensation policies.
Auxiliary Task Graph Convolution Network: A Skeleton-Based Action Recognition for Practical Use
Authors: Cho Junsu , Kim Seungwon , Oh Chi-Min , Park Jeong-Min
Published: December 2024

Graph convolution networks (GCNs) have been extensively researched for action recognition by estimating human skeletons from video clips. However, their image sampling methods are not practical because they require video-length information for sampling images. In this study, we propose an Auxiliary Task Graph Convolution Network (AT-GCN) with low and high-frame pathways while supporting a new sampling method. AT-GCN learns actions at a defined frame rate in the defined range with three losses: fuse, slow, and fast losses. AT-GCN handles the slow and fast losses in two auxiliary tasks, while the mainstream handles the fuse loss. AT-GCN outperforms the original State-of-the-Art model on the NTU RGB+D, NTU RGB+D 120, and NW-UCLA datasets while maintaining the same inference time. AT-GCN shows the best performance on the NTU RGB+D dataset at 90.3% from subjects, 95.2 from view benchmarks, on the NTU RGB+D 120 dataset at 86.5% from subjects, 87.6% from set benchmarks, and at 93.5% on the NW-UCLA dataset as top-1 accuracy.
AFT-SAM: Adaptive Fusion Transformer with a Sparse Attention Mechanism for Audio–Visual Speech Recognition
Authors: Che Na , Zhu Yiming , Wang Haiyan , Zeng Xianwei , Du Qinsheng
Published: December 2024

Aiming at the problems of serious information redundancy, complex inter-modal information interaction, and difficult multimodal fusion faced by the audio–visual speech recognition system when dealing with complex multimodal information, this paper proposes an adaptive fusion transformer algorithm (AFT-SAM) based on a sparse attention mechanism. The algorithm adopts the sparse attention mechanism in the feature-encoding process to reduce excessive attention to non-important regions and dynamically adjusts the attention weights through adaptive fusion to capture and integrate the multimodal information more effectively and reduce the impact of redundant information on the model performance. Experiments are conducted on the audio–visual speech recognition dataset LRS2 and compared with other algorithms, and the experimental results show that the proposed algorithm in this paper has significantly lower WERs in the audio-only, visual-only, and audio–visual bimodal cases.
Bayesian Hierarchical Risk Premium Modeling with Model Risk: Addressing Non-Differential Berkson Error
Authors: Kim Minkun , Bezbradica Marija , Crane Martin
Published: December 2024

For general insurance pricing, aligning losses with accurate premiums is crucial for insurance companies’ competitiveness. Traditional actuarial models often face challenges like data heterogeneity and mismeasured covariates, leading to misspecification bias. This paper addresses these issues from a Bayesian perspective, exploring connections between Bayesian hierarchical modeling, partial pooling techniques, and the Gustafson correction method for mismeasured covariates. We focus on Non-Differential Berkson (NDB) mismeasurement and propose an approach that corrects such errors without relying on gold standard data. We discover the unique prior knowledge regarding the variance of the NDB errors, and utilize it to adjust the biased parameter estimates built upon the NDB covariate. Using simulated datasets developed with varying error rate scenarios, we demonstrate the superiority of Bayesian methods in correcting parameter estimates. However, our modeling process highlights the challenge in accurately identifying the variance of NDB errors. This emphasizes the need for a thorough sensitivity analysis of the relationship between our prior knowledge of NDB error variance and varying error rate scenarios.
Word Vector Representation of Latin Cuengh Based on Root Feature Enhancement
Authors: Lyu Weibin , Chen Jinlong , Qin Xingguo , Li Jun
Published: December 2024

The Latin Cuengh is a kind of language used in China’s minority areas. Due to its complex pronunciation and semantic system, it is difficult to spread widely. To deal with and protect this language further, this paper considers using the current word vector representation technology to study it. Word vector representation is the basic method and an important foundation of current research on natural language processing. It relies on a large number of data resources and is obtained through the paradigm of pre-training and feature learning. Due to the extreme lack of Latin Cuengh corpus resources, it is very difficult to obtain word vectors by relying on a large amount of data training. In this study, we propose a word vector representation method that combines the root features of Latin Cuengh words. Specifically, while training and learning the Latin Cuengh language corpus, this method uses the special word roots in the Latin Cuengh language to modify the training process, which can enhance the expression ability of the root features. The method uses the mask method based on BERT to mask the word roots after word segmentation and predict the masked word roots in the output layer of the model to obtain a better vector representation of Latin Cuengh words. The experimental results show that the word vector representation method proposed in this paper is effective and has the ability to express Latin Cuengh semantics. The accuracy rate of words semantic is nearly 2% points higher than that of BERT representation, and the judgment of the semantic similarity of words is more accurate.
Study of the Effects of Different Dielectric Environments on the Characteristics of Electro-Explosive Discharge of Metal Wires and Shock Waves
Authors: Liu Jiawei , Wang Jin , Xue Qilong , Zhang Yuanyuan , Li Hufeng , Fang Song
Published: December 2024

The electrical explosive fragmentation technique has attracted widespread attention due to its environmental friendliness and high efficiency. However, the mechanism by which dielectrics influence rock fragmentation remains unclear. This study innovatively selected seven types of environmentally friendly dielectrics to systematically investigate their roles in the metallic wire electrical explosive rock fragmentation process. By precisely characterizing the crack morphology of concrete blocks, shock wave–strain responses, and discharge signal characteristics, the diverse mechanisms by which different dielectrics modulate rock fragmentation were revealed. The results indicate that oxide dielectrics release energy continuously through thermochemical reactions, highly conductive solutions accelerate energy deposition, and reductant suspensions generate strong secondary shock waves—all significantly outperforming tap water in terms of rock fragmentation performance. Notably, the energy deposition efficiency shows a nonlinear relationship with fragmentation effectiveness, influenced by factors such as energy release modes, dielectric composition, and bubble dynamics. The energy conversion mechanism of the electrical explosive rock fragmentation process studied in this paper provides a theoretical foundation for the fine-tuning, customization, and greening of electrical explosive rock fragmentation strategies in engineering practice.
A Method for Borehole Image Reverse Positioning and Restoration Based on Grayscale Characteristics
Authors: Chen Shuangyuan , Han Zengqiang , Wang Yiteng , Jiao Yuyong , Wang Chao , Wang Jinchao
Published: December 2024

Borehole imaging technology is a critical means for the meticulous measurement of rock mass structures. However, the inherent issue of probe eccentricity significantly compromises the quality of borehole images obtained during testing. This paper proposes a method based on grayscale feature analysis for reverse positioning of imaging probes and image restoration. An analysis of the response characteristics of probe eccentricity was conducted, leading to the development of a grayscale feature model and a method for reverse positioning analysis. By calculating the error matrix using the probe’s spatial trajectory, this method corrects and restores grayscale errors caused by probe eccentricity in images. Quantitative analysis was conducted on the azimuthal errors in borehole images caused by probe eccentricity, establishing a method for correcting image perspective errors based on probe spatial-positioning calibration. Results indicate significant enhancement in the effectiveness and measurement accuracy of borehole images.
A Conversational Agent for Empowering People with Parkinson’s Disease in Exercising Through Motivation and Support
Authors: Macedo Patricia , Madeira Rui Neves , Santos Pedro Albuquerque , Mota Pedro , Alves Beatriz , Pereira Carla Mendes
Published: December 2024

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms. The MoveONParkinson project aims to enhance exercise engagement among people with Parkinson’s Disease (PwPD) in the Portuguese context through the ONParkinson digital platform, which provides mobile and web interfaces. While the broader MoveONParkinson project has been previously described from a health-focused perspective, this study specifically focuses on the development and integration of an AI-driven conversational agent (CA) for the Portuguese language, called PANDORA, within the mobile interface of the solution to assist and motivate PwPD in their exercise routines. PANDORA (Parkinson Assistant in Natural Dialogue and Oriented by Rules and Assessments), designed based on Self-Determination Theory (SDT), addresses the psychological needs of autonomy, competence, and relatedness. A preliminary study involving 20 PwPD, 10 caregivers, and 5 healthcare professionals informed the design requirements for PANDORA. The development process involved four main phases: (1) Design of the Chatbot’s Motivation Model, (2) Design and implementation of the conversational agent, (3) Technical Performance Evaluation, and (4) User Experience Evaluation. Technical Performance Evaluation, conducted with three physiotherapists, assessed domain coverage, coherence response capacity, and dialog management capacity, achieving 100% accuracy in domain coverage and coherence response capacity and 89% in dialog management capacity. The User Experience Study involved eight PwPD users recruited from Portuguese healthcare units performing predefined tasks, with user satisfaction scores ranging from 4.2 to 4.9 on a five-point Likert scale. The findings indicate that integrating a conversational agent with motivational cues tends to increase patient engagement. However, further studies are required to determine PANDORA’s impact on exercise engagement in PwPD.
Modal-Guided Multi-Domain Inconsistency Learning for Face Forgery Detection
Authors: Guo Zishuo , Zhang Baopeng , Fan Jack , Teng Zhu , Fan Jianping
Published: December 2024

The remarkable development of deepfake models has facilitated the generation of fake content with various modalities, such as forged images, manipulated audio, and modified video with (or without) corresponding audio. However, many existing methods only analyze content with known and fixed modalities to identify deepfakes, which restricts their focus on intra-domain inconsistencies, and they fail to explore diverse modal and inter-domain hierarchical inconsistencies. In this work, we propose a novel unified neural network named MGDL-Net (Modal-Guided Domain Learning Network), which contains a spatial branch, a temporal branch, and a frequency branch. This diverse combination of branches endows our network with the ability to detect face-related input with flexible modalities and perceive both intra- and inter-domain inconsistencies, such as unimodal, bimodal, and trimodal modalities. To effectively and comprehensively capture the various inconsistencies, we propose implementing heterogeneous inconsistency learning (HIL) with a three-level joint extraction paradigm. In particular, HIL performs heterogeneous learning from spatial, temporal, and frequency perspectives to generate more generalized representations of forgery and eliminate the interference of static redundant information. Furthermore, a multi-modal deepfake dataset is also constructed. We have conducted extensive experiments, and our results have demonstrated that the proposed method can achieve an outstanding performance compared to that of numerous state-of-the-art methods, which implies that the cross-modal inconsistency learning we propose is beneficial for multi-modal face forgery detection.
Deep Reinforcement Learning for Selection of Dispatch Rules for Scheduling of Production Systems
Authors: Alexopoulos Kosmas , Mavrothalassitis Panagiotis , Bakopoulos Emmanouil , Nikolakis Nikolaos , Mourtzis Dimitris
Published: December 2024

Production scheduling is a critical task in the management of manufacturing systems. It is difficult to derive an optimal schedule due to the problem complexity. Computationally expensive and time-consuming solutions have created major issues for companies trying to respect their customers’ demands. Simple dispatching rules have typically been applied in manufacturing practice and serve as a good scheduling option, especially for small and midsize enterprises (SMEs). However, in recent years, the progress in smart systems enabled by artificial intelligence (AI) and machine learning (ML) solutions has revolutionized the scheduling approach. Under different production circumstances, one dispatch rule may perform better than others, and expert knowledge is required to determine which rule to choose. The objective of this work is to design and implement a framework for the modeling and deployment of a deep reinforcement learning (DRL) agent to support short-term production scheduling. The DRL agent selects a dispatching rule to assign jobs to manufacturing resources. The model is trained, tested and evaluated using a discrete event simulation (DES) model that simulates a pilot case from the bicycle production industry. The DRL agent can learn the best dispatching policy, resulting in schedules with the best possible production makespan.
Assessing Seismic Vulnerability and Pollution Risks of Wastewater Treatment Plants
Authors: Golfinopoulos Spyridon K. , Kerpelis Ploutarchos N. , Alexakis Dimitrios E.
Published: December 2024

Empirical studies are valuable for assessing soil and water pollution, as they can reduce costs and save time. The present study discusses previous research results using a questionnaire to gather experts’ judgments on technical issues and potential pollution related to the vulnerability of Wastewater Treatment Plants (WWTPs) in Greece. The questionnaire included 44 closed-type questions based on the Likert Scale. It was distributed to a representative sample of 116 operators over seven (7) months (April–November 2021). Geographical Information Systems (GISs) were employed to visualize the spatial distribution of the seismic vulnerability of WWTPs. The study outputs include eight (8) maps depicting the spatial distribution of seismic vulnerability, both with and considering soil–water pollution, by calculating the existence of seismic hazards and identifying potentially affected regions. Additionally, eight (8) tables support this analysis. The survey findings highlight the most vulnerable regions and WWTPs in the country. The results suggest that after excluding Zone III, the WWTPs of Zone II of the national Seismic Hazard Map (SHM) are estimated to be the most vulnerable. This study spatially visualizes the indicator of seismic vulnerability (ISV) and the seismic vulnerability index concerning potential soil–water pollution (ISV-REF), according to the SHM and regions. Most WWTPs have low ISV-REF, while maps illustrate the exceedance of that parameter, identifying the safest units and indicating that Zone I has the safest units according to the exceedance percentages. Integrating data on regions, ISV, ISV-REF, and their exceedance in GIS could lead to authorities’ and technicians’ decisions to implement quick measures. Researchers should also focus their studies more precisely, mitigating the seismic vulnerability of critical infrastructure, such as WWTPs.
Parameter Correction on Waste Heat Recovery System of a Gas Turbine Using Supercritical CO2 Based on Data Reconciliation
Authors: Xu Liang , You Jiarui , Wu Jiahua , Liu Yikang , Zhang Di
Published: December 2024

At present, the research significance of a waste heat recovery system of a gas turbine using CO2 is gradually becoming prominent, and accurate parameter measurement and performance monitoring are very necessary in the process of establishing coupled circulation system and actual operation. In this paper, a data reconciliation model is established based on the waste heat recovery system of a gas turbine using supercritical CO2 (S-CO2) cycle with two S-CO2 turbines, and several random errors and gross errors are added to verify the data reconciliation ability of the model. The calculation shows that the data reconciliation model established in this paper can obviously reduce the overall deviation level of each parameter in the thermal system, and can also identify and eliminate gross errors in the system. For some of the key parameters such as the total mass flow of CO2, data deviation is reduced to less than 1%, and high-precision power values of the equipment are calculated. This means that the measurement accuracy is effectively improved. In general, this paper makes a new attempt to use data reconciliation in parameter measurement and correction of a simple coupled cyclic system, and provides a certain reference for the subsequent application.