bluuebunny/crossref_metadata_2025 huggingface dataset:
Demand Forecasting Model for Airline Flights Based on Historical Passenger Flow Data
Authors: Lundaeva Karina A. , Saranin Zakhar A. , Pospelov Kapiton N. , Gintciak Aleksei M.
Published: December 2024

This paper addresses the problem of estimating passenger demand for flights, with a particular focus on the necessity of developing precise forecasts that incorporate intricate and interdependent variables for effective resource planning within the air transport industry. The present paper focuses on the development of a model for medium-term flight demand estimation by flight destinations. This is based on the analysis of historical airline data on dates, departure times, and passenger demand, as well as the consideration of the influence of macroeconomic indicators, namely gross regional product (GRP), median per capita income, and population of departure and arrival points. This paper reviews international experience in the development of demand forecasting models and their use for resource planning in the industry. The developed model was evaluated using historical data on demand for a single turnaround flight operated by an airline. The developed model allows for the forecasting of the distribution of potential demand for airline flight destinations in the medium term, utilizing comprehensive historical data on departure times and flight demand by destination.
Simulation and Experimental Research on the Energy Loss of Confluence Pipelines
Authors: Zhou Shenghao , Pang Chao , Lin Junzhe , Han Qingkai , Luo Zhong
Published: December 2024

Compared with T- and Y-shaped confluence pipelines, arc confluence pipelines have a smaller energy loss coefficient. In the study presented herein, numerical simulation analyses of the flow fields of T-shaped, Y-shaped, bifurcated, and arc confluence pipelines were carried out. An experimental system was designed to study the energy loss coefficient of the arc confluence pipeline by using five different pipe diameters and nine different radii with water as the working fluid and analyze the influence of pipe diameter, radius, and Reynolds number on the energy loss of the arc confluence pipeline. As the radius of the arc confluence pipeline increases, the energy loss coefficient of confluence first decreases and then increases, and the minimum energy loss corresponds to a radius of 65 mm; with an increase in Reynolds number, the energy loss coefficient of confluence first decreases gradually and tends to a certain value. With an increase in diameter, the energy loss coefficient of confluence decreases gradually.
Research on Damage Assessment Method for Reinforced Concrete T-Girder Bridges Under Munitions Strikes
Authors: Chen Xi , Zhai Hongbo , Xu Qipeng , Wei Wei
Published: December 2024

As a transportation hub, the girder bridge is often an important target for both sides in combat, and it is very important for wartime decision-making and subsequent fire planning to carry out assessment on the effect of the damage to the bridge after the fight. The use of images for target damage assessment is more secure and efficient compared with the traditional method of manual inspection. However, the current image-based damage assessment of girder bridges has the problems of neglecting the damage to bridge piers and the poor stability of the damage features. To address these issues, this paper proposes a functional damage assessment method for beam bridges after being hit by ammunition based on visible light images. This method decomposes the girder bridge into two main sub-targets (the abutment and deck), analyzes its functional characteristics, designs the residual bearing capacity of the bridge and the number of passable lanes in the damage features, proposes an algorithm for conversion from image features to damage features and a fusion algorithm for the damage features, and establishes the rules for the evaluation of the damage level accordingly. The MATLAB experiment verifies that the method is able to extract the residual bearing capacity and the number of passable lanes from the image of the post-fighting girder bridge and to obtain the damage assessment results, which provides a new method for the assessment of the effect of damage to girder bridges.
Backdoor Attack Against Dataset Distillation in Natural Language Processing
Authors: Chen Yuhao , Xu Weida , Zhang Sicong , Xu Yang
Published: December 2024

Dataset distillation has become an important technique for enhancing the efficiency of data when training machine learning models. It finds extensive applications across various fields, including computer vision (CV) and natural language processing (NLP). However, it essentially consists of a deep neural network (DNN) model, which remain susceptible to security and privacy vulnerabilities (e.g., backdoor attacks). Existing studies have primarily focused on optimizing the balance between computational efficiency and model performance, overlooking the accompanying security and privacy risks. This study presents the first backdoor attack targeting NLP models trained on distilled datasets. We introduce malicious triggers into synthetic data during the distillation phase to execute a backdoor attack on downstream models trained with these data. We employ several widely used datasets to assess how different architectures and dataset distillation techniques withstand our attack. The experimental findings reveal that the attack achieves strong performance with a high (above 0.9 and up to 1.0) attack success rate (ASR) in most cases. For backdoor attacks, high attack performance often comes at the cost of reduced model utility. Our attack maintains high ASR while maximizing the preservation of downstream model utility, as evidenced by results showing that the clean test accuracy (CTA) of the backdoored model is very close to that of the clean model. Additionally, we performed comprehensive ablation studies to identify key factors affecting attack performance. We tested our attack method against five defense strategies, including NAD, Neural Cleanse, ONION, SCPD, and RAP. The experimental results show that these defense methods are unable to reduce the attack success rate without compromising the model’s performance on normal tasks. Therefore, these methods cannot effectively defend against our attack.
Optimizing Vehicle Body Cross-Sections Using a Parametric Mathematical Model
Authors: Hu Zhaohui , Mo Shuai , Liu Huang , Mo Fuhao
Published: December 2024

This paper proposes a fast optimization method of body section at the conceptual design stage, based on the demand for body performance in body concept design. The study first establishes a geometrically simplified model of the truss body structure and uses the transfer matrix method to establish a fully parameterized model of the geometrically simplified body under bending conditions. Then, the stochastic gradient genetic algorithm is used to optimize the solution and determine the geometric parameters of each section. In the example of this paper, after the optimization of the established meshless model, the mass of the whole vehicle is reduced by 30%, and the stiffness of the whole vehicle is greater than that of the benchmark vehicle (5128 N/mm, 4386 N/mm), and at the same time, compared with the conceptual design method of the body of CAE technology, the modeling time is greatly reduced, and the computational efficiency of the analytical method is greatly improved compared with the finite element method.
Bioassays with Allium cepa for the Monitoring of Toxicity in the Groundwater of Yucatan, Mexico
Authors: Cauich-Suárez Loremy Yehudí , Sánchez-Sánchez Víctor Emmanuel , Moreno-Ortiz Gissela , Noreña-Barroso Elsa , Rodríguez-Fuentes Gabriela
Published: December 2024

This study employed the Allium cepa bioassay to evaluate the toxic effects of contaminants in the Yucatan aquifer. Seven monitoring wells were studied during September and October 2021. Nutrient concentrations showed significant variation between sites, with samples closer to the coast (P3 and P7) presenting higher ammonia and phosphate concentrations. The pesticides found at the highest concentration were δ-HCH and chlorpyrifos, with 141.44 and 175.92 ng/L, respectively. Heptachlor and aldrin were present in sites P4oct and P2sept. Interestingly, DDT values were highly correlated with caffeine concentrations. The PAHs acenaphthylene and the sum of B(k)fluoranthene and B(b)fluoranthene presented the highest prevalence. B(k)fluoranthene and B(b)fluoranthene were the PAHs found at the highest concentration. The results of the A. cepa bioassay indicated no nuclear abnormalities. The study also found no statistical differences in the mitotic index, root length, biomarkers of oxidative stress, and inhibition of B-esterases between sites and controls. In summary, the wells sampled in the present study had low concentrations of contaminants that can be used as a proxy of anthropogenic discharges; the lack of effect in the biomarkers used at organism, cellular, and biochemical levels indicated no toxic effect on A. cepa roots.
Broadband Millimeter-Wave Front-End Module Design Considerations in FD-SOI CMOS vs. GaN HEMTs
Authors: Sweeney Clint , Lie Donald Y. C. , Mayeda Jill C. , Lopez Jerry
Published: December 2024

Millimeter-wave (mm-Wave) phased array systems need to meet the transmitter (Tx) equivalent isotropic radiated power (EIRP) requirement, and that depends mainly on the design of two key sub-components: (1) the antenna array and (2) the Tx power amplifier (PA) in the front-end-modules (FEMs). Simulations using an electromagnetic (EM) solver carried out in Cadence AWR with AXIEM suggest that for two uniform square patch antenna arrays at 24 GHz, the 4 element array has ~6 dB lower antenna gain and twice the half power beam width (HPBW) compared to the 16 element array. We also present measurements and post-layout parasitic-extracted (PEX) EM simulation data taken on two broadband mm-Wave PAs designed in our lab that cover the key portions of the fifth-generation (5G) FR2-band (i.e., 24.25–52.6 GHz) that lies between the super-high-frequency (SHF, i.e., 3–30 GHz) band and the extremely-high-frequency (EHF, i.e., 30–300 GHz) band: one designed in a 22 nm fully depleted silicon on insulator (FD-SOI) CMOS process, and the other in an advanced 40 nm Gallium Nitride (GaN) high-electron-mobility transistor (HEMT) process. The FD-SOI PA achieves saturated output power (POUT,SAT) of ~14 dBm and peak power-added efficiency (PAE) of ~20% with ~14 dB of gain and 3 dB bandwidth (BW) from ~19.1 to 46.5 GHz in measurement, while the GaN PA achieves measured POUT,SAT of ~24 dBm and peak PAE of ~20% with ~20 dB gain and 3 dB BW from ~19.9 to 35.2 GHz. The PAs’ measured data are in good agreement with the PEX EM simulated data, and 3rd Watt-level GaN PA design data are also presented, but with simulated PEX EM data only. Assuming each antenna element will be driven by one FEM and each phased array targets the same 65 dBm EIRP, millimeter wave (mm-Wave) antenna arrays using the Watt-level GaN PAs and FEMs are expected to achieve roughly 2× wider HPBW with 4× reduction in the array size compared with the arrays using Si FEMs, which shall alleviate the thorny mm-Wave line-of-sight (LOS)-blocking problems significantly.
The Relationship Between Adolescent Orthodontic Treatment and Temporomandibular Disorders: A Systematic Review with Meta-Analysis
Authors: Jeong Seorin , Jih Myeong-Kwan , Ryu Ji-Won , Ahn Jong-Mo , Park Hyun-Jeong
Published: December 2024

The relationship between fixed orthodontic treatment and the development of temporomandibular disorders (TMDs) in adolescents has been a topic of considerable debate. This systematic review and meta-analysis aimed to evaluate the impact of fixed orthodontic treatment on the prevalence of TMDs in adolescents. A comprehensive literature search was conducted using PubMed, Web of Science, EMBASE, Google Scholar, and the Cochrane Library, yielding 886 records. After duplicate removal, 665 records were screened, and 8 studies were assessed for eligibility. Following quality assessment using the Joanna Briggs Institute checklist, 4 studies were included in the final analysis. Data were analyzed using a random-effects model in RevMan software. The meta-analysis revealed an overall odds ratio of 0.75 (95% CI: 0.37–1.51, p = 0.42), indicating no statistically significant association between fixed orthodontic treatment and the risk of developing TMDs. Substantial heterogeneity was observed (I2 = 73%), attributed to variations in study designs, populations, and outcome measures. The risk of bias analysis highlighted concerns in several domains, particularly selection bias and measurement of outcomes. While confounding bias and missing data bias were generally well-controlled, deviations in intervention and inconsistent outcome measurements were noted across the studies. These findings suggest that fixed orthodontic treatment does not significantly alter the risk of developing TMDs in adolescents. However, the substantial heterogeneity and potential biases across the included studies emphasize the need for further high-quality, standardized research to confirm these results and provide clearer clinical guidance.
Development of a Low-Cost Automated Demand Response Controller for Home Energy Management
Authors: Wu Yu-Chi , Chang Chao-Shu , Li Wei-Han
Published: December 2024

This research focuses on developing a low-cost automated demand response controller (DRC) with OpenADR 2.0a capability to enable existing infrared-controlled (IR-controlled) air conditioners (ACs) in homes and buildings to participate in automated demand response programs (ADRPs). The DRC consists of four modules: a smart socket module, an infrared module, a temperature sensor, and a voltage/current module. It can receive, analyze, and respond to demand response (DR) events and perform necessary demand and energy control strategies via IR. Power line communication (PLC) is used for communication without additional wiring. The system is tested under two conditions: participating in ADRPs via OpenADR and not participating in ADRPs. An 8.8% load reduction is observed with different temperature settings when not participating in ADRPs, and energy reductions of 21% to 46% are achieved using various cooling/fanning duty cycles in ADRPs. The proposed system can be integrated with any DR algorithm to meet demand management requirements under the OpenADR program, contributing to significant demand reductions.
Hyperspectral Imaging for Detecting Plastic Debris on Shoreline Sands to Support Recycling
Authors: Palmieri Roberta , Gasbarrone Riccardo , Bonifazi Giuseppe , Piccinini Giorgia , Serranti Silvia
Published: December 2024

Environmental pollution from plastic debris is raising concerns not only for the vulnerability of marine species to ingestion but also for potential human health hazards posed by small particles, known as microplastics. In this context, marine areas suffer from a lack of constant shoreline cleanups to remove accumulated debris, preventing their degradation and fragmentation. To establish optimal strategies for streamlining plastic recovery and recycling operations, it is important to have a system for recognizing plastic debris on the beach and, more specifically, for identifying the type of polymer and mapping (e.g., topologically assessing) the distribution of plastic debris on shoreline sands. This study aims to provide an operative tool finalized to perform an in situ detection, analysis, and characterization of plastic debris present in the coastal environment (i.e., beaches), adopting a near-infrared (NIR)-based hyperspectral imaging (HSI) approach. In more detail, the possibility of identifying and classifying polymers of plastic debris by NIR-HSI in three different areas along the Pontine coastline of the Lazio region (Latina, Italy) was investigated. The study focused on three distinct beaches (i.e., Foce Verde, Capo Portiere, and Sabaudia), each characterized by a different type of sand. For each location, the adopted approach allowed for the systematic classification of the various types of plastic waste found. Three Partial Least Squares Discriminant Analysis (PLS-DA) classification models were developed using a cascade detection strategy. The first model was designed to distinguish plastics from other materials in sand samples, the second to detect plastic particles in the sand, and the third to classify the type of polymer composing each identified plastic particle. Obtained results showed that, on the one hand, plastics were correctly detected from sand and other materials (i.e., sensitivity = 0.892–1.000 and specificity = 0.909–0.996), and on the other, the recognition of polymer type was satisfactory, according to the performance statistical parameters (i.e., sensitivity = 1.000 and specificity = 0.991–1.000). This research highlights the potential of the NIR-HSI approach as a reliable, non-invasive method for plastic debris monitoring and polymer classification. Its scalability and adaptability suggest possible future integration into mobile systems, enabling large-scale monitoring and efficient debris management.
Effects of Turbulence Modeling on the Simulation of Wind Flow over Typical Complex Terrains
Authors: Ma Guolin , Tian Linlin , Song Yilei , Zhao Ning
Published: December 2024

The correct prediction of the wind speed and turbulence levels over complex terrain is essential for accurately assessing wind turbine wake recovery, power production, safety, and wind farm design. In this paper, two modified RANS turbulence models are proposed, which are innovative variants of the conventional SST k-ω model and the linear Reynolds stress model (RSM) featuring optimized closure constants. Then, these two modified models and their origin models are applied to compare and analyze wind flows from a 3D hill wind tunnel experiment and two field measurements over typical complex terrain, including Askervein hill and Bolund island, with the aim of analyzing the sensitivity of wind flows to different RANS turbulence models. The study focuses on analyzing the effects of different turbulence models on the self-sustainability of wind speed and turbulent kinetic energy upstream of the computational domain and on the accuracy of wind flow prediction over complex terrain. The results show that our modified RSM model shows better agreement with the available experimental data on the upstream and leeward sides of all simulated hills. The wind speed on the leeward slope is particularly sensitive to the turbulence model, with a maximum difference in the relative root mean square error (RRMSE) that can reach 11% among the four models. The accuracy of the turbulent kinetic energy depends on the self-sustainability of the upstream turbulent kinetic energy and the predictive ability of the turbulence model for separated flows, and the maximum difference in the RRMSE of the four models can reach 47%. In addition, the advantages and disadvantages of the tested models are discussed to provide guidance for model selection during wind flow simulations in complex terrain.
A Rapid Evaluation Method of Permafrost Bearing Capacity in the Tibetan Plateau Region Based on the Correlation Between CPT-CBR
Authors: Wang Haowu , Tian Bo , Quan Lei , Zhang Panpan , Li Lihui , Hou Yitong , Hu Sen
Published: December 2024

Permafrost, as a soil type under unique environmental conditions, has bearing characteristics that are highly susceptible to thermo-hydraulic environments. Rapid evaluation of the bearing characteristics of in situ permafrost at various depths in perennially frozen regions is a critical scientific issue urgently needing resolution in the road design and construction sectors in China. To address this, this study investigates the correlation between CPT parameters and laboratory mechanical indices under the combined effects of multiple factors in frozen sandy soils. By conducting both CPT and CBR tests on frozen sandy soils, the study analyzes the trends in changes in cone tip resistance (qc) and CBR values under the influence of temperature (T), moisture content (ω), and compaction degree (K) and establishes a functional relationship between them. Based on the standard requirements for indoor CBR, an evaluation criterion using qc for assessing the bearing capacity of frozen sandy soil is proposed. The results indicate that both CBR values and qc increase initially and then stabilize as K increases. With decreasing temperature, both indices stabilize after an initial increase, with turning points at −3.1 °C for CBR values and −2.5 °C for qc. As ω increases, both indices first increase and then stabilize, with a turning point at 40%ω. There is a robust linear relationship between the CBR values and qc, with the ratio of predicted CBR values to actual values showing a histogram and log-normal distribution accounting for 81% and 51.3%, respectively, within a 20% accuracy level, indicating good predictive performance. Referring to the highway subgrade specifications for indoor CBR, a standard for evaluating the bearing capacity of frozen sandy soils using CPT technology is proposed. This study provides new insights for geological surveys in perennially frozen regions and a theoretical basis for the application of CPT technology in evaluating the bearing capacity of permafrost.
Process Optimization and Techno-Economic Analysis for the Production of Phycocyanobilin from Arthrospira maxima-Derived C-Phycocyanin
Authors: Lee Won-Kyu , Sunwoo In-Yung , Kim Junseong , Ryu Yong-Kyun , Koh Eun-Jeong , Kim Taeho , Choi Woon-Yong
Published: December 2024

C-Phycocyanin (C-PC) is a photosynthetic pigment found in cyanobacteria, notably in Arthrospira species. The extraction of phycocyanobilin (PCB), the chromophore of C-PC, is a common approach to address the instability of C-PC under light, heat, and acidic conditions. Methanol is typically used for PCB extraction. However, its use poses challenges for industrial applications owing to the need for solvent removal, extensive purification, and safety validation. Therefore, this study proposes ethanol as an alternative to methanol, optimizing the ethanol extraction conditions through response surface methodology (RSM) using a central composite design (CCD) and techno-economic analysis. The parameters evaluated were the extraction temperature, time, and C-PC/solvent ratio. Optimal conditions—68.81 °C, 14.91 h, and a C-PC to solvent ratio of 1:95 (w/v)— yielded a predicted PCB yield of 29.18%, closely aligning with the actual value of 29.67 ± 1.33%. A techno-economic analysis for pilot-scale PCB production showed that optimized ethanol extraction could yield 147.13 kg/year with 506 batches, compared with 84.31 kg/year standard methanol extraction with 317 batches. Furthermore, it was evaluated to have a unit production cost of USD 1,413,588/kg, an internal rate of return (IRR) of 53.36%, and a payback time of 1.6 years with increased yields and reduced toxic solvent disposal costs. This study supports scalable PCB production with a natural blue pigment suitable for the food, beverage, and cosmetics industries.
Distance Estimation with a Stereo Camera and Accuracy Determination
Authors: Zaremba Arnold , Nitkiewicz Szymon
Published: December 2024

Distance measurement plays a key role in many fields of science and technology, including robotics, civil engineering, and navigation systems. This paper focuses on analyzing the precision of a measurement system using stereo camera distance measurement technology in the context of measuring two objects of different sizes. The first part of the paper presents key information about stereoscopy, followed by a discussion of the process of building a measuring station. The Mask R-CNN algorithm, which is a deep learning model that combines object detection and instance segmentation, was used to identify objects in the images. In the following section, the calibration process of the system and the distance calculation method are presented. The purpose of the study was to determine the precision of the measurement system and to identify the distance ranges where the measurements are most precise. Measurements were made in the range of 20 to 70 cm. The system demonstrated a relative error of 0.95% for larger objects and 1.46% for smaller objects at optimal distances. A detailed analysis showed that for larger objects, the system exhibited higher precision over a wider range of distances, while for smaller objects, the highest accuracy was achieved over a more limited range. These results provide valuable information on the capabilities and limitations of the measurement system used, while pointing out directions for its further optimization.
Reverb and Noise as Real-World Effects in Speech Recognition Models: A Study and a Proposal of a Feature Set
Authors: Cesarini Valerio , Costantini Giovanni
Published: December 2024

Reverberation and background noise are common and unavoidable real-world phenomena that hinder automatic speaker recognition systems, particularly because these systems are typically trained on noise-free data. Most models rely on fixed audio feature sets. To evaluate the dependency of features on reverberation and noise, this study proposes augmenting the commonly used mel-frequency cepstral coefficients (MFCCs) with relative spectral (RASTA) features. The performance of these features was assessed using noisy data generated by applying reverberation and pink noise to the DEMoS dataset, which includes 56 speakers. Verification models were trained on clean data using MFCCs, RASTA features, or their combination as inputs. They validated on augmented data with progressively increasing noise and reverberation levels. The results indicate that MFCCs struggle to identify the main speaker, while the RASTA method has difficulty with the opposite class. The hybrid feature set, derived from their combination, demonstrates the best overall performance as a compromise between the two. Although the MFCC method is the standard and performs well on clean training data, it shows a significant tendency to misclassify the main speaker in real-world scenarios, which is a critical limitation for modern user-centric verification applications. The hybrid feature set, therefore, proves effective as a balanced solution, optimizing both sensitivity and specificity.
Intellectual Property Challenges in the Age of 3D Printing: Navigating the Digital Copycat Dilemma
Authors: Kantaros Antreas
Published: December 2024

This paper examines the intellectual property (IP) challenges introduced by 3D printing, a transformative technology enabling widespread innovation, but also raising critical legal questions. It focuses on three main areas: (1) IP issues associated with 3D printing hardware, (2) copyright and licensing complexities related to downloadable 3D files, and (3) IP concerns for commercialized 3D printed items. Through a comprehensive review, the paper highlights how existing IP frameworks struggle to keep pace with the flexibility and accessibility inherent in 3D printing. Proposed solutions, such as blockchain for secure design tracking and enhanced digital rights management (DRM), point towards a more controlled future for digital design protection. Additionally, the study underscores the need for international cooperation to harmonize IP laws, facilitating more effective cross-border enforcement. This work aims to contribute to the ongoing dialogue on IP rights in 3D printing, proposing adaptive strategies to support legal clarity while fostering technological advancement.
Extracellular Lipases of Yarrowia lipolytica Yeast in Media Containing Plant Oils—Studies Supported by the Design of Experiment Methodology
Authors: Fabiszewska Agata , Zieniuk Bartłomiej , Jasińska Karina , Nowak Dorota , Sasal Katarzyna , Kobus Joanna , Jankiewicz Urszula
Published: December 2024

Lipases are enzymes of great application importance in the food industry, in the cosmetic and detergent industries, in pharmacy and medicine, and in organic chemistry. Among lipases of various origins, those from microorganisms are currently the most commonly used. An excellent producer of lipases seems to be the nonconventional Yarrowia lipolytica yeast, but the biosynthesis of valuable metabolites depends on many factors. This study aimed to investigate the biodiversity of extracellular enzymes produced by four strains of Y. lipolytica, and to determine the optimal conditions of catalysis for the enzymes, according to temperature and pH, in a model hydrolysis reaction. Based on the obtained results, the biodiversity and strain dependence in lipase biosynthesis were observed. Using a Central Composite Design, it was found that temperature is the main factor in determining lipase activity. The enzymes produced by four different strains exhibited other substrate specificity, which was investigated using Latin square design methodology. Only two examined yeast strains, KKP 379 and W29, produced extracellular lipases at a high activity level towards medium- and long-chain fatty acid esters. Moreover, extracellular lipase from wild-type strain KKP 379 was further characterized, followed by exploring the activity of whole-cell biocatalyst and lyophilized enzyme solutions, and it was acknowledged that it was a “true” lipase with the highest affinity to p-nitrophenyl oleate.
Investigation of Corrosion Product Distribution and Induced Cracking Patterns in Reinforced Concrete Using Accelerated Corrosion Testing
Authors: Loukil Olfa , Adelaide Lucas , Bouteiller Véronique , Quiertant Marc , Ragueneau Frédéric , Chaussadent Thierry
Published: December 2024

The present study investigates the corrosion development and induced cracks in reinforced concrete specimens submitted to an accelerated corrosion test. The accelerated chloride-induced corrosion test was performed using an impressed current mode. Three current densities (50, 100 and 200 µA/cm2 of steel) and different exposure times were considered. The objective of the experiments is to analyse two distinct types of damage: firstly, internal damage near the steel/concrete interface, which can be observed in the distribution of corrosion products, as well as damage within the concrete cover, which manifests as cracking. Secondly, external damage, which can be observed in the form of rust spots and concrete surface cracks. The aim of this analysis is to elucidate the relationship between internal damage and external damage. The study confirmed that the corrosion products are non-uniformly distributed around and along the steel reinforcing bar. It also highlighted that the accelerated corrosion test conditions, such as current density, duration, environmental conditions and the specimen geometry, have a significant influence on the distribution of the corrosion products and their thickness around the steel reinforcement and therefore on the internal and external crack patterns. The data analysis revealed a substantial dispersion and contrast in terms of the data, which precluded the establishment of a definitive correlation between internal and external deterioration.
Determination of Lichens’ Bioaccumulation Capacity for Radioactive Isotopes Under Laboratory Conditions as a Basis for Their Application as Biomonitors
Authors: Saniewski Michał , Węgrzyn Michał Hubert , Fałowska Patrycja , Dziurowicz Patrycja , Waszkiewicz Karolina , Zalewska Tamara
Published: December 2024

This study investigates the bioaccumulation capacity of the lichen Cladonia uncialis for radioactive isotopes, aiming to establish its potential as a biomonitor. Conducted under controlled laboratory conditions, the experiment utilised gamma-emitting isotopes to simulate fallout. The lichens were exposed to varying concentrations of these isotopes in three aquariums. The results demonstrated that the bioaccumulation of 54Mn, 57Co, 60Co, 65Zn, 137Cs, and 241Am by C. uncialis was proportional to their initial deposition levels, and this occurred in the first days of the experiment. Analysis of isotope activity in washed and unwashed lichens showed that retention primarily occurred intracellularly, indicating effective bioaccumulation. The study derived the Aggregated Transfer Coefficient (Tag) for each isotope, which ranged from 0.34 to 0.64, and the absorption capacity of the elements increased in the following order: 54Mn < 57Co < 60Co ≤ 65Zn < 241Am < 137Cs. Absorption efficiency, amounting to approximately 50% for 137Cs and 241Am, highlights the potential for lichens to serve as reliable biomonitors for environmental monitoring and estimation of deposition when knowing only the activity of the isotopes in lichen.
How Does Induced Deceleration of One Treadmill’s Belt in the Pre-Swing Gait Phase Change Gait Pattern?
Authors: Chodkowska Katarzyna , Błażkiewicz Michalina , Kędziorek Justyna , Ortenburger Dorota , Wąsik Jacek
Published: December 2024

This study aimed to investigate how external perturbations caused by the treadmill belt’s deceleration during the pre-swing phase affect gait kinematics and kinetics in young adults. Twenty-one healthy young females walked on a treadmill in a virtual environment (GRAIL, Motek), where unexpected perturbations were applied to the left belt, mimicking a ‘trip-like’ effect at toe-off. The spatiotemporal, kinematic, and kinetic parameters were analyzed during two cycles. The first cycle involved the first perturbation and the response to it. The second included a gait cycle without the perturbation (treadmill gait). The perturbation resulted in an increased stride duration for both limbs when compared to the treadmill gait. The perturbed limb had a longer support phase, while the reactive limb had the longest double stance phase. The responding limb exhibited more than double the ankle plantarflexion compared to the normal treadmill gait and the perturbed limb. At the hip joint, both limbs showed significantly higher values, with a 40.8% increase in flexion and a 227% increase in extension for the perturbed limb, and a 24.5% increase in flexion and a 212% increase in extension for the responding limb, compared to the treadmill gait. Muscle torque was generally lower in most joints for both limbs, except for notably higher hip and knee extensor values for the perturbed limb. The responding limb exhibited lower values for the ankle, knee, and hip joints, indicating unexpected muscle activity patterns. Studying treadmill belt deceleration during pre-swing gait can provide valuable insights into biomechanical adaptations and motor control strategies.
Hybrid 3D Convolutional–Transformer Model for Detecting Stereotypical Motor Movements in Autistic Children During Pre-Meltdown Crisis
Authors: Jarraya Salma Kammoun , Masmoudi Marwa
Published: December 2024

Computer vision using deep learning algorithms has served numerous human activity identification applications, particularly those linked to safety and security. However, even though autistic children are frequently exposed to danger as a result of their activities, many computer vision experts have shown little interest in their safety. Several autistic children show severe challenging behaviors such as the Meltdown Crisis which is characterized by hostile behaviors and loss of control. This study aims to introduce a monitoring system capable of predicting the Meltdown Crisis condition early and alerting the children’s parents or caregivers before entering more difficult settings. For this endeavor, the suggested system was constructed using a combination of a pre-trained Vision Transformer (ViT) model (Swin-3D-b) and a Residual Network (ResNet) architecture to extract robust features from video sequences to extract and learn the spatial and temporal features of the Stereotyped Motor Movements (SMMs) made by autistic children at the beginning of the Meltdown Crisis state, which is referred to as the Pre-Meltdown Crisis state. The evaluation was conducted using the MeltdownCrisis dataset, which contains realistic scenarios of autistic children’s behaviors in the Pre-Meltdown Crisis state, with data from the Normal state serving as the negative class. Our proposed model achieved great classification accuracy, at 92%.
Evaluation of Snowboarding Helmets in Mitigation of the Biomechanical Responses of Head Surrogate
Authors: Harmukh Atul , Ganpule Shailesh G.
Published: December 2024

Traumatic brain injury (TBI) during snowboarding sports is a major concern. A robust evaluation of existing snowboarding helmets is desired. Head kinematics (i.e., linear acceleration, angular velocity, angular acceleration) and associated brain responses (brain pressure, equivalent (von Mises) stress, and maximum principal strain) of the head are a predominant cause of TBI or concussion. The conventional snowboarding helmet, which mitigates linear acceleration, is typically used in snow sports. However, the role of conventional snowboarding helmets in mitigating angular head kinematics is marginal or insignificant. In recent years, new anti-rotational technologies (e.g., MIPS, WaveCel) have been developed that seek to reduce angular kinematics (i.e., angular velocity, angular acceleration). However, investigations regarding the performance of snowboarding helmets in terms of the mitigation of head kinematics and brain responses are either extremely limited or not available. Toward this end, we have evaluated the performance of snowboarding helmets (conventional and anti-rotational technologies) against blunt impact. We also evaluated the performance of newly developed low-cost, silica-based anti-rotational pads by integrating them with conventional helmets. Helmets were mounted on a head surrogate–Hybrid III neck assembly. The head surrogate consisted of skin, skull, dura mater, and brain. The geometry of the head surrogate was based on the GHBMC head model. Substructures of the head surrogate was manufactured using additive manufacturing and/or molding. A linear impactor system was used to simulate/recreate snowfield hazards (e.g., tree stump, rock, pole) loading. Following the ASTM F2040 standard, an impact velocity of 4.6 ± 0.2 m/s was used. The head kinematics (i.e., linear acceleration, angular velocity, angular acceleration) and brain simulant pressures were measured in the head surrogate. Further, using the concurrent simulation, the brain simulant responses (i.e., pressure, von Mises stress, and maximum principal strain) were computed. The front and side orientations were considered. Our results showed that the helmets with anti-rotation technologies (i.e., MIPS, WaveCel) significantly reduced the angular kinematics and brain responses compared to the conventional helmet. Further, the performance of the silica pad-based anti-rotational helmet was comparable to the existing anti-rotational helmets. Lastly, the effect of a comfort liner on head kinematics was also investigated. The comfort liner further improved the performance of anti-rotational helmets. Overall, these results provide important data and novel insights regarding the performance of various snowboarding helmets. These data have utility in the design and development of futuristic snowboarding helmets and safety protocols.
Vehicle Mass Estimation via Practical Supervisory Artificial Neural Networks Using Perturbed Engine Torque and Acceleration Inputs
Authors: Kim Minsu , Jung Daeyi
Published: December 2024

Various model-based mass estimation approaches have been discussed for a long time. However, estimation performance often deteriorates in some driving situations and, in particular, slow convergence and excessive overshoot of estimates are a major issue for model-based approaches. Meanwhile, mass estimation approaches using ANN models have recently emerged to propose better solutions, but their usefulness has not been fully investigated. Therefore, this paper presents a vehicle mass estimation strategy using practical supervisory artificial neural networks to achieve more accurate results with better convergence. Here, the perturbed engine torque and vehicle longitudinal acceleration are selected as the inputs of the ANN (instead of the original engine torque and vehicle acceleration), which allows for the faster convergence of estimates with high accuracy; these inputs are existing sensor values already available in the vehicle system. The effectiveness of the proposed ANN approach was verified using simulation and software-in-the-loop simulation (SILS) with field test data, and it was found that the convergence speed of the proposed ANN is almost twice as fast as that of the model-based approach, the accuracy is much better, and the estimation quality is constantly stable without any excessive transient responses. This study will provide further insights into mass estimation using the ANN approach.
REDUCE—A Tool Supporting Inconsistencies Reduction in the Decision-Making Process
Authors: Kuraś Paweł , Strzałka Dominik , Kowal Bartosz , Organiściak Patryk , Demidowski Krzysztof , Vanivska Veronika
Published: December 2024

This paper presents REDUCE, a free online tool designed to support decision-making processes by addressing inconsistency in multiplicative pairwise comparison (PC) matrices, a key element of many multi-criteria decision-making (MCDM) methods, including the analytic hierarchy process (AHP). AHP relies on pairwise comparisons to assign weights to decision criteria or alternatives, but human-generated PC matrices often exhibit inconsistencies. Consistency is evaluated using Saaty’s consistency ratio (CR), where a value below 0.10 is considered acceptable. Higher inconsistency levels necessitate matrix corrections, which are challenging if the original expert is unavailable or revision constraints exist. REDUCE autonomously reduces inconsistency in PC matrices using two different algorithms that require no expert intervention. The tool accommodates different PC matrices, enabling users to specify the desired CR threshold (e.g., CR≤0.10) and select the algorithm for adjustment. It ensures the resulting matrix is consistent while preserving the original preference structure to the greatest extent possible. Additionally, REDUCE calculates weights for the compared entities, making it a valuable tool for applications of AHP and related methodologies. Quantitative evaluations demonstrate that REDUCE can improve matrices with high inconsistency (e.g., CR=0.25) to acceptable levels (e.g., CR=0.08) while retaining up to 95% of the original preference integrity, depending on the chosen algorithm. By addressing the accessibility gap for small and medium enterprises (SMEs) that lack resources for costly decision-making software or expert consultants, REDUCE facilitates broader adoption of MCDM tools. This work highlights the potential of REDUCE to enhance decision-making reliability and accessibility in resource-constrained environments.
Magnetosphere–Ionosphere Conjugate Harang Discontinuity and Sub-Auroral Polarization Streams (SAPS) Phenomena Observed by Multipoint Satellites
Authors: Horvath Ildiko , Lovell Brian C.
Published: December 2024

It is well understood that near midnight, the Harang Discontinuity separates the auroral duskside eastward electrojet (EEJ) and dawnside westward electrojet (WEJ) and associated plasma flows driven by enhanced magnetospheric convections via Magnetosphere–Ionosphere (M–I) coupling. There are conflicting reports regarding the significance of Region1 (R1) and R2 currents and the enhancement of Sub-Auroral Polarization Streams (SAPS) in the Harang region. We investigate the M–I conjugate Harang and SAPS phenomena using multipoint satellite observations. Results show the inner-magnetosphere (1) Harang region at midnight (between the plasmapause and the closed/open field-line boundary) with (2) a strong SAPS electric field (EX ≈ 30 mV/m; in magnitude) in a fast-time voltage generator (VGFT) near the plasmapause and the topside ionosphere (3) Harang Discontinuity (where R1 and R2 currents flow along) with (4) an enhanced SAPS flow (~1800 m/s) in the underlying VGFT system (requiring no R2 currents). From these (1–4) findings we conclude (i) the significance of both R1 and R2 currents in the observed M–I conjugate Harang phenomenon’s development, (ii) the different development of the reversing EEJ–WEJ compared to the regular auroral EEJ and WEJ in the topside ionosphere R1–R2 system, and (iii) the R2 currents’ absence in the enhanced SAPS flow newly formed in the VGFT system.
Study on the Response of Net Primary Productivity to Vegetation Phenology and Its Influencing Factors in Karst Ecologically Fragile Regions
Authors: Wang Jiale , Zhou Zhongfa , Zhu Meng , Wan Jiaxue , Wu Xiaopiao , Liu Rongping , Zheng Jiajia
Published: December 2024

Net primary productivity (NPP) is a crucial indicator of ecosystem function and sustainability. Quantifying the response of NPP to phenological dynamics is essential for understanding the impact of climate change on ecosystem processes. In this study, vegetation phenology data for Guizhou Province were extracted from the MCD12Q2 dataset, and NPP was estimated using the Normalized Difference Vegetation Index (NDVI) combined with meteorological data. Linear regression, trend analysis, and structural equation modeling were employed to clarify the spatiotemporal patterns of NPP and phenology as a basis for exploring the role of climatic factors in the NPP’s response to phenological changes. The results indicate that 72.15% of Guizhou Province shows an increasing trend in vegetation NPP (slope = 5.0981, p = 0.002). The start of the growing season (measured as SOS) tends to advance (slope = −0.4004, p = 0.0528), while the end of the growing season (measured as EOS) tends to delay (slope = 0.2747, p = 0.1011), resulting in an overall extension of the increasing length of the season (LOS) (slope = 0.64549, p = 0.0065). The spatiotemporal patterns of SOS, EOS, LOS, and NPP varied with elevation changes. For every 500 m increase in altitude, NPP decreased by 25.3 gC/m2, SOS was delayed by 7.1 days, EOS advanced by 1.25 days, and LOS decreased by 8.36 days. These findings suggest that the response of NPP to phenological changes is primarily controlled by local climatic and topographical conditions. Additionally, the indirect effects of climate on NPP through phenological changes were more significant than the direct effects. Climatic factors play varying roles in the NPP response to phenological dynamics, highlighting the profound influence of climate in regulating the mechanisms by which NPP responds to phenological changes.