bluuebunny/crossref_metadata_2025 huggingface dataset:
Mixed-Dimensional Heterostructure Photodetector Based on Bi2O2Se Nanosheets and PbS Quantum Dots
Authors: Zhang Bin , Liu Weijing , Wang Zhongxuan , Xie Yuee , Chen Yuanping
Published: July 2024

Due to their exceptional electronic and optical properties, two-dimensional materials have emerged as one of the most promising candidates for future optoelectronic detection. However, optoelectronic detectors based on two-dimensional transition metal materials still face challenges due to factors such as limited absorption coefficients and carrier recombination. In this study, we combine two-dimensional Bi2O2Se with PbS quantum dots to prepare a hybrid heterojunction, effectively broadening the detection range and significantly enhancing the photoresponse rate. The hybrid photodetector exhibited a remarkable photoresponsivity of 14.89 A/W at 450 nm and demonstrated broadband detection capabilities from visible (405 nm) to near-infrared (1350 nm) light illumination. Moreover, the hybrid device showed reduced photocurrent response and recovery times, highlighting its improved performance over bare Bi2O2Se photodetectors. This work underscores the potential of hybrid heterojunctions for enhancing optoelectronic detection capabilities, paving the way for advanced applications in various fields.
Multi-Objective Ship Route Optimisation Using Estimation of Distribution Algorithm
Authors: Dębski Roman , Dreżewski Rafał
Published: July 2024

The paper proposes an innovative adaptation of the estimation of distribution algorithm (EDA), intended for multi-objective optimisation of a ship’s route in a non-stationary environment (tidal waters). The key elements of the proposed approach—the adaptive Markov chain-based path generator and the dynamic programming-based local search algorithm—are presented in detail. The experimental results presented indicate the high effectiveness of the proposed algorithm in finding very good quality approximations of optimal solutions in the Pareto sense. Critical for this was the proposed local search algorithm, whose application improved the final result significantly (the Pareto set size increased from five up to nine times, and the Pareto front quality just about doubled). The proposed algorithm can also be applied to other domains (e.g., mobile robot path planning). It can be considered a framework for (simulation-based) multi-objective optimal path planning in non-stationary environments.
Predictors of Successful Maintenance Practices in Companies Using Fluid Power Systems: A Model-Agnostic Interpretation
Authors: Orošnjak Marko , Beker Ivan , Brkljač Nebojša , Vrhovac Vijoleta
Published: July 2024

The study identifies critical factors influencing companies’ operational and sustainability performance utilising fluid power systems. Firstly, the study performs Machine Learning (ML) modelling using variables extracted from survey instruments in the West Balkan region. The dataset comprises 115 companies (38.75% response rate). The survey data consist of 22 predictors, including meta-data and three target variables. The K-Nearest Neighbours algorithm offers the highest predictive accuracy compared to the other seven ML models, including Ridge Regression, Support Vector Regression, and ElasticNet Regression. Next, using a model-agnostic interpretation, we assess feature importance using mean dropout loss. After extracting the most essential features, we test hypotheses to understand individual variables’ local and global interpretation of maintenance performance metrics. The findings suggest that Failure Analysis Personnel, data analytics, and the usage of advanced technological solutions significantly impact the availability and sustainability of these systems.
Air Quality Predictions through Mathematical Modeling for Iron Ore Mine Project
Authors: Katariya Naresh Kumar , Choudhary Bhanwar Singh , Pandey Prerna
Published: July 2024

Mathematical modeling was deployed to predict air quality during the construction and operation phases of an iron ore mine project in Maharashtra, India. A survey of different models revealed that the ISCST3 model was the most applicable one to predict the air quality parameters, particularly the suspended particulate matter (SPM) and coarse particulate matter (PM10). Baseline air quality data, emission rates, local meteorology, and terrain information were used to simulate the ground-level concentrations. The simulation predicted SPM and PM10 peaks of 172 µg/m3 and 44 µg/m3, respectively. The prediction was within the prescribed limits of the national standards of 200 µg/m3 and 100 µg/m3, respectively, near the source, with minor exceedances in total SPM in two nearby villages and an impact on air quality due to proposed mining. Accordingly, mitigation strategies towards such villages were recommended and implemented. Later, the monitoring in the operation phase revealed that particulate matter could be controlled effectively with mitigation strategies and ensured compliance with air quality standards. The analysis also revealed strong correlations between the particulate matter and the distance of the localities and SPM and PM10. Continuous monitoring and adaptive mitigation based on real-time data were thus emphasized for long-term sustainability and responsible mining practices.
Effects of Different Tonic, Isometric and Isometric/Vibratory Strength Training Programs on Motor Symptomatology in People with Parkinson’s Disease: Study Protocol for a Randomized Trial
Authors: Andrades-Ramírez Oscar , Ulloa-Díaz David , Guede-Rojas Francisco , Araya-Sierralta Sergio , Muñoz-Bustos Gustavo , Arroyo-Jofré Patricio , Chirosa-Ríos Luis-Javier
Published: July 2024

Background: The Chilean population has experienced increased longevity in recent decades, leading to an increased incidence of and mortality from neurodegenerative diseases such as Parkinson’s disease (PD). PD is a chronic degenerative condition that affects the central nervous system. The main objective of this research is to evaluate the effect of 12-week programs of tonic, isometric, and isometric/vibratory muscular strength training while controlling the manipulation of the intensity variable on motor and non-motor symptomatology in PD patients. The secondary objective is to assess the levels of muscular strength in PD patients and their relationship with motor and non-motor symptomatology. Methods: A parallel-group, randomized trial will randomly assign (n = 34) people of both sexes with Parkinson’s disease between stages I–III Hoehn and Yahr (H&Y), aged between 50 and 70 years to one of the experimental groups, in which they will undergo a total of 24 strength training sessions during 12 weeks. During the intervention period, the participants will be advised not to undertake additional exercise programs, to avoid substances that may disrupt metabolism and circadian cycles, and to maintain their medication regimen. The primary or motor evaluation of rest tremor will be performed with an accelerometer (Actigraphy), balance with the Mini-BESTest balance test, gait speed with the Ten Meters Walk Test, and non-motor symptomatology through anxiety, depression (MDS-UPDRS), and quality of life (PDQ-39) questionnaires. The Secondary evaluation of muscle strength will be performed with a functional electromechanical dynamometer. Discussion: Established as a hypothesis is that manipulating intensity variables in 12-week tonic, isometric, and isometric/vibratory muscle strength training programs has an effect on motor and non-motor symptomatology in people with Parkinson’s disease. The research will establish the extent to which controlled muscular strength training has an effect on relevant factors related to motor and non-motor symptomatology.
Applications of Raman Microscopy/Spectroscopy-Based Techniques to Plant Disease Diagnosis
Authors: Vagelas Ioannis , Manthos Ioannis , Sotiropoulos Thomas
Published: July 2024

Plant diseases pose a significant threat to plant and crop health, leading to reduced yields and economic losses. The traditional methods for diagnosing plant diseases are often invasive and time-consuming and may not always provide accurate results. In recent years, there has been growing interest in utilizing Raman microscopy as a non-invasive and label-free technique for plant disease diagnosis. Raman microscopy is a powerful analytical tool that can provide detailed molecular information about samples by analyzing the scattered light from a laser beam. This technique has the potential to revolutionize plant disease diagnosis by offering rapid and accurate detection of various plant pathogens, including bacteria and fungi. One of the key advantages of Raman microscopy/spectroscopy is its ability to provide real-time and in situ analyses of plant samples. By analyzing the unique spectral fingerprints of different pathogens, researchers can quickly identify the presence of specific diseases without the need for complex sample preparation or invasive procedures. This article discusses the development of a Raman microspectroscopy system for disease diagnosis that can accurately detect and identify various plant pathogens, such as bacteria and fungi.
Dynamic Spatio-Temporal Adaptive Graph Convolutional Recurrent Networks for Vacant Parking Space Prediction
Authors: Gao Liangpeng , Fan Wenli , Jian Wenliang
Published: July 2024

The prediction of vacant parking spaces (VPSs) can reduce the time drivers spend searching for parking, thus alleviating traffic congestion. However, previous studies have mostly focused on modeling the temporal features of VPSs using historical data, neglecting the complex and extensive spatial characteristics of different parking lots within the transportation network. This is mainly due to the lack of direct physical connections between parking lots, making it challenging to quantify the spatio-temporal features among them. To address this issue, we propose a dynamic spatio-temporal adaptive graph convolutional recursive network (DSTAGCRN) for VPS prediction. Specifically, DSTAGCRN divides VPS data into seasonal and periodic trend components and combines daily and weekly information with node embeddings using the dynamic parameter-learning module (DPLM) to generate dynamic graphs. Then, by integrating gated recurrent units (GRUs) with the parameter-learning graph convolutional recursive module (PLGCRM) of DPLM, we infer the spatio-temporal dependencies for each time step. Furthermore, we introduce a multihead attention mechanism to effectively capture and fuse the spatio-temporal dependencies and dynamic changes in the VPS data, thereby enhancing the prediction performance. Finally, we evaluate the proposed DSTAGCRN on three real parking datasets. Extensive experiments and analyses demonstrate that the DSTAGCRN model proposed in this study not only improves the prediction accuracy but can also better extract the dynamic spatio-temporal characteristics of available parking space data in multiple parking lots.
Influence of Nonstationary Processes in Drill Rigs on the Durability of Structural Elements
Authors: Kharchenko Yevhen , Blikharskyy Yaroslav , Bilovus Andriy , Vira Volodymyr , Selejdak Jacek , Blikharskyy Zinoviy
Published: July 2024

Assessing the effects that nonstationary dynamic processes have on the durability of structural elements belongs to an important trend in modern dynamics and technical diagnostics of machines. Normally, fatigue strength calculations are performed taking into account only periodically variable stresses, as steady operating modes of machines are much longer in comparison with transient modes. However, a significant role in fatigue failure in machines and engineering structures is also played by nonstationary loads. This is explained by emerging intensive oscillations in the mechanical system during accelerating, braking, or changing the operation mode of a machine unit, which often lead to the accumulation of fatigue damages in the materials of parts in heavy loaded assemblies. The combination of stationary and nonstationary dynamic loads manifests itself, particularly in drilling rigs, where technological cycles include steady motion modes, starts, and stops. This paper represents a generalized mathematical model describing nonstationary processes in the lift system of a drill rig, which considers the relationship between electromagnetic processes in asynchronous motors and mechanical oscillatory phenomena, with the purpose of determining dynamic loads and stresses in structural elements of the rigs. Nonlinear physical systems include mechanical members with both concentrated and clearly expressed distributed parameters. The durability of structural elements is evaluated by means of a computer algorithm for analysis of crack growth rates using the NASGRO equation obtained with the presence of plastic deformation zones. An example of the crown block axis illustrates the influence of nonstationary dynamic processes in drill rigs on the durability of structural elements.
Evaluation of Fracture Strength of Roots to Determine the Duration Limit of Activated Ethylene Diamine Tetraacetic Acid Irrigation for Intracanal Calcium Hydroxide Dressing Removal In Vitro
Authors: Kolunsağ Özbek Sena , Kaşıkçı Sena , Özdemir Olcay
Published: July 2024

This study evaluated the effect of the use of EDTA solution at various intervals to remove intracanal calcium hydroxide medication on fracture resistance. One hundred and one extracted lower premolar teeth were randomly allocated into one control group (n = 5), two main experimental groups (CH(+), with calcium hydroxide dressing, and CH(−), without calcium hydroxide dressing), with four subgroups (EDTA intervals: 1 min, 3 min, 5 min, 10 min, n = 12). Ready-to-use injectable calcium hydroxide was used for the samples in the four CH(+) subgroups, and the samples were kept in an incubator at 37 °C and 100% humidity for seven days. All samples (CH(+), CH(−)) were irrigated with 17% EDTA, which was accompanied by activation with EndoActivator for 1, 3, 5, and 10 min, and then rinsed with 2.5% NaOCl in a standardized manner. The obturation was conducted using the warm obturation technique; experiments were conducted with a universal testing device, and their fracture strength was recorded. Compliance with the normal distribution was examined with ±2 skewness coefficients. Two-way ANOVA, Tukey’s, one-way ANOVA, and Dunnett’s tests were used for statistics (p < 0.050). A statistically significant difference was found between the mean values of the force between the groups (p = 0.009). The mean strength of the tooth at fracture that CH(−) was 401.7, CH(+) was 335.35. There was no statistically significant difference between the mean values of the forces according to time intervals (p = 0.387). While there was no significant difference among the experimental groups (p = 0.229), the mean fracture strength of the negative control group was found to be significantly higher (p < 0.001). The highest fracture resistance was observed in the nonprepared group. Irrigation with EDTA for up to 5 min showed more acceptable fracture resistance results than the other groups. Using calcium hydroxide significantly and negatively affected the root strength.
RFID (Radio Frequency Identification) Localization and Application
Authors: Liu Jia
Published: July 2024

RFID (Radio Frequency Identification) technology has witnessed widespread adoption across diverse industries and sectors due to its versatility and ability to provide real-time tracking, monitoring, and data capture [...]
Three-Dimensional Guidance Laws for Spacecraft Propelled by a SWIFT Propulsion System
Authors: Quarta Alessandro A.
Published: July 2024

This paper discusses the optimal control law, in a three-dimensional (3D) heliocentric orbit transfer, of a spacecraft whose primary propulsion system is a Solar Wind Ion Focusing Thruster (SWIFT). A SWIFT is an interesting concept of a propellantless thruster, proposed ten years ago by Gemmer and Mazzoleni, which deflects, collects, and accelerates the charged particles of solar wind to generate thrust in the interplanetary space. To this end, the SWIFT uses a large conical structure made of thin metallic wires, which is positively charged with the aid of an electron gun. In this sense, a SWIFT can be considered as a sort of evolution of the Janhunen’s E-Sail, which also uses a (nominally flat) mesh of electrically charged tethers to deflect the solar wind stream. In the recent literature, the optimal performance of a SWIFT-based vehicle has been studied by assuming a coplanar orbit transfer and a two-dimensional scenario. The mathematical model proposed in this paper extends that result by discussing the optimal guidance laws in the general context of a 3D heliocentric transfer. In this regard, a number of different forms of the spacecraft state vectors are considered. The validity of the obtained optimal control law is tested in a simplified Earth–Venus and Earth–Mars transfer by comparing the simulation results with the literature data in terms of minimum flight time.
A Galerkin Finite Element Method for the Reconstruction of a Time-Dependent Convection Coefficient and Source in a 1D Model of Magnetohydrodynamics
Authors: Koleva Miglena N. , Vulkov Lubin G.
Published: July 2024

The mathematical analysis of viscous magnetohydrodynamics (MHD) models is of great interest in recent years. In this paper, a finite element Galerkin method is employed for the estimation of an unknown time-dependent convection coefficient and source in a 1D magnetohydrodynamics flow system. In this inverse problem, two integral observations are posed and used to transform the inverse problem to a non-classical direct problem with a non-local parabolic operator. Then, the non-classical strongly coupled parabolic system is studied in various settings. The equivalence of the inverse problem (IP) and the direct one are proven. The Galerkin procedure is analyzed to proove the existence and uniqueness of the solution. The finite element method (FEM) has been developed for the solution of the variational problem. Test examples are discussed.
Towards Model-Driven Explainable Artificial Intelligence: Function Identification with Grammatical Evolution
Authors: Sepioło Dominik , Ligęza Antoni
Published: July 2024

Machine learning is a well-matured discipline, and exploration of datasets can be performed in an efficient way, leading to accurate and operational prediction and decision models. On the other hand, most methods tend to produce black-box-type models, which can be considered a serious drawback. This is so, since in case of numerous practical applications, it is also required to justify, explain, and uncover the inner decision mechanism so that an in-depth understanding of the causal and functional dependencies becomes possible and some responsibility for the decision can be considered. This paper addresses the critical need for model-driven eXplainable Artificial Intelligence (XAI) by exploring the limitations inherent in existing explanatory mechanisms, such as LIME or SHAP, which rely solely on input data. This seems to be an intrinsic limitation and a conceptual error, as no expert domain knowledge can come into play, and no analytical models of the phenomena under investigation are created. In order to deal with this issue, this paper puts forward the idea of building open, white-box explanatory models. To do that, we propose employing grammatical evolution tools combined with expert domain knowledge. The results demonstrate that the developed models can effectively explain the structure and behavior of decision models in terms of components, connections, causality, and simple functional dependencies.
Fuzzy Classification of the Maturity of the Orange (Citrus × sinensis) Using the Citrus Color Index (CCI)
Authors: Villaseñor-Aguilar Marcos J. , Cano-Lara Miroslava , Lopez Adolfo R. , Rostro-Gonzalez Horacio , Padilla-Medina José Alfredo , Barranco-Gutiérrez Alejandro Israel
Published: July 2024

The orange (Citrus sinensis) is a fruit of the Citrus genus, which is part of the Rutaceae family. The orange has gained considerable importance due to its extensive range of applications, including the production of juices, jams, sweets, and extracts. The consumption of oranges confers several nutritional benefits, including flavonoids, vitamin C, potassium, beta-carotene, and dietary fiber. It is crucial to acknowledge that the primary quality criterion employed by consumers and producers is maturity, which is correlated with the visual quality associated with the color of the epicarp. This study proposes the implementation of a computer vision system that estimates the degree of ripeness of oranges Valencia using fuzzy logic (FL); the soluble solids content was determined by refractometry, while the firmness of the fruit was evaluated through the fruit firmness test. The proposed method was divided into five distinct steps. The initial stage involved the acquisition of RGB images. The second stage presents the segmentation of the fruit, which entails the removal of extraneous noise and backgrounds. The third and fourth steps involve determining the centroid of the fruit, and five regions of interest were obtained in the centroid of the fruit of the Citrus Color Index (CII), ranging from 3 × 3 to 11 × 11 pixels. Finally, in the fifth step, a model was created to estimate maturity, °Brix, and firmness using Matlab 2024 and the Fuzzy Logic Designer and Neuro-Fuzzy Designer applications. Consequently, a statistically significant correlation was established between maturity, degree Brix, and firmness, with a value greater than 0.9, using the Citrus Color Index (CII), which reflects the physical–chemical changes that occur in the orange.
Exoland Simulator, a Laboratory Device for Reflectance Spectral Analyses of Planetary Soil Analogs: Design and Simulation
Authors: Dionigi Marco , Logozzo Silvia , Valigi Maria Cristina , Comodi Paola , Pisello Alessandro , Perugini Diego , Fastelli Maximiliano
Published: July 2024

In planetary science, visible (Vis) and near-infrared (NIR) reflectance spectra allow deciphering the chemical/mineralogical composition of celestial bodies’ surfaces by comparison between remotely acquired data and laboratory references. This paper presents the design of an automated test rig named Exoland Simulator equipped with two reflectance spectrometers covering the 0.38–2.2 µm range. It is designed to collect data of natural/synthetic rocks and minerals prepared in the laboratory that simulate the composition of planetary surfaces. The structure of the test rig is conceived as a Cartesian robot to automatize the acquisition. The test rig is also tested by simulating some project trajectories, and results are presented in terms of its ability to reproduce the programmed trajectories. Furthermore, preliminary spectral data are shown to demonstrate how the soil analogs’ spectra could allow an accurate remote identification of materials, enabling the creation of libraries to study the effect of multiple chemical–physical component variations on individual spectral bands. Despite the primary scope of Exoland, it can be advantageously used also for tribological purposes, to correlate the wear behavior of soils and materials with their composition by also analyzing the wear scars.
Machine Learning Models to Enhance the Berlin Questionnaire Detection of Obstructive Sleep Apnea in at-Risk Patients
Authors: Conte Luana , De Nunzio Giorgio , Giombi Francesco , Lupo Roberto , Arigliani Caterina , Leone Federico , Salamanca Fabrizio , Petrelli Cosimo , Angelelli Paola , De Benedetto Luigi , Arigliani Michele
Published: July 2024

The Berlin questionnaire (BQ), with its ten questions, stands out as one of the simplest and most widely implemented non-invasive screening tools for detecting individuals at a high risk of Obstructive Sleep Apnea (OSA), a still underdiagnosed syndrome characterized by the partial or complete obstruction of the upper airways during sleep. The main aim of this study was to enhance the diagnostic accuracy of the BQ through Machine Learning (ML) techniques. A ML classifier (hereafter, ML-10) was trained using the ten questions of the standard BQ. Another ML model (ML-2) was trained using a simplified variant of the BQ, BQ-2, which comprises only two questions out of the total ten. A 10-fold cross validation scheme was employed. Ground truth was provided by the Apnea–Hypopnea Index (AHI) measured by Home Sleep Apnea Testing. The model performance was determined by comparing ML-10 and ML-2 with the standard BQ in the Receiver Operating Characteristic (ROC) space and using metrics such as the Area Under the Curve (AUC), sensitivity, specificity, and accuracy. Both ML-10 and ML-2 demonstrated superior performance in predicting the risk of OSA compared to the standard BQ and were also capable of classifying OSA with two different AHI thresholds (AHI ≥ 15, AHI ≥ 30) that are typically used in clinical practice. This study underscores the importance of integrating ML techniques for early OSA detection, suggesting a direction for future research to improve diagnostic processes and patient outcomes in sleep medicine with minimal effort.
Construction Technology and Service Performance of Waterproof Curtain for Foundation Pit in Large-Particle Pebble Gravel Layer of Yangtze River Floodplain
Authors: Xu Wen , Liu Bo , Wu Jin
Published: July 2024

A foundation pit is constructed in the floodplain of Yangtze River, and a deep and thick layer of large-particle pebble gravel exists below the base slab, thus forming a connected supply channel with the adjacent Yangtze River. The large water volume, high water pressure, and strong permeability of this layer bring great risks to the foundation pit construction. In view of the fact that conventional waterproof curtain construction technologies such as the deep mixing column and high-pressure jet grouting column cannot meet the engineering requirements under these kinds of geological and environmental conditions, a new waterproof curtain construction technology that combines the trenching technology of the diaphragm wall with the TRD (Trench cutting Remixing Deep wall) technology is proposed, i.e., the trenching-and-replacing-style TRD technology, as well as the construction process of this technology, is presented. After the waterproof curtain is built using the proposed technology, the strength, integrity, uniformity, and service performance of the waterproof curtain wall are tested and evaluated by the comprehensive methods of coring, borehole television imaging, resistivity CT, and a group well pumping test. The results show that the proposed technology overcomes the adverse effects of underlying large-particle pebble gravel layer, and the waterproof curtain built by it effectively cuts off the hydraulic connection inside and outside the pit. The technical proposal can provide useful references for similar projects.
Automatic Definition of Traffic Analysis Zones Based on Big Data
Authors: Nagy Viktor , Horváth Balázs
Published: July 2024

The planning process for any transport system can be considered complete if it is accompanied by a modeling system to evaluate the intervention. The study area should always be divided into traffic zones. Correct zoning is the key to any transport system study. The basic principles of zone creation require a thorough understanding of the area and local traffic conditions. However, this is not always a given, especially if a universally applicable assessment system is to be developed. This has led to the need to develop an algorithm that is able to provide an estimate for the definition of traffic zones based on some automatically observable or measurable phenomena or sequence of events. The aim of this research is to identify the observable events that are suitable for characterizing the area, so that an automatic zone definition procedure can be developed based on these. In this paper, automatic WAZE-generated congestion data were processed in a selected district of Budapest. During the processing, the area was divided into a grid network and time series were developed that show the traffic flow on the grid network as a function of the congestion level. The area subdivisions were then clustered using spectral clustering to create spatially distinct districts with identical traffic behavior.
Chemical Composition of PM2.5-0.3 and PM0.3 Collected in Southern Lebanon and Assessment of Their Toxicity in BEAS-2B Cells
Authors: Badran Ghidaa , Chwaikani Malak , Verdin Anthony , Abbas Imane , Simonin Ophélie , Cazier Fabrice , Roumie Mohamad , Courcot Dominique , Lo Guidice Jean-Marc , Ledoux Frédéric , Garçon Guillaume
Published: July 2024

Fine particles (PM2.5) have generally been reported as the major contributor to the adverse health effects of air pollution. Lebanon is characterized by a high density of transport, the production of electricity by generators, and a problem of uncontrolled incineration of household waste. For the purpose of this paper, the physico-chemical properties of fine (PM2.5-0.3) and quasi-ultrafine (PM0.3) particulate matter sampled in Southern Lebanon, were studied. Then, an evaluation and comparison of the toxicity of the different extracted fractions from PM (i.e., native PM2.5-0.3 vs. organic extractable matter fraction (OEM2.5-0.3), and non-extractable matter fraction (NEM2.5-0.3)) was performed. Also, an examination of the toxicity of PM0.3 was conducted indirectly through the evaluation of the OEM0.3 harmfulness. The physico-chemical analysis showed that PM0.3 was much more concentrated than PM2.5-0.3 in organic compounds such as polycyclic aromatic hydrocarbons (PAHs) (28-fold) and their nitrated (N-PAHs, 14-fold) and oxygenated (O-PAHs, 10-fold) derivatives. Normal human bronchial epithelial cells (BEAS-2B) were exposed to PM2.5-0.3, its derived fractions (i.e., OEM2.5-0.3 and NEM2.5-0.3), and OEM0.3 before evaluating the global cytotoxicity, metabolic activation of organic compounds, genotoxicity, and inflammatory response. Different responses were observed depending on the considered fraction of particles. The global cytotoxicity showed a pronounced response related to ATP and LDH activities after exposure to the quasi-ultrafine organic extractable matter fraction (OEM0.3). There was no significant induction of the AhR cell-signaling pathway by NEM2.5-0.3. Despite the apparent difference in the kinetics of induction of the toxicological endpoints under study, OEM0.3 provoked a higher overall cytotoxicity and genotoxicity than OEM2.5-0.3 and total PM2.5-0.3. Taken together, these results clearly showed that the finest particles are more damaging to BEAS-2B cells than PM2.5-0.3 because they are richer in organic compounds, thereby inducing more remarkable toxic effects.
Interannual Fluctuations and Their Low-Frequency Modulation of Summertime Heavy Daily Rainfall Potential in Western Japan
Authors: Mochizuki Takashi
Published: July 2024

Heavy rainfall under the conditions of the changing climate has recently garnered considerable attention. The statistics on heavy daily rainfall offer vital information for assessing present and future extreme events and for clarifying the impacts of global climate variability and change, working to form a favorable background. By analyzing a set of large-ensemble simulations using a global atmospheric model, this study demonstrated that two different physical processes in global climate variability control the interannual fluctuations in the 99th- and 90th-percentile values of summertime daily rainfall (i.e., the potential amounts) on Kyushu Island in western Japan. The 90th-percentile values were closely related to large-scale horizontal moisture transport anomalies due to changes in the subtropical high in the northwestern Pacific, which was usually accompanied by basin-scale warming in the Indian Ocean subsequent to the wintertime El Niño events. The contributions of the sea surface temperatures over the northern Indian Ocean and the eastern tropical Pacific Ocean showed low-frequency modulations, mainly due to the influences of the global warming tendency and the interdecadal variability in the climate system, respectively. In contrast, tropical cyclone activity played a major role in changing the 99th-percentile value. The potentials of both the tropical cyclone intensity and the existence density fluctuated, largely owing to the summertime sea surface temperature over the tropical Pacific, which can be modulated by the El Niño diversity on interdecadal timescales.
Temporal and Spatial Variability of Ground Frost Indices in Northeast China
Authors: Wang Ting , Fan Gaohua , Zhang Hui , Shen Xiangjin
Published: July 2024

Frost is one of the most frequent, intense, and influential agrometeorological disasters that occurs frequently in Northeast China. The study of the spatiotemporal changes of ground frost is of great significance for farmers and policymakers in Northeast China, as it can inform decisions related to crop selection, planting schedules, and the development of regional climate adaptation plans. In this study, the spatiotemporal changes of frost indices (last spring frost (LSF), first fall frost (FFF), and frost-free period (FFP)) in Northeast China were analyzed from 1961 to 2020. Then, we investigated the mutation characteristics of the frost indices and their correlation with geographical factors. The results revealed that (1) the LSF, FFF, and FFP in Northeast China were concentrated at 120–140 DOY, 260–280 DOY, and 110–170 days, respectively. The spatial distribution of frost indices exhibited significant spatial heterogeneity. (2) The LSF, FFF, and FFP showed significant trends of advancement, delay, and extension, with trends of −1.94 days/10 a, 1.72 days/10 a, and 4.21 days/10 a, respectively. (3) More than 80% of the LSF, FFF, and FFP of the sites showed trends of advancement, delay, and extension, with greater variability in the central part of Heilongjiang Province. (4) The FFF and FFP experienced an abrupt change in the late 1990s. (5) The correlation between latitude and LSF, FFF, and FFP was the strongest, with correlation coefficients of 0.77, −0.79, and −0.78, respectively. This study provides a comprehensive understanding of the changing characteristics of ground frost indices that impact agricultural production in Northeast China against the backdrop of climate change. The findings hold significant scientific value in guiding the adaptation of agricultural production layouts in Northeast China to the evolving climatic conditions.
Generative Artificial Intelligence: Analyzing Its Future Applications in Additive Manufacturing
Authors: Westphal Erik , Seitz Hermann
Published: July 2024

New developments in the field of artificial intelligence (AI) are increasingly finding their way into industrial areas such as additive manufacturing (AM). Generative AI (GAI) applications in particular offer interesting possibilities here, for example, to generate texts, images or computer codes with the help of algorithms and to integrate these as useful supports in various AM processes. This paper examines the opportunities that GAI offers specifically for additive manufacturing. There are currently relatively few publications that deal with the topic of GAI in AM. Much of the information has only been published in preprints. There, the focus has been on algorithms for Natural Language Processing (NLP), Large Language Models (LLMs) and generative adversarial networks (GANs). This summarised presentation of the state of the art of GAI in AM is new and the link to specific use cases is this first comprehensive case study on GAI in AM processes. Building on this, three specific use cases are then developed in which generative AI tools are used to optimise AM processes. Finally, a Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis is carried out on the general possibilities of GAI, which forms the basis for an in-depth discussion on the sensible use of GAI tools in AM. The key findings of this work are that GAI can be integrated into AM processes as a useful support, making these processes faster and more creative, as well as to make the process information digitally recordable and usable. This current and future potential, as well as the technical implementation of GAI into AM, is also presented and explained visually. It is also shown where the use of generative AI tools can be useful and where current or future potential risks may arise.
Limbic Network and Papez Circuit Involvement in ALS: Imaging and Clinical Profiles in GGGGCC Hexanucleotide Carriers in C9orf72 and C9orf72-Negative Patients
Authors: Christidi Foteini , Kleinerova Jana , Tan Ee Ling , Delaney Siobhan , Tacheva Asya , Hengeveld Jennifer C. , Doherty Mark A. , McLaughlin Russell L. , Hardiman Orla , Siah We Fong , Chang Kai Ming , Lope Jasmin , Bede Peter
Published: July 2024

Background: While frontotemporal involvement is increasingly recognized in Amyotrophic lateral sclerosis (ALS), the degeneration of limbic networks remains poorly characterized, despite growing evidence of amnestic deficits, impaired emotional processing and deficits in social cognition. Methods: A prospective neuroimaging study was conducted with 204 individuals with ALS and 111 healthy controls. Patients were stratified for hexanucleotide expansion status in C9orf72. A deep-learning-based segmentation approach was implemented to segment the nucleus accumbens, hypothalamus, fornix, mammillary body, basal forebrain and septal nuclei. The cortical, subcortical and white matter components of the Papez circuit were also systematically evaluated. Results: Hexanucleotide repeat expansion carriers exhibited bilateral amygdala, hypothalamus and nucleus accumbens atrophy, and C9orf72 negative patients showed bilateral basal forebrain volume reductions compared to controls. Both patient groups showed left rostral anterior cingulate atrophy, left entorhinal cortex thinning and cingulum and fornix alterations, irrespective of the genotype. Fornix, cingulum, posterior cingulate, nucleus accumbens, amygdala and hypothalamus degeneration was more marked in C9orf72-positive ALS patients. Conclusions: Our results highlighted that mesial temporal and parasagittal subcortical degeneration is not unique to C9orf72 carriers. Our radiological findings were consistent with neuropsychological observations and highlighted the importance of comprehensive neuropsychological testing in ALS, irrespective of the underlying genotype.
Enzymatic Transglycosylation Features in Synthesis of 8-Aza-7-Deazapurine Fleximer Nucleosides by Recombinant E. coli PNP: Synthesis and Structure Determination of Minor Products
Authors: Eletskaya Barbara Z. , Mironov Anton F. , Fateev Ilya V. , Berzina Maria Ya. , Antonov Konstantin V. , Smirnova Olga S. , Zatsepina Alexandra B. , Arnautova Alexandra O. , Abramchik Yulia A. , Paramonov Alexander S. , Kayushin Alexey L. , Khandazhinskaya Anastasia L. , Matyugina Elena S. , Kochetkov Sergey N. , Miroshnikov Anatoly I. , Mikhailopulo Igor A. , Esipov Roman S. , Konstantinova Irina D.
Published: July 2024

Enzymatic transglycosylation of the fleximer base 4-(4-aminopyridine-3-yl)-1H-pyrazole using recombinant E. coli purine nucleoside phosphorylase (PNP) resulted in the formation of “non-typical” minor products of the reaction. In addition to “typical” N1-pyrazole nucleosides, a 4-imino-pyridinium riboside and a N1-pyridinium-N1-pyrazole bis-ribose derivative were formed. N1-Pyrazole 2′-deoxyribonucleosides and a N1-pyridinium-N1-pyrazole bis-2′-deoxyriboside were formed. But 4-imino-pyridinium deoxyriboside was not formed in the reaction mixture. The role of thermodynamic parameters of key intermediates in the formation of reaction products was elucidated. To determine the mechanism of binding and activation of heterocyclic substrates in the E. coli PNP active site, molecular modeling of the fleximer base and reaction products in the enzyme active site was carried out. As for N1-pyridinium riboside, there are two possible locations for it in the PNP active site. The presence of a relatively large space in the area of amino acid residues Phe159, Val178, and Asp204 allows the ribose residue to fit into that space, and the heterocyclic base can occupy a position that is suitable for subsequent glycosylation. Perhaps it is this “upside down” arrangement that promotes secondary glycosylation and the formation of minor bis-riboside products.
Investigating the Antifibrotic Effects of β-Citronellol on a TGF-β1-Stimulated LX-2 Hepatic Stellate Cell Model
Authors: Buakaew Watunyoo , Krobthong Sucheewin , Yingchutrakul Yodying , Potup Pachuen , Thongsri Yordhathai , Daowtak Krai , Ferrante Antonio , Usuwanthim Kanchana
Published: July 2024

Liver fibrosis, a consequence of chronic liver damage or inflammation, is characterized by the excessive buildup of extracellular matrix components. This progressive condition significantly raises the risk of severe liver diseases like cirrhosis and hepatocellular carcinoma. The lack of approved therapeutics underscores the urgent need for novel anti-fibrotic drugs. Hepatic stellate cells (HSCs), key players in fibrogenesis, are promising targets for drug discovery. This study investigated the anti-fibrotic potential of Citrus hystrix DC. (KL) and its bioactive compound, β-citronellol (β-CIT), in a human HSC cell line (LX-2). Cells exposed to TGF-β1 to induce fibrogenesis were co-treated with crude KL extract and β-CIT. Gene expression was analyzed by real-time qRT-PCR to assess fibrosis-associated genes (ACTA2, COL1A1, TIMP1, SMAD2). The release of matrix metalloproteinase 9 (MMP-9) was measured by ELISA. Proteomic analysis and molecular docking identified potential signaling proteins and modeled protein–ligand interactions. The results showed that both crude KL extract and β-CIT suppressed HSC activation genes and MMP-9 levels. The MAPK signaling pathway emerged as a potential target of β-CIT. This study demonstrates the ability of KL extract and β-CIT to inhibit HSC activation during TGF-β1-induced fibrogenesis, suggesting a promising role of β-CIT in anti-hepatic fibrosis therapies.
The Role of the RNA Helicase DDX3X in Medulloblastoma Progression
Authors: Swarup Akanksha , Bolger Timothy A.
Published: July 2024

Medulloblastoma is the most common pediatric brain cancer, with about five cases per million in the pediatric population. Current treatment strategies have a 5-year survival rate of 70% or more but frequently lead to long-term neurocognitive defects, and recurrence is relatively high. Genomic sequencing of medulloblastoma patients has shown that DDX3X, which encodes an RNA helicase involved in the process of translation initiation, is among the most commonly mutated genes in medulloblastoma. The identified mutations are 42 single-point amino acid substitutions and are mostly not complete loss-of-function mutations. The pathological mechanism of DDX3X mutations in the causation of medulloblastoma is poorly understood, but several studies have examined their role in promoting cancer progression. This review first discusses the known roles of DDX3X and its yeast ortholog Ded1 in translation initiation, cellular stress responses, viral replication, innate immunity, inflammatory programmed cell death, Wnt signaling, and brain development. It then examines our current understanding of the oncogenic mechanism of the DDX3X mutations in medulloblastoma, including the effect of these DDX3X mutations on growth, biochemical functions, translation, and stress responses. Further research on DDX3X’s mechanism and targets is required to therapeutically target DDX3X and/or its downstream effects in medulloblastoma progression.
Liposomes against Alzheimer’s Disease: Current Research and Future Prospects
Authors: Constantinou Christiana , Meliou Katerina , Skouras Athanasios , Siafaka Panoraia , Christodoulou Panayiota
Published: July 2024

Alzheimer’s disease, the most common neurodegenerative disease, affects more than 60 million people worldwide, a number that is estimated to double by 2050. Alzheimer’s disease is characterized by progressive memory loss, the impairment of behavior, and mood changes, as well as the disturbed daily routine of the patient. Although there are some active molecules that can be beneficial by halting the progression of the disease, the blood–brain barrier and other physiological barriers hinder their delivery and, consequently, the appropriate management of the disease. Therefore, drug delivery systems that effectively target and overcome the blood–brain barrier to reach the targeted brain area would improve treatment effectiveness. Liposomes are lipophilic carriers that consist of a phospholipid bilayer structure, simulating the physiological lipidic layer of the blood–brain barrier and enabling better delivery of the drug to the brain. Given that pure liposomes may have less targeting affinity than functionalized liposomes, modification with groups such as lactoferrin, poly(ethylene glycol), and transferrin may improve specificity. In this mini-review, we summarize the literature on the use of liposomes for the treatment of Alzheimer’s disease, focusing on the functionalization moieties of liposomes. In addition, challenges in brain delivery are also discussed.
LDD: High-Precision Training of Deep Spiking Neural Network Transformers Guided by an Artificial Neural Network
Authors: Liu Yuqian , Zhao Chujie , Jiang Yizhou , Fang Ying , Chen Feng
Published: July 2024

The rise of large-scale Transformers has led to challenges regarding computational costs and energy consumption. In this context, spiking neural networks (SNNs) offer potential solutions due to their energy efficiency and processing speed. However, the inaccuracy of surrogate gradients and feature space quantization pose challenges for directly training deep SNN Transformers. To tackle these challenges, we propose a method (called LDD) to align ANN and SNN features across different abstraction levels in a Transformer network. LDD incorporates structured feature knowledge from ANNs to guide SNN training, ensuring the preservation of crucial information and addressing inaccuracies in surrogate gradients through designing layer-wise distillation losses. The proposed approach outperforms existing methods on the CIFAR10 (96.1%), CIFAR100 (82.3%), and ImageNet (80.9%) datasets, and enables training of the deepest SNN Transformer network using ImageNet.
A 3D-Printed Do-It-Yourself ELISA Plate Reader as a Biosensor Tested on TNFα Assay
Authors: Pohanka Miroslav , Keresteš Ondřej , Žáková Jitka
Published: July 2024

Simple analytical devices suitable for the analysis of various biochemical and immunechemical markers are highly desirable and can provide laboratory diagnoses outside standard hospitals. This study focuses on constructing an easily reproducible do-it-yourself ELISA plate reader biosensor device, assembled from generally available and inexpensive parts. The colorimetric biosensor was based on standard 96-well microplates, 3D-printed parts, and a smartphone camera as a detector was utilized here as a tool to replace the ELISA method, and its function was illustrated in the assay of TNFα as a model immunochemical marker. The assay provided a limit of detection of 19 pg/mL when the B channel of the RGB color model was used for calibration. The assay was well correlated with the ELISA method, and no significant matrix effect was observed for standard biological samples or interference of proteins expected in a sample. The results of this study will inform the development of simple analytical devices easily reproducible by 3D printing and found on generally available electronics.