bluuebunny/crossref_metadata_2025 huggingface dataset:
Dynamics in Redox-Active Molecules Following Ischemic Preconditioning in the Brain
Authors: Lysikova Terezia , Tomascova Anna , Kovalska Maria , Lehotsky Jan , Leskova Majdova Katarina , Kaplan Peter , Tatarkova Zuzana
Published: May 2024

It is well known that the brain is quite vulnerable to oxidative stress, initiating neuronal loss after ischemia-reperfusion (IR) injury. A potent protective mechanism is ischemic preconditioning (IPC), where proteins are among the primary targets. This study explores redox-active proteins’ role in preserving energy supply. Adult rats were divided into the control, IR, and IPC groups. Protein profiling was conducted to identify modified proteins and then verified through activity assays, immunoblot, and immunohistochemical analyses. IPC protected cortex mitochondria, as evidenced by a 2.26-fold increase in superoxide dismutase (SOD) activity. Additionally, stable core subunits of respiratory chain complexes ensured sufficient energy production, supported by a 16.6% increase in ATP synthase activity. In hippocampal cells, IPC led to the downregulation of energy-related dehydrogenases, while a significantly higher level of peroxiredoxin 6 (PRX6) was observed. Notably, IPC significantly enhanced glutathione reductase activity to provide sufficient glutathione to maintain PRX6 function. Astrocytes may mobilize PRX6 to protect neurons during initial ischemic events, by decreased PRX6 positivity in astrocytes, accompanied by an increase in neurons following both IR injury and IPC. Maintained redox signaling via astrocyte-neuron communication triggers IPC’s protective state. The partnership among PRX6, SOD, and glutathione reductase appears essential in safeguarding and stabilizing the hippocampus.
The Influence of a Polyphenol-Rich Red Berry Fruit Juice on Recovery Process and Leg Strength Capacity after Six Days of Intensive Endurance Exercise in Recreational Endurance Athletes
Authors: Valder Sarah , Habersatter Elisabeth , Kostov Tihomir , Quenzer Sina , Herzig Lukas , von Bernuth Jakob , Matits Lynn , Herdegen Volker , Diel Patrick , Isenmann Eduard
Published: May 2024

Background: Various nutritional strategies are increasingly used in sports to reduce oxidative stress and promote recovery. Chokeberry is rich in polyphenols and can reduce oxidative stress. Consequently, chokeberry juices and mixed juices with chokeberry content are increasingly used in sports. However, the data are very limited. Therefore, this study investigates the effects of the short-term supplementation of a red fruit juice drink with chokeberry content or a placebo on muscle damage, oxidative status, and leg strength during a six-day intense endurance protocol. Methods: Eighteen recreational endurance athletes participated in a cross-over high intensity interval training (HIIT) design, receiving either juice or a placebo. Baseline and post-exercise assessments included blood samples, anthropometric data, and leg strength measurements. Results: A significant increase was measured in muscle damage following the endurance protocol in all participants (∆ CK juice: 117.12 ± 191.75 U/L, ∆ CK placebo: 164.35 ± 267.00 U/L; p = 0.001, η2 = 0.17). No group effects were detected in exercise-induced muscle damage (p = 0.371, η2 = 0.010) and oxidative status (p = 0.632, η2 = 0.000). The reduction in strength was stronger in the placebo group, but group effects are missing statistical significance (∆ e1RM juice: 1.34 ± 9.26 kg, ∆ e1RM placebo: −3.33 ± 11.49 kg; p = 0.988, η2 = 0.000). Conclusion: Although a reduction in strength can be interpreted for the placebo treatment, no statistically significant influence of chokeberry could be determined. It appears that potential effects may only occur with prolonged application and a higher content of polyphenols, but further research is needed to confirm this.
Synthesis, Activity, Toxicity, and In Silico Studies of New Antimycobacterial N-Alkyl Nitrobenzamides
Authors: Pais João P. , Antoniuk Olha , Pires David , Delgado Tiago , Fortuna Andreia , Costa Paulo J. , Anes Elsa , Constantino Luis
Published: May 2024

Tuberculosis (TB) is a disease that plagues the frailest members of society. We have developed a family of N-alkyl nitrobenzamides that exhibit promising antitubercular activities and can be considered a structural simplification of known inhibitors of decaprenylphosphoryl-β-D-ribofuranose 2′-oxidase (DprE1), an essential Mycobacterium tuberculosis (Mtb) enzyme and an emergent antitubercular target. Hereby, we report the development of these compounds via a simple synthetic methodology as well as their stability, cytotoxicity, and antitubercular activity. Studying their in vitro activity revealed that the 3,5-dinitro and the 3-nitro-5-trifluoromethyl derivatives were the most active, and within these, the derivatives with intermediate lipophilicities presented the best activities (MIC of 16 ng/mL). Additionally, in an ex vivo macrophage model of infection, the derivatives with chain lengths of six and twelve carbon atoms presented the best results, exhibiting activity profiles comparable to isoniazid. Although the proof is not definite, the assessment of susceptibility over multiple mycobacterial species, together with the structure similarities with known inhibitors of this enzyme, support DprE1 as a likely target of action for the compounds. This idea is also reinforced by the docking studies, where the fit of our more active compounds to the DprE1 binding pocket is very similar to what was observed for known inhibitors like DNB1.
Improved Mechanical Performance in FDM Cellular Frame Structures through Partial Incorporation of Faces
Authors: Ghosh Mahan , D’Souza Nandika Anne
Published: May 2024

The utilization of lattice-type cellular architectures has seen a significant increase, owing to their predictable shape and the ability to fabricate templated porous materials through low-cost 3D-printing methods. Frames based on atomic lattice structures such as face-centered cubic (FCC), body-centered cubic (BCC), or simple cubic (SC) have been utilized. In FDM, the mechanical performance has been impeded by stress concentration at the nodes and melt-solidification interfaces arising from layer-by-layer deposition. Adding plates to the frames has resulted in improvements with a concurrent increase in weight and hot-pocket-induced dimensional impact in the closed cells formed. In this paper, we explore compressive performance from the partial addition of plates to the frames of a SC-BCC lattice. Compression testing of both single unit cells and 4 × 4 × 4 lattices in all three axial directions is conducted to examine stress transfer to the nearest neighbor and assess scale-up stress transfer. Our findings reveal that hybrid lattice structure unit cells exhibit significantly improved modulus in the range of 125% to 393%, specific modulus in the range of 13% to 120%, and energy absorption in the range of 17% to 395% over the open lattice. The scaled-up lattice modulus increased by 8% to 400%, specific modulus by 2% to 107%, and energy absorption by 37% to 553% over the lattice frame. Parameters that emerged as key to improved lightweighting.
A Color-Detectable Vitamin C Controlled-Release System Fabricated Using Electrospinning
Authors: Shin Min Jae
Published: May 2024

This study develops a vitamin C controlled-release system, trackable via color changes as a function of vitamin C release. The system is composed of coaxial microfibers prepared via coaxial electrospinning, with a core of poly(ethylene oxide) (PEO) incorporating vitamin C, and a shell composed of polycaprolactone (PCL) containing polydiacetylene (PDA) as the color-changing material. The shell thickness is controlled by adjusting the amount of PCL ejected during electrospinning, allowing regulation of the release rate of vitamin C. When vitamin C added to PEO penetrates the PCL layer, the color of PDA changes from blue to red, indicating a color change. The results of this study can be applied to devices that require immediate detection of vitamin C release levels.
Utilizing Recycled Expanded Polystyrene Plastics to Stabilize Metal–Organic Frameworks for Heterogeneous Catalysis
Authors: Yin Ruizhi , Shen Enxi , Liang Chenjia , Song Dezhong , El Hankari Samir , Huo Jia
Published: May 2024

Polystyrene plastics present significant environmental and human health threats due to their poor recyclability and degradability. However, leveraging their properties to enhance material performance stands out as one of the most effective strategies for mitigating these issues. Here, we have employed recycled expanded polystyrene plastics to manufacture metal–organic framework/expanded polystyrene plastic composites (MOF@EPP) using an adverse solvent precipitation method. This method simultaneously recycles EPPs and safeguards moisture-sensitive MOFs. Due to the exceptional hydrophobic properties of EPPs, HKUST−1@EPP can maintain structural integrity even when immersed in water for 30 days. This method is applicable to other moisture-sensitive MOFs, such as MOF−74(Zn) and MIL−53(Al). The HKUST−1@EPP composite also exhibits desirable heterogeneous catalytic activity in the Knoevenagel condensation reaction between benzaldehyde and acrylonitrile. The conversion rate can reach 94.9% within 4 h at 90 °C and does not exhibit a significant decrease even after six cycles, even in the presence of water. This study not only introduces a novel concept for recycling polystyrene plastics, but also offers a practical strategy for safeguarding moisture-sensitive MOFs.
Influence of Supraglacial Lakes on Accuracy of Inversion of Greenland Ice Sheet Surface Melt Data in Different Passive Microwave Bands
Authors: Li Qian , Wang Che , An Lu , Ding Minghu
Published: May 2024

The occurrence of Supraglacial Lakes (SGLs) may influence the signals acquired with microwave radiometers, which may result in a degree of uncertainty when employing microwave radiometer data for the detection of surface melt. Accurate monitoring of surface melting requires a reasonable assessment of this uncertainty. However, there is a scarcity of research in this field. Therefore, in this study, we computed surface melt in the vicinity of Automatic Weather Stations (AWSs) by employing Defense Meteorological Satellite Program (DMSP) Ka-band data and Soil Moisture and Ocean Salinity (SMOS) satellite L-band data and extracted SGL pixels by utilizing Sentinel-2 data. A comparison between surface melt results derived from AWS air temperature estimates and those obtained with remote sensing inversion in the two different bands was conducted for sites below the mean snowline elevation during the summers of 2016 to 2020. Compared with sites with no SGLs, the commission error (CO) of DMSP morning and evening data at sites where these water bodies were present increased by 36% and 30%, respectively, and the number of days with CO increased by 12 and 3 days, respectively. The omission error (OM) of SMOS morning and evening data increased by 33% and 32%, respectively, and the number of days with OM increased by 17 and 21 days, respectively. Identifying the source of error is a prerequisite for the improvement of surface melt algorithms, for which this study provides a basis.
Determining Riverine Surface Roughness at Fluvial Mesohabitat Level and Its Influence on UAV-Based Thermal Imaging Accuracy
Authors: Kuhn Johannes , Pander Joachim , Habersetzer Luis , Casas-Mulet Roser , Geist Juergen
Published: May 2024

Water surface roughness (SR) is a highly relevant parameter governing data reliability in remote sensing applications, yet lacking appropriate methodology in riverine habitats. In order to assess thermal accuracy linked to SR of thermal imaging derived from an unmanned aerial vehicle (UAV), we developed the SR Measurement Device (SRMD). The SRMD uses the concept of in situ quantification of wave frequency and wave amplitude. Data of nine installed SRMDs in four different fluvial mesohabitat classes presented a range of 0 to 47 waves per 30 s and an amplitude range of 0 to 6 cm. Even subtle differences between mesohabitat classes run, riffle, and no-/low-flow still and pool areas could be detected with the SRMD. However, SR revealed no significant influence on the accuracy of thermal infrared (TIR) imagery data in our study case. Overall, the presented device expands existing methods of riverine habitat assessments and has the potential to produce highly relevant data of SR for various ecological and technical applications, ranging from remote sensing of surface water and habitat quality characterizations to bank stability and erosion risk assessments.
Consistency of Aerosol Optical Properties between MODIS Satellite Retrievals and AERONET over a 14-Year Period in Central–East Europe
Authors: Deaconu Lucia-Timea , Mereuță Alexandru , Radovici Andrei , Ștefănie Horațiu Ioan , Botezan Camelia , Ajtai Nicolae
Published: May 2024

Aerosols influence Earth’s climate by interacting with radiation and clouds. Remote sensing techniques aim to enhance our understanding of aerosol forcing using ground-based and satellite retrievals. Despite technological advancements, challenges persist in reducing uncertainties in satellite remote sensing. Our study examines retrieval biases in MODIS sensors on Terra and Aqua satellites compared to AERONET ground-based measurements. We assess their performance and the correlation with the AERONET aerosol optical depth (AOD) using 14 years of data (2010–2023) from 29 AERONET stations across 10 Central–East European countries. The results indicate discrepancies between MODIS Terra and Aqua retrievals: Terra overestimates the AOD at 16 AERONET stations, while Aqua underestimates the AOD at 21 stations. The examination of temporal biases in the AOD using the calculated estimated error (ER) between AERONET and MODIS retrievals reveals a notable seasonality in coincident retrievals. Both sensors show higher positive AOD biases against AERONET in spring and summer compared to fall and winter, with few ER values for Aqua indicating poor agreement with AERONET. Seasonal variations in correlation strength were noted, with significant improvements from winter to summer (from R2 of 0.58 in winter to R2 of 0.76 in summer for MODIS Terra and from R2 of 0.53 in winter to R2 of 0.74 in summer for MODIS Aqua). Over the fourteen-year period, monthly mean aerosol AOD trends indicate a decrease of −0.00027 from AERONET retrievals and negative monthly mean trends of the AOD from collocated MODIS Terra and Aqua retrievals of −0.00023 and −0.00025, respectively. An aerosol classification analysis showed that mixed aerosols comprised over 30% of the total aerosol composition, while polluted aerosols accounted for more than 22%, and continental aerosols contributed between 22% and 24%. The remaining 20% consists of biomass-burning, dust, and marine aerosols. Based on the aerosol classification method, we computed the bias between the AERONET AE and MODIS AE, which showed higher AE values for AERONET retrievals for a mixture of aerosols and biomass burning, while for marine aerosols, the MODIS AE was larger and for dust the results were inconclusive.
Analysis of Meteorological Drivers of Taihu Lake Algal Blooms over the Past Two Decades and Development of a VOCs Emission Inventory for Algal Bloom
Authors: Liao Zihang , Lv Shun , Zhang Chenwu , Zha Yong , Wang Suyang , Shao Min
Published: May 2024

Cyanobacterial blooms represent a common environmental issue in aquatic systems, and these blooms bring forth numerous hazards, with the generation of volatile organic compounds (VOCs) being one of them. Global climate change has led to alterations in various climatic factors affecting algal growth, indirectly impacting the quantity of VOCs released by algae. With advancements in remote sensing technology, exploration of the spatiotemporal distributions of algae in large water bodies has become feasible. This study focuses on Taihu Lake, characterized by frequent occurrences of cyanobacterial blooms. Utilizing MODIS satellite imagery from 2001 to 2020, we analyzed the spatiotemporal characteristics of cyanobacterial blooms in Taihu Lake and its subregions. Employing the LightGBM machine learning model and the (SHapley Additive exPlanations) SHAP values, we quantitatively analyzed the major meteorological drivers influencing cyanobacterial blooms in each region. VOC-related source spectra and emission intensities from cyanobacteria in Taihu Lake are collected based on the literature review and are used to compile the first inventory of VOC emissions from blue-green algae blooms in Taihu Lake. The results indicate that since the 21st century, the situation of cyanobacterial blooms in Taihu Lake has continued to deteriorate with increasing variability. The relative impact of meteorological factors varies across different regions, but temperature consistently shows the highest sensitivity in all areas. The VOCs released from the algal blooms increase with the proliferation of the blooms, posing a continuous threat to the atmospheric environment of the surrounding cities. This study aims to provide a scientific basis for further improvement of air quality in urban areas adjacent to large lakes.
A Signal Matching Method of In-Orbit Calibration of Altimeter in Tracking Mode Based on Transponder
Authors: Fang Qingyu , Guo Wei , Wang Caiyun , Liu Peng , Wang Te , Han Sijia , Yang Shijie , Zhang Yufei , Peng Hailong , Ma Chaofei , Mu Bo
Published: May 2024

In this paper, a matching method for altimeter and transponder signals in Sub-optimal Maximum Likelihood Estimate (SMLE) tracking mode is proposed. In the in-orbit calibration of the altimeter in SMLE tracking mode using the reconstructive transponder, it is necessary to separate the forwarding signal from the ground echo signal. At the same time, the fluctuations in the received signal of the altimeter, which are caused by the forwarding signal of the transponder, can be eliminated. The transponder generates a bias when measuring the arrival time of the transmitting signal from the altimeter and embeds this bias in both the transponder-recorded data and the altimeter-recorded data. Therefore, the two sets of data have one-to-one correspondence, and they are superimposed using the sliding sum method. Moreover, the distance between the altimeter and the transponder is a parabolic geometric relationship, and the outliers are eliminated by the fitting error minimization decision, and the transponder signal is separated from the ground echo. The final altimeter transmitting–receiving signal path is obtained. Furthermore, the principles underlying this method can be used for any transponder that can adjust the response signal delay during calibration.
A Novel Multi-Scale Feature Map Fusion for Oil Spill Detection of SAR Remote Sensing
Authors: Li Chunshan , Yang Yushuai , Yang Xiaofei , Chu Dianhui , Cao Weijia
Published: May 2024

The efficient and timely identification of oil spill areas is crucial for ocean environmental protection. Synthetic aperture radar (SAR) is widely used in oil spill detection due to its all-weather monitoring capability. Meanwhile, existing deep learning-based oil spill detection methods mainly rely on the classical U-Net framework and have achieved impressive results. However, SAR images exhibit high noise, blurry boundaries, and irregular shapes of target areas, as well as speckles and shadows, which lead to the loss of performance in existing algorithms. In this paper, we propose a novel network architecture to achieve more precise segmentation of oil spill areas by reintroducing rich semantic contextual information before obtaining the final segmentation mask. Specifically, the proposed architecture can re-fuse feature maps from different levels at the decoder end. We design a multi-convolutional layer (MCL) module to extract basic feature information from SAR images, and a feature extraction module (FEM) module further extracts and fuses feature maps generated by the U-Net decoder at different levels. Through these operations, the network can learn rich global and local contextual information, enable sufficient interaction of feature information at different stages, enhance the model’s contextual awareness, and improve its ability to recognize complex textures and blurry boundaries, thereby enhancing the segmentation accuracy of SAR images. Compared to many U-Net based segmentation networks, our method shows promising results and achieves state-of-the-art performance on multiple evaluation metrics.
High-Speed Spatial–Temporal Saliency Model: A Novel Detection Method for Infrared Small Moving Targets Based on a Vectorized Guided Filter
Authors: Aliha Aersi , Liu Yuhan , Zhou Guangyao , Hu Yuxin
Published: May 2024

Infrared (IR) imaging-based detection systems are of vital significance in the domains of early warning and security, necessitating a high level of precision and efficiency in infrared small moving target detection. IR targets often appear dim and small relative to the background and are easily buried by noise and difficult to detect. A novel high-speed spatial–temporal saliency model (HS-STSM) based on a guided filter (GF) is proposed, which innovatively introduces GF into IR target detection to extract the local anisotropy saliency in the spatial domain, and substantially suppresses the background region as well as the bright clutter false alarms present in the background. Moreover, the proposed model extracts the motion saliency of the target in the temporal domain through vectorization of IR image sequences. Additionally, the proposed model significantly improves the detection efficiency through a vectorized filtering process and effectively suppresses edge components in the background by integrating a prior weight. Experiments conducted on five real infrared image sequences demonstrate the superior performance of the model compared to existing algorithms in terms of the detection rate, noise suppression, real-time processing, and robustness to the background.
Spectral Superresolution Using Transformer with Convolutional Spectral Self-Attention
Authors: Liao Xiaomei , He Lirong , Mao Jiayou , Xu Meng
Published: May 2024

Hyperspectral images (HSI) find extensive application across numerous domains of study. Spectral superresolution (SSR) refers to reconstructing HSIs from readily available RGB images using the mapping relationships between RGB images and HSIs. In recent years, convolutional neural networks (CNNs) have become widely adopted in SSR research, primarily because of their exceptional ability to extract features. However, most current CNN-based algorithms are weak in terms of extracting the spectral features of HSIs. While certain algorithms can reconstruct HSIs through the fusion of spectral and spatial data, their practical effectiveness is hindered by their substantial computational complexity. In light of these challenges, we propose a lightweight network, Transformer with convolutional spectral self-attention (TCSSA), for SSR. TCSSA comprises a CNN-Transformer encoder and a CNN-Transformer decoder, in which the convolutional spectral self-attention blocks (CSSABs) are the basic modules. Multiple cascaded encoding and decoding modules within TCSSA facilitate the efficient extraction of spatial and spectral contextual information from HSIs. The convolutional spectral self-attention (CSSA) as the basic unit of CSSAB combines CNN with self-attention in the transformer, effectively extracting both spatial local features and global spectral features from HSIs. Experimental validation of TCSSA’s effectiveness is performed on three distinct datasets: GF5 for remote sensing images along with CAVE and NTIRE2022 for natural images. The experimental results demonstrate that the proposed method achieves a harmonious balance between reconstruction performance and computational complexity.
Printed Thick Film Resistance Temperature Detector for Real-Time Tube Furnace Temperature Monitoring
Authors: Hai Zhenyin , Su Zhixuan , Zhu Kaibo , Pan Yue , Luo Suying
Published: May 2024

Accurately acquiring crucial data on tube furnaces and real-time temperature monitoring of different temperature zones is vital for material synthesis technology in production. However, it is difficult to achieve real-time monitoring of the temperature field of tube furnaces with existing technology. Here, we proposed a method to fabricate silver (Ag) resistance temperature detectors (RTDs) based on a blade-coating process directly on the surface of a quartz ring, which enables precise positioning and real-time temperature monitoring of tube furnaces within 100–600 °C range. The Ag RTDs exhibited outstanding electrical properties, featuring a temperature coefficient of resistance (TCR) of 2854 ppm/°C, an accuracy of 1.8% FS (full scale), and a resistance drift rate of 0.05%/h over 6 h at 600 °C. These features ensured accurate and stable temperature measurement at high temperatures. For demonstration purposes, an array comprising four Ag RTDs was installed in a tube furnace. The measured average temperature gradient in the central region of the tube furnace was 5.7 °C/mm. Furthermore, successful real-time monitoring of temperature during the alloy sintering process revealed approximately a 20-fold difference in resistivity for silver-palladium alloys sintered at various positions within the tubular furnace. The proposed strategy offers a promising approach for real-time temperature monitoring of tube furnaces.
Electric Susceptibility at Partial Coverage of a Circular One-Side Access Capacitive Sensor with Rigid Polyurethane Foams
Authors: Beverte Ilze
Published: May 2024

The capability of dielectric measurements was significantly increased with the development of capacitive one-side access physical sensors. Complete samples give no opportunity to study electric susceptibility at a partial coverage of the one-side access sensor’s active area; therefore, partial samples are proposed. The electric susceptibility at the partial coverage of a circular one-side access sensor with cylinders and shells is investigated for polyurethane materials. The implementation of the relative partial susceptibility permitted us to transform the calculated susceptibility data to a common scale of 0.0–1.0 and to outline the main trends for PU materials. The partial susceptibility, relative partial susceptibility, and change rate of relative partial susceptibility exhibited dependence on the coverage coefficient of the sensor’s active area. The overall character of the curves for the change rate of the relative partial susceptibility, characterised by slopes of lines and the ratio of the change rate in the centre and near the gap, corresponds with the character of the surface charge density distribution curves, calculated from mathematical models. The elaborated methods may be useful in the design and optimization of capacitive OSA sensors of other configurations of electrodes, independent of the particular technical solution.
Human Activity Recognition Algorithm with Physiological and Inertial Signals Fusion: Photoplethysmography, Electrodermal Activity, and Accelerometry
Authors: Gilmore Justin , Nasseri Mona
Published: May 2024

Inertial signals are the most widely used signals in human activity recognition (HAR) applications, and extensive research has been performed on developing HAR classifiers using accelerometer and gyroscope data. This study aimed to investigate the potential enhancement of HAR models through the fusion of biological signals with inertial signals. The classification of eight common low-, medium-, and high-intensity activities was assessed using machine learning (ML) algorithms, trained on accelerometer (ACC), blood volume pulse (BVP), and electrodermal activity (EDA) data obtained from a wrist-worn sensor. Two types of ML algorithms were employed: a random forest (RF) trained on features; and a pre-trained deep learning (DL) network (ResNet-18) trained on spectrogram images. Evaluation was conducted on both individual activities and more generalized activity groups, based on similar intensity. Results indicated that RF classifiers outperformed corresponding DL classifiers at both individual and grouped levels. However, the fusion of EDA and BVP signals with ACC data improved DL classifier performance compared to a baseline DL model with ACC-only data. The best performance was achieved by a classifier trained on a combination of ACC, EDA, and BVP images, yielding F1-scores of 69 and 87 for individual and grouped activity classifications, respectively. For DL models trained with additional biological signals, almost all individual activity classifications showed improvement (p-value < 0.05). In grouped activity classifications, DL model performance was enhanced for low- and medium-intensity activities. Exploring the classification of two specific activities, ascending/descending stairs and cycling, revealed significantly improved results using a DL model trained on combined ACC, BVP, and EDA spectrogram images (p-value < 0.05).
In Situ Assessment of Uplink Duty Cycles for 4G and 5G Wireless Communications
Authors: Vermeeren Günter , Verloock Leen , Aerts Sam , Martens Luc , Joseph Wout
Published: May 2024

In this presented study, we measured in situ the uplink duty cycles of a smartphone for 5G NR and 4G LTE for a total of six use cases covering voice, video, and data applications. The duty cycles were assessed at ten positions near a 4G and 5G base-station site in Belgium. For Twitch, VoLTE, and WhatsApp, the duty cycles ranged between 4% and 22% in time, both for 4G and 5G. For 5G NR, these duty cycles resulted in a higher UL-allotted time due to time division duplexing at the 3.7 GHz frequency band. Ping showed median duty cycles of 2% for 5G NR and 50% for 4G LTE. FTP upload and iPerf resulted in duty cycles close to 100%.
Photothermal Radiometry Data Analysis by Using Machine Learning
Authors: Xiao Perry , Chen Daqing
Published: May 2024

Photothermal techniques are infrared remote sensing techniques that have been used for biomedical applications, as well as industrial non-destructive testing (NDT). Machine learning is a branch of artificial intelligence, which includes a set of algorithms for learning from past data and analyzing new data, without being explicitly programmed to do so. In this paper, we first review the latest development of machine learning and its applications in photothermal techniques. Next, we present our latest work on machine learning for data analysis in opto-thermal transient emission radiometry (OTTER), which is a type of photothermal technique that has been extensively used in skin hydration, skin hydration depth profiles, skin pigments, as well as topically applied substances and skin penetration measurements. We have investigated different algorithms, such as random forest regression, gradient boosting regression, support vector machine (SVM) regression, and partial least squares regression, as well as deep learning neural network regression. We first introduce the theoretical background, then illustrate its applications with experimental results.
A Study on the Dynamic Evolution Paths of Social Risks in PPP Projects of Water Environmental Governance—From the Vulnerability Perspective
Authors: Chen Xu , Zhao Ying , Xue Song
Published: May 2024

The Chinese economy is transitioning from high-speed development to high-quality development, and water environmental governance is a key factor promoting economic transformation. Due to low returns and high investment in China’s water environmental governance, the PPP (public–private-partnership) model is often adopted. However, the PPP model has historically faced challenges adapting to local conditions in China, leading to social risks in the PPP projects of water environmental governance. To reduce these risks, this paper takes a vulnerability perspective, employing the system dynamics simulation method to explore the dynamic evolution process of social risks in the PPP projects of water environmental governance. The main results show the following: (1) the external manifestations of social risks during the construction and operation periods vary significantly, exhibiting notable fluctuations; (2) during the construction period, there is a surge in social risks followed by a returns to lower than normal levels, then a gradual upward trend; during the operation period, social risks initially decrease to a lower level before gradually increasing; and (3) city class, relevant legal systems, and resource reserves emerge as critical factors influencing vulnerability and social risks. The higher the city class, the lower the project vulnerability; the soundness of the legal system for PPP projects can effectively reduce vulnerability and social risks; sufficient resource reserves can reduce social risks. Based on the above findings, this paper proposes several suggestions aiming to reduce the vulnerability and social risks in PPP projects, optimize the process of water environmental governance, and further promote the sustainable development of water environmental governance and the high-quality economy of China.
Sustainable Food Value Chains: Approaches to Transaction Costs in Agro-Alimentary Systems of Developing Countries—A Chile Case Study
Authors: Valdés Rodrigo
Published: May 2024

This study delves into the dynamics of price linkages and transaction costs in agricultural markets, emphasizing the sustainability of food supply chains. By exploring vertical and horizontal price linkages in agro-farming value chains of a developing country, it addresses the efficiency of market information transmission and the capacity for arbitrage among chain participants. The methodological core of the research involves analyzing price linkages in Chilean horticultural wholesale markets, focusing on key vegetables such as, onions, lettuce, maize, and tomatoes. This analysis is underpinned by a novel approach that models and estimates time-dependent, conditional threshold bands, extending the traditional cointegration models. This method allows a more nuanced understanding of how agricultural market linkages evolve over time, enhancing our comprehension of price transmission behavior and market integration. The results reveal significant non-linear relationships between fuel prices and vegetable prices, particularly in central Chilean regions. This finding challenges the traditional linear perspective, suggesting that factors such as storage capacity and arbitrage behavior can influence price signal transmission. Such insights are crucial for stakeholders in the agribusiness value chain, offering a deeper understanding of market dynamics and aiding in the development of more sustainable and efficient market strategies. This research contributes significantly to the field of agricultural economics by providing a more robust framework for analyzing market behaviors and transaction costs in the context of sustainability and value chains. Its findings have profound implications for both theory and practice, informing policy-making and strategic decision-making in the agribusiness sector.
Delimitation and Prioritization of Climate-Affected Areas Using the Example of a Post-Industrial City
Authors: Bronder Joachim , Kurianowicz Anna , Piasecka Joanna , Krupanek Janusz , Krzyżak Jacek
Published: May 2024

This study adapted and tested an approach to identifying areas that are particularly vulnerable to climate-related hazards using the example of the post-industrial city of Knurów in Poland. This study applied a multi-criteria method using the analytical hierarchy process based on GIS map data. The analysis was divided into statistical regions defined for the city. Fifteen attributes were defined for each statistical region. The applied methods provided verified spatial information related to specific climate change hazards. The results showed that the most vulnerable areas were the areas with intensive development in the city center and in the southwestern part of the city. Among the 15 attributes, the most significant were T1 (number of inhabitants in zones with a higher potential thermal risk index by statistical district) with a value of 0.163, G2 (percentage of the sum of tree-shaded areas in built-up areas within the territory of a given statistical district) with 0.143 and H3 (number of buildings in areas of drainless basins and 100-year water). This method effectively identified the most vulnerable areas. The use of such a method can help in the preparation of planning documents and urban adaptation plans by determining the thermally and hydrologically vulnerable areas with the least developed green infrastructure as an exposure-mitigating factor.
Environmental Regulations and Urban Technological Innovation: China’s Two Control Zones Policy as Evidence
Authors: Zhu Boen , Zhou Yujie
Published: May 2024

In China, the Two Control Zones (TCZ) policy is an important practice in promoting sustainable development. This study aimed to investigate whether the TCZ policy promotes urban technological innovation. In this research, a DID model was built with the TCZ policy as an exogenous shock using panel data covering over 223 Chinese cities from 1995 to 2003. The empirical outcomes indicate that the TCZ policy is beneficial for augmenting urban technological innovation. Through heterogeneity analysis, it is further observed that the promotional effects of technological innovation resulting from the TCZ policy are highest in the Western region, followed by the Eastern region, and finally the Central region, and higher in cities with a higher proportion of state-owned enterprises than in cities with a higher proportion of non-state-owned enterprises. The findings of this paper align with Porter’s hypothesis and hold significant importance for other countries in devising appropriate environmental regulation policies to facilitate sustainable development.
Variations in Gender Perceptions of Summer Comfort and Adaptation in Colonial Revival-Style Homes
Authors: Adekunle Timothy O.
Published: May 2024

Past investigations have assessed gender variations in thermal comfort and adaptation in different buildings. However, no reported study has evaluated differences in gender perceptions of the thermal environment in US Colonial Revival-style homes. As a result, this study aims to provide an understanding of variations in gender perceptions of summer comfort and adaptation in the buildings. The study evaluated data collected during the field studies of thermal comfort (FSTC), including physical measurements of environmental variables and subjects’ votes. In this study, 67% females and 33% males participated in the field surveys in summertime. The mean neutral temperature of 25.5 °C and preferred temperature of 24.9 °C were noted for females. For males, the mean neutral and preferred temperatures of 25.8 °C and 25.6 °C were observed. Females feel neutral and prefer “no change” to the thermal environment at lower temperatures than males. A difference of 0.3 °C was noted between the average heat indices for females and males. Male respondents who feel warm perceive less air quality more than females. Females and males who perceive being thermally comfortable also perceive good air quality. Females and males who are thermally comfortable rated air humidity to be acceptable. There are similarities and differences between gender perceptions of the thermal environment. The investigation suggests interventions that can help users regulate their skin temperatures and use control measures that are sensitive to clothing insulation. The research also calls for policies that can promote building users’ pivotal adaptive measures to improve their indoor thermal environment. The study enhances our understanding of sustainable indoor environments and how to improve living conditions and adaptive modifications among different groups of occupants in buildings.
Tourism Development and Urban Landscape Conservation in Rural Areas: Opportunities and Ambivalences in Local Regulations—The Case of Spain
Authors: Albarrán José David
Published: May 2024

This article analyzes local-level normative regulations aimed at directly or indirectly conserving the urban landscape in rural areas. Using a discursive analysis methodology on regulatory documents being enforced in a series of localities assigned to a tourism promotion program, the evidence suggests that promotional activity retroactively influences the phraseology of these municipal regulations. The results obtained point to the existence of perpetuating historicist approaches within the current regulations, which appear to largely derive from the search for success in the tourism market and the resulting benefits to the local economy.
Bacterial Contamination of Syringes and Fluids in Diagnostic and Interventional Neuroangiography
Authors: Wiesmann Martin , Honecker Sophia , Fleu Claudia , Franz Christiane , Schmiech Manuela , Ridwan Hani , Bürkle Franziska , Nikoubashman Omid , Lemmen Sebastian
Published: May 2024

(1) Background: Bacterial contamination has been shown to occur during angiographies, although data on its frequency and relevance are sparse. Our aim was to evaluate the incidence of bacterial contamination of syringes used under sterile conditions during neuroangiographies. We sought to differentiate between contamination of the outside of the syringes and the inside and to detect the frequency, extent and germ spectrum of bacterial contamination. (2) Methods: We prospectively collected 600 samples from 100 neuroangiographies. Per angiography, fluid samples from the three routinely used syringes as well as the syringes themselves were analyzed. We analyzed the frequency and extent of contamination and determined the germ spectrum. (3) Results: The majority of samples (56.9%) were contaminated. There was no angiography that showed no contamination (0%). The outer surfaces of the syringes were contaminated significantly more frequently and to a higher extent than the inner surfaces. Both the frequency and extent of contamination of the samples increased with longer duration of angiographic procedures. Most of the bacterial species were environmental or skin germs (87.7%). (4) Conclusions: Bacterial contamination is a frequent finding during neuroangiographies, although its clinical significance is believed to be small. Bacterial contamination increases with longer duration of angiographic procedures.
Exploring the Relationship between the Dynamics of the Urban–Rural Interface and Regional Development in a Post-Socialist Transition
Authors: Ianoș Ioan , Cocheci Radu-Matei , Petrișor Alexandru-Ionuț
Published: May 2024

This study offers, by an empirical analysis, another perspective on post-socialist development, highlighting the role of the urban–rural interface in regional dynamics. The current literature on the relationships between both issues is not too rich and our paper analyzes the relationships between core cities, their peri-urban areas, and their regions, through a comparative overview of their growth over the last three decades. Romania, as a special case study for a contradictory transition, due to the great step from a drastic dictatorial regime to a democracy and a market economy, is a good example to test these complex relationships. Considering the new development trend at the urban–rural interfaces, our key idea was to depict their contribution to regional development (NUTS 3) compared to city cores. The second question was how this differentiated contribution can be measured, using the simplest tool. The starting point was the fact that population dynamics reflect all changes in the city core and at the urban–rural interface, and less so at a regional level. Consequently, we selected the dynamics of the number of inhabitants for the first two, as well as the dynamics of GDP per capita at the regional level. We found higher and significant correlations between GDP per capita and urban–rural interfaces, but no significant correlations in the case of city cores. Our conclusion is that, in the transition period, the dynamics of urban–rural interfaces influenced more regional development dynamics, than those of city cores. This means that urban–rural interfaces amplify the development coming from cities, adding their own contribution and then dissipating it regionally. Future research should identify what the urban–rural interface offers to regions, in addition to the city core.
Presence and Significance of Multiple Respiratory Viral Infections in Children Admitted to a Tertiary Pediatric Hospital in Italy
Authors: Di Maio Velia Chiara , Scutari Rossana , Forqué Lorena , Colagrossi Luna , Coltella Luana , Ranno Stefania , Linardos Giulia , Gentile Leonarda , Galeno Eugenia , Vittucci Anna Chiara , Pisani Mara , Cristaldi Sebastian , Villani Alberto , Raponi Massimiliano , Bernaschi Paola , Russo Cristina , Perno Carlo Federico
Published: May 2024

Viral co-infections are frequently observed among children, but whether specific viral interactions enhance or diminish the severity of respiratory disease is still controversial. This study aimed to investigate the type of viral mono- and co-infections by also evaluating viral correlations in 3525 respiratory samples from 3525 pediatric in/outpatients screened by the Allplex Respiratory Panel Assays and with a Severe Acute Respiratory Syndrome-COronaVirus 2 (SARS-CoV-2) test available. Overall, viral co-infections were detected in 37.8% of patients and were more frequently observed in specimens from children with lower respiratory tract infections compared to those with upper respiratory tract infections (47.1% vs. 36.0%, p = 0.003). SARS-CoV-2 and influenza A were more commonly detected in mono-infections, whereas human bocavirus showed the highest co-infection rate (87.8% in co-infection). After analyzing viral pairings using Spearman’s correlation test, it was noted that SARS-CoV-2 was negatively associated with all other respiratory viruses, whereas a markedly significant positive correlation (p < 0.001) was observed for five viral pairings (involving adenovirus/human bocavirus/human enterovirus/metapneumoviruses/rhinovirus). The correlation between co-infection and clinical outcome may be linked to the type of virus(es) involved in the co-infection rather than simple co-presence. Further studies dedicated to this important point are needed, since it has obvious implications from a diagnostic and clinical point of view.
Establishment of a Multilocus Sequence Typing Scheme for the Characterization of Avibacterium paragallinarum
Authors: Guo Mengjiao , Jin Yikun , Wang Haonan , Zhang Xiaorong , Wu Yantao
Published: May 2024

Infectious coryza is an acute respiratory infection caused by Avibacterium paragallinarum, which is widely distributed throughout the world. However, there is no effective molecular typing scheme to obtain basic knowledge about the Av. paragallinarum population structure. This study aimed to develop a multilocus sequence typing (MLST) scheme for Av. paragallinarum that allows for the worldwide comparison of sequence data. For this purpose, the genetic variability of 59 Av. paragallinarum strains from different geographical origins and serovars was analyzed to identify correlations. The MLST scheme was developed using seven conserved housekeeping genes, which identified eight STs that clustered all of the strains into three evolutionary branches. The analytical evaluation of the clone group relationship between the STs revealed two clone complexes (CC1 and CC2) and three singletons (ST2, ST5, and ST6). Most of the isolates from China belonged to ST1 and ST3 in CC1. ST8 from Peru and ST7 from North America together formed CC2. Our results showed that the Av. paragallinarum strains isolated from China had a distant genetic relationship with CC2, indicating strong regional specificity. The MLST scheme established in this study can monitor the dynamics and genetic differences of Av. paragallinarum transmission.