bluuebunny/crossref_metadata_2025 huggingface dataset:
Ex-Vivo Hippocampus Segmentation Using Diffusion-Weighted MRI
Authors: Tang  Haoteng , Dai Siyuan , Zou Eric M. , Liu Guodong , Ahearn Ryan , Krafty Ryan , Modo Michel , Zhan Liang
Published: March 2024

The hippocampus is a crucial brain structure involved in memory formation, spatial navigation, emotional regulation, and learning. An accurate MRI image segmentation of the human hippocampus plays an important role in multiple neuro-imaging research and clinical practice, such as diagnosing neurological diseases and guiding surgical interventions. While most hippocampus segmentation studies focus on using T1-weighted or T2-weighted MRI scans, we explore the use of diffusion-weighted MRI (dMRI), which offers unique insights into the microstructural properties of the hippocampus. Particularly, we utilize various anisotropy measures derived from diffusion MRI (dMRI), including fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity, for a multi-contrast deep learning approach to hippocampus segmentation. To exploit the unique benefits offered by various contrasts in dMRI images for accurate hippocampus segmentation, we introduce an innovative multimodal deep learning architecture integrating cross-attention mechanisms. Our proposed framework comprises a multi-head encoder designed to transform each contrast of dMRI images into distinct latent spaces, generating separate image feature maps. Subsequently, we employ a gated cross-attention unit following the encoder, which facilitates the creation of attention maps between every pair of image contrasts. These attention maps serve to enrich the feature maps, thereby enhancing their effectiveness for the segmentation task. In the final stage, a decoder is employed to produce segmentation predictions utilizing the attention-enhanced feature maps. The experimental outcomes demonstrate the efficacy of our framework in hippocampus segmentation and highlight the benefits of using multi-contrast images over single-contrast images in diffusion MRI image segmentation.
Testing Homogeneity of Proportion Ratios for Stratified Bilateral Correlated Data
Authors: Tian Wanqing , Ma Changxing
Published: March 2024

Intraclass correlation in bilateral data has been investigated in recent decades with various statistical methods. In practice, stratifying bilateral data by some control variables will provide more sophisticated statistical results to satisfy different research proposed in randomized clinical trials. In this article, we propose three test statistics (the likelihood ratio test, score test, and Wald-type test statistics) to evaluate the homogeneity of proportion ratios for stratified bilateral correlated data under an equal correlation assumption. Monte Carlo simulations of Type I error and power are performed, and the score test yields a robust outcome based on empirical Type I error and power. Lastly, two real data examples are conducted to illustrate the proposed three tests.
Memristor–CMOS Hybrid Circuits Implementing Event-Driven Neural Networks for Dynamic Vision Sensor Camera
Authors: Yoon Rina , Oh Seokjin , Cho Seungmyeong , Min Kyeong-Sik
Published: March 2024

For processing streaming events from a Dynamic Vision Sensor camera, two types of neural networks can be considered. One are spiking neural networks, where simple spike-based computation is suitable for low-power consumption, but the discontinuity in spikes can make the training complicated in terms of hardware. The other one are digital Complementary Metal Oxide Semiconductor (CMOS)-based neural networks that can be trained directly using the normal backpropagation algorithm. However, the hardware and energy overhead can be significantly large, because all streaming events must be accumulated and converted into histogram data, which requires a large amount of memory such as SRAM. In this paper, to combine the spike-based operation with the normal backpropagation algorithm, memristor–CMOS hybrid circuits are proposed for implementing event-driven neural networks in hardware. The proposed hybrid circuits are composed of input neurons, synaptic crossbars, hidden/output neurons, and a neural network’s controller. Firstly, the input neurons perform preprocessing for the DVS camera’s events. The events are converted to histogram data using very simple memristor-based latches in the input neurons. After preprocessing the events, the converted histogram data are delivered to an ANN implemented using synaptic memristor crossbars. The memristor crossbars can perform low-power Multiply–Accumulate (MAC) calculations according to the memristor’s current–voltage relationship. The hidden and output neurons can convert the crossbar’s column currents to the output voltages according to the Rectified Linear Unit (ReLU) activation function. The neural network’s controller adjusts the MAC calculation frequency according to the workload of the event computation. Moreover, the controller can disable the MAC calculation clock automatically to minimize unnecessary power consumption. The proposed hybrid circuits have been verified by circuit simulation for several event-based datasets such as POKER-DVS and MNIST-DVS. The circuit simulation results indicate that the neural network’s performance proposed in this paper is degraded by as low as 0.5% while saving as much as 79% in power consumption for POKER-DVS. The recognition rate of the proposed scheme is lower by 0.75% compared to the conventional one, for the MNIST-DVS dataset. In spite of this little loss, the power consumption can be reduced by as much as 75% for the proposed scheme.
Gestational Diabetes and the Gut Microbiota: Fibre and Polyphenol Supplementation as a Therapeutic Strategy
Authors: Pheiffer Carmen , Riedel Sylvia , Dias Stephanie , Adam Sumaiya
Published: March 2024

Gestational diabetes mellitus (GDM) is an escalating public health concern due to its association with short- and long-term adverse maternal and child health outcomes. Dysbiosis of microbiota within the gastrointestinal tract has been linked to the development of GDM. Modification of microbiota dysbiosis through dietary adjustments has attracted considerable attention as adjunct strategies to improve metabolic disease. Diets high in fibre and polyphenol content are associated with increased gut microbiota alpha diversity, reduced inflammation and oxidative processes and improved intestinal barrier function. This review explores the potential of fibre and polyphenol supplementation to prevent GDM by investigating their impact on gut microbiota composition and function.
Influence of Age of Infection on the Gut Microbiota in Worker Honey Bees (Apis mellifera iberiensis) Experimentally Infected with Nosema ceranae
Authors: Aguado-López Daniel , Urbieta Magro Almudena , Higes Mariano , Rodríguez Juan Miguel , Martín-Hernández Raquel
Published: March 2024

The gut microbiota of honey bees has received increasing interest in the past decades due to its crucial role in their health, and can be disrupted by pathogen infection. Nosema ceranae is an intracellular parasite that affects the epithelial cells of the midgut, altering gut homeostasis and representing a major threat to honey bees. Previous studies indicated that younger worker bees are more susceptible to experimental infection by this parasite, although the impact of infection and of age on the gut bacterial communities remains unclear. To address this, honey bees were experimentally infected with a consistent number of N. ceranae spores at various ages post-emergence (p.e.) and the gut bacteria 7 days post-infection (p.i.) were analysed using real-time quantitative PCR, with the results compared to non-infected controls. Infected bees had a significantly higher proportion and load of Gilliamella apicola. In respect to the age of infection, the bees infected just after emergence had elevated loads of G. apicola, Bifidobacterium asteroides, Bombilactobacillus spp., Lactobacillus spp., Bartonella apis, and Bombella apis. Moreover, the G. apicola load was higher in bees infected at nearly all ages, whereas older non-infected bees had higher loads of Bifidobacterium asteroides, Bombilactobacillus spp., Lactobacillus spp., Ba. apis, and Bo apis. These findings suggest that N. ceranae infection and, in particular, the age of bees at infection modulate the gut bacterial community, with G. apicola being the most severely affected species.
Comparison of Rhizosphere Bacterial Communities of Pinus squamata, a Plant Species with Extremely Small Populations (PSESP) in Different Conservation Sites
Authors: Li Fengrong , Lu Shugang , Sun Weibang
Published: March 2024

Pinus squamata is one of the most threatened conifer species in the world. It is endemic to northeastern Yunnan Province, China, and has been prioritized as a Plant Species with Extremely Small Populations (PSESP). The integrated study of soil properties and rhizosphere bacteria can assist conservation to understand the required conditions for the protection and survival of rare and endangered species. However, differences between the rhizospheric bacterial communities found in the soil surrounding P. squamata at different conservation sites remain unclear. In this study, Samples were collected from wild, ex situ, and reintroduced sites. Bacterial communities in different conservation sites of P. squamata rhizosphere soils were compared using Illumina sequencing. The soil physicochemical properties were determined, the relationships between the bacterial communities and soil physicochemical factors were analyzed, and the potential bacterial ecological functions were predicted. The reintroduced site Qiaojia (RQ) had the highest richness and diversity of bacterial community. Actinobacteria, Proteobacteria, and Acidobacteriota were the dominant phyla, and Bradyrhizobium, Mycobacterium, Acidothermus were the most abundant genera. Samples were scattered (R = 0.93, p = 0.001), indicating significant difference between the different conservation sites. The abundance of Mycobacterium differed between sites (0.01 < p ≤ 0.05), and the relative abundances of Bradyrhizobium and Acidothermus differed significantly among different sites (0.001 < p ≤ 0.01). Soil total potassium (TK) and available nitrogen (AN) were the main factors driving bacterial community at the phylum level (0.01 < p ≤ 0.05). This study generated the first insights into the diversity, compositions, and potential functions of bacterial communities associated with the rhizosphere soils of P. squamata in different conservation sites and provides a foundation to assess the effect of conservation based on bacterial diversity and plant growth-promoting rhizobacteria (PGPR) to guide future research into the conservation of P. squamata.
Unsupervised Clustering-Assisted Method for Consensual Quantitative Analysis of Methanol–Gasoline Blends by Raman Spectroscopy
Authors: Lu Biao , Wu Shilong , Liu Deliang , Wu Wenping , Zhou Wei , Yuan Lei-ming
Published: March 2024

Methanol–gasoline blends have emerged as a promising and environmentally friendly bio-fuel option, garnering widespread attention and promotion globally. The methanol content within these blends significantly influences their quality and combustion performance. This study explores the qualitative and qualitative analysis of methanol–gasoline blends using Raman spectroscopy coupled with machine learning methods. Experimentally, methanol–gasoline blends with varying methanol concentrations were artificially configured, commencing with initial market samples. For qualitative analysis, the partial least squares discriminant analysis (PLS-DA) model was employed to classify the categories of blends, demonstrating high prediction performance with an accuracy of nearly 100% classification. For the quantitative analysis, a consensus model was proposed to accurately predict the methanol content. It integrates member models developed on clustered variables, using the unsupervised clustering method of the self-organizing mapping neural network (SOM) to accomplish the regression prediction. The performance of this consensus model was systemically compared to that of the PLS model and uninformative variable elimination (UVE)–PLS model. Results revealed that the unsupervised consensus model outperformed other models in predicting the methanol content across various types of methanol gasoline blends. The correlation coefficients for prediction sets consistently exceeded 0.98. Consequently, Raman spectroscopy emerges as a suitable choice for both qualitative and quantitative analysis of methanol–gasoline blend quality. This study anticipates an increasing role for Raman spectroscopy in analysis of fuel composition.
A Study on the Biological Activity of Optically Pure Aziridine Phosphines and Phosphine Oxides
Authors: Kowalczyk Aleksandra , Pieczonka Adam M. , Kassassir Hassan , Rachwalski Michał , Stączek Paweł
Published: March 2024

A series of optically pure aziridine phosphines and their corresponding phosphine oxides were synthesized through established chemical methodologies. The compounds were systematically investigated for their biological properties. Notably, all synthesized compounds demonstrated moderate antibacterial activity only against the reference strain of Staphylococcus aureus. However, compounds 5 and 7 exhibited noteworthy cell viability inhibition of human cervical epithelioid carcinoma HeLa cells and endometrial adenocarcinoma Ishikawa cells. Further studies of these compounds revealed additional biological effects, including disruption of the cell membrane in high concentrations, cell cycle arrest in the S phase, and the induction of reactive oxygen species (ROS). Comparative analysis of the two classes of chiral organophosphorus derivatives of aziridines indicated that chiral phosphine oxides displayed significantly higher biological activity. Consequently, these findings suggest that chiral phosphine oxides may be potential candidates for the development of anticancer drugs. In light of the significant interest in preparations whose structure is based on a three-membered aziridine ring in terms of potential anticancer therapy, this research fits into the current research trend and should constitute a valuable addition to the current state of knowledge and the existing library of aziridine derivatives with anticancer properties.
Is Diet a Determining Factor in the Induction of Gingival Inflammation by Dental Plaque? A Secondary Analysis of Clinical Studies
Authors: Woelber Johan Peter , Bartha Valentin , Baumgartner Stefan , Tennert Christian , Schlagenhauf Ulrich , Ratka-Krüger Petra , Vach Kirstin
Published: March 2024

The aim was to determine the association between plaque and gingival inflammation reported by dietary interventions. Data of four clinical studies dealing with changed nutrition and gingival examination were reanalyzed with regard to gingival inflammation (GI), plaque (PI), and bleeding on probing (BOP). Dietary changes basically involved avoiding sugar, white flour and sweetened drinks and focusing on whole foods for 4 weeks. The control groups were to maintain their usual diet. All participants had to reduce their oral hygiene efforts. Linear regression models taking the clustering of the data due to several studies into account were applied. In total, data of 92 participants (control groups: 39, test-groups 53) were reanalyzed. While both groups showed a slight increase in dental plaque, only the test groups showed a significant decrease in inflammatory parameters: GI (mean value difference End-Baseline (Δ): −0.31 (±SD 0.36)) and BOP (Δ: −15.39% (±16.07)), both p < 0.001. In the control groups, there was a constant relation between PI and GI, while the experimental group showed a decreasing relationship in GI/PI (p = 0.016), and even an inverted relationship BOP/PI under a changed diet (p = 0.031). In conclusion, diet seems to be a determining factor how the gingiva reacts towards dental plaque.
Polymeric Nanoparticles Enable mRNA Transfection and Its Translation in Intervertebral Disc and Human Joint Cells, Except for M1 Macrophages
Authors: Muenzebrock Katrin Agnes , Ho Fiona Y. W. , Pontes Adriano P. , Jorquera-Cordero Carla , Utomo Lizette , Garcia Joao Pedro , Willems Paul C. , Welting Tim J. M. , Rip Jaap , Creemers Laura B.
Published: March 2024

Chronic lower back pain caused by intervertebral disc degeneration and osteoarthritis (OA) are highly prevalent chronic diseases. Although pain management and surgery can alleviate symptoms, no disease-modifying treatments are available. mRNA delivery could halt inflammation and degeneration and induce regeneration by overexpressing anti-inflammatory cytokines or growth factors involved in cartilage regeneration. Here, we investigated poly(amidoamine)-based polymeric nanoparticles to deliver mRNA to human joint and intervertebral disc cells. Human OA chondrocytes, human nucleus pulposus (NP) cells, human annulus fibrosus (AF) cells, fibroblast-like synoviocytes (FLS) and M1-like macrophages were cultured and transfected with uncoated or PGA-PEG-coated nanoparticles loaded with EGFP-encoding mRNA. Cell viability and transfection efficiency were analyzed for all cell types. Nanoparticle internalization was investigated in FLS and M1-like macrophages. No significant decrease in cell viability was observed in most conditions. Only macrophages showed a dose-dependent reduction of viability. Transfection with either nanoparticle version resulted in EGFP expression in NP cells, AF cells, OA chondrocytes and FLS. Macrophages showed internalization of nanoparticles by particle–cell co-localization, but no detectable expression of EGFP. Taken together, our data show that poly (amidoamine)-based nanoparticles can be used for mRNA delivery into cells of the human joint and intervertebral disc, indicating its potential future use as an mRNA delivery system in OA and IVDD, except for macrophages.
Synergy of Nd:YAG Picosecond Pulsed Laser Irradiation and Electrochemical Anodization in the Formation of TiO2 Nanostructures for the Photocatalytic Degradation of Pesticide Carbofuran
Authors: Tošić Miloš , Rajić Vladimir , Pjević Dejan , Stojadinović Stevan , Krstulović Nikša , Dimitrijević-Branković Suzana , Momčilović Miloš
Published: March 2024

This study proposes a simple and controlled method for producing TiO2 with phase junction, oxygen vacancies, and Ti3+ by combining picosecond pulsed laser irradiation and electrochemical anodization. Ti mesh was pretreated by irradiating with a picosecond pulsed laser technique using an Nd:YAG laser (1064 nm) at two fluencies, 15 J/cm2 and 30 J/cm2. The samples were then subjected to electrochemical anodization to form TiO2 nanotube arrays on the previously laser-treated surface. This study will investigate the possibility of forming TiO2 nanotube arrays on a pre-laser-treated Ti substrate and determine their physicochemical and photocatalytic properties. The samples were characterized by FESEM, XRD, Raman, XPS, and UV-Vis DRS. UV-Vis spectroscopy was used to observe the progress of photocatalytic degradation for all samples, and degradation products were determined using GC-MS. With the synergistic effects of phase junction, oxygen vacancies, and Ti3+, the laser-treated TiO2 with 30 J/cm2 showed a higher photocatalytic degradation rate (85.1%) of the pesticide carbofuran compared to non-laser-treated TiO2 (54.8%), remaining stable during successive degradation cycles, which has promising practical applications.
Enhancing Rubber Vulcanization Cure Kinetics: Lowering Vulcanization Temperature by Addition of MgO as Co-Cure Activator in ZnO-Based Cure Activator Systems
Authors: Alam Md Najib , Kumar Vineet , Jeong Seok U , Park Sang-Shin
Published: March 2024

Vulcanization is a chemical modification of rubber that requires a considerable amount of thermal energy. To save thermal energy, the kinetics of rubber vulcanization should be improved. In this article, the curing properties of rubber vulcanization are thoroughly investigated using the moving die rheometer (MDR) technique. To enhance the kinetics in different stages of ZnO-based sulfur vulcanization systems, small amounts of MgO were added. The results revealed that the small amount of 1 to 2 phr (per hundred grams of rubber) of MgO in the controlled 5 phr ZnO-based curing systems can significantly improve the curing kinetics. For example, the optimum curing time of 1 phr MgO added to the 5 phr ZnO-containing semi-efficient vulcanization system at different temperatures was more than half that of the controlled 5 phr ZnO-only compound. While maintaining a similar rate of vulcanization, the vulcanization temperature can be reduced by up to 20 °C by using MgO as a co-cure activator, which exhibits similar or better rheometric mechanical properties compared to the controlled compounds. With the addition of MgO as a co-cure activator, the vulcanization reactions become very fast, enabling vulcanization to be completed, even at the boiling point of water (100 °C) with an affordable curing time (<1 h). By reducing the vulcanization temperature, the scorch safety time can be enhanced in the ZnO/MgO-based binary cure activator-containing vulcanizates. Overall, MgO could be a potential candidate as a co-cure activator with ZnO for the vulcanization of rubber, offering better economical and eco-friendly methods.
A Molecularly Imprinted Fluorescence Sensor Based on Upconversion-Nanoparticle-Grafted Covalent Organic Frameworks for Specific Detection of Methimazole
Authors: Liu Yan , Zhao Tian , Li Shuzhen , Cao Yichuan , Fang Guozhen
Published: March 2024

Rapid detection and sensitive analysis of MMZ is of great importance for food safety. Herein, a fluorescent molecularly imprinted sensor based on upconversion nanoparticles (UCNPs) grafted onto covalent organic frameworks (COFs) was designed for the detection of MMZ. COFs with a high specific surface area and excellent affinity serve as substrates for grafting of UCNPs, which can inhibit the aggregation burst of UCNPs and improve the mass transfer rate of the sensor. Through a series of characterizations, it was found that the proposed UCNP-grafted COFs@MIP-based sensor had good optical stability, high adsorption efficiency, strong anti-interference ability, and high sensitivity owing to the integration of the advantages of UCNPs, COFs and MIPs. Under the optimal conditions, a good linear relationship was presented between the fluorescence intensity of UCNP-grafted COFs@MIPs and the methimazole concentration in the range of 0.05–3 mg L−1, and the detection limit was 3 μg L−1. The as-prepared UCNP-grafted COFs@MIPs were successfully applied for the detection of MMZ in actual samples, and the results were relevant with those determined by high-performance liquid chromatography. The sensor has good sensitivity, reusability, and high selectivity, which are highly valuable in the rapid analysis and detection of food safety.
Path Optimization of Aircraft-Gear-Tooth-Surface Detection Based on Improved Genetic Algorithm
Authors: Chu Xiaomeng , Zhou Zhiji
Published: March 2024

Aiming at the problems of low detection efficiency and complexity of aircraft gear tooth surfaces, a path optimization algorithm based on an improved genetic algorithm is proposed. The detection area of the tooth surface is planned, the sampling points of the tooth surface are determined based on the digital technology of the tooth surface, and the sampling mesh is obtained by the truncated plane method to reduce the sampling distortion of the shape and improve the sampling efficiency. Adaptive crossover and mutation probability are used to improve the convergence speed and accuracy of the genetic algorithm. The selected individuals of the binary tournament are used to guide the global optimal search by a simulated annealing algorithm, and the local optimal is avoided by the Metropolis criterion. In the simulation experiment, the proposed method and other algorithms are used to optimize the detection path. The optimized tooth-surface-detection path has the shortest distance and the shortest time, that is, the tooth-surface-detection path efficiency is improved, verifying the practicability of the algorithm.
Multi-Energy Flow Integrated Energy System Considering Economic Efficiency Targets: Capacity Allocation and Scheduling Study
Authors: Zhang Liwen , He Shan , Han Lu , Yuan Zhi , Xu Lijun
Published: March 2024

An integrated energy system (IES) breaks down barriers between different energy subsystems, enhancing energy reliability and efficiency. However, issues such as uneven equipment capacity allocation and suboptimal scheduling persist in multi-energy flow IES. To maximize economic benefits while ensuring energy balance and the operational characteristics of the equipment, a capacity matching optimization and scheduling strategy model for IES was developed. Firstly, mathematical models for the electricity, gas, and thermal networks within the IES were established. Secondly, considering the efficiency of energy conversion between different forms and constraints of energy storage in the electricity–thermal–gas interconnected energy system, optimization solutions were obtained using regional contraction algorithms and sequential quadratic programming methods. Finally, case studies conducted in a real park demonstrated that, through optimized capacity matching, unit prices for electricity, heat, and gas decreased by 39.9%, 90.5%, and 74.2%, respectively, effectively improving the economic viability of the system.
A Numerical Study on the Performance of a Pumping Station with Bell-Mouth-Based Vertical Pumps during an Accidental Shutdown
Authors: Sedlář Milan , Abrahámek Petr
Published: March 2024

This study presents a numerical simulation of a pump’s performance during a power-cut event and connected hazards resulting from the failure of non-return flap valves. The vertical mixed-flow pumps with suction bells were mounted inside the suction basins of a pumping station. Different regimes of the pump operation during the time were analyzed based on the pump’s 4-quadrant characteristics and the dynamics of rotating parts in the pump, gearbox and electric engine. The resulting development of flow rates, rotor speed and forces in the course of time were used to analyze the hazards of failure of any pumping system component and the flooding of the suction object and its surroundings. The presented results show a deep insight into the flow phenomena in vertical mixed-flow pumps with suction bells during the runaway process and confirm that the developed methodology can be successfully applied to monitor the critical regimes in a pumping station in real time. The simulations were verified with some experimentally obtained data.
A Production Prediction Model of Tight Gas Well Optimized with a Back Propagation (BP) Neural Network Based on the Sparrow Search Algorithm
Authors: Zhao Zhengyan , Ren Zongxiao , He Shun’an , Tang Shanjie , Tian Wei , Wang Xianwen , Zhao Hui , Fan Weichao , Yang Yang
Published: March 2024

The production of tight gas wells decreases rapidly, and the traditional method is difficult to accurately predict the production of tight gas wells. At present, intelligent algorithms based on big data have been applied in oil and gas well production prediction, but there are still some technical problems. For example, the traditional error back propagation neural network (BP) still has the problem of finding the local optimal value, resulting in low prediction accuracy. In order to solve this problem, this paper establishes the output prediction method of BP neural network optimized with the sparrow search algorithm (SSA), and optimizes the hyperparameters of BP network such as activation function, training function, hidden layer, and node number based on examples, and constructs a high-precision SSA-BP neural network model. Data from 20 tight gas wells, the SSA-BP neural network model, Hongyuan model, and Arps model are predicted and compared. The results indicate that when the proportion of the predicted data is 20%, the SSA-BP model predicts an average absolute mean percentage error of 20.16%. When the proportion of forecast data is 10% of the total data, the SSA-BP algorithm has high accuracy and high stability. When the proportion of predicted data is 10%, the mean absolute average percentage error is 3.97%, which provides a new method for tight gas well productivity prediction.
Research on the Influencing Factors of the Void Volume of Insoluble Sediment in Salt Cavern Gas Storage
Authors: Sun Xiangrui , Ding Guosheng , Li Kang , Xin Chuanqi , Wu Zhide , Gou Yanxia , Ran Li’na , Li Haitao , Bai Song , Wu Jia’nan
Published: March 2024

Utilizing voids of insoluble sediment (IS) to store gas is an effective way to improve the efficiency of salt cavern gas storage (SCGS) in China. In this study, a suitable method for predicting the void volume of insoluble sediments (VVISs) is established. This study explores three key factors affecting the VVISs through laboratory experiments. Firstly, in order to make the experimental results more in line with production realities, an analysis of the characteristics of IS in X SCGS was conducted to provide a basis for setting parameters for subsequent experiments. Secondly, experimental setups and methods for measuring the VVISs were designed. Finally, the experimental results were used to predict the VVISs in on-site cavity wells. The results indicate that the higher the proportion of quartz, illite, and large-grain particles in IS, the larger the VVISs. Under different parameters, the VVISs can account for approximately 10–40% of the IS accumulation volume. Different particle sizes can cause a variation of approximately 5–30% in the VVISs, while different mineral compositions can result in a difference of 6–23% in the VVISs. With increasing compaction pressure, the VVISs can decrease by around 5–80%. The prediction of the VVISs in on-site cavity wells shows a high degree of fit with empirical algorithms. This study can provide a reference basis for the utilization of the void space of IS in SCGS.
Effect of COVID-19 Lockdown on Urban Heat Island Dynamics in Prague, Czechia
Authors: Dogan Tugba , Urban Aleš , Hanel Martin
Published: March 2024

Urban heat islands (UHI) are a well-known phenomenon adversely affecting human health and urban environments. The worldwide COVID-19 lockdown in 2020 provided a unique opportunity to investigate the effects of decreased emission of air pollution and anthropogenic heat flux (AHF) on UHI. Although studies have suggested that reduced AHF during lockdown decreased atmospheric UHI (AUHI) and surface UHI (SUHI), these results contain inherent uncertainties due to unaccounted weather variability and urban-rural dynamics. Our study comprehensively analyzes the impact of the COVID-19 lockdown on AUHI and SUHI in Prague, Czechia. By selecting days with similar weather conditions, we examined changes in mean SUHI using MODIS satellite images and in AUHI based on air temperature from Prague weather stations for the Lockdown period during March–April 2020 versus a Reference period from March–April 2017–2019. Our results show that, in comparison to the Reference period, the Lockdown period was associated with a 15% (0.1 °C) reduction of SUHI in urbanized areas of Prague and a 0.7 °C decline in AUHI in the city center. Additionally, the observed decreases in satellite-based aerosol optical depth and nitrogen dioxide by 12% and 29%, respectively, support our hypothesis that the weakened UHI effects were linked to reduction in anthropogenic activities during the lockdown. Revealing the largest decrease of mean SUHI magnitude around the periphery of Prague, which has predominantly rural land cover, our study emphasizes the need to consider the effects of urban-rural dynamics when attributing changes in SUHI to AHF. Our findings provide additional insights into the role of reduced anthropogenic activities in UHI dynamics during the COVID-19 lockdown and offer policymakers a comprehensive understanding of how the complex interaction between urban and rural microclimate dynamics influences the SUHI phenomenon.
Joint Gravity and Magnetic Inversion Using CNNs’ Deep Learning
Authors: Bai Zhijing , Wang Yanfei , Wang Chenzhang , Yu Caixia , Lukyanenko Dmitry , Stepanova Inna , Yagola Anatoly G.
Published: March 2024

Enhancing the reliability of inversion results has always been a prominent issue in the field of geophysics. In recent years, data-driven inversion methods leveraging deep neural networks (DNNs) have gained prominence for their ability to address non-uniqueness issues and reduce computational costs compared to traditional physically model-driven methods. In this study, we propose a GMNet machine learning method, i.e., a CNN-based inversion method for gravity and magnetic field data. This method relies more on data-driven training, and in the prediction phase after the model is trained, it does not heavily depend on a priori assumptions, unlike traditional methods. By forward modeling gravity and magnetic fields, we obtain a substantial dataset to train the CNN model, enabling the direct mapping from field data to subsurface property distribution. Applying this method to synthetic data and one-field data yields promising inversion results.
Flexible Textile Sensors-Based Smart T-Shirt for Respiratory Monitoring: Design, Development, and Preliminary Validation
Authors: Romano Chiara , Lo Presti Daniela , Silvestri Sergio , Schena Emiliano , Massaroni Carlo
Published: March 2024

Respiratory rate (fR) monitoring through wearable devices is crucial in several scenarios, providing insights into well-being and sports performance while minimizing interference with daily activities. Strain sensors embedded into garments stand out but require thorough investigation for optimal deployment. Optimal sensor positioning is often overlooked, and when addressed, the quality of the respiratory signal is neglected. Additionally, sensor metrological characterization after sensor integration is often omitted. In this study, we present the design, development, and feasibility assessment of a smart t-shirt embedded with two flexible sensors for fR monitoring. Guided by a motion capture system, optimal sensor design and position on the chest wall were defined, considering both signal magnitude and quality. The sensors were developed, embedded into the wearable system, and metrologically characterized, demonstrating a remarkable response to both static (sensitivity 9.4 Ω⋅%−1 and 9.1 Ω⋅%−1 for sensor A and sensor B, respectively) and cyclic loads (min. hysteresis span 20.4% at 36 bpm obtained for sensor A). The feasibility of the wearable system was assessed on healthy volunteers both under static and dynamic conditions (such as running, walking, and climbing stairs). A mean absolute error of 0.32 bpm was obtained by averaging all subjects and tests using the combination of the two sensors. This value was lower than that obtained using both sensor A (0.53 bpm) and sensor B (0.78 bpm) individually. Our study highlights the importance of signal amplitude and quality in optimal sensor placement evaluation, as well as the characterization of the embedded sensors for metrological assessment.
Real-Time Direction Judgment System for Dual-Frequency Laser Interferometer
Authors: Zeng Qilin , Chen Wenwei , Du Hua , Zhang Wentao , Xiong Xianming , Zhao Zhengyi , Zhou Fangjun , Guo Xin , Xu Le
Published: March 2024

Current real-time direction judgment systems are inaccurate and insensitive, as well as limited by the sampling rate of analog-to-digital converters. To address this problem, we propose a dynamic real-time direction judgment system based on an integral dual-frequency laser interferometer and field-programmable gate array technology. The optoelectronic signals resulting from the introduction of a phase subdivision method based on the amplitude resolution of the laser interferometer when measuring displacement are analyzed. The proposed system integrates the optoelectronic signals to increase the accuracy of its direction judgments and ensures these direction judgments are made in real time by dynamically controlling the integration time. Several experiments were conducted to verify the performance of the proposed system. The results show that, compared with current real-time direction judgment systems, the proposed system makes accurate judgements during low-speed motions and can update directions within 0.125 cycles of the phase difference change at different speeds. Moreover, a sweep frequency experiment confirmed the system’s ability to effectively judge dynamic directions. The proposed system is capable of accurate and real-time directional judgment during low-speed movements of a table in motion.
Semaglutide as a Possible Calmodulin Binder: Ligand-Based Computational Analyses and Relevance to Its Associated Reward and Appetitive Behaviour Actions
Authors: Floresta Giuseppe , Arillotta Davide , Catalani Valeria , Papanti Pelletier Gabriele Duccio , Corkery John Martin , Guirguis Amira , Schifano Fabrizio
Published: March 2024

Semaglutide, a glucagon-like peptide-1 (GLP-1) receptor agonist, has gained considerable attention as a therapeutic agent for type 2 diabetes mellitus and obesity. Despite its clinical success, the precise mechanisms underlying its pharmacological effects remain incompletely understood. In this study, we employed ligand-based drug design strategies to investigate potential off-target interactions of semaglutide. Through a comprehensive in silico screening of semaglutide’s structural properties against a diverse panel of proteins, we have identified calmodulin (CaM) as a putative novel target of semaglutide. Molecular docking simulations revealed a strong interaction between semaglutide and CaM, characterized by favourable binding energies and a stable binding pose. Further molecular dynamics simulations confirmed the stability of the semaglutide–CaM complex, emphasizing the potential for a physiologically relevant interaction. In conclusion, our ligand-based drug design approach has uncovered calmodulin as a potential novel target of semaglutide. This discovery sheds light on the complex pharmacological profile of semaglutide and offers a promising direction for further research into the development of innovative therapeutic strategies for metabolic disorders. The CaM, and especially so the CaMKII, system is central in the experience of both drug- and natural-related reward. It is here hypothesized that, due to semaglutide binding, the reward pathway-based calmodulin system may be activated, and/or differently regulated. This may result in the positive semaglutide action on appetitive behaviour. Further studies are required to confirm these findings.
The Effects of Single-Bout Exercise Interventions with Different Exercise Modalities on Executive Function in Youths
Authors: Tam Chung-Kit , Chang Yu-Hua , Tan Kok-Hwa
Published: March 2024

Background: This study aimed to investigate how single-bout open-skill exercise (OSE), closed-skill exercise (CSE), and mixed-skill exercise intervention (MSE) influence executive function. Method: A total of 120 students aged between 18 and 25 were separated into three groups: closed-skill exercise, open-skill exercise, and mixed-skill exercise. A task-switching test was performed before and after a single bout of exercise intervention. The simple reaction time, choice reaction time, switch cost, and correction rate were tested in a task-switching test. The results were analyzed via a two-way analysis of variance, with a significance level of α = 0.05, to compare the effects of the intervention. Results: Only open-skill exercise exhibited a significant effect on the simple reaction time (p < 0.05). In terms of choice reaction time and switch cost, all three intervention groups exhibited significant improvements, with no significant differences observed between the three groups (p < 0.05). The correction rate did not show a significant effect post-intervention, and no significant differences were observed between the groups. The correction rate showed no significant effect after the intervention or between groups. Conclusion: All three types of exercise can shorten choice reaction time and switch cost, but only OSE can reduce simple reaction time.
Do Innovation and Entrepreneurship Support Policies Promote Urban Green Transformation?—The Mediating Role of Fiscal Technology Expenditure
Authors: Wen Junqi , Lan Yong , Bu Guoqin
Published: March 2024

This article takes the establishment of two batches of mass entrepreneurship and innovation demonstration bases in 2016 and 2017 as a quasi-natural experiment. It utilizes panel data from 284 cities in China from 2010 to 2021 to construct a multiple time point difference-in-difference (DID) model. This study finds that innovation and entrepreneurship support policies can effectively promote urban green transformation. The robustness of the model is ensured through parallel trend tests, placebo tests, difference-in-difference-in-difference models, PSM-DID, and other methods. A mechanism analysis reveals that fiscal technology expenditure partially mediates the process of innovation and entrepreneurship support policies affecting urban green transformation. A heterogeneity analysis indicates that innovation and entrepreneurship support policies have only a significant positive impact on the green transformation of eastern cities, (sub-)provincial cities, and cities with high green total factor productivity, suggesting that the effectiveness of innovation and entrepreneurship support policies may be influenced by the size of the urban economy. This article provides a theoretical basis and policy recommendations for better leveraging the effects of innovation and entrepreneurship support policies to address the dual challenges of economic and green transformation jointly.
Collaborative Control of Reactive Power and Voltage in a Coupled System Considering the Available Reactive Power Margin
Authors: Li Jiahe , Guo Dongbo , Liu Chuang , Gu Yichen , Duan Yonglin , Li Yangyang
Published: March 2024

This article proposes a hierarchical collaborative reactive power and voltage control method aimed at meeting the internal voltage requirements of a coupled system composed of centralized new energy stations such as wind and solar power and traditional thermal power units, with particular attention paid to the reactive power regulation ability of power units in new energy stations. This method is implemented in two layers: At the transmission layer, the overall reactive power compensation capacity of thermal power units and stations on the transmission lines within the system is determined according to the engineering requirements of different node voltages in the coupled system, the regulation voltages of the high-voltage grid-connected points and the point of common coupling (PCC) of the stations are found, and the actual regulation voltages of the PCCs of the stations are given as commands. At the station layer, based on determining reactive power distribution between power units and continuous reactive power compensation devices within each station, voltage optimization is carried out using the voltage command issued by the transmission layer as a reference. At the same time, consistency coordination between the transmission layer and the station layer is achieved based on the Analytical Target Cascading (ATC) method at different layers. Finally, simulation analysis was conducted on a regional coupled system in Dalian. The results showed that the method proposed in this paper can effectively control the voltage safety requirements of each region and PCC in the coupled system and, to some extent, improve the economic efficiency of system operation.
Perfluorooctanesulfonic Acid Alters Pro-Cancer Phenotypes and Metabolic and Transcriptional Signatures in Testicular Germ Cell Tumors
Authors: Boyd Raya I. , Shokry Doha , Fazal Zeeshan , Rennels Brayden C. , Freemantle Sarah J. , La Frano Michael R. , Prins Gail S. , Madak Erdogan Zeynep , Irudayaraj Joseph , Singh Ratnakar , Spinella Michael J.
Published: March 2024

The potential effects of poly- and perfluoroalkyl substances (PFAS) are a recently emergent human and environmental health concern. There is a consistent link between PFAS exposure and cancer, but the mechanisms are poorly understood. Although epidemiological evidence supporting PFAS exposure and cancer in general is conflicting, there is relatively strong evidence linking PFAS and testicular germ cell tumors (TGCTs). However, no mechanistic studies have been performed to date concerning PFAS and TGCTs. In this report, the effects of the legacy PFAS perfluorooctanesulfonic acid (PFOS) and the newer “clean energy” PFAS lithium bis(trifluoromethylsulfonyl)imide (LiTFSi, called HQ-115), on the tumorigenicity of TGCTs in mice, TGCT cell survival, and metabolite production, as well as gene regulation were investigated. In vitro, the proliferation and survival of both chemo-sensitive and -resistant TGCT cells were minimally affected by a wide range of PFOS and HQ-115 concentrations. However, both chemicals promoted the growth of TGCT cells in mouse xenografts at doses consistent with human exposure but induced minimal acute toxicity, as assessed by total body, kidney, and testis weight. PFOS, but not HQ-115, increased liver weight. Transcriptomic alterations of PFOS-exposed normal mouse testes were dominated by cancer-related pathways and gene expression alterations associated with the H3K27me3 polycomb pathway and DNA methylation, epigenetic pathways that were previously showed to be critical for the survival of TGCT cells after cisplatin-based chemotherapy. Similar patterns of PFOS-mediated gene expression occurred in PFOS-exposed cells in vitro. Metabolomic studies revealed that PFOS also altered metabolites associated with steroid biosynthesis and fatty acid metabolism in TGCT cells, consistent with the proposed ability of PFAS to mimic fatty acid-based ligands controlling lipid metabolism and the proposed role of PFAS as endocrine disrupters. Our data, is the first cell and animal based study on PFAS in TGCTs, support a pro-tumorigenic effect of PFAS on TGCT biology and suggests epigenetic, metabolic, and endocrine disruption as potential mechanisms of action that are consistent with the non-mutagenic nature of the PFAS class.
An In-Field Assessment of the P.ALP Device in Four Different Real Working Conditions: A Performance Evaluation in Particulate Matter Monitoring
Authors: Fanti Giacomo , Borghi Francesca , Campagnolo Davide , Rovelli Sabrina , Carminati Alessio , Zellino Carolina , Cattaneo Andrea , Cauda Emanuele , Spinazzè Andrea , Cavallo Domenico Maria
Published: March 2024

This study aimed to assess the performance, in terms of precision and accuracy, of a prototype (called “P.ALP”—Ph.D. Air Quality Low-cost Project) developed for monitoring PM2.5 concentration levels. Four prototypes were co-located with reference instrumentation in four different microenvironments simulating real-world and working conditions, namely (i) office, (ii) home, (iii) outdoor, and (iv) occupational environments. The devices were evaluated for a total of 20 monitoring days (approximately 168 h) under a wide range of PM2.5 concentrations. The performances of the prototypes (based on the light-scattering working principle) were tested through different statistical methods. After the data acquisition and data cleaning processes, a linear regression analysis was performed to assess the precision (by comparing all possible pairs of devices) and the accuracy (by comparing the prototypes against the reference instrumentation) of the P.ALP. Moreover, the United States Environmental Protection Agency (US EPA) criteria were applied to assess the possible usage of this instrumentation, and to evaluate the eventual error trends of the P.ALP in the data storage process, Bland–Altman plots were also adopted. The outcomes of this study underlined that the P.ALP performed differently depending on the microenvironment in which it was tested and, consequently, on the PM2.5 concentrations. The device can monitor PM2.5 variations with acceptable results, but the performance cannot be considered satisfactory at extremely low and remarkably high PM2.5 concentrations. Thanks to modular components and open-source software, the tested device has the potential to be customized and adapted to better fit specific study design needs, but it must be implemented with ad hoc calibration factors depending on the application before being used in field.