bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of Out-of-Hospital First Aid for Recovery of Spontaneous Circulation after Cardiac Arrest in Korea
Authors: Lee Hyeon-Ji , Choi Mi-Young , Choi Young-Soon
Published: January 2024

The characteristics of an individual patient experiencing out-of-hospital cardiac arrest who recovered spontaneous circulation with the assistance of witnesses and paramedics were examined. The analysis of bystander cardiopulmonary resuscitation (CPR) and the professional first aid efforts of paramedics in the pre-hospital environment is pivotal to enhancing the survival rate of out-of-hospital cardiac arrest patients. The data used in this study were extracted from the Korea Centers for Disease Control and Prevention (KCDC) nationally recognized statistics, Acute Heart Failure big data survey. Out-of-hospital cardiac arrest (OHCA) customer data were collected from the Gangwon Fire Headquarters public information database as social management data. The data were analyzed using SPSS 24. The study’s results emphasized the significance of offering basic CPR training to the public. This is evident from the fact that 90.5% of the first witnesses in the study performed CPR on OHCA patients, resulting in the recovery of spontaneous circulation (ROSC). The majority of patients with ROSC were male, with the highest age group being 41–50 years. Heart disease, hypertension, and diabetes were common medical conditions. The rate of witnessing cardiac arrest was high. Among the first witnesses, about 78.4% were of cardiac arrest incidents involving family members, co-workers, or acquaintances; 12.2% were on-duty medical healthcare personnel; and 9.5% were off-duty healthcare personnel. Cardiac arrest was treated in 83.8% of cases, with 90% of witnesses performing CPR. The percentage of witnesses that used an automated external defibrillator (AED) was 13.5%. In this study, the rates of ECG monitoring, CPR performance, and defibrillation performed by paramedics were high, but intravascular access and drug administration had a lower rate of performance. The time elapsed depended on the patient’s physical fitness. The study found that paramedics had the highest CPC restoration rate in patients with cardiac arrest, followed by EMTs and nurses. Significant differences were observed in cerebral performance scores after care by these paramedics and nurses. To increase the performance of AEDs, more AEDs should be installed in public spaces so that the public can access them conveniently in cases of emergency. In addition, it is necessary to improve the quality of professional first aid physical activity services performed by first-class paramedics.
An Enhanced LightGBM-Based Breast Cancer Detection Technique Using Mammography Images
Authors: Sait Abdul Rahaman Wahab , Nagaraj Ramprasad
Published: January 2024

Breast cancer (BC) is the leading cause of mortality among women across the world. Earlier screening of BC can significantly reduce the mortality rate and assist the diagnostic process to increase the survival rate. Researchers employ deep learning (DL) techniques to detect BC using mammogram images. However, these techniques are resource-intensive, leading to implementation complexities in real-life environments. The performance of convolutional neural network (CNN) models depends on the quality of mammogram images. Thus, this study aimed to build a model to detect BC using a DL technique. Image preprocessing techniques were used to enhance image quality. The authors developed a CNN model using the EfficientNet B7 model’s weights to extract the image features. Multi-class classification of BC images was performed using the LightGBM model. The Optuna algorithm was used to fine-tune LightGBM for image classification. In addition, a quantization-aware training (QAT) strategy was followed to implement the proposed model in a resource-constrained environment. The authors generalized the proposed model using the CBIS-DDSM and CMMD datasets. Additionally, they combined these two datasets to ensure the model’s generalizability to diverse images. The experimental findings revealed that the suggested BC detection model produced a promising result. The proposed BC detection model obtained an accuracy of 99.4%, 99.9%, and 97.0%, and Kappa (K) values of 96.9%, 96.9%, and 94.1% in the CBIS-DDSM, CMMD, and combined datasets. The recommended model streamlined the BC detection process in order to achieve an exceptional outcome. It can be deployed in a real-life environment to support physicians in making effective decisions. Graph convolutional networks can be used to improve the performance of the proposed model.
An Objective and Robust Bayes Factor for the Hypothesis Test One Sample and Two Population Means
Authors: Almodóvar-Rivera Israel A. , Pericchi-Guerra Luis R.
Published: January 2024

It has been over 100 years since the discovery of one of the most fundamental statistical tests: the Student’s t test. However, reliable conventional and objective Bayesian procedures are still essential for routine practice. In this work, we proposed an objective and robust Bayesian approach for hypothesis testing for one-sample and two-sample mean comparisons when the assumption of equal variances holds. The newly proposed Bayes factors are based on the intrinsic and Berger robust prior. Additionally, we introduced a corrected version of the Bayesian Information Criterion (BIC), denoted BIC-TESS, which is based on the effective sample size (TESS), for comparing two population means. We studied our developed Bayes factors in several simulation experiments for hypothesis testing. Our methodologies consistently provided strong evidence in favor of the null hypothesis in the case of equal means and variances. Finally, we applied the methodology to the original Gosset sleep data, concluding strong evidence favoring the hypothesis that the average sleep hours differed between the two treatments. These methodologies exhibit finite sample consistency and demonstrate consistent qualitative behavior, proving reasonably close to each other in practice, particularly for moderate to large sample sizes.
Cervical Intervertebral Disc Segmentation Based on Multi-Scale Information Fusion and Its Application
Authors: Yang Yi , Wang Ming , Ma Litai , Zhang Xiang , Zhang Kerui , Zhao Xiaoyao , Teng Qizhi , Liu Hao
Published: January 2024

The cervical intervertebral disc, a cushion-like element between the vertebrae, plays a critical role in spinal health. Investigating how to segment these discs is crucial for identifying abnormalities in cervical conditions. This paper introduces a novel approach for segmenting cervical intervertebral discs, utilizing a framework based on multi-scale information fusion. Central to this approach is the integration of multi-level features, both low and high, through an encoding–decoding process, combined with multi-scale semantic fusion, to progressively refine the extraction of segmentation characteristics. The multi-scale semantic fusion aspect of this framework is divided into two phases: one leveraging convolution for scale interaction and the other utilizing pooling. This dual-phase method markedly improves segmentation accuracy. Facing a shortage of datasets for cervical disc segmentation, we have developed a new dataset tailored for this purpose, which includes interpolation between layers to resolve disparities in pixel spacing along the longitudinal and transverse axes in CT image sequences. This dataset is good for advancing cervical disc segmentation studies. Our experimental findings demonstrate that our network model not only achieves good segmentation accuracy on human cervical intervertebral discs but is also highly effective for three-dimensional reconstruction and printing applications. The dataset will be publicly available soon.
Comparative Analysis of Machine Learning Models for Predictive Maintenance of Ball Bearing Systems
Authors: Farooq Umer , Ademola Moses , Shaalan Abdu
Published: January 2024

In the era of Industry 4.0 and beyond, ball bearings remain an important part of industrial systems. The failure of ball bearings can lead to plant downtime, inefficient operations, and significant maintenance expenses. Although conventional preventive maintenance mechanisms like time-based maintenance, routine inspections, and manual data analysis provide a certain level of fault prevention, they are often reactive, time-consuming, and imprecise. On the other hand, machine learning algorithms can detect anomalies early, process vast amounts of data, continuously improve in almost real time, and, in turn, significantly enhance the efficiency of modern industrial systems. In this work, we compare different machine learning and deep learning techniques to optimise the predictive maintenance of ball bearing systems, which, in turn, will reduce the downtime and improve the efficiency of current and future industrial systems. For this purpose, we evaluate and compare classification algorithms like Logistic Regression and Support Vector Machine, as well as ensemble algorithms like Random Forest and Extreme Gradient Boost. We also explore and evaluate long short-term memory, which is a type of recurrent neural network. We assess and compare these models in terms of their accuracy, precision, recall, F1 scores, and computation requirement. Our comparison results indicate that Extreme Gradient Boost gives the best trade-off in terms of overall performance and computation time. For a dataset of 2155 vibration signals, Extreme Gradient Boost gives an accuracy of 96.61% while requiring a training time of only 0.76 s. Moreover, among the techniques that give an accuracy greater than 80%, Extreme Gradient Boost also gives the best accuracy-to-computation-time ratio.
Simulating Quantum Pauli Noise with Three Independently Controlled Pauli Gates
Authors: Chapeau-Blondeau François
Published: January 2024

A quantum Pauli noise is a nonunitary process that alters the state of a qubit by random application of the four Pauli operators. We investigate a four-qubit quantum circuit, consisting of a pipeline of three independently controlled Pauli gates, for simulating the general class of qubit Pauli noises. The circuit with a fixed architecture is controllable by three separable quantum states from three auxiliary qubits in order to adjust the parameters of the targeted Pauli noise on the principal qubit. Important Pauli noises such as bit flip, phase flip, bit phase flip, and depolarizing noise are readily simulated, along with an infinite subset of other Pauli noises. However, the quantum circuit with its simple and fixed architecture cannot simulate all conceivable Pauli noises, and a characterization is proposed, in the parameter space of the Pauli noises, denoting those that are simulable by the circuit and those that are not. The circuit is a useful tool to contribute to controlled simulation, on current or future quantum processors, of nonunitary processes of noise and decoherence.
CMOS Voltage-Controlled Oscillator with Complementary and Adaptive Overdrive Voltage Control Structures
Authors: Chang Yu-Hsin , Luo Yong-Lun
Published: January 2024

This paper displays a voltage-controlled oscillator (VCO) with high performance implemented in 0.18 µm CMOS. The proposed CMOS VCO adopts a current-reused method, analog coarse and fine tuning mechanisms, and an adaptive overdrive voltage control structure to increase the overall performance, such as the power dissipation, phase noise, and tuning range, and has a robust start-up condition. The current-reused complementary structure with higher transistor transconductances is to save power consumption; the analog coarse and fine tuning mechanisms are to effectively widen the tuning range; and the adaptive overdrive voltage control technique is to change the transconductances of the transistors to improve power consumption by reasonably biasing the gate and body terminals in a class-AB mode to adjust the threshold voltage of the NMOS transistors. The proposed CMOS VCO adopts the class-AB mode to improve the overall performance and the start-up condition. The figure-of-merit (FOM) and FOM with tuning range (FOMT) are used in evaluating the CMOS VCO performance. The measured phase noise at 1 MHz and 10 MHz offsets is –130.34 dBc/Hz and –150.96 dBc/Hz at the 3.38 GHz operating frequency, respectively. The proposed CMOS VCO has a tuning range between 2.85 and 3.62 GHz corresponding to 23.8% for the fifth-generation (5G) wireless communication applications. The proposed CMOS VCO core using a 1.4-V supply consumes 7.5 mW DC power. The FOMs and FOMTs at 1- and 10-MHz offsets are −192.2, −192.8, −199.7, and −200.3 dBc/Hz, respectively, from the 3.38 GHz output frequency.
Data Validity Analysis Based on Reinforcement Learning for Mixed Types of Anomalies Coexistence in Intelligent Connected Vehicle (ICV)
Authors: Gao Jiahao , Hu Chuangye , Wang Luyao , Ding Nan
Published: January 2024

Compared with traditional anomaly analysis, intelligent connected vehicle (ICV) data validity analysis is faced with a variety of data anomalies, including sensor anomalies, driving behavior anomalies, malicious tampering, and so on, which eventually leads to anomalies in the data. How to integrate the vehicle moving characteristics, driving style, and traffic flow conditions to provide an effective data detection method has become a new problem in the field of intelligent networked vehicles. Based on ICV data, a particle swarm optimization data validity detection algorithm (TE-PSO-SVM) was proposed by combining driving style and traffic flow theory to realize the effective detection of driving data. In addition, aiming at the problem of mixed types of anomalies in complex scenes, a model pool is constructed, and a model selection algorithm based on reinforcement learning (RLBMS) is proposed. Experiments on the real data set HighD show that RLBMS has a better detection effect in complex scenes of mixed types of anomalies.
Research on Path Planning with the Integration of Adaptive A-Star Algorithm and Improved Dynamic Window Approach
Authors: Liao Tianjian , Chen Fan , Wu Yuting , Zeng Huiquan , Ouyang Sujian , Guan Jiansheng
Published: January 2024

In response to the shortcomings of the traditional A-star algorithm, such as excessive node traversal, long search time, unsmooth path, close proximity to obstacles, and applicability only to static maps, a path planning method that integrates an adaptive A-star algorithm and an improved Dynamic Window Approach (DWA) is proposed. Firstly, an adaptive weight value is added to the heuristic function of the A-star algorithm, and the Douglas–Pucker thinning algorithm is introduced to eliminate redundant points. Secondly, a trajectory point estimation function is added to the evaluation function of the DWA algorithm, and the path is optimized for smoothness based on the B-spline curve method. Finally, the adaptive A-star algorithm and the improved DWA algorithm are integrated into the fusion algorithm of this article. The feasibility and effectiveness of the fusion algorithm are verified through obstacle avoidance experiments in both simulation and real environments.
Multi-Branch Spectral Channel Attention Network for Breast Cancer Histopathology Image Classification
Authors: Cao Lu , Pan Ke , Ren Yuan , Lu Ruidong , Zhang Jianxin
Published: January 2024

Deep-learning-based breast cancer image diagnosis is currently a prominent and growingly popular area of research. Existing convolutional-neural-network-related methods mainly capture breast cancer image features based on spatial domain characteristics for classification. However, according to digital signal processing theory, texture images usually contain repeated patterns and structures, which appear as intense energy at specific frequencies in the frequency domain. Motivated by this, we make an attempt to explore a breast cancer histopathology classification application in the frequency domain and further propose a novel multi-branch spectral channel attention network, i.e., the MbsCANet. It expands the interaction of frequency domain attention mechanisms from a multi-branch perspective via combining the lowest frequency features with selected high frequency information from two-dimensional discrete cosine transform, thus preventing the loss of phase information and gaining richer context information for classification. We thoroughly evaluate and analyze the MbsCANet on the publicly accessible BreakHis breast cancer histopathology dataset. It respectively achieves the optimal image-level and patient-level classification results of 99.01% and 98.87%, averagely outperforming the spatial-domain-dominated models by a large margin, and visualization results also demonstrate the effectiveness of the MbsCANet for this medical image application.
A Comparative Study of Optimal PV Allocation in a Distribution Network Using Evolutionary Algorithms
Authors: Bai Wenlei , Zhang Wen , Allmendinger Richard , Enyekwe Innocent , Lee Kwang Y.
Published: January 2024

The growing distributed energy resource (DER) penetration into distribution networks, such as through residential and commercial photovoltaics (PV), has emerged through a transition from passive to active networks, which takes the complexity of planning and operations to the next level. Optimal PV allocation (sizing and location) is challenging because it involves mixed-integer non-linear programming with three-phase non-linear unbalanced power flow equations. Meta-heuristic algorithms have proven their effectiveness in many complex engineering problems. Thus, in this study, we propose to achieve optimal PV allocation by using several basic evolutionary algorithms (EAs), particle swarm optimization (PSO), artificial bee colony (ABC), differential evolution (DE), and their variants, all of which are applied for a study of their performance levels. Two modified unbalanced IEEE test feeders (13 and 37 bus) are developed to evaluate these performance levels, with two objectives: one is to maximize PV penetration, and the other is to minimize the voltage deviation from 1.0 p.u. To handle the computational burden of the sequential power flow and unbalanced network, we adopt an efficient iterative load flow algorithm instead of the commonly used and yet highly simplified forward–backward sweep method. A comparative study of these basic EAs shows their general success in finding a near-optimal solution, except in the case of the DE, which is known for solving continuous optimization problems efficiently. From experiments run 30 times, it is observed that PSO-related algorithms are more efficient and robust in the maximum PV penetration case, while ABC-related algorithms are more efficient and robust in the minimum voltage deviation case.
Study on the Properties of Compound Surfactants with PO Groups
Authors: Wang Biao , Wang Xue , Hu Futang , Wang Xiangyu , Yang Zhongji , Zhu Xiuyu , Li Gen , Wang Keliang
Published: January 2024

The compound surfactant system has considerable development prospects for improving oil recovery. A synergistic effect can be achieved through the orderly arrangement of the two surfactants on the interface, which can make up for the performance defects of a single surfactant. In this paper, the effects of the number of propylene oxides (PO) in composite surfactants on interfacial tension and emulsion stability were systematically studied. The results showed that the solubility of composite surfactants was significantly improved under high-salinity conditions by adding anionic–nonionic surfactants to a single anionic surfactant. The interfacial tension of composite surfactants shows a V-shaped change of first decreasing and then increasing with the increase in PO groups. As the number of PO groups increases, the emulsion first increases and then decreases. Among the series of composite surfactants, LBAS/C12PO10S has the lowest interfacial tension, reaching the order of 10−3 mN/m, and the emulsion formed by it exhibits superior stability. This indicates that the synergistic effect of the two surfactants can be improved by adjusting the number of PO groups. Furthermore, the LBAS/C12PO10S composite system can be mixed with crude oil to form Winsor III microemulsion, which has great potential for application in enhanced oil recovery via chemical flooding.
Prospects of Hydrogen Application as a Fuel for Large-Scale Compressed-Air Energy Storages
Authors: Iliev Iliya K. , Fedyukhin Alexander V. , Semin Daniil V. , Valeeva Yulia S. , Dronov Stanislav A. , Beloev Ivan H.
Published: January 2024

A promising method of energy storage is the combination of hydrogen and compressed-air energy storage (CAES) systems. CAES systems are divided into diabatic, adiabatic, and isothermal cycles. In the diabatic cycle, thermal energy after air compression is discharged into the environment, and the scheme implies the use of organic fuel. Taking into account the prospects of the decarbonization of the energy industry, it is advisable to replace natural gas in the diabatic CAES scheme with hydrogen obtained by electrolysis using power-to-gas technology. In this article, the SENECA-1A project is considered as a high-power hybrid unit, using hydrogen instead of natural gas. The results show that while keeping the 214 MW turbines powered, the transition to hydrogen reduces carbon dioxide emissions from 8.8 to 0.0 kg/s, while the formation of water vapor will increase from 17.6 to 27.4 kg/s. It is shown that the adiabatic CAES SENECA-1A mode, compared to the diabatic, has 0.0 carbon dioxide and water vapor emission with relatively higher efficiency (71.5 vs. 62.1%). At the same time, the main advantage of the diabatic CAES is the possibility to produce more power in the turbine block (214 vs. 131.6 MW), having fewer capital costs. Thus, choosing the technology is a subject of complex technical, economic, and ecological study.
Performance Evaluation of Multiple Machine Learning Models in Predicting Power Generation for a Grid-Connected 300 MW Solar Farm
Authors: Aldosari Obaid , Batiyah Salem , Elbashir Murtada , Alhosaini Waleed , Nallaiyagounder Kanagaraj
Published: January 2024

Integrating renewable energy sources (RES), such as photovoltaic (PV) systems, into power system networks increases uncertainty, leading to practical challenges. Therefore, an accurate photovoltaic (PV) power prediction model is required to provide essential data that supports smooth power system operation. Hence, the work presented in this paper compares and discusses the results of different machine learning (ML) techniques in predicting the power produced by the 300 MW Sakaka PV Power Plant in the north of Saudi Arabia. The validation of the presented work is performed using real-world operational data obtained from the specified solar farm. Several performance measures, including accuracy, precision, recall, F1 Score, and mean square error (MSE), are used in this work to evaluate the performance of the different ML approaches and determine the most precise prediction model. The obtained results show that the Support Vector Machine (SVM) with a Radial basis function (RBF) is the most effective approach for optimizing solar power prediction in large-scale solar farms.
Machine Learning-Based Automated Fault Detection and Diagnostics in Building Systems
Authors: Nelson William , Dieckert Christopher
Published: January 2024

Automated fault detection and diagnostics analysis in commercial building systems using machine learning (ML) can improve the building’s efficiency and conserve energy costs from inefficient equipment operation. However, ML can be challenging to implement in existing systems due to a lack of common data standards and because of a lack of building operators trained in ML techniques. Additionally, results from ML procedures can be complicated for untrained users to interpret. Boolean rule-based analysis is standard in current automated fault detection and diagnostics (AFDD) solutions but limits analysis to the rules defined and calibrated by energy engineers. Boolean rule-based analysis and ML can be combined to create an effective fault detection and diagnostics (FDD) tool. Three examples of ML’s advantages over rule-based analysis are explored by analyzing functional building equipment. ML can detect long-term faults in the system caused by a lack of system maintenance. It can also detect faults in system components with incomplete sets of sensors by modeling expected system operations and by making comparisons to actual system operations. An example of ML detecting a failure in a building is shown along with a demonstration of the soft decision boundaries of ML-based FDD compared to Boolean rule-based FDD analysis. The results from the three examples are used to demonstrate the strengths and weaknesses of using ML for AFDD analysis.
Ozonation and Changes in Biodegradable Organic Substances in Drinking Water Treatment: The Future of Green Technology
Authors: Rosińska Agata , Rakocz Klaudia
Published: January 2024

Studies were carried out to assess changes in biodegradable dissolved organic carbon (BDOC) and assimilable organic carbon (AOC) in groundwater and surface waters after two processes: ozonation and ozonation/UV. The tested water was in contact with O3 firstly for 4 and secondly for 15 min. Three doses of disinfectant were used: 1.6 mg/L, 5.0 mg/L, and 10.0 mg/L. The UV radiation time was 10 and 30 min. The greatest change in AOC and BDOC for groundwater was observed at an O3 dose of 10.0 mg/L and a contact time of 15 min, by 400 and 197%, respectively. On the other hand, for surface water, it was shown that after the ozonation/UV process, the AOC and BDOC content decreased after both 10 and 30 min of radiation in comparison to the water after ozonation. The AOC content decreased by 33% and 22%, respectively, and the BDOC content by 27% and 31%, respectively. The results obtained in this study provide new information on the effect of different ozonation conditions and the combined method on the level of biodegradable organic fraction of water. It is recommended that BDOC and AOC should be monitored in Poland as routine indicators during the preparation of drinking water.
A Study on the Measurement of Regional Energy Consumption Efficiency and Decomposition of Its Influencing Factors in China: New Evidence for Achieving SDGs
Authors: Miao Xiumei , Wu Yong , Ren Fangrong
Published: January 2024

With the growth of global population and economic development, people are facing the problem of increasing scarcity of renewable energy and unsustainable energy use. To achieve the sustainable development goals (SDGs) proposed by the United Nations, research on energy consumption efficiency has become particularly important. This research evaluates the energy consumption efficiency of 270 cities in China through an improved EBM model and finds a common phenomenon of low energy consumption efficiency in the cities, with the highest efficiency in northeast China and the lowest efficiency in eastern China. In addition, the efficiency of industrial exhaust emissions most significantly positively correlates with the efficiency of employed population and total energy consumption efficiency, while the efficiency of regional GDP does not significantly correlate with the efficiency of the two input variables. Using the LMDI method to decompose the driving factors of energy consumption efficiency in the cities, we find that the most important factor affecting energy consumption efficiency is their own energy endowment. Therefore, to improve the energy consumption efficiency of its cities, the China government should comprehensively consider factors such as regional economic development level, industrial structure, and technological level differences, formulate relevant energy-saving and emission-reduction policies, focus on optimizing the energy consumption structure, encourage technological progress and innovation, and help increase investment in science and technology.
Independent Control of Active and Reactive Power Flow for a Single-Phase, Unidirectional Onboard Power Converter Connecting the DC Power Bus to the AC Bus
Authors: Binkowski Tomasz , Szcześniak Paweł
Published: January 2024

The paper presents a proposed system that supplies a 400 Hz single-phase onboard grid from the DC onboard bus. This system enables independent compensation of reactive power in the AC grid. Independent control of active and reactive power flow requires the decomposition of current in the grid into active and reactive components. Independent control of active and reactive power requires the use of synchronizers that operate in the dq frame system. If synchronization is performed with a single-phase grid, the transformation of dq requires the virtual quadrature signals. Standard quadrature signal generation systems use a second-order generalized integrator. To improve the dynamics of the system, the paper proposes a new quadrature generator that operates on the basis of trigonometric calculations instead of a second-order integration system. The developed system was implemented in a proportional-resonant current control system. Tests carried out in steady state and in dynamic states related to typical grid disturbances proved significantly better dynamic properties than those of a standard integrator-based system.
Economical Operation and Hazardous Air Pollutant Emissions of Biodegradable Sludge Combustion Process in Commercial Fluidized Bed Plant
Authors: Jang Ha-Na , Choi Myung Kyu , Choi Hang Seok
Published: January 2024

Waste sludge is characterized by high moisture, volatile compounds, toxic compounds, and ashes. The efficient operation of a commercial fluidized bed combustion (FBC) plant is important for reducing operational costs. We selected a commercial FBC plant for industrial waste sludge combustion to investigate the mass balance of the FBC process and the performance of the air pollution control device. Based on fuel analysis, the flow rate of incineration air was calculated as 4567 Nm3/h. After FBC combustion, the flow rate of the incineration gas increased to 8493.8 Nm3/h. Analysis of the heat balance showed that some heat potential was lost through leakage during the combustion process. The temperature of the incineration gas decreased to 200 °C at the inlet of the air pollution control device. According to the hazardous air pollutant emission testing of sampling points, the operation factors of lime slurry injection for SOx and HCl in the semi-dry reactor were 64.20 and 4.81 kg/h, respectively. In the wet scrubber, the operation factors of NaOH for SOx and HCl were 23.88 and 3.14 kg/h, respectively. At these operation factors, the available waste generation in the semi-dry reactor and wet scrubber was optimized to 76.6 and 42.57 kg/h, respectively.
Investigation of the Performance of Battery Thermal Management Based on Direct Refrigerant Cooling: Simulation, Validation of Results, and Parametric Studies
Authors: Jamsawang Suparat , Chanthanumataporn Saharat , Sutthivirode Kittiwoot , Thongtip Tongchana
Published: January 2024

This study proposes a simulation technique for investigating a battery thermal management system based on direct refrigerant cooling (BTMS-DRC). The main focus is to investigate the temperature uniformity and working temperature of the module housing. The simulation technique employs a finite element method for a combined conduction–convection heat transfer to predict the module housing temperature. The refrigerant side is based on two-phase flow evaporation, which is represented by the convection heat transfer under a certain refrigerant saturation temperature. The real BTMS-DRC, which is based on the dual-evaporator vapor compression refrigeration system, is constructed for experimentation with the test bench. The simulated result is validated with the experimental results to ensure correction of the modelling. Error rates of approximately 2.9–7.2% are noted throughout the specified working conditions. The BTMS can produce temperatures of less than 35 °C under conditions where 80–320 W heat is generated. The difference in the temperature of the module is around 1.7–4.2 °C. This study also investigates the impact of heat generation, the convection heat transfer coefficient (href), the refrigerant saturation temperature, and thermal conductivity on the module’s temperature. The thermal conductivity ranges from 25 to 430 W/m·K, while the href ranges from 80 to 400 W/m2·K.
Global Wildfire Danger Predictions Based on Deep Learning Taking into Account Static and Dynamic Variables
Authors: Ji Yuheng , Wang Dan , Li Qingliang , Liu Taihui , Bai Yu
Published: January 2024

Climate change will intensify the danger of wildfires, significantly impacting human life. Deep Learning (DL) has been extensively applied in wildfire prediction research. In the realm of wildfire prediction, previous deep learning methods have overlooked the inherent differences between static positional information and dynamic variables. Additionally, most existing deep learning models have not integrated the global system characteristics of the Earth’s features and teleconnection during the learning phase. Here, we propose a static location-aware ConvLSTM (SLA-ConvLSTM) model that is aware of static positional elements and interconnected with global information and teleconnection. The model we propose can discern the influence of dynamic variables across various geographical locations on predictive outcomes. Compared with other deep learning models, our SLA-ConvLSTM model has achieved commendable performance. The outcomes indicate that the collaborative interplay of spatiotemporal features and the extraction of static positional information present a promising technique for wildfire prediction. Moreover, the incorporation of climate indices and global feature variables enhances the predictive capability of the model in wildfire prediction.
Two Centuries of Winter Temperature Variability Inferred from Betula ermanii Ring Widths near the Forests/Tundra Ecotone in the Changbai Mountain, China
Authors: Li Siwen , Cui Xiaoyang , Jiang Yangao
Published: January 2024

In this study, we constructed a ring-width chronology derived from Betula ermanii (BE) near the transitional zone between forests and tundra within the Changbai Mountain (CBM) region. This chronology was established utilizing 55 cores obtained from 30 trees. Our analysis of growth/climate responses underscores the pivotal role of the mean maximum winter temperature in influencing radial growth. Drawing upon these growth/climate associations, we reconstructed the mean maximum temperature series for December of the preceding year through January of the current year for the years 1787 and 2005 CE, employing a standardized chronology. During the calibration period (1960–2005), the reconstructed series exhibited an explained variance of 36%. This reconstruction provides crucial insights into historical temperature fluctuations within the study area. Our findings indicate that year-to-year temperature variations did not manifest synchronously along the altitude gradient of Changbai Mountain. Notably, the response to recent winter warming exhibited disparities with the altitude on Changbai Mountain. Specifically, the higher altitude range (1950–2000 m a.s.l.) displayed a response to warming around 1960, the mid-altitude range (765–1188 m a.s.l.) responded around 1975, and the lowest altitude (650 m a.s.l.) responded by 1977. Consequently, the paleotemperature research outcomes from Changbai Mountain alone may not adequately characterize climate change in this region. We recommend future high-resolution temperature records be obtained through sampling at various altitudes to enhance the comprehensiveness of our understanding.
Application of Ultrasound Treatments in the Processing and Production of High-Quality and Safe-to-Drink Kiwi Juice
Authors: Bhutkar Sharayu , Brandão Teresa R. S. , Silva Cristina L. M. , Miller Fátima A.
Published: January 2024

This study explores the potential of thermosonication as an alternative to traditional heat treatments, such as pasteurization, in the processing of fruit juices. Conventional methods often lead to undesirable quality changes in fruit juices, whereas thermosonication offers promising results regarding microbial inactivation and quality preservation. This work focused on the inactivation kinetics of Listeria innocua 2030c, a surrogate for pathogenic L. monocytogenes, in kiwifruit juice using thermosonication at 45 °C, 50 °C, and 55 °C. These treatments were compared with equivalent heat treatments. Quality attributes of the juice were also evaluated to assess process efficiency. Survival data of L. innocua were fitted with the Weibull model, estimating first decimal reduction times (δ) and shape parameters (n). The results reveal temperature and process dependencies on δ, while n remains mostly temperature and treatment independent. Thermosonication outperforms heat treatment, achieving higher L. innocua reductions while retaining quality attributes like pH, soluble solid content, and total phenolics and chlorophylls. Thermosonication at 55 °C stands out, providing a 6.2-log-cycle reduction in just 3 min with superior quality retention. These findings highlight the synergistic effect of temperature and ultrasound, making mild heat processes feasible while enhancing product quality. Thermosonication, particularly at 55 °C, emerges as an effective alternative to traditional thermal treatments for fruit juices, offering improved microbial safety without compromising product quality.
Tracing of Di-Ethylhexyl Phthalate in the Tequila Production Process
Authors: Ornelas-Salas Jose Tomas , Tapia-Picazo Juan Carlos , De Leon-Rodriguez Antonio
Published: January 2024

The purpose of this study was to determine the origin, presence, and fate of the endocrine disruptor di-ethylhexil phthalate (DEHP) during tequila production. For this, three tequila factories (small, medium, and large) were monitored. DEHP concentrations in water, agave, additives, lubricating greases, neoprene seals, and materials of each stage process were analyzed using gas chromatography/mass spectrometry. DEHP mass balances were performed to identify the processes with significant changes in the inputs/outputs. DEHP was detected in agave at up to 0.08 ± 0.03 mg kg−1, water 0.02 ± 0.01 mg kg−1, lubricant greases 131.05 ± 2.80 mg kg−1, and neoprene seals 369.11 ± 22.52 mg kg−1. Whereas, tequila produced in the large, medium, and small factories contained 0.05 ± 0.01, 0.24 ± 0.04, and 1.43 ± 0.48 mg kg−1 DEHP, respectively. Furthermore, in waste materials (vinasses and bagasse) released, 534.26 ± 349.02, 947.18 ± 65.84, and 5222.60 ± 2836.94 mg of DEHP was detected for every 1000 L of tequila produced. The most significant increase in DEHP occurred during the sugar extraction and distillation stages. Results demonstrate that main raw materials, such as agave and water, contain DEHP, but lubricant greases and neoprene seals are the major sources of DEHP contamination. Identification of the contamination sources can help the tequila industry to take actions to reduce it, protect consumer health and the environment, and prevent circular contamination.
Determination of Mycotoxins in Plant-Based Meat Alternatives (PBMAs) and Ingredients after Microwave Cooking
Authors: Galluzzo Francesco , Cammilleri Gaetano , Pulvirenti Andrea , Mannino Erika , Pantano Licia , Calabrese Vittorio , Buscemi Maria , Messina Elisa , Alfano Calogero , Macaluso Andrea , Ferrantelli Vincenzo
Published: January 2024

In this study, we investigate the role of microwave cooking in reducing mycotoxin contamination in plant-based food matrices, with a focus on veggie burgers (purchased and home-made) and their ingredients (soybean, potatoes, zucchini, carrots). Two different conditions were studied (Max–Min) that were 800 W for 60 s and 800 W for 90 s, respectively. The degradation patterns of aflatoxins (AFB1, AFB2, AFG1, AFG2), fumonisins (FB1, FB2, FB3), trichothecenes (T2, HT2, ZEA), and ochratoxin A (OTA) were studied. The extraction procedures were conducted with the QuEChERS extraction, and the analyses were conducted with liquid chromatography–tandem mass spectrometry (LC-MS/MS). Principal component analysis (PCA) showed that degradation under microwave cooking varies considerably across different food matrices and cooking conditions. This study provides valuable insights into the degradation of mycotoxins during microwave cooking and underscores the need for more research in this area to ensure food safety.
The Molecular Mechanism Underlying the Therapeutic Effect of Dihydromyricetin on Type 2 Diabetes Mellitus Based on Network Pharmacology, Molecular Docking, and Transcriptomics
Authors: Wen Xinnian , Lv Chenghao , Zhou Runze , Wang Yixue , Zhou Xixin , Qin Si
Published: January 2024

Type 2 diabetes mellitus (T2DM) is a chronic and complex disease, and traditional drugs have many side effects. The active compound dihydromyricetin (DHM), derived from natural plants, has been shown in our previous study to possess the potential for reducing blood glucose levels; however, its precise molecular mechanism remains unclear. In the present study, network pharmacology and transcriptomics were performed to screen the molecular targets and signaling pathways of DHM disturbed associated with T2DM, and the results were partially verified by molecular docking, RT-PCR, and Western blotting at in vivo levels. Firstly, the effect of DHM on blood glucose, lipid profile, and liver oxidative stress in db/db mice was explored and the results showed that DHM could reduce blood glucose and improve oxidative stress in the liver. Secondly, GO analysis based on network pharmacology and transcriptomics results showed that DHM mainly played a significant role in anti-inflammatory, antioxidant, and fatty acid metabolism in biological processes, on lipoprotein and respiratory chain on cell components, and on redox-related enzyme activity, iron ion binding, and glutathione transferase on molecular functional processes. KEGG system analysis results showed that the PI3K-Akt signaling pathway, IL17 signaling pathway, HIF signaling pathway, MAPK signaling pathway, AGE-RAGE signaling pathway in diabetic complications, and TNF signaling pathway were typical signaling pathways disturbed by DHM in T2DM. Thirdly, molecular docking results showed that VEGFA, SRC, HIF1A, ESR1, KDR, MMP9, PPARG, and MAPK14 are key target genes, five genes of which were verified by RT-PCR in a dose-dependent manner. Finally, Western blotting results revealed that DHM effectively upregulated the expression of AKT protein and downregulated the expression of MEK protein in the liver of db/db mice. Therefore, our study found that DHM played a therapeutic effect partially by activation of the PI3K/AKT/MAPK signaling pathway. This study establishes the foundation for DHM as a novel therapeutic agent for T2DM. Additionally, it presents a fresh approach to utilizing natural plant extracts for chemoprevention and treatment of T2DM.
The Effect of Sociodemographic and Anthropometric Variables on Nutritional Knowledge and Nutrition Literacy
Authors: Sanlier Nevin , Kocaay Funda , Kocabas Sule , Ayyildiz Pinar
Published: January 2024

Nutrition literacy, which is one of the important components of health literacy, includes basic nutritional information and understanding, interpreting and having the ability to make healthy decisions on nutrition-related issues. This study aims to dwell upon the relationship between sociodemographic and anthropometric variables and nutritional knowledge and nutrition literacy. A total of 1600 people aged 19–64 years, 934 women and 666 men, voluntarily participated in the research in the capital city of Turkey. The mean age is 28.2 ± 10.9 years. More than half of the participants (57.4%) have a university graduate/postgraduate education level, and 66.2% are unemployed. This cross-sectional study evaluated demographic information, anthropometric measurements, nutritional information and nutrition literacy. Nearly all the respondents (94.6%) were determined to have sufficient nutrition literacy. Body mass index (BMI) and age were negatively associated with nutrition literacy, whilst nutrition knowledge was positively associated. Respondents with nutrition education at school had the highest nutrition knowledge and nutrition literacy scores, and primary school graduates had the lowest. Participants who received nutrition education scored higher in all the subgroups of the GNKQ. Age, gender, marital status, education status, employment status, BMI and nutrition education were significantly associated with nutrition literacy. The results will be useful in developing food and nutrition policies that will pave the way for making decisions on the most useful themes of health and nutrition campaigns.
Comprehensive Evaluation of Genome Gap-Filling Tools Utilizing Long Reads
Authors: Zhao Xianjia , Liu Fang , Pan Weihua
Published: January 2024

The availability of the complete genome of an organism plays a crucial role in the comprehensive analysis of the entire biological entity. Despite the rapid advancements in sequencing technologies, the inherent complexities of genomes inevitably lead to gaps during genome assembly. To obviate this, numerous genome gap-filling tools utilizing long reads have emerged. However, a comprehensive evaluation of these tools is currently lacking. In this study, we evaluated seven software under various ploidy levels and different data generation methods, and assessing them using QUAST and two additional criteria such as accuracy and completeness. Our findings revealed that the performance of the different tools varied across diverse ploidy levels. Based on accuracy and completeness, FGAP emerged as the top-performing tool, excelling in both haploid and tetraploid scenarios. This evaluation of commonly used genome gap-filling tools aims to provide users with valuable insights for tool selection, assisting them in choosing the most suitable genome gap-filling tool for their specific needs.
The Causal Relationship between PCSK9 Inhibitors and Malignant Tumors: A Mendelian Randomization Study Based on Drug Targeting
Authors: Wang Wenxin , Li Wei , Zhang Dan , Mi Yongrun , Zhang Jingyu , He Guoyang
Published: January 2024

Objective: This study explores the potential causal association between proprotein convertase subtilisin/kexin 9 (PCSK9) inhibitors and tumor development using Mendelian randomization (MR) based on drug targets. Methods: Instrumental variables within ±100 kb of the PCSK9 gene locus, impacting low-density lipoprotein cholesterol (LDL-C), were utilized for MR analysis. Coronary heart disease (CHD) served as a positive control to validate the causal relationship between PCSK9 inhibitors and various cancers. We employed reverse MR to address the reverse causation concerns. Data from positive controls and tumors were sourced from OpenGWAS. Results: MR analysis suggested a negative causal relationship between PCSK9 inhibitors and both breast and lung cancers (95%CIBreast cancer 0.81~0.99, p = 2.25 × 10−2; 95%CILung cancer 0.65~0.94, p = 2.55 × 10−3). In contrast, a positive causal link was observed with gastric, hepatic, and oral pharyngeal cancers and cervical intraepithelial neoplasia (95%CIGastric cancer 1.14~1.75, p = 1.88 × 10−2; 95%CIHepatic cancer 1.46~2.53, p = 1.16 × 10−2; 95%CIOral cavity and pharyngeal cancer 4.49~6.33, p = 3.36 × 10−4; 95%CICarcinoma in situ of cervix uteri 4.56~7.12, p = 6.91 × 10−3), without heterogeneity or pleiotropy (p > 0.05). Sensitivity analyses confirmed these findings. The results of MR of drug targets suggested no causal relationship between PCSK9 inhibitors and bladder cancer, thyroid cancer, pancreatic cancer, colorectal cancer, malignant neoplasms of the kidney (except for renal pelvis tumors), malignant neoplasms of the brain, and malignant neoplasms of the esophagus (p > 0.05). Reverse MR helped mitigate reverse causation effects. Conclusions: The study indicates a divergent causal relationship of PCSK9 inhibitors with certain cancers. While negatively associated with breast and lung cancers, a positive causal association was observed with gastric, hepatic, oral cavity, and pharyngeal cancers and cervical carcinoma in situ. No causal links were found with bladder, thyroid, pancreatic, colorectal, certain kidney, brain, and esophageal cancers.