bluuebunny/crossref_metadata_2025 huggingface dataset:
Deep Learning-Based Context-Aware Recommender System Considering Change in Preference
Authors: Jeong Soo-Yeon , Kim Young-Kuk
Published: May 2023

In order to predict and recommend what users want, users’ information is required, and more information is required to improve the performance of the recommender system. As IoT devices and smartphones have made it possible to know the user’s context, context-aware recommender systems have emerged to predict preferences by considering the user’s context. A context-aware recommender system uses contextual information such as time, weather, and location to predict preferences. However, a user’s preferences are not always the same in a given context. They may follow trends or make different choices due to changes in their personal environment. Therefore, in this paper, we propose a context-aware recommender system that considers the change in users’ preferences over time. The proposed method is a context-aware recommender system that uses Matrix Factorization with a preference transition matrix to capture and reflect the changes in users’ preferences. To evaluate the performance of the proposed method, we compared the performance with the traditional recommender system, context-aware recommender system, and dynamic recommender system, and confirmed that the performance of the proposed method is better than the existing methods.
Diagnostics and Fault Tolerance in DC–DC Converters and Related Industrial Electronics Technologies
Authors: Cardoso Antonio , Bento Fernando
Published: May 2023

The deployment of DC energy systems is an attractive alternative to conventional AC-based energy distribution systems, improving the efficiency of energy supplies and promoting renewable energies [...]
Reducing Red Light Running (RLR) with Adaptive Signal Control: A Case Study
Authors: Li Hongbo , Chang Xiao , Lu Pingping , Ren Yilong
Published: May 2023

Traffic accidents are a leading cause of premature death for citizens, with millions of injuries and fatalities occurring annually. Due to the fact that a large proportion of accidents are caused by red light running, reduction of the frequency of red light running (RLR) has been extensively researched in recent years. However, most of the previous studies have focused on reducing RLR frequency through driver education or warning sign design, with little attention paid to the relationship between RLR behavior and traffic signal control. Considering RLR is significantly affected by the number of vehicles arriving during yellow, it is possible to identify RLR behaviors in advance by analyzing data on yellow-arriving vehicles. Meanwhile, based on the strong correlation between yellow arriving and RLR frequency, it is possible to reduce RLR by traffic signal control. In this paper, we propose a quantitative model of correlation between RLR frequency and yellow light arrival based on high-resolution traffic and signal event data from Twin Cities, Minnesota. On this basis, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is implemented to find trade-offs between minimizing the RLR frequency and the traffic delay. A case study of a 6-intersection arterial road reveals that in unsaturation, saturation, and supersaturation flow, our approach can converge to a Pareto optimal front in 30–50 iterations, which shows that is possible to simultaneously reduce RLR frequency and enhance traffic efficiency safety, which is conducive to ensuring the life safety of traffic participants.
A Fast and Accurate Prediction of Distortions in DCT-Based Lossy Image Compression
Authors: Abramova Victoriya , Lukin Vladimir , Abramov Sergey , Kryvenko Sergii , Lech Piotr , Okarma Krzysztof
Published: May 2023

Since the number of acquired images and their size have the tendency to increase, their lossy compression is widely applied for their storage, transfer, and dissemination. Simultaneously with providing a relatively large compression ratio, lossy compression produces distortions that are inevitably introduced and have to be controlled. The properties of these distortions depend on several factors such as image properties, the coder used, and a parameter that controls compression, which is different for particular coders. Then, one has to set a parameter that controls compression individually for an image to be compressed to provide image quality appropriate for a given application, and it is often desirable to do this quickly. Iterative procedures are usually not fast enough, and therefore fast and accurate procedures for providing a desired quality are needed. In the paper, such a procedure for two coders based on discrete cosine transform is proposed. This procedure is based on a prediction of mean square errors for a given quantization step using a simple analysis of image complexity (local activity in blocks). The statistical and spatial–spectral characteristics of distortions introduced by DCT-based coders are analyzed, and it is shown that they depend on the quantization step and local content. Generalizing the data for sets of grayscale test images and quantization step values, it is shown that the MSE can be easily predicted. These predictions are accurate enough and can be used to set the quantization step properly, as verified by experiments performed using more than 300 remote sensing and conventional optical images. The proposed approach is applicable to the lossy compression of grayscale images and the component-wise compression of multichannel data.
Advances in Modeling and Suppression Methods of EMI in Power Electronic Converters of Third-Generation Semiconductor Devices
Authors: Wu Xiaogang , Gao Xinjia , Wang Jiulong , Li Zheng , Du Shirui , Gao Shuchun , Li Feiqiang , Du Jiuyu , Shchurov Nickolay I. , Zhang Xinyang
Published: May 2023

With the development of high-frequency, miniaturized, and lightweight power electronic devices, third-generation semiconductor devices are more and more used in the main circuits of power electronic converters. The electromagnetic interference (EMI) generated by their fast switching can affect the performance of power electronic converters. Therefore, it is necessary to investigate the modeling and suppression methods of conducted noise in power electronic converters of third-generation semiconductor devices. This paper describes the EMI sources and coupling paths of EMI in third-generation semiconductor devices used in power electronic converters. The modeling methods of EMI are summarized from the perspectives of power devices and coupling paths. The suppression methods of conducted noise are summarized by suppressing EMI sources and improving coupling path characteristics. This paper provides a reference for the electromagnetic compatibility design of power electronic converters for third-generation semiconductor devices.
DVL Model Prediction Based on Fuzzy Multi-Output Least Squares Support Vector Machine in SINS/DVL
Authors: Zhao Bo , Gao Wei , Xia Xiuwei
Published: May 2023

For an underwater Strapdown Inertial Navigation System/Doppler velocity log (SINS/DVL) integrated navigation system, the short-term failure of DVL may lead to the loss of reliable external velocity information from DVL, which will cause the SINS errors to accumulate. To circumvent this problem, this paper proposes a velocity predictor based on fuzzy multi-output least squares support vector machine (FMLS-SVM) to predict DVL measurements when DVL malfunctions occur. Firstly, the single-output least squares support vector machine (LS-SVM) model is extended to the multi-output LS-SVM model (MLS-SVM), and the self-adaptive fuzzy membership is introduced to fuzzify the input samples to overcome the over-fitting problem caused by the excessive sensitivity to the outlier points. Secondly, the fuzzy membership function is designed from the idea of the K nearest neighbor (KNN) algorithm. Finally, considering the influence of vehicle maneuver on the prediction model of DVL, the dynamic attitude angles are extended to the input samples of the prediction model to improve the adaptability of the DVL prediction model under large maneuver conditions. The performance of the method is verified by lake experiments. The comparison results show that the velocity predictor based on FMLS-SVM can correctly provide the estimated DVL measurements, effectively prolong the fault tolerance time of DVL faults, and improve the accuracy and reliability of the SINS/DVL integrated navigation system.
iDehaze: Supervised Underwater Image Enhancement and Dehazing via Physically Accurate Photorealistic Simulations
Authors: Mousavi Mehdi , Estrada Rolando , Ashok Ashwin
Published: May 2023

Underwater image enhancement and turbidity removal (dehazing) is a very challenging problem, not only due to the sheer variety of environments where it is applicable, but also due to the lack of high-resolution, labelled image data. In this paper, we present a novel, two-step deep learning approach for underwater image dehazing and colour correction. In iDehaze, we leverage computer graphics to physically model light propagation in underwater conditions. Specifically, we construct a three-dimensional, photorealistic simulation of underwater environments, and use them to gather a large supervised training dataset. We then train a deep convolutional neural network to remove the haze in these images, then train a second network to transform the colour space of the dehazed images onto a target domain. Experiments demonstrate that our two-step iDehaze method is substantially more effective at producing high-quality underwater images, achieving state-of-the-art performance on multiple datasets. Code, data and benchmarks will be open sourced.
Rule-Based System with Machine Learning Support for Detecting Anomalies in 5G WLANs
Authors: Uszko Krzysztof , Kasprzyk Maciej , Natkaniec Marek , Chołda Piotr
Published: May 2023

The purpose of this paper is to design and implement a complete system for monitoring and detecting attacks and anomalies in 5G wireless local area networks. Regrettably, the development of most open source systems has been stopped, making them unable to detect emerging forms of threats. The system provides a modular framework to create and add new detection rules as new attacks emerge. The system is based on packet analysis modules and rules and incorporates machine learning models to enhance its efficiency. The use of rule-based detection establishes a strong basis for the identification of recognized threats, whereas the additional implementation of machine learning models enables the detection of new and emerging attacks at an early stage. Therefore, the ultimate aim is to create a tool that constantly evolves by integrating novel attack detection techniques. The efficiency of the system is proven experimentally with accuracy levels up to 98.57% and precision as well as recall scores as high as 92%.
MFF-IoT: A Multi-Granularity Formal Framework of User Authentication for IoT
Authors: Fei Yuan , Yin Jiaqi , Yan Lijun
Published: May 2023

The Internet of Things (IoT) generates vast amounts of data from numerous applications. However, since wireless channels are the primary means of communication, IoT networks are vulnerable to several security threats, which can compromise their security and privacy. To address these issues, various user authentication protocols have been proposed. Thus, it is still a challenge to provide multi-granularity verifications for different authentications of the IoT. In this paper, we propose a multi-granularity formal framework of user authentication for the IoT (MFF-IoT). Our framework builds different formal models (specification language HLPSL models, process algebra CSP models, Timed CSP models, and timed automata) to complete multi-granularity formal verification. By using both coarse-grained and fine-grained modeling, we can balance the tradeoff between model complexity and verification accuracy. Specifically, our fine-grained models provide a more detailed representation of the framework’s behavior and enable us to perform timing-related probability analysis. As these formal models can be implemented by model-checking tools (AVISPA, PAT with C#, and UPPAAL), important properties and features can be analyzed and verified. We also propose several algorithms for better formal model building and evaluate our framework with a case study to show its practicality and effectiveness.
Magnetic Interference Compensation Using the Adaptive LMS Algorithm
Authors: Ponikvar Dušan , Zupanič Erik , Jeglič Peter
Published: May 2023

The aim of this study is to evaluate the method for the compensation of magnetic field interference from the mains, which consists primarily of components with a frequency of 50 (60) Hz and its multiples. The setup compensates for the interfering field using three Helmholtz coils, magnetic field sensors, a digital processing unit, and power amplifiers. The processing unit implements an adaptive least-mean-square (LMS) algorithm to calculate the currents through coils that are necessary for compensation. The prototype fully compensates for interfering with the magnetic field at 50, 150, 250, and 350 Hz down to the noise level of the sensor arrangement within 20 s and reduces higher harmonics. The overall reduction in the rms value of the interfering field is better than 28 dB, and a significantly better reduction is achieved in the frequency range from 0 Hz to 400 Hz.
An MPPT Strategy for Wind Turbines Combining Feedback Linearization and Model Predictive Control
Authors: Jiang Ping , Zhang Tianyi , Geng Jinpeng , Wang Peiguang , Fu Lei
Published: May 2023

This paper proposes a model predictive controller (MPC) design based on the optimal tip-speed ratio method for maximum power point tracking (MPPT) of a direct-driven permanent magnet synchronous generator (D-PMSG)-based wind energy conversion system (WECS). To eliminate system nonlinearity and time-varying characteristics, a control variable was added at the wind turbine and the system model was feedback-linearized to create a linear time-invariant system, reducing the computational burden of the MPC and improving system performance. MATLAB/Simulink simulations were performed and the results show that the linearized system has high fidelity. Compared to traditional MPC that use an operating point to linearize the system, it has better adaptability to turbulent wind speeds, improving the stability and rapidity of the system.
In Situ Measurement of Wall Thermal Properties: Parametric Investigation of the Heat Flow Meter Methods through Virtual Experiments Data
Authors: Alongi Andrea , Sala Luca , Angelotti Adriana , Mazzarella Livio
Published: May 2023

Energy retrofit of existing buildings is based on the assessment of the starting performance of the envelope. The procedure for the in situ measurement of thermal conductance is described in the ISO 9869-1:2014, which provides two techniques for data processing: the average method (AM) and the dynamic method (DM). This work studies their effectiveness using virtual data from numerical simulations based on a finite difference model applied to different wall kinds, considering winter and summer boundary conditions alternatively (Italian Milan-Linate TMY). The estimated thermal conductances are compared to the reference theoretical values. The main purposes are: (i) defining the shortest test duration that provides acceptable results; (ii) assess the reliability of the criteria provided by the standard to evaluate the measurement quality; (iii) evaluate the sensitivity of both methods to variables such as wall properties, boundary conditions and others more specific to the DM (namely, the number of time constants and linear equations). The AM always provides acceptable estimates in winter (−3.1% ÷ 10% error), with better outcomes when indoor heat flux is considered, except for the highly insulated wall, but is not effective in summer, despite the fulfillment of the acceptance criteria for the highly insulated wall. The DM provides improvements in both seasons (0.05% ÷ 8.6% absolute values of error), for most virtual samples, and requires shorter sampling periods, even below the 3 days limit suggested by the standard. The test on the confidence interval indicated by the ISO 9869-1:2014 is not reliable and measurements are sensitive to the number of linear equations, that is left to the user’s discretion without strict indications. This work suggests a possible approach for overcoming this issue, which requires deeper future investigation.
A Frequency Support Approach for Hybrid Energy Systems Considering Energy Storage
Authors: Li Dahu , Zhou Hongyu , Chen Yuan , Zhou Yue , Rao Yuze , Yao Wei
Published: May 2023

In hybrid energy systems, the intermittent and fluctuating nature of new energy sources poses major challenges for the regulation and control of power systems. To mitigate these challenges, energy storage devices have gained attention for their ability to rapidly charge and discharge. Collaborating with wind power (WP), energy storage (ES) can participate in the frequency control of regional power grids. This approach has garnered extensive interest from scholars worldwide. This paper proposes a two-region load frequency control model that accounts for thermal power, hydropower, ES, and WP. To address complex, nonlinear optimization problems, the dingo optimization algorithm (DOA) is employed to quickly obtain optimal power dispatching commands under different power disturbances. The DOA algorithm’s effectiveness is verified through the simulation of the two-region model. Furthermore, to further validate the proposed method’s optimization effect, the DOA algorithm’s optimization results are compared with those of the genetic algorithm (GA) and proportion method (PROP). Simulation results show that the optimization effect of DOA is more significant than the other methods.
Scenario-Based Uncertainty Modeling for Power Management in Islanded Microgrid Using the Mixed-Integer Distributed Ant Colony Optimization
Authors: Kreishan Maen Z. , Zobaa Ahmed F.
Published: May 2023

Reliable droop-controlled islanded microgrids are necessary to expand coverage and maximize renewables potential. Nonetheless, due to uncertainties surrounding renewable generation and load forecast, substantial power mismatch is expected at off-peak hours. Existing energy management systems such as storage and demand response are not equipped to handle a large power mismatch. Hence, utilizing dump loads to consume excess power is a promising solution to keep frequency and voltage within permissible limits during low-load hours. Considering the uncertainty in wind generation and demand forecast during off-peak hours, the dump load allocation problem was modeled within a scenario-based stochastic framework. The multi-objective optimization with uncertainty was formulated to minimize total microgrid cost, maximum voltage error, frequency deviation, and total energy loss. The mixed-integer distributed ant colony optimization was utilized in a massive parallelization framework for the first time in microgrids to solve the decomposed deterministic problem of the most probable scenarios. Moreover, a flexible and robust load-flow method called general backward/forward sweep was used to obtain the load-flow solution. The optimization problem was applied to the IEEE 69-bus and 118-bus systems. Furthermore, a cost benefit analysis was provided to highlight the proposed method’s advantage over battery-based power management solutions. Lastly, the obtained results further demonstrate the fundamental role of dump load as power management solution while minimizing costs and energy losses.
Cycle Characteristics of a New High-Temperature Heat Pump Based on Absorption–Compression Revolution
Authors: Sun Jian , Qin Yu , Liu Ran , Wang Guoshun , Liu Dingqun , Yang Yongping
Published: May 2023

A large amount of the waste heat generated during industrial production is not used, which leads to a low energy utilization rate. The recovery of industrial waste heat using heat pumps has the advantages of low energy consumption, high efficiency, safety, and environmental protection. Industrial waste heat has a wide temperature distribution range. Traditional absorption and compression heat pumps can only work in a narrow temperature range due to the thermodynamic cycle, the thermal properties of the working medium, the temperature and pressure resistance of the compressor, and other factors; they cannot simultaneously meet the requirements of a “high heating temperature” and “wide temperature-range heat transfer”. To solve the above problems, this paper proposes a high-temperature heat pump unit based on a coupled cycle of absorption and compression, which can recover low-temperature steam and 50 °C waste heat and produce hot water at 110–130 °C. EES software is used for the mathematical modeling and simulation analysis of the heat pump unit. The results show that, when the driving steam temperature is 140 °C and the waste heat temperature is 50 °C, the heating temperature can reach 110~130 °C and the COP of the system can reach 4.22. Increasing the waste heat outlet temperature and the condensation temperature of the absorption cycle strengthens the COP of the coupled cycle; meanwhile, increasing the evaporation temperature and heating temperature of the absorption cycle reduces the COP of the coupled cycle. The results of this study significantly broaden the operating temperature range and heating temperature of electric heat pumps; our findings therefore have essential research significance for improving energy efficiency in industrial fields.
Eventual Increase in Solar Electricity Production and Desalinated Water through the Formation of a Channel between the Mediterranean and the Dead Sea
Authors: Lineykin Simon , Sharma Abhishek , Averbukh Moshe
Published: May 2023

Currently, the Israeli energy industry faces the challenge of a considerable increase in solar electricity production. As a relatively isolated system, the significant expansion of solar electricity may cause problems with electricity quality. Electrical storage installation can resolve this problem. In Israel’s situation, the optimal solution could be the creation of a channel between the Mediterranean and the Dead Sea. The channel can solve three closely related problems: the increased production of desalinated water for domestic, industrial, and agricultural needs; the prevention of a permanent Dead Sea level decline and its imminent disappearance; the development of hydro-pumping electrical storage stations; and the creation of numerous PV facilities in the Negev area for national electricity generation. However, detailed analysis should be conducted for the estimation of the possible increase in solar electric generation with consideration of a stochastic PV outcome and the potential ability to use the Dead Sea for the brine discharge of electrical hydro-storage plants.
Do Distance-Dependent Competition Indices Contribute to Improve Diameter and Total Height Tree Growth Prediction in Juvenile Cork Oak Plantations?
Authors: Firmino Paulo Neves , Tomé Margarida , Paulo Joana Amaral
Published: May 2023

Competition indices may improve tree growth modelling in high-density stands, found often in new cork oak plantations. Distance-dependent competition indices have hardly been considered for juvenile cork oak plantations since existing models were developed for low-density mature stands. This study aims at inspecting the potential of including distance-dependent competition indices into diameter at breast height (d) and total height (h) growth models for Quercus suber L., comparing several distance-dependent and distance-independent competition indices. Annual d and h growth were modelled with linear and non-linear growth functions, formulated as difference equations. Base models were initially fitted considering parameter estimates depending only on site index (S) and/or stand density (N). They were refitted, testing the significance of adding each competition index to the model parameters. Selected models included the best-performing distance-dependent or -independent competition indices as additional predictors. Best base d and h growth models showed a modelling efficiency (ef) of ef = 0.9833 and ef = 0.9900, respectively. Adding a distance-dependent competition index slightly improved growth models, to an ef = 0.9851 for d and ef = 0.9902 for h. Best distance-dependent competition indices slightly overperformed distance-independent ones in diameter growth models. Neither S nor N were included on best fitted models. If inter-tree competition is present in juvenile undebarked cork oak plantations, it does not yet strongly impact individual tree growth, which may diminish the importance of using, at this stage, more complex spatially explicit competition indices on predicting individual tree growth.
Improving Carbon Sequestration Capacity of Forest Vegetation in China: Afforestation or Forest Management?
Authors: Gao Li , Li Hua
Published: May 2023

Both forest management—especially forest tending—and afforestation help to enhance the carbon sequestration of forest vegetation. However, with limited resources, appropriate measures need to be selected to increase the vegetation carbon sinks based on regional endowments. This study aimed to assess the differences in the effects and costs of afforestation and forest tending on vegetation carbon sequestration and to help select suitable afforestation and forest tending areas. In this paper, we used panel fixed effects models to analyze the effects of afforestation and forest tending on vegetation carbon sequestration and conducted a regional heterogeneity analysis to identify suitable afforestation and tending areas. Our results show that the vegetation carbon sequestration capacity of forest tending is 4.48 times higher than that of afforestation, and there is obvious spatial heterogeneity in the effects of afforestation and forest tending on vegetation carbon sequestration. Specifically, the marginal contribution of afforestation is higher than that of tending in northwest and southwest China, whereas the marginal contribution of tending is greater in other regions. Additionally, the afforestation cost for vegetation carbon sequestration is 44.44 times higher than that of tending. Therefore, the management of existing forests must be enhanced, especially in northeastern, southern, and northern China. Similar to the northwest and southwest regions of China, there is still a need to emphasize the use of suitable space for afforestation.
What’s Your Pension Story? Women’s Perspectives during the COVID-19 Pandemic on Their Old-Age Pension Status, Past and Present
Authors: Herbst-Debby Anat
Published: May 2023

This study examines the present and retrospective views of mothers who are nearing or are at retirement age regarding their economic status, pension planning, and perceptions of state pension policy. The paper addresses gaps in the literature on the cross-intersections of employment history, vulnerable economic retirement status, and marital and parental status, thereby adopting a life course perspective. Based on in-depth interviews of thirty-one mothers (ages 59–72) during the COVID-19 pandemic, the findings revealed five themes—economic abuse: an unequal distribution of pension funds following divorce; regrets over past choices; COVID-19 and pensions; the state’s responsibility for old-age economic security; and knowledge is important, and I can help others. The study concludes that the majority of women at these ages perceive their current economic situation as a product of insufficient familiarity with pension plans, while voicing opinions about the state’s irresponsibility regarding people of retirement age.
Pediatric Behavioral Health during the COVID-19 Pandemic: Expert Advice for Preparedness, Response, and Recovery
Authors: Burkhart Kimberly , Ievers-Landis Carolyn E.
Published: May 2023

The COVID-19 pandemic exacerbated the child mental health crisis and existing disparities. Child anxiety, depression, suicide attempts and completions, and mental-health-related emergency department visits significantly increased. In response to this crisis, the Administration for Strategic Preparedness and Response (ASPR) developed behavioral health task forces associated with funded pediatric centers of disaster excellence. The Health Resources and Services Administration (HRSA) funded the Pediatric Pandemic Network (PPN) to prepare for future endemics and pandemics, with behavioral health identified as a priority in mitigation, preparedness, response, and recovery. This commentary provides insights from pediatric disaster preparedness and response behavioral health subject matter experts. Our roles have been to identify how to build behavioral health professional competencies across disciplines and various medical settings and to strengthen emergency interdisciplinary behavioral health care capability regionally and at the national level. Specific examples of interdisciplinary training and demonstration projects are included as models for enhancing behavioral health situational awareness and developing curricula to support preparedness and response for the current ongoing pandemic and future natural and biological disasters. This commentary also includes a call to action for workforce development to move beyond a boots-on-the-ground mentality for pediatric behavioral health disaster preparedness and response toward a more inclusive role for behavioral health providers of varied specialties. This means that behavioral health providers should become more informed of federal programs in this area, seek further training, and find innovative ways to collaborate with their medical colleagues and community partners.
Conducting Violence and Mental Health Research with Female Sex Workers during the COVID-19 Pandemic: Ethical Considerations, Challenges, and Lessons Learned from the Maisha Fiti Study in Nairobi, Kenya
Authors: Kung’u Mary , Kabuti Rhoda , Babu Hellen ,   on behalf of The Maisha Fiti Study Champions, Nyamweya Chrispo , Okumu Monica , Mahero Anne , Jama Zaina , Ngurukiri Polly , Nyariki Emily , Panneh Mamtuti , Shah Pooja , Beksinska Alicja , Irungu Erastus , Adhiambo Wendy , Muthoga Peter , Kaul Rupert , Weiss Helen A. , Seeley Janet , Kimani Joshua , Beattie Tara S.
Published: May 2023

Conducting violence and mental health research during the COVID-19 pandemic with vulnerable groups such as female sex workers (FSWs) required care to ensure that participants and the research team were not harmed. Potential risks and harm avoidance needed to be considered as well as ensuring data reliability. In March 2020, COVID-19 restrictions were imposed in Kenya during follow-up data collection for the Maisha Fiti study (n = 1003); hence data collection was paused. In June 2020, the study clinic was re-opened after consultations with violence and mental health experts and the FSW community. Between June 2020 and January 2021, data were collected in person and remotely following ethical procedures. A total of 885/1003 (88.2%) FSWs participated in the follow-up behavioural–biological survey and 47/47 (100%) participated in the qualitative in-depth interviews. A total of 26/885 (2.9%) quantitative surveys and 3/47 (6.4%) qualitative interviews were conducted remotely. Researching sensitive topics like sex work, violence, and mental health must guarantee study participants’ safety and privacy. Collecting data at the height of COVID-19 was crucial in understanding the relationships between the COVID-19 pandemic, violence against women, and mental health. Relationships established with study participants during the baseline survey—before the pandemic—enabled us to complete data collection. In this paper, we discuss key issues involved in undertaking violence and mental health research with a vulnerable population such as FSWs during a pandemic. Lessons learned could be useful to others researching sensitive topics such as violence and mental health with vulnerable populations.
Health Promotion in Debate: The Role of Women Leaders in the Favelas of Rio de Janeiro, Brazil
Authors: Nunes Nilza Rogéria de Andrade , Rocha Dais , Rodriguez Andréa
Published: May 2023

This study aimed to discuss the actions of women leaders in favelas in Rio de Janeiro, Brazil, regarding the health promotion of people residing in territories affected by urban violence and inequalities. The understanding of social determinants of health (SDH) is not unequivocal and challenges us to expand our practices in health promotion and equity. A mixed-methods study was conducted with 200 women living in 169 favelas of Rio de Janeiro between 2018 and 2022. Questionnaires and semi-structured face-to-face interviews followed by thematic analysis were conducted. The analysis focused on the socio-demographic profile, community activism, and health promotion strategies undertaken by these groups, expanding knowledge on the experiences of these leaders in confronting social injustices. Results showed that participants performed health promotion actions in their communities by 1. strengthening popular participation and human rights, 2. creating environments favorable to health, and 3. developing personal skills towards social participation in policy design through mobilizing health services and third-sector organizations. With the limited presence of government public agents in these spaces, participants assumed roles as managers of local demands, and, by means of resistance, intersectionality, and solidarity, they transformed this micro-power into the potential for social transformation.
Intelligent Short-Term Multiscale Prediction of Parking Space Availability Using an Attention-Enhanced Temporal Convolutional Network
Authors: Shang Ke , Wan Zeyu , Zhang Yulin , Cui Zhiwei , Zhang Zihan , Jiang Chenchen , Zhang Feizhou
Published: May 2023

The accurate and rapid prediction of parking availability is helpful for improving parking efficiency and to optimize traffic systems. However, previous studies have suffered from limited training sample sizes and a lack of thorough investigation into the correlations among the factors affecting parking availability. The purpose of this study is to explore a prediction method that can account for multiple factors. Firstly, a dynamic prediction method based on a temporal convolutional network (TCN) model was confirmed to be efficient for ultra-short-term parking availability with an accuracy of 0.96 MSE. Then, an attention-enhanced TCN (A-TCN) model based on spatial attention modules was proposed. This model integrates multiple factors, including related dates, extreme weather, and human control, to predict the daily congestion index of parking lots in the short term, with a prediction period of up to one month. Experimental results on real data demonstrate that the MSE of A-TCN is 0.0061, exhibiting better training efficiency and prediction accuracy than a traditional TCN for the short-term prediction time scale.
Exploring Public Transportation Supply–Demand Structure of Beijing from the Perspective of Spatial Interaction Network
Authors: Liu Jian , Meng Bin , Xu Jun , Li Ruoqian
Published: May 2023

A comprehensive understanding of the relationship between public transportation supply and demand is crucial for the construction and sustainable development of urban transportation. Due to the spatial and networked nature of public transportation, revealing the spatial configuration and structural disparities between public transportation supply and demand networks (TSN and TDN) can provide significant insights into complex urban systems. In this study, we explored the spatial configuration and structural disparities between TSN and TDN in the complex urban environment of Beijing. By constructing subdistrict-scale TSN and TDN using urban public transportation operation data and mobile phone data, we analyzed the spatial characteristics and structural disparities of these networks from various dimensions, including global indicators, three centralities, and community structure, and measured the current public transportation supply and demand matching pattern in Beijing. Our findings revealed strong structural and geographic heterogeneities of TSN and TDN, with significant traffic supply–demand mismatch being observed in urban areas within the Sixth Ring Road. Moreover, based on the percentage results of supply–demand matching patterns, we identified that the current public transportation supply–demand balance in Beijing is approximately 64%, with around 18% of both excess and shortage of traffic supply. These results provide valuable insights into the structure and functioning of public transportation supply–demand networks for policymakers and urban planners; these can be used to facilitate the development of a sustainable urban transportation system.
Using Enhanced Gap-Filling and Whittaker Smoothing to Reconstruct High Spatiotemporal Resolution NDVI Time Series Based on Landsat 8, Sentinel-2, and MODIS Imagery
Authors: Liang Jieyu , Ren Chao , Li Yi , Yue Weiting , Wei Zhenkui , Song Xiaohui , Zhang Xudong , Yin Anchao , Lin Xiaoqi
Published: May 2023

Normalized difference vegetation index (NDVI) time series data, derived from optical images, play a crucial role for crop mapping and growth monitoring. Nevertheless, optical images frequently exhibit spatial and temporal discontinuities due to cloudy and rainy weather conditions. Existing algorithms for reconstructing NDVI time series using multi-source remote sensing data still face several challenges. In this study, we proposed a novel method, an enhanced gap-filling and Whittaker smoothing (EGF-WS), to reconstruct NDVI time series (EGF-NDVI) using Google Earth Engine. In EGF-WS, NDVI calculated from MODIS, Landsat-8, and Sentinel-2 satellites were combined to generate high-resolution and continuous NDVI time series data. The MODIS NDVI was employed as reference data to fill missing pixels in the Sentinel–Landsat NDVI (SL-NDVI) using the gap-filling method. Subsequently, the filled NDVI was smoothed using a Whittaker smoothing filter to reduce residual noise in the SL-NDVI time series. With reference to the all-round performance assessment (APA) metrics, the performance of EGF-WS was compared with the conventional gap-filling and Savitzky–Golay filter approach (GF-SG) in Fusui County of Guangxi Zhuang Autonomous Region. The experimental results have demonstrated that the EGF-WS can capture more accurate spatial details compared with GF-SG. Moreover, EGF-NDVI of Fusui County exhibited a low root mean square error (RMSE) and a high coefficient of determination (R2). In conclusion, EGF-WS holds significant promise in providing NDVI time series images with a spatial resolution of 10 m and a temporal resolution of 8 days, thereby benefiting crop mapping, land use change monitoring, and various ecosystems, among other applications.
Modulation of Gut Microbiome in Ecstasy/MDMA-Induced Behavioral and Biochemical Impairment in Rats and Potential of Post-Treatment with Anacyclus pyrethrum L. Aqueous Extract to Mitigate Adverse Effects
Authors: Baslam Abdelmounaim , Aitbaba Abdelfatah , Lamrani Hanchi Asmae , Tazart Zakaria , Aboufatima Rachida , Soraa Nabila , Ait-El-Mokhtar Mohamed , Boussaa Samia , Baslam Marouane , Chait Abderrahman
Published: May 2023

The use of illicit substances continues to pose a substantial threat to global health, affecting millions of individuals annually. Evidence suggests the existence of a ‘brain–gut axis’ as the involving connection between the central nervous system and gut microbiome (GM). Dysbiosis of the GM has been associated with the pathogenesis of various chronic diseases, including metabolic, malignant, and inflammatory conditions. However, little is currently known about the involvement of this axis in modulating the GM in response to psychoactive substances. In this study, we investigated the effect of MDMA (3,4-methylenedioxymethamphetamine, “Ecstasy”)-dependence on the behavioral and biochemical responses, and the diversity and abundance of the gut microbiome in rats post-treated (or not) with aqueous extract of Anacyclus pyrethrum (AEAP), which has been reported to exhibit anticonvulsant activity. The dependency was validated using the conditioned place preference (CPP) paradigm, behavioral, and biochemical tests, while the gut microbiota was identified using matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS). The CPP and behavioral tests confirmed the presence of MDMA withdrawal syndrome. Interestingly, treatment with AEAP led to a compositional shift in the GM compared to the MDMA-treated rats. Specifically, the AEAP group yielded a higher relative abundance of Lactobacillus and Bifidobacter, while animals receiving MDMA had higher levels of E. coli. These findings suggest that A. pyrethrum therapy may directly modulate the gut microbiome, highlighting a potential target for regulating and treating substance use disorders.
Gut Microbiota and Cardiovascular Disease: Evidence on the Metabolic and Inflammatory Background of a Complex Relationship
Authors: Nesci Antonio , Carnuccio Claudia , Ruggieri Vittorio , D’Alessandro Alessia , Di Giorgio Angela , Santoro Luca , Gasbarrini Antonio , Santoliquido Angelo , Ponziani Francesca Romana
Published: May 2023

Several studies in recent years have demonstrated that gut microbiota–host interactions play an important role in human health and disease, including inflammatory and cardiovascular diseases. Dysbiosis has been linked to not only well-known inflammatory diseases, such as inflammatory bowel diseases, rheumatoid arthritis, and systemic lupus erythematous, but also to cardiovascular risk factors, such as atherosclerosis, hypertension, heart failure, chronic kidney disease, obesity, and type 2 diabetes mellitus. The ways the microbiota is involved in modulating cardiovascular risk are multiple and not only related to inflammatory mechanisms. Indeed, human and the gut microbiome cooperate as a metabolically active superorganism, and this affects host physiology through metabolic pathways. In turn, congestion of the splanchnic circulation associated with heart failure, edema of the intestinal wall, and altered function and permeability of the intestinal barrier result in the translocation of bacteria and their products into the systemic circulation, further enhancing the pro-inflammatory conditions underlying cardiovascular disorders. The aim of the present review is to describe the complex interplay between gut microbiota, its metabolites, and the development and evolution of cardiovascular diseases. We also discuss the possible interventions intended to modulate the gut microbiota to reduce cardiovascular risk.
Metabolomic Analysis of the Response of Haloxylon ammodendron and Haloxylon persicum to Drought
Authors: Yang Fang , Lv Guanghui
Published: May 2023

Haloxylon ammodendron and Haloxylon persicum, as typical desert plants in arid areas, show strong drought tolerance and environmental adaptability and are therefore ideal model plants for studying the molecular mechanisms of drought tolerance. A metabolomic analysis of H. ammodendron and H. persicum in their natural environment is lacking, and their metabolic response to drought therefore remains unclear. To elucidate the response of H. ammodendron and H. persicum to drought at the metabolic level, a non-targeted metabolomics analysis was carried out herein. Under a dry environment, H. ammodendron exhibited 296 and 252 differentially expressed metabolites (DEMs) in the positive and negative ion modes, respectively, whereas 452 and 354 DEMs were identified in the positive and negative ion modes in H. persicum, respectively. The results indicated that H. ammodendron responds to drought by increasing the content of organic nitrogen compounds and lignans, neolignans, and related compounds, and reducing the content of alkaloids and derivatives. By contrast, H. persicum adapts to the dry environment by increasing the content of organic acids and their derivatives and reducing the content of lignans, neolignans, and related compounds. In addition, H. ammodendron and H. persicum improved their osmoregulation ability, reactive oxygen species detoxification ability, and cell membrane stability by regulating the key metabolic pathways and anabolism of associated metabolites. This is the first metabolomics report on the response of H. ammodendron and H. persicum to drought in their natural environment, providing a foundation for the further study of their regulatory mechanisms under drought stress.
Friunavirus Phage-Encoded Depolymerases Specific to Different Capsular Types of Acinetobacter baumannii
Authors: Timoshina Olga Y. , Kasimova Anastasia A. , Shneider Mikhail M. , Matyuta Ilya O. , Nikolaeva Alena Y. , Evseev Peter V. , Arbatsky Nikolay P. , Shashkov Alexander S. , Chizhov Alexander O. , Shelenkov Andrey A. , Mikhaylova Yulia V. , Slukin Pavel V. , Volozhantsev Nikolay V. , Boyko Konstantin M. , Knirel Yuriy A. , Miroshnikov Konstantin A. , Popova Anastasia V.
Published: May 2023

Acinetobacter baumannii is a critical priority nosocomial pathogen that produces a variety of capsular polysaccharides (CPSs), the primary receptors for specific depolymerase-carrying phages. In this study, the tailspike depolymerases (TSDs) encoded in genomes of six novel Friunaviruses, APK09, APK14, APK16, APK86, APK127v, APK128, and one previously described Friunavirus phage, APK37.1, were characterized. For all TSDs, the mechanism of specific cleavage of corresponding A. baumannii capsular polysaccharides (CPSs) was established. The structures of oligosaccharide fragments derived from K9, K14, K16, K37/K3-v1, K86, K127, and K128 CPSs degradation by the recombinant depolymerases have been determined. The crystal structures of three of the studied TSDs were obtained. A significant reduction in mortality of Galleria mellonella larvae infected with A. baumannii of K9 capsular type was shown in the example of recombinant TSD APK09_gp48. The data obtained will provide a better understanding of the interaction of phage–bacterial host systems and will contribute to the formation of principles of rational usage of lytic phages and phage-derived enzymes as antibacterial agents.
Potential Binding Sites of Pharmacological Chaperone NCGC00241607 on Mutant β-Glucocerebrosidase and Its Efficacy on Patient-Derived Cell Cultures in Gaucher and Parkinson’s Disease
Authors: Kopytova Alena E. , Rychkov George N. , Cheblokov Alexander A. , Grigor’eva Elena V. , Nikolaev Mikhail A. , Yarkova Elena S. , Sorogina Diana A. , Ibatullin Farid M. , Baydakova Galina V. , Izyumchenko Artem D. , Bogdanova Daria A. , Boitsov Vitali M. , Rybakov Akim V. , Miliukhina Irina V. , Bezrukikh Vadim A. , Salogub Galina N. , Zakharova Ekaterina Y. , Pchelina Sofya N. , Emelyanov Anton K.
Published: May 2023

Mutations in the GBA1 gene, encoding the lysosomal enzyme glucocerebrosidase (GCase), cause Gaucher disease (GD) and are the most common genetic risk factor for Parkinson’s disease (PD). Pharmacological chaperones (PCs) are being developed as an alternative treatment approach for GD and PD. To date, NCGC00241607 (NCGC607) is one of the most promising PCs. Using molecular docking and molecular dynamics simulation we identified and characterized six allosteric binding sites on the GCase surface suitable for PCs. Two sites were energetically more preferable for NCGC607 and located nearby to the active site of the enzyme. We evaluated the effects of NCGC607 treatment on GCase activity and protein levels, glycolipids concentration in cultured macrophages from GD (n = 9) and GBA-PD (n = 5) patients as well as in induced human pluripotent stem cells (iPSC)—derived dopaminergic (DA) neurons from GBA-PD patient. The results showed that NCGC607 treatment increased GCase activity (by 1.3-fold) and protein levels (by 1.5-fold), decreased glycolipids concentration (by 4.0-fold) in cultured macrophages derived from GD patients and also enhanced GCase activity (by 1.5-fold) in cultured macrophages derived from GBA-PD patients with N370S mutation (p < 0.05). In iPSC-derived DA neurons from GBA-PD patients with N370S mutation NCGC607 treatment increased GCase activity and protein levels by 1.1-fold and 1.7-fold (p < 0.05). Thus, our results showed that NCGC607 could bind to allosteric sites on the GCase surface and confirmed its efficacy on cultured macrophages from GD and GBA-PD patients as well as on iPSC-derived DA neurons from GBA-PD patients.