bluuebunny/crossref_metadata_2025 huggingface dataset:
Community Pharmacies in the Asian Countries of Developing Health System: Formation, Regulation, and Implication
Authors: Alenezi Shaymaa , Alanazi Mona , Aljazaeri Reaam , Almuzaini Marwah , Alrasheidi Shorouq , Shamlan Wafa Bin , Aljohani Raghad , Alhawiti Ghadeer , Alqarni Meaad , Aljabri Ehdaa , Qmmash Majd , Kanan Mohammed
Published: August 2023

Community pharmacies (CPs) in Asian countries are often the first point of contact for patients withinthe healthcare system and their preferred place to purchase medicines. The number of CPs may vary across Asian countries, and each country has developed its own design and functioning. The regulatory environment plays a crucial role in shaping and governing CPs. The aim of this study was to conduct a comprehensive literature review in order to enhance understanding of the establishment and regulation of CPs. By undertaking this review, the researchers, policymakers, and practitioners sought to gain a deeper insight into the performance and functions of CPs and the regulatory frameworks that govern them. Moreover, this review shed light on implementation strategies, effects on patient outcomes, and the barriers and challenges associated with their establishment. A narrative literature review method was adopted with specific inclusion and exclusion criteria. Significant disparities can be observed when comparing the stated intentions of regulations with their actual implementation. Recently, there has been an inclusion of public health practices. Unfortunately, pharmacy procedures conducted in such environments have been characterized by inadequate understanding and inappropriate care. This poor performance can be attributed to employees’ focus on maximizing profits. Several shortcomings can arise, including incomplete patient history documentation, failure to refer patients who require medical attention, unauthorized dispensing of prescription-only medicines (POM), dispensing clinically inappropriate or excessive medication doses, selling incomplete antibiotic courses, and inadequate information and counseling services. Regulatory interventions can help strengthen these services.
Vessel Anatomical Features of ‘Picual’ and ‘Frantoio’, Two Olive Cultivars Different in Resistance against Verticillium Wilt of Olive
Authors: Santos-Rufo Antonio , Molina-Molina Martín , Alcántara-Vara Esteban , Weiland-Ardáiz Carlos , López-Escudero Fco. Javier
Published: August 2023

The olive tree (Olea europaea), a non-tropical woody crop that occupies the largest area in the world, is severely affected by the fungus Verticillium dahliae worldwide. In this regard, there is currently detailed information on the level of resistance to this pathogen in the main olive varieties. However, there is little information on quantitative aspects of its anatomy and on the existence of anatomical differences between varieties that could be related to the differential resistance response observed. In the present work, a quantitative study of the xylem of ‘Picual’, susceptible, and ‘Frantoio’, resistant, to V. dahliae is carried out. This study also provides quantitative data on the xylem in different areas of the plant, an aspect on which there is not much information for the olive tree. Among the parameters evaluated, it is probably the greater conductive capacity in the xylem tissue that ‘Frantoio’ presents, mainly due to the greater density of its vessels, which has a more relevant role in the resistance and natural recovery that this cultivar manifests to the disease. In any case, these constitutive anatomical differences, and those others that can be induced in plants during infections, should be investigated in future work that includes inoculation with the pathogen.
First Report of Colleters in Araceae: A Case Study in Anthurium andraeanum Reveals Diverse Mucilage Glands Associated with the Developing Shoot
Authors: Pereira-Silva Carlos Gabriel , Ballego-Campos Igor , Sakuragui Cássia Mônica , Gonçalves Eduardo Gomes , Paiva Elder Antônio Sousa
Published: August 2023

Araceae comprises a diverse group of plants that grow in various habitats, ranging from submerged aquatics to lithophytes. Thus, aroids are likely to show diverse glands acting in several plant–environment interactions, including colleters that protect young shoots. Based on this premise and the lack of studies regarding secretory structures in Araceae, we employed standard light and electron microscopy methods to test the hypothesis that colleters are present in Anthurium. Our main goals were to identify mucilage glands in A. andraeanum by conducting a detailed anatomical study of their structure, ultrastructure, and secretory activity. We found finger-like colleters in the apex of young leaves, spathes, and unexpanded cataphylls as well as secreting zones at the apex of expanded cataphylls, at the margins of non-fused cataphylls, and throughout the keels in two-keeled cataphylls. The colleters develop precociously and senesce shortly afterwards. Ultrastructural data and histochemistry confirmed the production of a polysaccharide-rich secretion that fills the spaces within the developing shoot. As far we know, this is the first time that colleters have been reported for Araceae. The functional roles of the secretion and the position of finger-like colleters concerning the ‘precursor tip’ of monocotyledons are discussed. Future research correlating secretory activity in colleters of species from different habitats might reveal a great diversity of mucilage glands with ecological and evolutionary significance to the family.
Soil Microbial Community, Soil Quality, and Productivity along a Chronosequence of Larix principis-rupprechtii Forests
Authors: Zhang Jing , Liu Qiang , Wang Dongzhi , Zhang Zhidong
Published: August 2023

Elucidating the correlation between soil microbial communities and forest productivity is the focus of research in the field of forest ecology. Nonetheless, the relationship between stand age, soil quality, soil microorganisms, and their combined influence on productivity is still unclear. In this study, five development stages (14, 25, 31, 39, and >80 years) of larch (Larix principis-rupprechtii) forests were investigated in Inner Mongolia and Shanxi provinces of China. We evaluated soil quality using the Integrated Soil Quality Index (SQI) and analyzed changes in bacterial and fungal communities using high-throughput sequencing. Regression models were also established to examine the impacts of stand age, microbial diversity, and SQI on productivity. The findings revealed an ascending trend in soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), available potassium (AK), and SQI in 14, 25, 31, and 39-year-old stands. The abundance of oligotrophic bacteria Acidobacteria exhibited a gradual decline with increasing forest age, whereas copiotroph bacteria Proteobacteria displayed a progressive increase. Stands older than 80 years exhibited a higher abundance of both the saprophytic fungus Ascomycota and mycorrhizal fungus Basidiomycota. Forest age had a significant impact on microbial diversity, particularly in terms of bacterial diversity, impacting both α and β diversity. The soil bacterial community structure was influenced by AK, SOM, TN, TP, and pH. Conversely, the fungal community structure was regulated by crucial factors including SOM, TN, TP, TK, AK, and pH. Fungal diversity demonstrated a significant and positive correlation with the basal area increment (BAI) of larch. Furthermore, microbial diversity accounted for 23.6% of the variation in BAI. In summary, the findings implied a robust association between microbial composition, diversity, and soil chemical properties throughout the chronosequence of larch forests. These factors collectively played a crucial role in influencing the productivity of larch forest.
Monitoring and Characterizing the Flame State of a Bluff-Body Stabilized Burner by Electrical Capacitance Tomography
Authors: Chang Liuyong , Cui Boxuan , Zhang Chenglin , Xu Zheng , Li Guangze , Chen Longfei
Published: August 2023

Unstable combustion phenomena such as flame flashback, flame liftoff, extinction and blowout frequently take place during the operation of the bluff-body stabilized burner. Therefore, flame state monitoring is necessary for the safe operation of the bluff-body stabilized burner. In the present study, an electrical capacitance tomography (ECT) system was deployed to detect the permittivity distribution in the premixing channel and further characterize the flame states of stabilization, flashback, liftoff, extinction and blowout. A calderon-based reconstruction method was modified to reconstruct the permittivity distribution in the annular premixing channel. The detection results indicate that the permittivity in the premixing channel increases steeply when the flame flashback takes place and decreases obviously when the flame lifts off from the combustor rim. Based on the varied permittivity distribution at different flame states, a flame state index was proposed to characterize the flame state in quantification. The flame state index is 0, positive, in the range of −0.64–0, and lower than −0.64 when the flame is at the state of stable, flashback, liftoff and blowout, respectively. The flame state index at the flame state of extinction is the same as that at the flame state of liftoff. The extinction state and the blowout state can be distinguished by judging whether the flame flashback takes place before the flame is extinguished. These results reveal that the ECT system is capable of monitoring the flame state, and that the proposed flame state index can be used to characterize the flame state.
Method for Dynamic Prediction of Oxygen Demand in Steelmaking Process Based on BOF Technology
Authors: Zhang Kaitian , Zheng Zhong , Zhang Liu , Liu Yu , Chen Sujun
Published: August 2023

Oxygen is an important energy medium in the steelmaking process. The accurate dynamic prediction of oxygen demand is needed to guarantee molten steel quality, improve the production rhythm, and promote the collaborative optimization of production and energy. In this work, a analysis of the mechanism and of industrial big data was undertaken, and we found that the characteristic factors of Basic Oxygen Furnace (BOF) oxygen consumption were different in different modes, such as duplex dephosphorization, duplex decarbonization, and the traditional mode. Based on this, a dynamic-prediction modeling method for BOF oxygen demand considering mode classification is proposed. According to the characteristics of BOF production organization, a control module based on dynamic adaptions of the production plan was researched to realize the recalculation of the model predictions. A simulation test on industrial data revealed that the average relative error of the model in each BOF mode was less than 5% and the mean absolute error was about 450 m3. Moreover, an accurate 30-minute-in-advance prediction of dynamic oxygen demand was realized. This paper provides the method support and basis for the long-term demand planning of the static balance and the short-term real-time scheduling of the dynamic balance of oxygen.
Numerical Optimization Study of the Resistance Coefficient of U-Shaped Air Distributor
Authors: Wu Zhijing , Wang Jinfeng , Xie Jing
Published: August 2023

In this paper, the optimization of the flow channel structure of the U-shaped air distributor is proposed. Fluent meshing was used to mesh the multipatch meshing of the original model of the grid air distributor, and then the CFD numerical simulation was carried out by using Fluent 2022R1 to obtain the internal air flow state of the air distributor flow channel. Through the orthogonal experimental design and a comprehensive analysis method, the optimal size structure for resistance performance is obtained as S = 60 mm, RL = 125 mm, L = 160 mm, D = 100 mm, the resistance coefficient of the new structure as 1.375, and the pressure loss as 56.87 Pa, by using 3D modeling software (SOLIDWORKS 2015) and Fluent. Compared with the initial scheme, the resistance coefficient and pressure loss are reduced by 3.03% and 6.29%, respectively. To summarize, the research in this paper offers a substantial contribution to the realm of energy conservation and emission abatement in ship air conditioning systems, simultaneously furnishing invaluable guidance for the design of air distributors.
Comparison between Air-Exposed and Underground Thermal Energy Storage for Solar Cooling Applications
Authors: Ríos-Arriola Juan , Velázquez-Limón Nicolás , Aguilar-Jiménez Jesús Armando , Islas Saúl , López-Sánchez Juan Daniel , Caballero-Talamantes Francisco Javier , Corona-Sánchez José Armando , Cásares-De la Torre Cristian Ascención
Published: August 2023

Solar energy is one of the main alternatives for the decarbonization of the electricity sector and the reduction of the existing energy deficit in some regions of the world. However, one of its main limitations lies in its storage, since this energy source is intermittent. This paper evaluates the potential of an underground thermal energy storage tank supplied by solar thermal collectors to provide hot water for the activation of a single-effect absorption cooling system. A simulator was developed in TRNSYS 17 software. Experimentally on-site measured data of soil temperature were used in order to increase the accuracy of the simulation. The results show that the underground tank reduces thermal energy losses by 27.6% during the entire hot period compared with the air-exposed tank. The electrical energy savings due to the reduction in pumping time during the entire hot period was 639 kWh, which represents 23.6% of the electrical energy consumption of the solar collector pump. It can be concluded that using an underground thermal energy storage tank is a feasible option in areas with high levels of solar radiation, especially in areas where ambient temperature drops significantly during night hours and/or when access to electrical energy is limited.
Sequential Processing Using Supercritical Carbon Dioxide and High-Intensity Ultrasound in Sunflower Protein Flour Production: Nutritional Value, Microstructure, and Technological Functionality
Authors: dos Santos Friolli Mariana Pacífico , Silva Eric Keven , Chaves Janaíne , da Silva Marcos Fellipe , Goldbeck Rosana , Galland Fabiana Andrea Barrera , Pacheco Maria Teresa Bertoldo
Published: August 2023

Sunflowers are among the world’s most widely cultivated oilseeds with an interesting nutritional composition. A biomass composed mainly of carbohydrates, fibers, and proteins is generated from sunflower oil production. In this context, the objective of this study was to investigate the application of emerging technologies to sunflower biomass to obtain an edible protein-rich flour with the potential to be exploited in the food industry. The effects of the optimized conditions for the sequential processing of sunflower meal using supercritical carbon dioxide (SCO2) and high-intensity ultrasound (HIUS) were investigated. The protein structure was preserved even after the application of HIUS to the flour as verified through differential scanning calorimetry (DSC) and the electrophoresis curves. The fact that the HIUS treatment did not modify the protein structure demonstrates that this emerging technology could be incorporated into the processing chain of this new food ingredient (sunflower flour) without promoting damage to the nutritional value of the product regarding its protein content. At a pH of 7.0, the flour showed only 30% solubility, and HIUS application improved both the formation and the stability of the emulsion when compared to the other samples. The preliminary evaluation of cell viability (caco2 cells) showed its protective potential against reactive oxygen species. Therefore, the flour resulting from the green processes presented the potential to be employed as an ingredient in the food industry, presenting a technological and nutritional potential when considering its chemical composition. In addition to the novel edible flour, the phenolic compounds obtained a present potential as a functional ingredient to be incorporated into foods.
HOMER-Based Multi-Scenario Collaborative Planning for Grid-Connected PV-Storage Microgrids with Electric Vehicles
Authors: Zhang Yifan , Yan Shiye , Yin Wenqian , Wu Chao , Ye Jilei , Wu Yuping , Liu Lili
Published: August 2023

One of the crucial methods for adapting distributed PV generation is the microgrid. However, solar resources, load characteristics, and the essential microgrid system components are all directly tied to the optimal planning scheme for microgrids. This article conducts a collaborative planning study of grid-connected PV-storage microgrids under electric vehicle integration in various scenarios using HOMER 1.8.9 software. To be more specific, in multiple scenarios, we built capacity optimization models for PV modules, energy storage, and converters in microgrids, with several scenarios each accounting for the cleanliness, economic performance, and overall performance of microgrids. For multiple scenarios, this paper used the net present value cost and levelized cost of electricity as indicators of microgrid economics, and carbon dioxide emissions and the fraction of renewable energy were used as indicators of microgrid cleanliness. The optimal capacity allocation for economy, cleanliness, and a combination of economy and cleanliness were separately derived. Finally, on a business park in Wuhan, China, we conducted thorough case studies to compare and debate the planning performance under various scenarios and to undertake sensitivity analyses on the cases. The sensitivity analyses were conducted for the optimal configuration of microgrids in terms of the EV charging scale, carbon dioxide emissions, PV module unit cost, and storage unit cost. The results of the simulation and optimization show that the optimization approach could determine the ideal configuration for balancing economy and cleanliness. As the EV charging demand increased, the energy storage capacity required in the microgrid gradually increased, while the carbon dioxide emission limit was negatively correlated with the energy storage capacity demand. The unit investment cost of PV module units had a greater impact on the optimal system configuration than the cost of batteries.
Feature Selection of Microarray Data Using Simulated Kalman Filter with Mutation
Authors: Ahmad Zamri Nurhawani , Ab. Aziz Nor Azlina , Bhuvaneswari Thangavel , Abdul Aziz Nor Hidayati , Ghazali Anith Khairunnisa
Published: August 2023

Microarrays have been proven to be beneficial for understanding the genetics of disease. They are used to assess many different types of cancers. Machine learning algorithms, like the artificial neural network (ANN), can be trained to determine whether a microarray sample is cancerous or not. The classification is performed using the features of DNA microarray data, which are composed of thousands of gene values. However, most of the gene values have been proven to be uninformative and redundant. Meanwhile, the number of the samples is significantly smaller in comparison to the number of genes. Therefore, this paper proposed the use of a simulated Kalman filter with mutation (SKF-MUT) for the feature selection of microarray data to enhance the classification accuracy of ANN. The algorithm is based on a metaheuristics optimization algorithm, inspired by the famous Kalman filter estimator. The mutation operator is proposed to enhance the performance of the original SKF in the selection of microarray features. Eight different benchmark datasets were used, which comprised: diffuse large b-cell lymphomas (DLBCL); prostate cancer; lung cancer; leukemia cancer; “small, round blue cell tumor” (SRBCT); brain tumor; nine types of human tumors; and 11 types of human tumors. These consist of both binary and multiclass datasets. The accuracy is taken as the performance measurement by considering the confusion matrix. Based on the results, SKF-MUT effectively selected the number of features needed, leading toward a higher classification accuracy ranging from 95% to 100%.
Effect of Inclined Orifice in Air Impingement Freezer on Heat Transfer Characteristics of Steel Strip Surface
Authors: Xie Jing , Luo Xilan , Wang Jinfeng , Liu Yuyan
Published: August 2023

In order to improve the heat transfer characteristics of the air impingement freezer, an impingement freezer experimental table was designed as the research object in this paper. Numerical simulation technology was used to simulate the impingement freezer experimental table on the basis of test verification. When the other structural parameters in the impingement freezer experimental table were constant, the effect of the inclination angle of the orifice plate (θ = 60°, 65°, 70°, 75°, 80°, 85°, and 90°) on the heat transfer characteristics of a steel strip surface was analyzed by two aspects, the average Nusselt number and the heat transfer uniformity. The results showed that with the increase in the inclination angle of the orifice plate (60° ≤ θ ≤ 90°), the average Nusselt number of the steel strip surface was increased by 19.39%, and the heat transfer uniformity index was decreased by 33.69%. When θ = 90°, the average Nusselt number on steel strip was the maximum, which was 263.68, and the heat transfer uniformity index was the minimum, which was 0.2039. Therefore, the heat transfer intensity and heat transfer uniformity in the air impingement freezer could be improved when the inclination angle of the orifice plates was 90°. This helps to improve the output of the air impingement freezer, reduce energy consumption, and improve the quality of frozen food.
Microparticles’ Lateral Oscillation Motion in Serpentine Micro-Channels without Inertial Lift Effects
Authors: Liu Yang , Hu Xintao , Ma Jiayuan , Gao Feng , Gao Yanan , Yan Linbo
Published: August 2023

Micro-particle manipulation, based solely on the Dean drag force, has begun to be advocated for with the goal of lowering the pumping pressure and simplifying the complexity of the coupling effects of the inertial lift force and the Dean drag force, thus reducing the difficulty of theoretically predicting particle motion. We employed the CFD-DEM two-way coupling method in this work to quantitatively study the lateral (z in axis) motion of particles (7–10 μm), in square or half-circle segment serpentine microchannels, that was only reliant on Dean drag with the blockage ratio dDh= 0.04 (the inertial lift effects show at dDh>0.07). In the square-segment serpentine channel, under the conditions of single-side-wall sheath flow and sedimentation, we discovered that the particles exhibit a twist-type lateral trajectory around each turn, with the larger particles always twisting in the opposite direction of the smaller particles, as a result of the four-grid-pattern distribution of the lateral velocity values at each turn. The large and small particles are separated at the channel’s exit at Re = 56.7, De = 17.8, indicating the likelihood of separation only due to the Dean drag. This separation efficiency decreases as Re and De decreases. The lateral position and velocity values of the particles oscillate, as time passes, due to the twist trajectory, with the oscillation amplitude increasing as Re or De decreases and deflecting toward the inner side of z. In the cases of the two-side-wall-symmetric sheath flow, the particles exhibit only a little lateral deflection, and particle separation is not achieved. The deflection of the oscillation is uncertain and does not change regularly with any physical quantity.
Study on the Spatial and Temporal Distribution of Urban Vegetation Phenology by Local Climate Zone and Urban–Rural Gradient Approach
Authors: Li Shan , Li Qiang , Zhang Jiahua , Zhang Shichao , Wang Xue , Yang Shanshan , Zhang Sha
Published: August 2023

Understanding variations in the temporal and spatial distribution of vegetation phenology is essential for adapting to and mitigating future climate change and urbanization. However, there have been limited vegetation phenology studies within small-scale areas such as urban environments over the past decades. Therefore, the present study focuses on Jinan city, Shandong Province, China as the study area and employs a more refined local climate zone (LCZ) approach to investigate spatial and temporal variations in vegetation phenology. The three phenological indicators used in this study from 2007 to 2018, namely, the start of growing season (SOS), the end of growing season (EOS), and the length of growing season (LOS), were provided by MODIS satellite data. The SOS, EOS, and LOS were superimposed on the LCZ and urban–rural gradient to analyze the changes in vegetation phenology, and the applicability of these two analysis methods in the study of urban vegetation phenology was compared by the honest significant difference test. We found that the SOS, EOS, and LOS of vegetation in the study area generally showed an advance, delay, and extension trend, respectively. The means of the SOS and EOS along different LCZ types varied noticeably more than those along urban–rural gradients. In 2016, 77.5%, 80.0%, and 75.8% of LCZ pairs indicated statistically significant differences for SOS, EOS, and LOS, respectively. This study provides a new perspective for the study of urban vegetation phenology which can help in management of urban-scale environments, identification of areas rich in biodiversity, and conservation and restoration of biodiversity in urban areas.
Wind Direction Extraction from X-Band Marine Radar Images Based on the Attenuation Horizontal Component
Authors: Yu Huanyu , Lu Zhizhong , Wang Hui
Published: August 2023

This paper presents a novel algorithm based on the attenuation horizontal component for wind direction retrieval from X-band marine radar images. The range dependence of radar return on the ocean surface can be presented in radar images, and the radar return decreases with the increase in range. The traditional curve-fitting method averages the radar return of the whole range to retrieve the wind direction, but it is vulnerable to the interference of fixed objects and long-range low-intensity pixel points. For the pixels with the same range in the polar coordinates of the radar image, the ideal range attenuation model is derived by selecting the pixels with the highest intensity value. The ideal attenuation model is used to fit the attenuation data and calculate the attenuation horizontal component at each azimuth direction. To eliminate the effect of outliers, the iterative optimization method is used in the estimation of the attenuation horizontal component and the weights of the data are continuously updated. Finally, the wind direction is determined based on the azimuthal dependence of the attenuation horizontal component. This algorithm was tested using shipboard radar images and anemometer data collected in the East China Sea. The results show that, compared with the single curve-fitting method, the proposed algorithm can improve the wind direction retrieval accuracy in the case of more fixed targets. Under the condition of more fixed targets, the deviation and root mean square error are reduced by 16.3° and 16.2°, respectively.
On-Board Multi-Class Geospatial Object Detection Based on Convolutional Neural Network for High Resolution Remote Sensing Images
Authors: Shen Yanyun , Liu Di , Chen Junyi , Wang Zhipan , Wang Zhe , Zhang Qingling
Published: August 2023

Multi-class geospatial object detection in high-resolution remote sensing images has significant potential in various domains such as industrial production, military warning, disaster monitoring, and urban planning. However, the traditional process of remote sensing object detection involves several time-consuming steps, including image acquisition, image download, ground processing, and object detection. These steps may not be suitable for tasks with shorter timeliness requirements, such as military warning and disaster monitoring. Additionally, the transmission of massive data from satellites to the ground is limited by bandwidth, resulting in time delays and redundant information, such as cloud coverage images. To address these challenges and achieve efficient utilization of information, this paper proposes a comprehensive on-board multi-class geospatial object detection scheme. The proposed scheme consists of several steps. Firstly, the satellite imagery is sliced, and the PID-Net (Proportional-Integral-Derivative Network) method is employed to detect and filter out cloud-covered tiles. Subsequently, our Manhattan Intersection over Union (MIOU) loss-based YOLO (You Only Look Once) v7-Tiny method is used to detect remote-sensing objects in the remaining tiles. Finally, the detection results are mapped back to the original image, and the truncated NMS (Non-Maximum Suppression) method is utilized to filter out repeated and noisy boxes. To validate the reliability of the scheme, this paper creates a new dataset called DOTA-CD (Dataset for Object Detection in Aerial Images-Cloud Detection). Experiments were conducted on both ground and on-board equipment using the AIR-CD dataset, DOTA dataset, and DOTA-CD dataset. The results demonstrate the effectiveness of our method.
Impact of Uncertainty Estimation of Hydrological Models on Spectral Downscaling of GRACE-Based Terrestrial and Groundwater Storage Variation Estimations
Authors: Eshagh Mehdi , Fatolazadeh Farzam , Goïta Kalifa
Published: August 2023

Accurately estimating hydrological parameters is crucial for comprehending global water resources and climate dynamics. This study addresses the challenge of quantifying uncertainties in the global land data assimilation system (GLDAS) model and enhancing the accuracy of downscaled gravity recovery and climate experiment (GRACE) data. Although the GLDAS models provide valuable information on hydrological parameters, they lack uncertainty quantification. To enhance the resolution of GRACE data, a spectral downscaling approach can be employed, leveraging uncertainty estimates. In this study, we propose a novel approach, referred to as method 2, which incorporates parameter magnitudes to estimate uncertainties in the GLDAS model. The proposed method is applied to downscale GRACE data over Alberta, with a specific focus on December 2003. The groundwater storage extracted from the downscaled terrestrial water storage (TWS) are compared with measurements from piezometric wells, demonstrating substantial improvements in accuracy. In approximately 80% of the wells, the root mean square (RMS) and standard deviation (STD) were improved to less than 5 mm. These results underscore the potential of the proposed approach to enhance downscaled GRACE data and improve hydrological models.
User Pairing for Delay-Limited NOMA-Based Satellite Networks with Deep Reinforcement Learning
Authors: Zhang Qianfeng , An Kang , Yan Xiaojuan , Xi Hongxia , Wang Yuli
Published: August 2023

In this paper, we investigate a user pairing problem in power domain non-orthogonal multiple access (NOMA) scheme-aided satellite networks. In the considered scenario, different satellite applications are assumed with various delay quality-of-service (QoS) requirements, and the concept of effective capacity is employed to characterize the effect of delay QoS limitations on achieved performance. Based on this, our objective was to select users to form a NOMA user pair and utilize resource efficiently. To this end, a power allocation coefficient was firstly obtained by ensuring that the achieved capacity of users with sensitive delay QoS requirements was not less than that achieved with an orthogonal multiple access (OMA) scheme. Then, considering that user selection in a delay-limited NOMA-based satellite network is intractable and non-convex, a deep reinforcement learning (DRL) algorithm was employed for dynamic user selection. Specifically, channel conditions and delay QoS requirements of users were carefully selected as state, and a DRL algorithm was used to search for the optimal user who could achieve the maximum performance with the power allocation factor, to pair with the delay QoS-sensitive user to form a NOMA user pair for each state. Simulation results are provided to demonstrate that the proposed DRL-based user selection scheme can output the optimal action in each time slot and, thus, provide superior performance than that achieved with a random selection strategy and OMA scheme.
Real-Time Monocular Skeleton-Based Hand Gesture Recognition Using 3D-Jointsformer
Authors: Zhong Enmin , del-Blanco Carlos R. , Berjón Daniel , Jaureguizar Fernando , García Narciso
Published: August 2023

Automatic hand gesture recognition in video sequences has widespread applications, ranging from home automation to sign language interpretation and clinical operations. The primary challenge lies in achieving real-time recognition while managing temporal dependencies that can impact performance. Existing methods employ 3D convolutional or Transformer-based architectures with hand skeleton estimation, but both have limitations. To address these challenges, a hybrid approach that combines 3D Convolutional Neural Networks (3D-CNNs) and Transformers is proposed. The method involves using a 3D-CNN to compute high-level semantic skeleton embeddings, capturing local spatial and temporal characteristics of hand gestures. A Transformer network with a self-attention mechanism is then employed to efficiently capture long-range temporal dependencies in the skeleton sequence. Evaluation of the Briareo and Multimodal Hand Gesture datasets resulted in accuracy scores of 95.49% and 97.25%, respectively. Notably, this approach achieves real-time performance using a standard CPU, distinguishing it from methods that require specialized GPUs. The hybrid approach’s real-time efficiency and high accuracy demonstrate its superiority over existing state-of-the-art methods. In summary, the hybrid 3D-CNN and Transformer approach effectively addresses real-time recognition challenges and efficient handling of temporal dependencies, outperforming existing methods in both accuracy and speed.
Miniaturization and Model-Integration of the Optical Measurement System for Temperature-Sensitive Paint Investigations
Authors: Lemarechal Jonathan , Dimond Benjamin Daniel , Barth Hans Peter , Hilfer Michael , Klein Christian
Published: August 2023

The temperature-sensitive paint (TSP) method, an optical measurement technique, is used for qualitative skin friction visualizations in a wide variety of aerodynamic applications. One such application is the visualization of the laminar–turbulent boundary-layer transition. Optical access to the surface of interest is mandatory for the measurement system, which consists of scientific cameras and LEDs. But the optical access to the area of interest is often impeded by the available windows of the wind tunnel and the wind tunnel model itself, reducing the field of view and the spatial resolution. In some cases, it is of interest to increase the flexibility of the installation of the optical measurement system by reducing its physical dimensions and placing the installation inside the plenum. The DLR Swept flat PlatE Cross-flow TRAnsition (SPECTRA-A) configuration was selected to investigate the influence of two-dimensional steps on the cross-flow-induced boundary layer transition by means of TSP, as part of the EU project Clean Sky 2. The SPECTRA-A configuration consists of two main elements: a flat plate and a displacement body mounted within a very close distance of each other, creating a narrow gap between the two elements. The surface of interest is the area on the flat plate facing the displacement body. The narrow gap limits the utilization of an external camera setup due to poor optical access. A new optical setup consisting of four miniature CMOS machine-vision cameras and five miniature high-power LEDs was integrated into the displacement body. The characteristics of the camera system were analyzed in laboratory tests, establishing that the miniature CMOS machine-vision cameras are suitable for qualitative TSP skin friction visualizations. This was confirmed by successfully measuring the laminar–turbulent boundary-layer transition on the SPECTRA-A configuration. The integrated TSP system is capable of resolving even small variations of the transition location caused by changing the amplitude of the stationary cross-flow instability. The quality of the TSP visualization with the integrated optical system allows for the measurement of the transition location and the wavelength of the stationary cross-flow instability. Overall, a cost-effective TSP visualization system with small space requirements was developed and tested for future applications in wind tunnel models, model support, or side walls of wind tunnels.
Facial Expressions Track Depressive Symptoms in Old Age
Authors: Kim Hairin , Kwak Seyul , Yoo So Young , Lee Eui Chul , Park Soowon , Ko Hyunwoong , Bae Minju , Seo Myogyeong , Nam Gieun , Lee Jun-Young
Published: August 2023

Facial expressions play a crucial role in the diagnosis of mental illnesses characterized by mood changes. The Facial Action Coding System (FACS) is a comprehensive framework that systematically categorizes and captures even subtle changes in facial appearance, enabling the examination of emotional expressions. In this study, we investigated the association between facial expressions and depressive symptoms in a sample of 59 older adults without cognitive impairment. Utilizing the FACS and the Korean version of the Beck Depression Inventory-II, we analyzed both “posed” and “spontaneous” facial expressions across six basic emotions: happiness, sadness, fear, anger, surprise, and disgust. Through principal component analysis, we summarized 17 action units across these emotion conditions. Subsequently, multiple regression analyses were performed to identify specific facial expression features that explain depressive symptoms. Our findings revealed several distinct features of posed and spontaneous facial expressions. Specifically, among older adults with higher depressive symptoms, a posed face exhibited a downward and inward pull at the corner of the mouth, indicative of sadness. In contrast, a spontaneous face displayed raised and narrowed inner brows, which was associated with more severe depressive symptoms in older adults. These findings suggest that facial expressions can provide valuable insights into assessing depressive symptoms in older adults.
TinyML-Sensor for Shelf Life Estimation of Fresh Date Fruits
Authors: Srinivasagan Ramasamy , Mohammed Maged , Alzahrani Ali
Published: August 2023

Fresh dates have a limited shelf life and are susceptible to spoilage, which can lead to economic losses for producers and suppliers. The problem of accurate shelf life estimation for fresh dates is essential for various stakeholders involved in the production, supply, and consumption of dates. Modified atmosphere packaging (MAP) is one of the essential methods that improves the quality and increases the shelf life of fresh dates by reducing the rate of ripening. Therefore, this study aims to apply fast and cost-effective non-destructive techniques based on machine learning (ML) to predict and estimate the shelf life of stored fresh date fruits under different conditions. Predicting and estimating the shelf life of stored date fruits is essential for scheduling them for consumption at the right time in the supply chain to benefit from the nutritional advantages of fresh dates. The study observed the physicochemical attributes of fresh date fruits, including moisture content, total soluble solids, sugar content, tannin content, pH, and firmness, during storage in a vacuum and MAP at 5 and 24 ∘C every 7 days to determine the shelf life using a non-destructive approach. TinyML-compatible regression models were employed to predict the stages of fruit development during the storage period. The decrease in the shelf life of the fruits begins when they transition from the Khalal stage to the Rutab stage, and the shelf life ends when they start to spoil or ripen to the Tamr stage. Low-cost Visible–Near–Infrared (VisNIR) spectral sensors (AS7265x—multi-spectral) were used to capture the internal physicochemical attributes of the fresh fruit. Regression models were employed for shelf life estimation. The findings indicated that vacuum and modified atmosphere packaging with 20% CO2 and N balance efficiently increased the shelf life of the stored fresh fruit to 53 days and 44 days, respectively, when maintained at 5 ∘C. However, the shelf life decreased to 44 and 23 days when the vacuum and modified atmosphere packaging with 20% CO2 and N balance were maintained at room temperature (24 ∘C). Edge Impulse supports the training and deployment of models on low-cost microcontrollers, which can be used to predict real-time estimations of the shelf life of fresh dates using TinyML sensors.
ADFireNet: An Anchor-Free Smoke and Fire Detection Network Based on Deformable Convolution
Authors: Li Bin , Liu Peng
Published: August 2023

In this paper, we propose an anchor-free smoke and fire detection network, ADFireNet, based on deformable convolution. The proposed ADFireNet network is composed of three parts: The backbone network is responsible for feature extraction of input images, which is composed of ResNet added to deformable convolution. The neck network, which is responsible for multi-scale detection, is composed of the feature pyramid network. The head network outputs results and adopts pseudo intersection over union combined with anchor-free network structure. The head network consists of two full convolutional subnetworks: the first is the classification sub-network, which outputs a classification confidence score, and the second is the regression sub-network, which predicts the parameters of bounding boxes. The deformable convolution (DCN) added to the backbone network enhances the shape feature extraction capability for fire and smoke, and the pseudo intersection over union (pseudo-IoU) added to the head network solves the label assignment problem that exists in anchor-free object detection networks. The proposed ADFireNet is evaluated using the fire smoke dataset. The experimental results show that ADFireNet has higher accuracy and faster detection speeds compared with other methods. Ablation studies have demonstrated the effectiveness of DCN and pseudo IoU.
GANMasker: A Two-Stage Generative Adversarial Network for High-Quality Face Mask Removal
Authors: Mahmoud Mohamed , Kang Hyun-Soo
Published: August 2023

Deep-learning-based image inpainting methods have made remarkable advancements, particularly in object removal tasks. The removal of face masks has gained significant attention, especially in the wake of the COVID-19 pandemic, and while numerous methods have successfully addressed the removal of small objects, removing large and complex masks from faces remains demanding. This paper presents a novel two-stage network for unmasking faces considering the intricate facial features typically concealed by masks, such as noses, mouths, and chins. Additionally, the scarcity of paired datasets comprising masked and unmasked face images poses an additional challenge. In the first stage of our proposed model, we employ an autoencoder-based network for binary segmentation of the face mask. Subsequently, in the second stage, we introduce a generative adversarial network (GAN)-based network enhanced with attention and Masked–Unmasked Region Fusion (MURF) mechanisms to focus on the masked region. Our network generates realistic and accurate unmasked faces that resemble the original faces. We train our model on paired unmasked and masked face images sourced from CelebA, a large public dataset, and evaluate its performance on multi-scale masked faces. The experimental results illustrate that the proposed method surpasses the current state-of-the-art techniques in both qualitative and quantitative metrics. It achieves a Peak Signal-to-Noise Ratio (PSNR) improvement of 4.18 dB over the second-best method, with the PSNR reaching 30.96. Additionally, it exhibits a 1% increase in the Structural Similarity Index Measure (SSIM), achieving a value of 0.95.
Enhancing Cross-Lingual Entity Alignment in Knowledge Graphs through Structure Similarity Rearrangement
Authors: Liu Guiyang , Jin Canghong , Shi Longxiang , Yang Cheng , Shuai Jiangbing , Ying Jing
Published: August 2023

Cross-lingual entity alignment in knowledge graphs is a crucial task in knowledge fusion. This task involves learning low-dimensional embeddings for nodes in different knowledge graphs and identifying equivalent entities across them by measuring the distances between their representation vectors. Existing alignment models use neural network modules and the nearest neighbors algorithm to find suitable entity pairs. However, these models often ignore the importance of local structural features of entities during the alignment stage, which may lead to reduced matching accuracy. Specifically, nodes that are poorly represented may not benefit from their surrounding context. In this article, we propose a novel alignment model called SSR, which leverages the node embedding algorithm in graphs to select candidate entities and then rearranges them by local structural similarity in the source and target knowledge graphs. Our approach improves the performance of existing approaches and is compatible with them. We demonstrate the effectiveness of our approach on the DBP15k dataset, showing that it outperforms existing methods while requiring less time.
Urban Waste Management and Prediction through Socio-Economic Values and Visualizing the Spatiotemporal Relationship on an Advanced GIS-Based Dashboard
Authors: Xu Shixiong , Shirowzhan Sara , Sepasgozar Samad M. E.
Published: August 2023

Enhancing data-driven decision-making is vital for waste authorities. Although few studies have explored the influence of socio-economic indicators on waste tonnage, comprehensive analysis of urban waste data focusing on geographical information is also scarce. There is a dearth of dashboards for visualizing waste tonnage with spatial relationship maps. This study aims to present a prediction model useful for estimating urban waste by using personal income (I), the number of income earners (E), land values (L), the estimated resident population (P) and population density (D), called the IELPD measures. An innovative approach is developed to identify the correlation between urban household waste data and socio-economic factors and develop an advanced dashboard based on a geographic information system (GIS). To accomplish this, relationship maps and regression analysis are deployed to visualize household waste data spanning six years of waste production in New South Wales, Australia, classified into three categories: recyclable, residual and organic (RRO) wastes. Furthermore, this classification enables accessing the association between these three waste categories and the IELPD metrics. And there are four types of visualization generated, that is, thematic mapping, spatial relationship maps, correlation matrices and dashboard development. The regression analysis shows a substantial association between RRO waste tonnage, population changes and a minor correlation with land values. Overall, this study contributes to urban waste data storytelling and its spatiotemporal associations with socio-economic determinants. This paper offers a valuable prediction model of the IELPD metrics to estimate urban waste and visualize them in a dashboard allowing practitioners and decision-makers to track trends in the RRO waste stream in urban waste generally.