bluuebunny/crossref_metadata_2025 huggingface dataset:
Enriching NPK Mineral Fertilizer with Plant-Stimulating Peptides Increases Soilless Tomato Production, Grower Profit, and Environmental Sustainability
Authors: Ciriello Michele , Rajabi Hamedani Sara , Rouphael Youssef , Colla Giuseppe , Cardarelli Mariateresa
Published: July 2024

The need to increase agricultural production to feed a steadily growing population may clash with the more environmentally friendly but less efficient production methods required. Therefore, it is important to try to reduce the use of chemical inputs without compromising production. In this scenario, natural biostimulants have become one of the most sought-after and researched technologies. In the present study, the results of a greenhouse experiment on hydroponic tomatoes (Solanum lycopersicum L.) are presented, which involved comparing the use of ordinary NPK fertilizer (Cerbero®) with the use of NPK fertilizers enriched with 0.5% protein hydrolysate of plant origin (Cerbero Green®) at both standard (100%) and reduced (70%) fertilization rates. The results highlight how the use of Cerbero Green® fertilizers improves the production performance of tomatoes. More specifically, they show that the use of Cerbero Green® leads to higher marketable yields, especially under reducing fertilizer use, ensuring a positive net change in profit for the grower. In addition, carbon footprint analysis has revealed that the use of Cerbero Green® reduces the environmental impact of hydroponic tomato growing practices by up to 8%. The observed higher yield of hydroponically grown tomatoes even with reduced fertilization rates underlines once again the key role of natural biostimulants in increasing both the economic and environmental sustainability of horticultural production.
Metabolic Aspects of Lentil–Fusarium Interactions
Authors: Foti Chrysanthi , Zambounis Antonios , Bataka Evmorfia P. , Kalloniati Chrysanthi , Panagiotaki Evangelia , Nakas Christos T. , Flemetakis Emmanouil , Pavli Ourania I.
Published: July 2024

Fusarium oxysporum f. sp. lentis (Fol) is considered the most destructive disease for lentil (Lens culinaris Medik.) worldwide. Despite the extensive studies elucidating plants’ metabolic response to fungal agents, there is a knowledge gap in the biochemical mechanisms governing Fol-resistance in lentil. Τhis study aimed at comparatively evaluating the metabolic response of two lentil genotypes, with contrasting phenotypes for Fol-resistance, to Fol-inoculation. Apart from gaining insights into the metabolic reprogramming in response to Fol-inoculation, the study focused on discovering novel biomarkers to improve early selection for Fol-resistance. GC-MS-mediated metabolic profiling of leaves and roots was employed to monitor changes across genotypes and treatments as well as their interaction. In total, the analysis yielded 178 quantifiable compounds, of which the vast majority belonged to the groups of carbohydrates, amino acids, polyols and organic acids. Despite the magnitude of metabolic fluctuations in response to Fol-inoculation in both genotypes under study, significant alterations were noted in the content of 18 compounds, of which 10 and 8 compounds referred to roots and shoots, respectively. Overall data underline the crucial contribution of palatinitol and L-proline in the metabolic response of roots and shoots, respectively, thus offering possibilities for their exploitation as metabolic biomarkers for Fol-resistance in lentil. To the best of our knowledge, this is the first metabolomics-based approach to unraveling the effects of Fol-inoculation on lentil’s metabolome, thus providing crucial information related to key aspects of lentil–Fol interaction. Future investigations in metabolic aspects of lentil–Fol interactions will undoubtedly revolutionize the search for metabolites underlying Fol-resistance, thus paving the way towards upgrading breeding efforts to combat fusarium wilt in lentil.
Effects of Drought Stress on Leaf Functional Traits and Biomass Characteristics of Atriplex canescens
Authors: Wang Shuai , Zhou Hai , He Zhibin , Ma Dengke , Sun Weihao , Xu Xingzhi , Tian Quanyan
Published: July 2024

Drought is a critical factor constraining plant growth in arid regions. However, the performance and adaptive mechanism of Atriplex canescens (A. canescens) under drought stress remain unclear. Hence, a three-year experiment with three drought gradients was performed in a common garden, and the leaf functional traits, biomass and biomass partitioning patterns of A. canescens were investigated. The results showed that drought stress had significant effects on A. canescens leaf functional traits. A. canescens maintained the content of malondialdehyde (MDA) and the activity of superoxide dismutase (SOD), but the peroxidase (POD) and catalase (CAT) activity decreased, and the content of proline (Pro) and soluble sugar (SS) increased only under heavy drought stress. Under drought stress, the leaves became smaller but denser, the specific leaf area (SLA) decreased, but the dry matter content (LDMC) maintained stability. Total biomass decreased 60% to 1758 g under heavy drought stress and the seed and leaf biomass was only 10% and 20% of non-stress group, but there had no significant difference on root biomass. More biomass was allocated to root under drought stress. The root biomass allocation ratio was doubled from 9.62% to 19.81% under heavy drought, and the root/shoot ratio (R/S) increased from 0.11 to 0.25. The MDA was significantly and negatively correlated with biomass, while the SPAD was significantly and positively correlated with total and aboveground organs biomass. The POD, CAT, Pro and SS had significant correlations with root and seed allocation ratio. The leaf morphological traits related to leaf shape and weight had significant correlations with total and aboveground biomass and biomass allocation. Our study demonstrated that under drought stress, A. canescens made tradeoffs between growth potential and drought tolerance and evolved with a conservative strategy. These findings provide more information for an in-depth understanding of the adaption strategies of A. canescens to drought stress and provide potential guidance for planting and sustainable management of A. canescens in arid and semi-arid regions.
Outbreak of Alternaria Black Spot of Pomegranate (Punica granatum L.) in Italy as a Consequence of Unusual Climatic Conditions
Authors: Tirrò Gaetano , Conti Taguali Sebastiano , Pane Antonella , Riolo Mario , Ezra David , Cacciola Santa Olga
Published: July 2024

Alternaria black spot of pomegranate (Punica granatum) was reported for the first time in Italy. In spring 2023, an outbreak of this disease was noticed in commercial pomegranate ‘Wonderful’ orchards of the municipality of Misterbianco (Sicily), following an unusually rainy period. A total of 30 randomly selected Alternaria isolates recovered from typical necrotic spots of leaves and fruits were characterized. Based on the colony morphology on solid agar media (PDA and MEA), isolates were separated into three distinct morphotypes (1, 2, and 3). The first two morphotypes comprised only isolates from fruits, while morphotype 3 comprised only isolates from leaves. Multigene phylogenetic analysis of four DNA regions, including internal transcribed spacer (ITS), translation elongation factor 1-α (EF-1α), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and a SCAR marker (OPA10–2), identified the isolates of morphotypes 1 and 2 as Alternaria alternata and morphotype 3 isolates as A. arborescens. In pathogenicity tests on unwounded leaves and fruit, the isolates of all three morphotypes produced symptoms on the leaves of three pomegranate cultivars, ‘Acco’, ‘Wonderful’, and ‘Etna’. The symptoms on ‘Acco’ leaves were the least severe. Conversely, the fruits of ‘Acco’ were the most susceptible. The isolates of morphotypes 2 and 3 were not pathogenic on the fruits of ‘Wonderful’ and ‘Etna’. This is the first report of Alternaria black spot in Italy and of A. arborescens associated with Alternaria black spot of pomegranate worldwide.
The Role of Liriodendron Dof Gene Family in Abiotic Stress Response
Authors: Liao Bojun , Liang Pengxiang , Tong Lu , Lu Lu , Lu Ye , Zheng Renhua , Zheng Xueyan , Chen Jinhui , Hao Zhaodong
Published: July 2024

The DOF (DNA-binding with one finger) transcription factors are exclusive to plants and play crucial roles in plant growth, development, and environmental adaptation. Although extensive research has been conducted on the Dof gene family in Arabidopsis, maize, and Solanum, investigations concerning the role of this gene family in Liriodendron remain unreported, leaving its biological function largely unknown. In this study, we performed a comprehensive genome-wide identification of the Dof gene family based on the Liriodendron genome, resulting in the discovery of a total of 17 LcDof gene members. Based on the results of phylogenetic analysis, the 17 LcDof proteins were classified into eight subfamilies. The motif analysis revealed the diverse nature of motifs within the D1 subfamily, which includes a distinct type of Dof transcription factor known as CDF (Cycling Dof Factor). We further characterized the chromosomal distribution, gene structure, conserved protein motifs, and cis-elements in the promoter regions. Additionally, utilizing transcriptome data from Liriodendron hybrids and conducting RT-qPCR experiments, we investigated the expression patterns of LhDofs under various abiotic stresses such as drought, cold, and heat stress. Notably, we found that several LhDofs, particularly LhDof4 and LhDof6, were significantly upregulated in response to abiotic stress. Furthermore, we cloned LhDof4 and LhDof6 genes and found that its encoding protein was mainly located in the nucleus by transient transformation in Liriodendron hybrids protoplast. Subsequently, we used LhDof6-overexpressing Liriodendron hybrid seedlings. We found that overexpression of LhDof6 enhanced the cold tolerance of the plants, increasing their survival rate at −20 °C. This result was further validated by changes in physiological indicators.
Utilizing Edge-Oxidized Graphene Oxide to Enhance Cement Mortar’s Properties Containing Crumb Rubber: Toward Achieving Sustainable Materials
Authors: Alamri Mohammed , Khawaji Mohammad
Published: July 2024

Scrap tires have become one of the most serious environmental issues worldwide in recent years. Exploiting this scrap has caught the attention of researchers in their efforts to conserve the environment. From a structural engineering materials perspective, a partial fine aggregate in cement mortar can be replaced by crumb rubber produced from scrap tires. This research mainly emphasizes the role of adding 0.1% edge-oxidized graphene oxide EOGO (by the weight of cement) in enhancing the properties of cement mortars containing 5%, 10%, and 15% of crumb rubber (by sand replacement). Cube and prism specimens were employed to investigate compressive and flexural strengths at 7- and 28-day curing ages. A porosity test was also conducted after 28 days of curing. In addition, a scanning electron microscopy (SEM) test was performed to investigate the effect of incorporating EOGO on the interfacial transition zone (ITZ). Results showed an enhancement of the mechanical properties of cement mortar, including compressive and flexural strengths, with the inclusion of EOGO in the mixes. The findings demonstrated that adding EOGO can improve the mechanical properties of mixes containing crumb rubber particles. Specifically, the mortar mix with 0.1% EOGO and 5% crumb rubber exhibited better performance compared with the virgin mix without rubber particles. Therefore, crumb rubber is viable for use as a sand replacement when EOGO is included.
Comparative Evaluation of Mechanical Properties and Color Stability of Dental Resin Composites for Chairside Provisional Restorations
Authors: Yu Haikun , Yao Jiaqi , Du Zhili , Guo Jingmei , Lei Wenlong
Published: July 2024

Resin composites have become the preferred choice for chairside provisional dental restorations. However, these materials may undergo discoloration, changes in surface roughness, and mechanical properties with aging in the oral cavity, compromising the aesthetics, functionality, and success of dental restorations. To investigate the color and mechanical stability of chairside provisional composite resins, this study evaluated the optical, surface, and mechanical properties of four temporary restoration resin materials before and after aging, stimulated by thermal cycling in double-distilled water. Measurements, including CIE LAB color analysis, three-point bending test, nanoindentation, scanning electron microscopy (SEM), and atomic force microscopy (AFM), were conducted (n = 15). Results showed significant differences among the materials in terms of optical, surface, and mechanical properties. Revotek LC (urethane dimethacrylate) demonstrated excellent color stability (ΔE00 = 0.53-Black/0.32-White), while Artificial Teeth Resin (polymethyl methacrylate) exhibited increased mechanical strength with aging (p < 0.05, FS = 68.40 MPa-non aging/87.21 MPa-aging). Structur 2 SC (Bis-acrylic) and Luxatemp automix plus (methyl methacrylate bis-acrylate) demonstrated moderate stability in optical and mechanical properties (Structur 2 SC: ΔE00 = 1.97-Black/1.38-White FS = 63.20 MPa-non aging/50.07 MPa-aging) (Luxatemp automix plus: ΔE00 = 2.49-Black/1.77-White FS = 87.72 MPa-non aging/83.93 MPa-aging). These results provide important practical guidance for clinical practitioners, as well as significant theoretical and experimental bases for the selection of restorative composite resins.
Optical Characteristics of a New Molecular Complex: “Nafion–Colloidal CdSe/CdS/ZnS Nanocrystals”
Authors: Timchenko Svetlana L. , Ambrozevich Sergey A. , Zadorozhnyi Evgenii N. , Zadorozhnyi Nikolai A. , Skrabatun Alexander V. , Sharandin Evgenii A.
Published: July 2024

Here, the optical properties of the Nafion polymer membrane containing colloidal CdSe/CdS/ZnS nanocrystals embedded by diffusion have been studied. The CdSe/CdS/ZnS nanocrystals have a core/shell/shell appearance. All experiments were carried out at room temperature (22 ± 2) °C. A toluene solution was used to provide mobility to the active sulfone groups of the Nafion membrane and to embed the nanocrystals inside the membrane. The diffusion process of colloidal CdSe/CdS/ZnS nanocrystals into Nafion proton exchange membrane has resulted in a new molecular complex “Nafion–colloidal CdSe/CdS/ZnS nanocrystals”. The kinetics of the nanocrystals embedding into the membrane matrix was investigated using luminescence analysis and absorption spectroscopy techniques. The embedding rate of CdSe/CdS/ZnS nanocrystals into the Nafion polymer membrane was approximately 4·10−3 min−1. The presence of new luminescence centers in the membrane was proved independently by laser emission spectroscopy. The luminescence spectrum of the resulting molecular complex contains intensity maxima at wavelengths of 538, 588, 643 and 700 nm. The additional luminescence maximum observed at the 643 nm wavelength was not recorded in the original membrane, solvent or in the spectrum of the semiconductor nanoparticles. The luminescence maximum of the colloidal CdSe/CdS/ZnS nanocrystals was registered at a wavelength of 634 nm. The intensity of the luminescence spectrum of the membrane with embedded nanocrystals was found to be higher than the intensity of the secondary emission peak of the initial nanocrystals, which is important for the practical use of the “Nafion–colloidal nanocrystals” complex in optical systems. The lines contained in the luminescence spectrum of the membrane, which has been in solution with colloidal nanocrystals for a long time, registered upon its drying, show the kinetics of the formation of the molecular complex “Nafion membrane–nanocrystals”. Colloidal nanocrystals located in the Nafion matrix represent an analog of a luminescent transducer.
Leakage Quantification in Metallic Pipes under Different Corrosion Exposure Times
Authors: Agala Alaa , Khan Muhammad A. , He Feiyang , Alnuman Abdulaaziz
Published: July 2024

The combined effects of aqueous corrosion, stress factors, and seeded cracks on leakage in cast iron pipes have not been thoroughly examined due to the complexity and difficulty in predicting their interactions. This study seeks to address this gap by investigating the interdependencies between corrosion, stress, and cracks in cast iron pipes to optimise the material selection and design in corrosive environments. Leakage experiments were conducted under simulated localised corrosive conditions and internal pressure, revealing that leakage increased from 0 to 25 mL with crack sizes of 0.5 mm, 0.8 mm, 1 mm, and 1.2 mm, along with corrosion times of 0, 120, 160, and 200 h, and varying stress levels. An empirical model was developed using a curve-fitting approach to map the relationships among corrosion time, crack propagation, and leakage amount. The results demonstrate that the interaction between corrosion, stress, and crack propagation was complex and nonlinear, and the leakage amount increased from 0.7 to 0.10 mm every 15 min, as evidenced by SEM microstructure images and empirical data.
Optimization of Sample Preparation Procedure for Determination of Fat-Soluble Vitamins in Milk and Infant Food by HPLC Technique
Authors: Bošnir Jasna , Bevardi Martina , Hećimović Ida , Budeč Maja , Juranović Cindrić Iva , Kober Robert , Jurak Gordana , Lasić Dario , Brkić Danijel , Racz Aleksandar
Published: July 2024

Background: The analysis of vitamins in baby food is a challenging task given the complexity of the food matrix, vitamin stability, and strict regulations of the European Union regarding permissible deviations from declared values. Vitamins in food exist in different concentrations and forms and have different stabilities; thus, the preparation of samples for a reliable analysis using the same procedure is not straightforward. Therefore, significant attention has been devoted to optimizing sample preparation in the analysis of vitamins. Methods: This study aims to determine which of the sample preparation and extraction methods is the most efficient for the simultaneous determination of vitamins A, D, E, and K in milk and baby food using high-performance liquid chromatography (HPLC). Different samples of baby food were prepared in seven different ways based on four methods (saponification, enzymatic hydrolysis, solvent extraction, and solid-phase extraction). Results and Conclusions: According to the validation parameters, the optimal preparation method proved to be solid-phase extraction with a C18 stationary phase, with recoveries of 97.4%, 96.1%, 98.3%, and 96.2% for vitamins A, D, E, and K, respectively, and HPLC with a UV–Vis detector was identified as a sufficiently sensitive technique for the identification and quantification of fat-soluble vitamins in milk and baby food.
Assessing the Impact of Sustainable Pasture Systems on Lamb Meat Quality
Authors: Stanišić Nikola , Ružić-Muslić Dragana , Maksimović Nevena , Cekić Bogdan , Caro Petrović Violeta , Ćosić Ivan , Lazarević Marina
Published: July 2024

The global demand for sustainable lamb production is increasing due to the need for high-quality meat with minimal environmental impact, making the choice of feeding systems crucial. This study investigates the effects of supplemented pasture feeding during the last 60 days of rearing on the meat fatty acid profile, pH value, colour characteristics, and mineral composition of lambs, highlighting the benefits of such feeding systems. Ninety lambs (MIS sheep breed) were divided into three distinct feeding regimes: Group I (alfalfa and concentrate feeding), Group II (white clover [Trifolium repens] pasture with concentrate supplementation), and Group III (birds’ foot trefoil [Lotus corniculatus] pasture with concentrate supplementation). The results have shown that supplemented pasture feeding improves the fatty acid profile by increasing n-3 content and desirable fatty acids, while reducing the n-6/n-3 ratio and atherogenic index (p < 0.05), particularly in lambs finished on an L. corniculatus diet. However, forage-supplemented feeding also reduces meat colour lightness and redness (p < 0.05). On the other hand, it enhances the meat’s mineral profile, with higher calcium, selenium, and iron levels, especially in lambs fed L. corniculatus. These findings underscore the benefits of moderate grazing with supplemental concentrates in optimising lamb meat quality. Importantly, they also highlight the potential of forage legumes like T. repens and L. corniculatus to significantly enhance the nutritional profile of lamb meat, offering a promising outlook for the future of sustainable lamb production. Additionally, this research provides valuable insights that could guide the development of future agricultural practices, dietary guidelines, and environmental policies to advance sustainable and nutritious food systems.
Forecasting Age- and Sex-Specific Survival Functions: Application to Annuity Pricing
Authors: Wang Shaokang , Shang Han Lin , Tickle Leonie , Li Han
Published: July 2024

We introduce the function principal component regression (FPCR) forecasting method to model and forecast age-specific survival functions observed over time. The age distribution of survival functions is an example of constrained data whose values lie within a unit interval. Because of the constraint, such data do not reside in a linear vector space. A natural way to deal with such a constraint is through an invertible logit transformation that maps constrained onto unconstrained data in a linear space. With a time series of unconstrained data, we apply a functional time-series forecasting method to produce point and interval forecasts. The forecasts are then converted back to the original scale via the inverse logit transformation. Using the age- and sex-specific survival functions for Australia, we investigate the point and interval forecast accuracies for various horizons. We conclude that the functional principal component regression (FPCR) provides better forecast accuracy than the Lee–Carter (LC) method. Therefore, we apply FPCR to calculate annuity pricing and compare it with the market annuity price.
Evaluating Coseismic Landslide Susceptibility Following the 2022 Luding Earthquake: A Comparative Analysis of Six Displacement Regression Models Integrating Epicentral and Seismogenic Fault Distances within the Permanent-Displacement Framework
Authors: Liu Tianhao , Zang Mingdong , Peng Jianbing , Xu Chong
Published: July 2024

Coseismic landslides have the potential to cause catastrophic disasters. Thus, it is of crucial importance to conduct a comprehensive regional assessment of susceptibility to coseismic landslides. This study rigorously interprets 13,759 coseismic landslides triggered by the 2022 Luding earthquake within the seismic zone. Employing the Newmark method, we systematically assess the susceptibility to coseismic landslides through the application of six distinct displacement regression models. The efficacy of these models is validated against the actual landslide inventory using the area under the receiver operating characteristic (ROC) curve. A hazard map of coseismic landslides is generated based on the displacement regression model with the highest degree of fit. The results show that Moxi Town, Detuo Town, the flanks of the Daduhe River, Wandonghe River, Hailuogou River, and Yanzigou River are high-susceptibility areas for coseismic landslides. This study explores factors influencing model fit, revealing that the inclusion of the epicentral distance and the distance to the seismogenic fault in displacement prediction enhances model performance. Nevertheless, in close proximity to fault zones, the distance to the seismogenic fault exerts a more significant influence on the spatial distribution density of coseismic landslides compared to the epicentral distance. Conversely, in regions situated further from fault zones, the epicentral distance has a greater impact on the spatial distribution density of coseismic landslides compared to the distance to the seismogenic fault. These findings contribute to a nuanced understanding of coseismic landslide susceptibility and offer valuable insights for future Newmark method-based coseismic landslide displacement calculations.
Spatiotemporal Feature Fusion Transformer for Precipitation Nowcasting via Feature Crossing
Authors: Xiong Taisong , Wang Weiping , He Jianxin , Su Rui , Wang Hao , Hu Jinrong
Published: July 2024

Precipitation nowcasting plays an important role in mitigating the damage caused by severe weather. The objective of precipitation nowcasting is to forecast the weather conditions 0–2 h ahead. Traditional models based on numerical weather prediction and radar echo extrapolation obtain relatively better results. In recent years, models based on deep learning have also been applied to precipitation nowcasting and have shown improvement. However, the forecast accuracy is decreased with longer forecast times and higher intensities. To mitigate the shortcomings of existing models for precipitation nowcasting, we propose a novel model that fuses spatiotemporal features for precipitation nowcasting. The proposed model uses an encoder–forecaster framework that is similar to U-Net. First, in the encoder, we propose a spatial and temporal multi-head squared attention module based on MaxPool and AveragePool to capture every independent sequence feature, as well as a global spatial and temporal feedforward network, to learn the global and long-distance relationships between whole spatiotemporal sequences. Second, we propose a cross-feature fusion strategy to enhance the interactions between features. This strategy is applied to the components of the forecaster. Based on the cross-feature fusion strategy, we constructed a novel multi-head squared cross-feature fusion attention module and cross-feature fusion feedforward network in the forecaster. Comprehensive experimental results demonstrated that the proposed model more effectively forecasted high-intensity levels than other models. These results prove the effectiveness of the proposed model in terms of predicting convective weather. This indicates that our proposed model provides a feasible solution for precipitation nowcasting. Extensive experiments also proved the effectiveness of the components of the proposed model.
Optimizing Robotic Mobile Fulfillment Systems for Order Picking Based on Deep Reinforcement Learning
Authors: Zhu Zhenyi , Wang Sai , Wang Tuantuan
Published: July 2024

Robotic Mobile Fulfillment Systems (RMFSs) face challenges in handling large-scale orders and navigating complex environments, frequently encountering a series of intricate decision-making problems, such as order allocation, shelf selection, and robot scheduling. To address these challenges, this paper integrates Deep Reinforcement Learning (DRL) technology into an RMFS, to meet the needs of efficient order processing and system stability. This study focuses on three key stages of RMFSs: order allocation and sorting, shelf selection, and coordinated robot scheduling. For each stage, mathematical models are established and the corresponding solutions are proposed. Unlike traditional methods, DRL technology is introduced to solve these problems, utilizing a Genetic Algorithm and Ant Colony Optimization to handle decision making related to large-scale orders. Through simulation experiments, performance indicators—such as shelf access frequency and the total processing time of the RMFS—are evaluated. The experimental results demonstrate that, compared to traditional methods, our algorithms excel in handling large-scale orders, showcasing exceptional superiority, capable of completing approximately 110 tasks within an hour. Future research should focus on integrated decision-making modeling for each stage of RMFSs and designing efficient heuristic algorithms for large-scale problems, to further enhance system performance and efficiency.
BAFusion: Bidirectional Attention Fusion for 3D Object Detection Based on LiDAR and Camera
Authors: Liu Min , Jia Yuanjun , Lyu Youhao , Dong Qi , Yang Yanyu
Published: July 2024

3D object detection is a challenging and promising task for autonomous driving and robotics, benefiting significantly from multi-sensor fusion, such as LiDAR and cameras. Conventional methods for sensor fusion rely on a projection matrix to align the features from LiDAR and cameras. However, these methods often suffer from inadequate flexibility and robustness, leading to lower alignment accuracy under complex environmental conditions. Addressing these challenges, in this paper, we propose a novel Bidirectional Attention Fusion module, named BAFusion, which effectively fuses the information from LiDAR and cameras using cross-attention. Unlike the conventional methods, our BAFusion module can adaptively learn the cross-modal attention weights, making the approach more flexible and robust. Moreover, drawing inspiration from advanced attention optimization techniques in 2D vision, we developed the Cross Focused Linear Attention Fusion Layer (CFLAF Layer) and integrated it into our BAFusion pipeline. This layer optimizes the computational complexity of attention mechanisms and facilitates advanced interactions between image and point cloud data, showcasing a novel approach to addressing the challenges of cross-modal attention calculations. We evaluated our method on the KITTI dataset using various baseline networks, such as PointPillars, SECOND, and Part-A2, and demonstrated consistent improvements in 3D object detection performance over these baselines, especially for smaller objects like cyclists and pedestrians. Our approach achieves competitive results on the KITTI benchmark.
DOA Estimation on One-Bit Quantization Observations through Noise-Boosted Multiple Signal Classification
Authors: Pan Yan , Zhang Li , Xu Liyan , Duan Fabing
Published: July 2024

Due to the low-complexity implementation, direction-of-arrival (DOA) estimation-based one-bit quantized data are of interest, but also, signal processing struggles to obtain the demanded estimation accuracy. In this study, we injected a number of noise components into the receiving data before the uniform linear array (ULA) composed of one-bit quantizers. Then, based on this designed noise-boosted quantizer unit (NBQU), we propose an efficient one-bit multiple signal classification (MUSIC) method for estimating the DOA. Benefiting from the injected noise, the numerical results show that the proposed NBQU-based MUSIC method outperforms existing one-bit MUSIC methods in terms of estimation accuracy and resolution. Furthermore, with the optimal root mean square (RMS) of the injected noise, the estimation accuracy of the proposed method for estimating DOA can approach that of the MUSIC method based on the complete analog data.
Trustworthy Environmental Monitoring Using Hardware-Assisted Security Mechanisms
Authors: Segers Laurent , Talebi Borna , da Silva Bruno , Touhafi Abdellah , Braeken An
Published: July 2024

Environmental monitoring is essential for safeguarding the health of our planet and protecting human health and well-being. Without trust, the effectiveness of environmental monitoring and the ability to address environmental challenges are significantly compromised. In this paper, we present a sensor platform capable of performing authenticated and trustworthy measurements, together with a lightweight security protocol for sending the data from the sensor to a central server anonymously. Besides presenting a new and very efficient symmetric-key-based protocol, we also demonstrate on real hardware how existing embedded security modules can be utilized for this purpose. We provide an in-depth evaluation of the performance and a detailed security analysis.
Local and Global Context-Enhanced Lightweight CenterNet for PCB Surface Defect Detection
Authors: Chen Weixun , Meng Siming , Wang Xueping
Published: July 2024

Printed circuit board (PCB) surface defect detection is an essential part of the PCB manufacturing process. Currently, advanced CCD or CMOS sensors can capture high-resolution PCB images. However, the existing computer vision approaches for PCB surface defect detection require high computing effort, leading to insufficient efficiency. To this end, this article proposes a local and global context-enhanced lightweight CenterNet (LGCL-CenterNet) to detect PCB surface defects in real time. Specifically, we propose a two-branch lightweight vision transformer module with local and global attention, named LGT, as a complement to extract high-dimension features and leverage context-aware local enhancement after the backbone network. In the local branch, we utilize coordinate attention to aggregate more powerful features of PCB defects with different shapes. In the global branch, Bi-Level Routing Attention with pooling is used to capture long-distance pixel interactions with limited computational cost. Furthermore, a Path Aggregation Network (PANet) feature fusion structure is incorporated to mitigate the loss of shallow features caused by the increase in model depth. Then, we design a lightweight prediction head by using depthwise separable convolutions, which further compresses the computational complexity and parameters while maintaining the detection capability of the model. In the experiment, the LGCL-CenterNet increased the mAP@0.5 by 2% and 1.4%, respectively, in comparison to CenterNet-ResNet18 and YOLOv8s. Meanwhile, our approach requires fewer model parameters (0.542M) than existing techniques. The results show that the proposed method improves both detection accuracy and inference speed and indicate that the LGCL-CenterNet has better real-time performance and robustness.
TTFDNet: Precise Depth Estimation from Single-Frame Fringe Patterns
Authors: Cai Yi , Guo Mingyu , Wang Congying , Lu Xiaowei , Zeng Xuanke , Sun Yiling , Ai Yuexia , Xu Shixiang , Li Jingzhen
Published: July 2024

This work presents TTFDNet, a transformer-based and transfer learning network for end-to-end depth estimation from single-frame fringe patterns in fringe projection profilometry. TTFDNet features a precise contour and coarse depth (PCCD) pre-processor, a global multi-dimensional fusion (GMDF) module and a progressive depth extractor (PDE). It utilizes transfer learning through fringe structure consistency evaluation (FSCE) to leverage the transformer’s benefits even on a small dataset. Tested on 208 scenes, the model achieved a mean absolute error (MAE) of 0.00372 mm, outperforming Unet (0.03458 mm) models, PDE (0.01063 mm) and PCTNet (0.00518 mm). It demonstrated precise measurement capabilities with deviations of ~90 μm for a 25.4 mm radius ball and ~6 μm for a 20 mm thick metal part. Additionally, TTFDNet showed excellent generalization and robustness in dynamic reconstruction and varied imaging conditions, making it appropriate for practical applications in manufacturing, automation and computer vision.
Trustworthy High-Performance Multiplayer Games with Trust-but-Verify Protocol Sensor Validation
Authors: Joens Alexander , Jillepalli Ananth A. , Sheldon Frederick T.
Published: July 2024

Multiplayer online video games are a multibillion-dollar industry, to which widespread cheating presents a significant threat. Game designers compromise on game security to meet demanding performance targets, but reduced security increases the risk of potential malicious exploitation. To mitigate this risk, game developers implement alternative security sensors. The alternative sensors themselves become a liability due to their intrusive and taxing nature. Online multiplayer games with real-time gameplay are known to be difficult to secure due to the cascading exponential nature of many-many relationships among the components involved. Behavior-based security sensor schemes, or referees (a trusted third party), could be a potential solution but require frameworks to obtain the game state information they need. We describe our Trust-Verify Game Protocol (TVGP), which is a sensor protocol intended for low-trust environments and designed to provide game state information to help support behavior-based cheat-sensing detection schemes. We argue TVGP is an effective solution for applying an independent trusted referee capability to trust-lacking subdomains and demands high-performance requirements. Our experimental results validate high efficiency and performance standards for TVGP. We identify and discuss the operational domain assumptions of the TVGP validation testing presented here.
Exploring the Real-Time Variability and Complexity of Sitting Patterns in Office Workers with Non-Specific Chronic Spinal Pain and Pain-Free Individuals
Authors: Oliosi Eduarda , Júlio Afonso , Probst Phillip , Silva Luís , Vilas-Boas João Paulo , Pinheiro Ana Rita , Gamboa Hugo
Published: July 2024

Chronic spinal pain (CSP) is a prevalent condition, and prolonged sitting at work can contribute to it. Ergonomic factors like this can cause changes in motor variability. Variability analysis is a useful method to measure changes in motor performance over time. When performing the same task multiple times, different performance patterns can be observed. This variability is intrinsic to all biological systems and is noticeable in human movement. This study aims to examine whether changes in movement variability and complexity during real-time office work are influenced by CSP. The hypothesis is that individuals with and without pain will have different responses to office work tasks. Six office workers without pain and ten with CSP participated in this study. Participant’s trunk movements were recorded during work for an entire week. Linear and nonlinear measures of trunk kinematic displacement were used to assess movement variability and complexity. A mixed ANOVA was utilized to compare changes in movement variability and complexity between the two groups. The effects indicate that pain-free participants showed more complex and less predictable trunk movements with a lower degree of structure and variability when compared to the participants suffering from CSP. The differences were particularly noticeable in fine movements.
Coming Out as Undocumented: Identity Celebrations and Political Change
Authors: Puggioni Raffaela
Published: July 2024

By focusing on the notion of ‘coming out as undocumented’, this concept paper critically explores whether identity politics has the potential to transform dominant ‘illegalisation’ processes. It argues that although the coming out strategy has helped make marginalised communities more visible and audible, it does not necessarily help disrupt dominant stigmas. Drawing upon insights from Disability Studies and Fat and Queer Politics, it suggests that the celebration of stigmatised identities through coming out events does not necessarily disrupt and transform unequal systems. What is needed is a radical shift in focus: from identity celebration to political projects, from an ‘unliveable’ present to an inclusive tomorrow.
May I Come In? EU Policies to Control Migration: The EUTF
Authors: da Costa Mangueira Ana Beatriz
Published: July 2024

What types of policies has the European Union (EU) implemented to control migration flows in recent decades, and what are their strategies? This paper aims to explore the measures developed by the EU to manage migration flows and identify how they operate. While a securitisation approach, such as activities of border control, has been widely discussed by scholars in this field, it is worth exploring and understanding other kinds of instruments aimed at curbing irregular flows through executing programs such as the Emergency Trust Fund for Africa (EUTF), developed in the aftermath of the Arab uprisings to address the “root causes” of the displacement. In light of this, this research conducts a case study and qualitative content and descriptive analysis of documents on the EUTF. Preliminary findings indicate patterns in what motivated the EU to undertake these actions and present the main strategies of the Fund in the North Africa region. However, some factors may have led to disappointing outcomes for the EUTF, such as the increase, in 2019, of nationals leaving the North Africa region towards Europe, as reported by UNDESA.
Dementia Care Decisions, Caregiving Situations, and Formal Service Use in Korean Immigrant Families: A Qualitative Application of a Sociocultural Model
Authors: Jang Yuri , Oh Hans , Park Juyoung , Rhee Min-Kyoung , Park Nan Sook , Chung Soondool , Kim Miyong T.
Published: July 2024

Given the significant role of culture and place in dementia caregiving, we conducted a qualitative study with Korean American family caregivers of persons with dementia. Guided by the sociocultural model of dementia caregivers’ service use, we explored the categories of care decisions, caregiving situations, and formal service use within the context of culture and place. Data from in-depth interviews with 16 Korean American dementia caregivers living in the greater Los Angeles area were analyzed using the constant comparison method. Across the three conceptual categories, we derived eight themes: (1) personal motivation; (2) family context; (3) attitude toward formal care; (4) strains; (5) rewards; (6) language and geographic region; (7) knowledge and awareness; and (8) social support and cultural stigma. Our findings demonstrate not only varied experiences in care decisions, caregiving situations, and formal service use but also their interconnectedness. Supporting the influential role of culture and place in dementia caregiving, the findings include positive and negative elements within each domain and provide implications for programs and services to respond to identified needs and barriers.
Reconceptualizing ICTD: Prioritizing Place-Based Learning Experiences, Socio-Economic Realities, and Individual Aspirations of Young Students in India
Authors: Pathak-Shelat Manisha , Bhatia Kiran Vinod
Published: July 2024

This paper critically examines the neo-liberal conceptualization of Information and Communication Technology for Development (ICTD), which imposes the linear and simplistic notions of empowerment and development on the users from the global South. Using the rapidly growing EdTech segment in India as a case, this paper observes that EdTech has been touted as a magic multiplier and a savior for countries like India that aspire to educate their large populations. This has prompted EdTech companies to pursue platformization and templatization to accomplish scalability and standardization in EdTech use. Based on immersive ethnographic research with youth from low-income families in three Indian cities—Ahmedabad, Delhi, and Vadodara—we argue that the practices of young people concerning EdTech resist standardization. Our analysis reveals that three major factors—challenges of access and autonomy, continued relevance of place-based learning and in-person interactions, and uneven quality and rigor—influence low-income students and families to not completely buy the promise of access, equity, and quality that EdTech companies and governments advance. We explore the significance of the socio-economic and cultural contexts of young learners in the global South context and argue that they aspire for personalization, place-based experiences, guidance/mentorship, high grades, and in-person interactions instead of standardization. They do not fully benefit by the experimentation, DIY practices, and tech-lead learning opportunities and resources offered by EdTech platforms in their current state.
Ethical Considerations in Artificial Intelligence Interventions for Mental Health and Well-Being: Ensuring Responsible Implementation and Impact
Authors: Saeidnia Hamid Reza , Hashemi Fotami Seyed Ghasem , Lund Brady , Ghiasi Nasrin
Published: July 2024

AI has the potential to revolutionize mental health services by providing personalized support and improving accessibility. However, it is crucial to address ethical concerns to ensure responsible and beneficial outcomes for individuals. This systematic review examines the ethical considerations surrounding the implementation and impact of artificial intelligence (AI) interventions in the field of mental health and well-being. To ensure a comprehensive analysis, we employed a structured search strategy across top academic databases, including PubMed, PsycINFO, Web of Science, and Scopus. The search scope encompassed articles published from 2014 to 2024, resulting in a review of 51 relevant articles. The review identifies 18 key ethical considerations, including 6 ethical considerations associated with using AI interventions in mental health and wellbeing (privacy and confidentiality, informed consent, bias and fairness, transparency and accountability, autonomy and human agency, and safety and efficacy); 5 ethical principles associated with the development and implementation of AI technologies in mental health settings to ensure responsible practice and positive outcomes (ethical framework, stakeholder engagement, ethical review, bias mitigation, and continuous evaluation and improvement); and 7 practices, guidelines, and recommendations for promoting the ethical use of AI in mental health interventions (adhere to ethical guidelines, ensure transparency, prioritize data privacy and security, mitigate bias and ensure fairness, involve stakeholders, conduct regular ethical reviews, and monitor and evaluate outcomes). This systematic review highlights the importance of ethical considerations in the responsible implementation and impact of AI interventions for mental health and well-being. By addressing privacy, bias, consent, transparency, human oversight, and continuous evaluation, we can ensure that AI interventions like chatbots and AI-enabled medical devices are developed and deployed in an ethically sound manner, respecting individual rights, promoting fairness, and maximizing benefits while minimizing potential harm.
Factors Affecting the Running Performance of Soccer Teams in the Turkish Super League
Authors: Plakias Spyridon , Michailidis Yiannis
Published: July 2024

Performance analysis in sports is a rapidly evolving field, where academics and applied performance analysts work together to improve coaches’ decision making through the use of performance indicators (PIs). This study aimed to provide a comprehensive analysis of factors affecting running performance (RP) in soccer teams, focusing on low (LI), medium (MI), and high-speed distances (HI) and the number of high-speed runs (NHI). Data were collected from 185 matches in the Turkish first division’s 2021–2022 season using InStat Fitness’s optical tracking technology. Four linear mixed-model analyses were conducted on the RP metrics with fixed factors, including location, team quality, opponent quality, ball possession, high-press, counterattacks, number of central defenders, and number of central forwards. The findings indicate that high-press and opponent team quality affect MI (d = 0.311, d = 0.214) and HI (d = 0.303, d = 0.207); team quality influences MI (d = 0.632); location and counterattacks impact HI (d = 0.228, d = 0.450); high-press and the number of central defenders affects NHI (d = 0.404, d = 0.319); and ball possession affects LI (d = 0.287). The number of central forwards did not influence any RP metrics. This study provides valuable insights into the factors influencing RP in soccer, highlighting the complex interactions between formations and physical, technical–tactical, and contextual variables. Understanding these dynamics can help coaches and analysts optimize team performance and strategic decision making.