bluuebunny/crossref_metadata_2025 huggingface dataset:
Improving Undernutrition with Microalgae
Authors: Panchal Sunil K. , Heimann Kirsten , Brown Lindsay
Published: September 2024

Undernutrition is an important global health problem, especially in children and older adults. Both reversal of maternal and child undernutrition and heathy ageing have become United Nations-supported global initiatives, leading to increased attention to nutritional interventions targeting undernutrition. One feasible option is microalgae, the precursor of all terrestrial plants. Most commercially farmed microalgae are photosynthetic single-celled organisms producing organic carbon compounds and oxygen. This review will discuss commercial opportunities to grow microalgae. Microalgae produce lipids (including omega-3 fatty acids), proteins, carbohydrates, pigments and micronutrients and so can provide a suitable and underutilised alternative for addressing undernutrition. The health benefits of nutrients derived from microalgae have been identified, and thus they are suitable candidates for addressing nutritional issues globally. This review will discuss the potential benefits of microalgae-derived nutrients and opportunities for microalgae to be converted into food products. The advantages of microalgae cultivation include that it does not need arable land or pesticides. Additionally, most species of microalgae are still unexplored, presenting options for further development. Further, the usefulness of microalgae for other purposes such as bioremediation and biofuels will increase the knowledge of these microorganisms, allowing the development of more efficient production of these microalgae as nutritional interventions.
Dietary Behaviour of Pregnant Women in Ethiopia: The Missing Aspect of Care
Authors: Alamirew Simegn Kassa , Lemke Stefanie , Freyer Bernhard , Stadlmayr Barbara
Published: September 2024

Background: Nutrition and adequate dietary intake during pregnancy strongly influence the health and well-being of the mother, as well as the physical and cognitive development of the unborn child. While previous studies have documented factors associated with the dietary behaviour of pregnant women in Ethiopia, a comprehensive overview is missing. Objective: The aim of this study was to close this research gap. Methodology: We conducted a mapping review, including 37 studies published between 2000 and 2022 in our analysis. Dietary behaviour refers to all phenomena related to food choice, eating behaviour and dietary intake. We used an innovative approach by integrating a socio-ecological framework with UNICEF’s conceptual framework on maternal and child nutrition, which specifies multidimensional individual, underlying and enabling determinants associated with the nutritional status of women. Importantly, we integrated a focus on care for women and healthy environments. Results: A total of 68 factors were identified as influencing the dietary behaviour of pregnant women, with a focus on the intra- (31/68) and interpersonal (21/68) levels, while factors at the community (11/68) and the institutional levels (5/68) were scarce. Few studies investigated socio-cultural aspects, such as gender roles, decision-making power and workload of women, psychological factors and eating practices related to food taboos. None of the studies explored the influence of resources at the institutional level. Conclusions: This attests that the focus in maternal nutrition is still placed on the individual responsibility of women, instead of addressing the structural conditions that would enable women to access resources such as land, education and nutrition information.
Brucella Species Circulating in Smallholder Dairy Cattle in Tanzania
Authors: Mengele Isaac Joseph , Akoko James Miser , Shirima Gabriel Mkilema , Bwatota Shedrack Festo , Motto Shabani Kiyabo , Hernandez-Castro Luis E. , Komwihangilo Daniel Mushumbusi , Lyatuu Eliamoni , Bronsvoort Barend Mark de Clare , Cook Elizabeth Anne Jessie
Published: September 2024

Brucellosis is a zoonosis caused by bacteria of the genus Brucella, which results in economic losses relating to livestock and threatens public health. A cross-sectional study was conducted to determine the molecular prevalence of Brucella species in smallholder dairy cattle in six regions of Tanzania from July 2019 to October 2020. Dairy cattle (n = 2048) were sampled from 1371 farms. DNA extracted from blood and vaginal swabs was tested for Brucella using qPCR targeting the IS711 gene and positives were tested for the alkB marker for B. abortus and BMEI1172 marker for B. melitensis. The molecular prevalence was 3.5% (95% CI: 2.8–4.4) with the highest prevalence 8.1% (95% CI: 4.6–13.0) in Njombe region. B. melitensis was the predominant species detected (66.2%). Further studies are recommended to understand the source of B. melitensis and its implications for veterinary public health. Livestock keepers should be informed of the risks and biosecurity practices to reduce the introduction and control of Brucella. Cattle and small ruminant vaccination programs could be implemented to control brucellosis in high-risk populations in the country.
Harnessing Lignocellulosic Crops for Phytomanagement of Contaminated Soils: A Multi-Country Study
Authors: Testa Giorgio , Ciaramella Barbara Rachele , Fernando Ana Luisa , Kotoula Danai , Scordia Danilo , Gomes Leandro Augusto , Cosentino Salvatore Luciano , Alexopoulou Efthymia , Papazoglou Eleni G.
Published: September 2024

The dwindling availability of agricultural land, caused by factors such as rapid population growth, urban expansion, and soil contamination, has significantly increased the pressure on food production. To address this challenge, cultivating non-food crops on contaminated land has emerged as a promising solution. This approach not only frees up fertile soil for food production but also mitigates human exposure to contaminants. This work aimed to examine the impact of soil contamination with Cd, Pb, Ni, and Zn on the growth, productivity, metal accumulation, and the tolerance of five lignocellulosic non-food crops: switchgrass (Panicum virgatum L.), biomass sorghum (Sorghum bicolor L. Moench), giant reed (Arundo donax L.), African fodder cane (Saccharum spontaneum L. spp. aegyptiacum Willd. Hackel), and miscanthus (Miscanthus × giganteus Greef et Deu.). A two-year pot experiment was conducted in Greece, Italy, and Portugal, following the same protocols and applying various levels of metals: Cd (0, 4, 8 mg kg−1), Pb and Zn (0, 450, 900 mg kg−1), and Ni (0, 110, 220 mg kg−1). The experimental design was completely randomized, with three replicates for each treatment. The results showed that switchgrass and sorghum generally maintained their height and productivity under Cd and Pb stress but were adversely affected by high Zn and Ni concentrations. Giant reed and African fodder cane showed reduced height and productivity at higher Ni and Zn levels. Miscanthus exhibited resilience in height but experienced productivity reductions only at the highest Zn concentration. Heavy metal uptake varied among crops, with switchgrass and sorghum showing high Cd and Pb uptake, while giant reed accumulated the most Cd and Zn. Miscanthus had the highest Ni accumulation. The tolerance indices indicated that switchgrass and sorghum were more tolerant to Cd and Zn at lower concentrations, whereas miscanthus had lower tolerance to Cd but a higher tolerance to Zn at higher concentrations. Giant reed and African fodder cane demonstrated stable tolerance across most heavy metals. Accumulation indices highlighted the effectiveness of switchgrass and sorghum in Cd and Pb uptake, while miscanthus excelled in Ni and Zn accumulation. The cluster analysis revealed similar responses to heavy metal stress between African fodder cane and giant reed, as well as between sorghum and miscanthus, with switchgrass displaying distinct behavior. Overall, the study highlights the differential tolerance and accumulation capacities of these crops, indicating the potential for phytoremediation applications and biomass production in heavy metal-contaminated soils.
Ethylene, a Signaling Compound Involved in Seed Germination and Dormancy
Authors: Corbineau Françoise
Published: September 2024

The present review is focused on current findings on the involvement of ethylene in seed biology. The responsiveness of seeds to ethylene depends on the species and the dormancy status, improving concentrations ranging from 0.1 to 200 μL L−1. The signaling pathway of ethylene starts with its binding to five membrane-anchored receptors, which results in the deactivation of Constitutive Triple Response 1 (CTR1, a protein kinase) that does not exert its inhibitory effect on Ethylene Insensitive 2 (EIN2) by phosphorylating its cytosolic C-terminal domain. An analysis of germination in the presence of inhibitors of ethylene synthesis or action, and using seeds from mutant lines altered in terms of the genes involved in ethylene synthesis (acs) and the signaling pathway (etr1, ein2, ein4, ctr1 and erf1), demonstrates the involvement of ethylene in the regulation of seed dormancy. The promoting effect of ethylene is also regulated through crosstalk with abscisic acid (ABA) and gibberellins (GAs), essential hormones involved in seed germination and dormancy, and Reactive Oxygen Species (ROS). Using a mutant of the proteolytic N-degron pathway, Proteolysis (PRT6), the Ethylene Response Factors (ERFs) from group VII (HRE1, HRE2, RAP 2.2, RAP2.3 and RAP 2.12) have also been identified as being involved in seed insensitivity to ethylene. This review highlights the key roles of EIN2 and EIN3 in the ethylene signaling pathway and in interactions with different hormones and discusses the responsiveness of seeds to ethylene, depending on the species and the dormancy status.
Role of Natural Phytoconstituents as a Potential Bioenhancer of Anti-Cancer and Anti-Microbial Agents: Spotlight on the Mechanism of Action, Clinical Studies and Patents
Authors: Unnikrishnan Meenakshi Dhanalekshmi , Narde Gurpreet Kaur , Ahuja Alka , Akhtar Md Jawaid , Khan Shah Alam
Published: September 2024

A drug design strategy with reduced side effects and economic feasibility is desirable for fatal diseases. Increasing the bioavailability of a drug using a bioenhancer is a smart strategy. Herbal/natural bioenhancers with no probable side effects are an ideal choice to enhance the pharmacokinetics of a therapeutic drug synergistically. The mechanism of bioenhancers relies on the retention of the drug molecule in the cell without causing any changes in the metabolic activity. Most of the herbal bioenhancers achieve this feat by inhibiting metabolic enzymes such as cytochrome P450 and Uridine 5′-diphospho-glucuronosyltransferase. The efflux pump p-glycoprotein, responsible for removal of xenobiotics, is also inhibited by herbal/natural bioenhancers. The increased bioavailability because of the higher Cmax and tmax of chemotherapeutics or anti-infectious agents such as rifampicin can result in a lower drug dosage regimen. The reduction in drug dosage is directly linked to fewer side effects and economic viability. Further, there is a significant effort in clinical trials to incorporate bioenhancers in drug regimens for cancer. The role of herbal/natural bioenhancers and their potential to augment the bioavailability of therapeutics used in cancer and infectious diseases, with a focus on the mechanisms of action, clinical studies and patents, have been summarized in this review article.
Influence Factors on the Energy Regulation Law of a Coal Seam after Hydraulic Slotting
Authors: Liu Shanshan , Yao Chuanru , Gao Deying , Wang Xinyuan
Published: September 2024

Hydraulic slotting technology is an effective pressure relief method for coal seams with high stress and burst risks. Based on FLAC3D and field applications, the stress and energy evolution of coal under different slotting radiuses, slotting spacings, and slotting ranges are studied. The results show that the pressure relief effect of slotting is mainly affected by the spacing and radius of the slotting. When the cutting radius increases from 0.5 m to 1.5 m, the average stress in the cutting range decreases from 10 MPa to 7.1 MPa, and the average energy decreases from 155.7 kJ/m3 to 117.1 kJ/m3. When the slotting spacing decreases from 3 m to 1 m, the stress release increases from 62% to 72%, and the energy release increases from 77.8% to 80.3%. The difference in the slotting area only affects the transfer distance of the peak point. In the field application, the microseismic frequency near the test area after hydraulic slotting is reduced from 32 times to 19 times, and the total microseismic energy is reduced from 2.67 × 104 J to 1.02 × 103 J, which can effectively realize the high stress transfer of the roadway. It can be seen that the hydraulic slotting technology can strongly relieve the pressure at fixed points in the high stress concentration area of the coal seam.
Modelling and Validating the Nonthermal Plasma Parameters for Producing Liquid Hydrocarbon from Solid Polyolefin Wastes
Authors: Khan Mohammad Jakir Hossain , Kryzevicius Zilvinas , Senulis Audrius , Zukauskaite Audrone , Uebe Jochen
Published: September 2024

This study solved a set of equations to verify the dynamic optimal conditions of nonthermal plasma (NTP)-chemical conversion of solid polyolefin wastes into liquid petroleum hydrocarbons. Furthermore, a novel optimisation model was validated with non-linear experimental conditions to assess the quantitative relationship between the process variables responsible for the degradation rate of wastes. The central composite design (CCD) experimental design was developed based on the Response Surface Model (RSM) technique. These techniques significantly improved the model predictions because of the more-detailed electrochemical description. Experiments were conducted in an in-house-designed and -developed NTP system with advanced data acquisition schemes. Both experimental and the numerical findings exhibited a good agreement, and the results indicated that the electrical factors of NTP could significantly affect the conversion yield (Yconv%) of solid polyolefin-derived wastes to liquid hydrocarbons. Additionally, the model investigation indicated that factors such as power discharge (x1), voltage intensity (x2), and reaction retention time (RTT) (x3) significantly influenced the conversion yield. After optimisation, a maximum conversion percentage (Yconv%) of ≈93% was achieved. The findings indicated that this recommended framework could be effectively employed for scaling the plasma synergistic pyrolysis technique for generating the maximal Yconv% of plastic wastes to yield an oil. Thereafter, the analysis of variance (ANOVA) technique was applied to examine the accuracy of the developed structure in order to upgrade this laboratory-scale processes to an industrial-scale process with >95% effectiveness. The calorific value of the produced oil was seen to be from 43,570.5 J/g to 46,025.5 J/g due to changes of the arrangements of the process factors, which specified that the liquid hydrocarbons showed similar characteristics like commercial diesel in this respect.
The Tripartite Qibla Wall as a Visual Form of Embodied Belief: From Al-Andalus to Mudejar and Morisco Mosques in Exile—Memory and Identity
Authors: Cuenca-Abellán Belén
Published: September 2024

The maqṣūrah commissioned by Caliph al-Hakam II in the 10th-century Mosque of Córdoba epitomizes the blend of sacred grandeur and political symbolism in Islamic architecture. This structure enhances aesthetic experiences, allowing worshippers to connect with divinity during Friday prayers. A distinctive feature is its qibla wall, divided into three parts, with a central empty miḥrāb symbolizing sacred force. This spatial organization, reminiscent of Late Antique basilicas in the Iberian Peninsula and the Mediterranean, persisted in peninsular mosques even after the Christian conquest of al-Andalus. Mudejar Islamic communities replicated this design in smaller mosques, perhaps to preserve al-Andalus’s memory amidst growing repression. Following the 17th-century expulsion of the Moriscos, similar architectural elements appeared in Testour, Tunisia, built by Morisco exiles. These visual codes, shared among Hispanic Islamic communities, reflect the enduring memory of the exile.
Endoscopic Guided Dilations without Intralesional Corticosteroid Injections: Pediatric Crohn’s Patients Case Series
Authors: Fawaz Leo , Slim Yousif , Freswick Peter N.
Published: September 2024

Background and Clinical Significance: The treatment for pediatric Crohn’s disease (CD) has shifted over the years from steroids and immunomodulators to biologics with the goal of histological and clinical remission. Endoscopic balloon dilation (EBD) has been utilized for stricturing disease, even in the pediatric population. EBD has been shown to be effective and minimally invasive, though historically, has been performed on patients with persistent mucosal inflammation. As such, intralesional corticosteroid (ILC) injections have been traditionally utilized during EBD. However, intralesional corticosteroid efficacy among pediatrics patients in deep endoscopic remission is unknown. Case Presentation: We report four patients that demonstrated at least initial successful dilations without intralesional steroid injections. Conclusions: The use of ILC injections during routine EBDs in pediatric patients should be further explored in randomized control trials.
Identification and Characterization of Reclaimed and Underclaimed Mine Features Using Lidar and Temporal Remote Sensing Methods within the Coastal Plain Uranium Mining Region of Texas
Authors: Stengel Victoria G. , Gallegos Tanya J. , Hubbard Bernard E. , Cahan Steven M. , Wallace David S.
Published: September 2024

We developed a spatiotemporal mapping approach utilizing multiple techniques for distinguishing and mapping known reclaimed mine sites from “unreclaimed” mine sites in a historic uranium mining district along the South Texas Coastal Plains. Lidar laser scanning penetrates the vegetation canopy to expose anthropogenic modifications to the landscape. The Lidar analysis (bare earth elevation surface, slope, topographic contours, topographic textures, and overland-flow hydrography) revealed mine features. Visual interpretation of Landsat imagery and time-series analysis augmented the Lidar analysis revealing the temporal life cycle of mining. The combination of bare earth texture with time-lapse and time-series analyses revealed areas of disturbance for reclaimed mines. The spatiotemporal mapping approach proved to be most useful in identifying and characterizing the known mine pit and pile features, reclamation status, and areas of disturbance due to mining. Two mine waste volume estimation methods resulted in a 21% difference indicating that although the approach helps to map mine features and areas of mining disturbance for the purposes of mine land inventory, additional information is needed to improve the estimation of buried mine waste at reclaimed mine sites.
Response of Upper Ocean to Parameterized Schemes of Wave Breaking under Typhoon Condition
Authors: Cao Xuhui , Chen Jie , Shi Jian , Xia Jingmin , Zhang Wenjing , Yi Zhenhui , Wang Hanshi , Zhang Shaoze , Lv Jialei , Zhao Zeqi , Wang Qianhui
Published: September 2024

The study of upper ocean mixing processes, including their dynamics and thermodynamics, has been a primary focus for oceanographers and meteorologists. Wave breaking in deep water is believed to play a significant role in these processes, affecting air–sea interactions and contributing to the energy dissipation of surface waves. This, in turn, enhances the transfer of gas, heat, and mass at the ocean surface. In this paper, we use the FVCOM-SWAVE coupled wave and current model, which is based on the MY-2.5 turbulent closure model, to examine the response of upper ocean turbulent kinetic energy (TKE) and temperature to various wave breaking parametric schemes. We propose a new parametric scheme for wave breaking energy at the sea surface, which is based on the correlation between breaking wave parameter RB and whitecap coverage. The impact of this new wave breaking parametric scheme on the upper ocean under typhoon conditions is analyzed by comparing it with the original parametric scheme that is primarily influenced by wave age. The wave field simulated by SWAVE was verified using Jason-3 satellite altimeter data, confirming the effectiveness of the simulation. The simulation results for upper ocean temperature were also validated using OISST data and Argo float observational data. Our findings indicate that, under the influence of Typhoon Nanmadol, both parametric schemes can transfer the energy of sea surface wave breaking into the seawater. The new wave breaking parameter RB scheme effectively enhances turbulent mixing at the ocean surface, leading to a decrease in sea surface temperature (SST) and an increase in mixed layer depth (MLD). This further improves upon the issue of uneven mixing of seawater at the air–sea interface in the MY-2.5 turbulent closure model. However, it is important to note that wave breaking under typhoon conditions is only one aspect of wave impact on ocean disturbances. Therefore, further research is needed to fully understand the impact of waves on upper ocean mixing, including the consideration of other wave mechanisms.
Weighted Contrastive Prototype Network for Few-Shot Hyperspectral Image Classification with Noisy Labels
Authors: Zhang Dan , Ren Yiyuan , Liu Chun , Han Zhigang , Wang Jiayao
Published: September 2024

Few-shot hyperspectral image classification aims to develop the ability of classifying image pixels by using relatively few labeled pixels per class. However, due to the inaccuracy of the localization system and the bias of the ground survey, the potential noisy labels in the training data pose a very significant challenge to few-shot hyperspectral image classification. To solve this problem, this paper proposes a weighted contrastive prototype network (WCPN) for few-shot hyperspectral image classification with noisy labels. WCPN first utilizes a similarity metric to generate the weights of the samples from the same classes, and applies them to calibrate the class prototypes of support and query sets. Then the weighted prototype network will minimize the distance between features and prototypes to train the network. WCPN also incorporates a weighted contrastive regularization function that uses the sample weights as gates to filter the fake positive samples whose labels are incorrect to further improve the discriminative power of the prototypes. We conduct experiments on multiple hyperspectral image datasets with artificially generated noisy labels, and the results show that the WCPN has excellent performance that can sufficiently mitigate the impact of noisy labels.
Identification of Spatial Distribution of Afforestation, Reforestation, and Deforestation and Their Impacts on Local Land Surface Temperature in Yangtze River Delta and Pearl River Delta Urban Agglomerations of China
Authors: Tai Zhiguo , Su Xiaokun , Shen Wenjuan , Wang Tongyu , Gu Chenfeng , He Jiaying , Huang Chengquan
Published: September 2024

Forest change affects local and global climate by altering the physical properties of the land surface. Accurately assessing urban forest changes in local land surface temperature (LST) is a scientific and crucial strategy for mitigating regional climate change. Despite this, few studies have attempted to accurately characterize the spatial and temporal pattern of afforestation, reforestation, and deforestation to optimize their effects on surface temperature. We used the China Land Cover Dataset and knowledge criterion-based spatial analysis model to map urban forestation (e.g., afforestation and reforestation) and deforestation. We then analyzed the impacts of these activities on LST from 2010 to 2020 based on the moving window strategy and the spatial–temporal pattern change analysis method in the urban agglomerations of the Yangtze River Delta (YRD) and Pearl River Delta (PRD), China. The results showed that forest areas declined in both regions. Most years, the annual deforestation area is greater than the yearly afforestation areas. Afforestation and reforestation had cooling effects of −0.24 ± 0.19 °C and −0.47 ± 0.15 °C in YRD and −0.46 ± 0.10 °C and −0.86 ± 0.11 °C in PRD. Deforestation and conversion of afforestation to non-forests led to cooling effects in YRD and warming effects of 1.08 ± 0.08 °C and 0.43 ± 0.19 °C in PRD. The cooling effect of forests is more evident in PRD than in YRD, and it is predominantly caused by reforestation. Moreover, forests demonstrated a significant seasonal cooling effect, except for December in YRD. Two deforestation activities exhibited seasonal warming impacts in PRD, mainly induced by deforestation, while there were inconsistent effects in YRD. Overall, this study provides practical data and decision-making support for rational urban forest management and climate benefit maximization, empowering policymakers and urban planners to make informed decisions for the benefit of their communities.
Source Range Estimation Using Linear Frequency-Difference Matched Field Processing in a Shallow Water Waveguide
Authors: Song Penghua , Wang Haozhong , Su Bolin , Wang Liang , Gao Wei
Published: September 2024

Matched field processing (MFP) is an established technique for source localization in known multipath acoustic environments. Unfortunately, in many situations, imperfect knowledge of the actual propagation environment and sidelobes due to modal interference prevent accurate propagation modeling and source localization via MFP. To suppress the sidelobes and improve the method’s robustness, a linear frequency-difference matched field processing (LFDMFP) method for estimating the source range is proposed. A two-neighbor-frequency high-order cross-spectrum between the measurement and the replica of each hydrophone of the vertical line array is first computed. The cost function can then be derived from the dual summation or double integral of the high-order cross-spectrum with respect to the depth of the hydrophones and the candidate sources of the replicas, where the range that corresponds to the minimum is the optimal estimation. Because of the larger modal interference distances, LFDMFP can efficiently provide only one optimal range within the same range search interval rather than some conventional matched field processing. The efficiency of the presented method was verified using simulations and experiments. The LFDMFP unambiguously estimated the source range in two experimental datasets with average relative errors of 2.2 and 1.9%.
PGNN-Net: Parallel Graph Neural Networks for Hyperspectral Image Classification Using Multiple Spatial-Spectral Features
Authors: Guo Ningbo , Jiang Mingyong , Wang Decheng , Jia Yutong , Li Kaitao , Zhang Yanan , Wang Mingdong , Luo Jiancheng
Published: September 2024

Hyperspectral image (HSI) shows great potential for application in remote sensing due to its rich spectral information and fine spatial resolution. However, the high dimensionality, nonlinearity, and complex relationship between spectral and spatial features of HSI pose challenges to its accurate classification. Traditional convolutional neural network (CNN)-based methods suffer from detail loss in feature extraction; Transformer-based methods rely too much on the quantity and quality of HSI; and graph neural network (GNN)-based methods provide a new impetus for HSI classification by virtue of their excellent ability to handle irregular data. To address these challenges and take advantage of GNN, we propose a network of parallel GNNs called PGNN-Net. The network first extracts the key spatial-spectral features of HSI using principal component analysis, followed by preprocessing to obtain two primary features and a normalized adjacency matrix. Then, a parallel architecture is constructed using improved GCN and ChebNet to extract local and global spatial-spectral features, respectively. Finally, the discriminative features obtained through the fusion strategy are input into the classifier to obtain the classification results. In addition, to alleviate the over-fitting problem, the label smoothing technique is embedded in the cross-entropy loss function. The experimental results show that the average overall accuracy obtained by our method on Indian Pines, Kennedy Space Center, Pavia University Scene, and Botswana reaches 97.35%, 99.40%, 99.64%, and 98.46%, respectively, which are better compared to some state-of-the-art methods.
Automatic Radar Intra-Pulse Signal Modulation Classification Using the Supervised Contrastive Learning
Authors: Cai Jingjing , Guo Yicheng , Cao Xianghai
Published: September 2024

The modulation classification technology for radar intra-pulse signals is important in the electronic countermeasures field. As the high quality labeled radar signals are difficult to be captured in the real applications, the signal modulation classification base on the limited number of labeled samples is playing a more and more important role. To relieve the requirement of the labeled samples, many self-supervised learning (SeSL) models exist. However, as they cannot fully explore the information of the labeled samples and rely significantly on the unlabeled samples, highly time-consuming processing of the pseudo-labels of the unlabeled samples is caused. To solve these problems, a supervised learning (SL) model, using the contrastive learning (CL) method (SL-CL), is proposed in this paper, which achieves a high classification accuracy, even adopting limited number of labeled training samples. The SL-CL model uses a two-stage training structure, in which the CL method is used in the first stage to effectively capture the features of samples, then the multilayer perceptron is applied in the second stage for the classification. Especially, the supervised contrastive loss is constructed to fully exploring the label information, which efficiently increases the classification accuracy. In the experiments, the SL-CL outperforms the comparison models in the situation of limited number of labeled samples available, which reaches 94% classification accuracy using 50 samples per class at 5 dB SNR.
Semantic Segmentation and Classification of Active and Abandoned Agricultural Fields through Deep Learning in the Southern Peruvian Andes
Authors: Zimmer-Dauphinee James , Wernke Steven A.
Published: September 2024

The monumental scale agricultural infrastructure systems built by Andean peoples during pre-Hispanic times have enabled intensive agriculture in the high-relief, arid/semi-arid landscape of the Southern Peruvian Andes. Large tracts of these labor-intensive systems have been abandoned, however, owing in large measure to a range of demographic, economic, and political crises precipitated by the Spanish invasion of the 16th century CE. This research seeks to better understand the dynamics of agricultural intensification and deintensification in the Andes by inventorying through the semantic segmentation of active and abandoned agricultural fields in satellite imagery across approximately 77,000 km2 of the Southern Peruvian Highlands. While manual digitization of agricultural fields in satellite imagery is time-consuming and labor-intensive, deep learning-based semantic segmentation makes it possible to map and classify en masse Andean agricultural infrastructure. Using high resolution satellite imagery, training and validation data were manually produced in distributed sample areas and were used to transfer-train a convolutional neural network for semantic segmentation. The resulting dataset was compared to manual surveys of the region and results suggest that deep learning can generate larger and more accurate datasets than those generated by hand.
The Detection of Maize Seedling Quality from UAV Images Based on Deep Learning and Voronoi Diagram Algorithms
Authors: Ren Lipeng , Li Changchun , Yang Guijun , Zhao Dan , Zhang Chengjian , Xu Bo , Feng Haikuan , Chen Zhida , Lin Zhongyun , Yang Hao
Published: September 2024

Assessing the quality of maize seedlings is crucial for field management and germplasm evaluation. Traditional methods for evaluating seedling quality mainly rely on manual field surveys, which are not only inefficient but also highly subjective, while large-scale satellite detection often lacks sufficient accuracy. To address these issues, this study proposes an innovative approach that combines the YOLO v8 object detection algorithm with Voronoi spatial analysis to rapidly evaluate maize seedling quality based on high-resolution drone imagery. The YOLO v8 model provides the maize coordinates, which are then used for Voronoi segmentation of the field after applying the Convex Hull difference method. From the generated Voronoi diagram, three key indicators are extracted: Voronoi Polygon Uniformity Index (VPUI), missing seedling rate, and repeated seedling rate to comprehensively evaluate maize seedling quality. The results show that this method effectively extracts the VPUI, missing seedling rate, and repeated seedling rate of maize in the target area. Compared to the traditional plant spacing variation coefficient, VPUI performs better in representing seedling uniformity. Additionally, the R2 for the estimated missing seedling rate and replanting rate based on the Voronoi method were 0.773 and 0.940, respectively. Compared to using the plant spacing method, the R2 increased by 0.09 and 0.544, respectively. The maize seedling quality evaluation method proposed in this study provides technical support for precision maize planting management and is of great significance for improving agricultural production efficiency and reducing labor costs.
Implications of Aperiodic and Periodic EEG Components in Classification of Major Depressive Disorder from Source and Electrode Perspectives
Authors: Zandbagleh Ahmad , Sanei Saeid , Azami Hamed
Published: September 2024

Electroencephalography (EEG) is useful for studying brain activity in major depressive disorder (MDD), particularly focusing on theta and alpha frequency bands via power spectral density (PSD). However, PSD-based analysis has often produced inconsistent results due to difficulties in distinguishing between periodic and aperiodic components of EEG signals. We analyzed EEG data from 114 young adults, including 74 healthy controls (HCs) and 40 MDD patients, assessing periodic and aperiodic components alongside conventional PSD at both source and electrode levels. Machine learning algorithms classified MDD versus HC based on these features. Sensor-level analysis showed stronger Hedge’s g effect sizes for parietal theta and frontal alpha activity than source-level analysis. MDD individuals exhibited reduced theta and alpha activity relative to HC. Logistic regression-based classifications showed that periodic components slightly outperformed PSD, with the best results achieved by combining periodic and aperiodic features (AUC = 0.82). Strong negative correlations were found between reduced periodic parietal theta and frontal alpha activities and higher scores on the Beck Depression Inventory, particularly for the anhedonia subscale. This study emphasizes the superiority of sensor-level over source-level analysis for detecting MDD-related changes and highlights the value of incorporating both periodic and aperiodic components for a more refined understanding of depressive disorders.
A New Measure for Quantifying Four-Limb Coordination of Human Gait Based on Mobility Sensors
Authors: Galor Noam , Zeilig Gabi , Plotnik Meir
Published: September 2024

Coordinated movement of four limbs is a hallmark of healthy locomotion. No measures exist to quantify four-limb coordination. This study aimed to investigate temporal four-limb coordination and proposed a new metric for quantifying the inter-limb phase of rhythmic locomotion-related movements. Kinetic data of arm and leg movements generated during walking (self-selected speed) from healthy adults were used to extract the phases (φ) between all possible limb pairings. The φ series were used to calculate each pair’s Phase Coordination Index (PCI). The PCI quantifies the accuracy and consistency of generating anti-phased rhythmic movements (lower PCI values mean better coordination). We also calculated the Quadruple-PCI (Q-PCI) by combining all φ values of all limb pairs. We found a significant correlation between the PCI values of all limb pairings and the Q-PCI (pairs involving arms: Pearson’s R > 0.79, p < 0.001; leg–leg: Pearson’s R = 0.3, p < 0.01). The PCI values that involve arms (median values between 6.5% and 8.3%) were significantly higher than the leg–leg PCI (median values between 3.8% and 4.1%), and the Q-PCI (median values between 8.3% and 9.7%) was significantly higher than all other PCI values. We also found a negative correlation between the arm swing amplitude and the PCI values (Spearman’s Rho of different limb pairings ranging from −0.25 to −0.5, p < 0.05), suggesting that higher arm swing amplitude leads to better coordination. Four-limb coordination analysis is a novel method for comprehensive assessment of gait coordination, which is often compromised among persons with disabilities.
Estimation of Leaf Area Index across Biomes and Growth Stages Combining Multiple Vegetation Indices
Authors: Lv Fangyi , Sun Kaimin , Li Wenzhuo , Miao Shunxia , Hu Xiuqing
Published: September 2024

The leaf area index (LAI) is a key indicator of vegetation canopy structure and growth status, crucial for global ecological environment research. The Moderate Resolution Spectral Imager-II (MERSI-II) aboard Fengyun-3D (FY-3D) covers the globe twice daily, providing a reliable data source for large-scale and high-frequency LAI estimation. VI-based LAI estimation is effective, but species and growth status impacts on the sensitivity of the VI–LAI relationship are rarely considered, especially for MERSI-II. This study analyzed the VI–LAI relationship for eight biomes in China with contrasting leaf structures and canopy architectures. The LAI was estimated by adaptively combining multiple VIs and validated using MODIS, GLASS, and ground measurements. Results show that (1) species and growth stages significantly affect VI–LAI sensitivity. For example, the EVI is optimal for broadleaf crops in winter, while the RDVI is best for evergreen needleleaf forests in summer. (2) Combining vegetation indices can significantly optimize sensitivity. The accuracy of multi-VI-based LAI retrieval is notably higher than using a single VI for the entire year. (3) MERSI-II shows good spatial–temporal consistency with MODIS and GLASS and is more sensitive to vegetation growth fluctuation. Direct validation with ground-truth data also demonstrates that the uncertainty of retrievals is acceptable (R2 = 0.808, RMSE = 0.642).
Design of an FPGA-Based Controller for Fast Scanning Probe Microscopy
Authors: Gregorat Leonardo , Cautero Marco , Carrato Sergio , Giuressi Dario , Panighel Mirco , Cautero Giuseppe , Esch Friedrich
Published: September 2024

Atomic-scale imaging using scanning probe microscopy is a pivotal method for investigating the morphology and physico-chemical properties of nanostructured surfaces. Time resolution represents a significant limitation of this technique, as typical image acquisition times are on the order of several seconds or even a few minutes, while dynamic processes—such as surface restructuring or particle sintering, to be observed upon external stimuli such as changes in gas atmosphere or electrochemical potential—often occur within timescales shorter than a second. In this article, we present a fully redesigned field programmable gate array (FPGA)-based instrument that can be integrated into most commercially available standard scanning probe microscopes. This instrument not only significantly accelerates the acquisition of atomic-scale images by orders of magnitude but also enables the tracking of moving features such as adatoms, vacancies, or clusters across the surface (“atom tracking”) due to the parallel execution of sophisticated control and acquisition algorithms and the fast exchange of data with an external processor. Each of these measurement modes requires a complex series of operations within the FPGA that are explained in detail.
Estimation Model for Maize Multi-Components Based on Hyperspectral Data
Authors: Xue Hang , Xu Xiping , Meng Xiang
Published: September 2024

Assessing the quality of corn seeds necessitates evaluating their water, fat, protein, and starch content. This study integrates hyperspectral imaging technology with chemometric analysis techniques to achieve non-invasive and rapid detection of multiple key components in corn seeds. Hyperspectral images of the embryo surface of maize seeds were collected within the wavelength range of 1100~2498 nm. Subsequently, image segmentation techniques were applied to extract the germ structure of the corn seeds as the region of interest. Seven spectral data preprocessing algorithms were employed, and the Detrending Transformation (DT) algorithm was identified as the optimal preprocessing method through comparative analysis using the Partial Least Squares Regression (PLSR) model. To reduce spectral redundancy and streamline the prediction model, three algorithms were employed for characteristic wavelength extraction: Successive Projections Algorithm (SPA), Competitive Adaptive Reweighted Sampling (CARS), and Uninformative Variable Elimination (UVE). Using the original spectra and extracted characteristic wavelengths, PLSR, BP, RBF, and LSSVM models were constructed to detect the content of four components. The analysis indicated that the CARS-LSSVM algorithm had the best prediction performance. The PSO algorithm was employed to further optimize the parameters of the LSSVM model, thereby improving the model’s prediction performance. The R values for the four components in the test set were 0.9884, 0.9490, 0.9864, and 0.9687, respectively. This indicates that hyperspectral technology combined with the DT-CARS-PSO-LSSVM algorithm can effectively detect the main component content of corn seeds. This study not only provides a scientific basis for the evaluation of corn seed quality but also opens up new avenues for the development of non-destructive testing technology in related fields.
Synchronous End-to-End Vehicle Pedestrian Detection Algorithm Based on Improved YOLOv8 in Complex Scenarios
Authors: Lei Shi , Yi He , Sarmiento Jeffrey S.
Published: September 2024

In modern urban traffic, vehicles and pedestrians are fundamental elements in the study of traffic dynamics. Vehicle and pedestrian detection have significant practical value in fields like autonomous driving, traffic management, and public security. However, traditional detection methods struggle in complex environments due to challenges such as varying scales, target occlusion, and high computational costs, leading to lower detection accuracy and slower performance. To address these challenges, this paper proposes an improved vehicle and pedestrian detection algorithm based on YOLOv8, with the aim of enhancing detection in complex traffic scenes. The motivation behind our design is twofold: first, to address the limitations of traditional methods in handling targets of different scales and severe occlusions, and second, to improve the efficiency and accuracy of real-time detection. The new generation of dense pedestrian detection technology requires higher accuracy, less computing overhead, faster detection speed, and more convenient deployment. Based on the above background, this paper proposes a synchronous end-to-end vehicle pedestrian detection algorithm based on improved YOLOv8, aiming to solve the detection problem in complex scenes. First of all, we have improved YOLOv8 by designing a deformable convolutional improved backbone network and attention mechanism, optimized the network structure, and improved the detection accuracy and speed. Secondly, we introduced an end-to-end target search algorithm to make the algorithm more stable and accurate in vehicle and pedestrian detection. The experimental results show that, using the algorithm designed in this paper, our model achieves an 11.76% increase in precision and a 6.27% boost in mAP. In addition, the model maintains a real-time detection speed of 41.46 FPS, ensuring robust performance even in complex scenarios. These optimizations significantly enhance both the efficiency and robustness of vehicle and pedestrian detection, particularly in crowded urban environments. We further apply our improved YOLOv8 model for real-time detection in intelligent transportation systems and achieve exceptional performance with a mAP of 95.23%, outperforming state-of-the-art models like YOLOv5, YOLOv7, and Faster R-CNN.
Jointly Optimization of Delay and Energy Consumption for Multi-Device FDMA in WPT-MEC System
Authors: Qiao Danxia , Sun Lu , Li Dianju , Xiong Huajie , Liang Rina , Han Zhenyuan , Wan Liangtian
Published: September 2024

With the rapid development of mobile edge computing (MEC) and wireless power transfer (WPT) technologies, the MEC-WPT system makes it possible to provide high-quality data processing services for end users. However, in a real-world WPT-MEC system, the channel gain decreases with the transmission distance, leading to “double near and far effect” in the joint transmission of wireless energy and data, which affects the quality of the data processing service for end users. Consequently, it is essential to design a reasonable system model to overcome the “double near and far effect” and make reasonable scheduling of multi-dimensional resources such as energy, communication and computing to guarantee high-quality data processing services. First, this paper designs a relay collaboration WPT-MEC resource scheduling model to improve wireless energy utilization efficiency. The optimization goal is to minimize the normalization of the total communication delay and total energy consumption while meeting multiple resource constraints. Second, this paper imports a BK-means algorithm to complete the end terminals cluster to guarantee effective energy reception and adapts the whale optimization algorithm with adaptive mechanism (AWOA) for mobile vehicle path-planning to reduce energy waste. Third, this paper proposes an immune differential enhanced deep deterministic policy gradient (IDDPG) algorithm to realize efficient resource scheduling of multiple resources and minimize the optimization goal. Finally, simulation experiments are carried out on different data, and the simulation results prove the validity of the designed scheduling model and proposed IDDPG.
Anomaly Detection Method for Industrial Control System Operation Data Based on Time–Frequency Fusion Feature Attention Encoding
Authors: Liu Jiayi , Sha Yun , Zhang Wenchang , Yan Yong , Liu Xuejun
Published: September 2024

Anomaly detection in industrial control system (ICS) data is one of the key technologies for ensuring the security monitoring of ICSs. ICS data are characterized as complex, multi-dimensional, and long-sequence time-series data that embody ICS business logic. Due to its complex and varying periodic characteristics, as well as the presence of long-distance and misaligned temporal associations among features, current anomaly detection methods in ICS are insufficient for feature extraction. This paper proposes an anomaly detection method named TFANet, based on time–frequency fusion feature attention encoding. Considering that periodic variations are more concentrated in the frequency domain, this method first transforms the time-domain data into the frequency domain, obtaining both amplitude and phase data. Then, these data, together with the original time-series data, are used to extract features from two perspectives: long-term temporal changes and long-distance associations. Finally, the six features learned from both the time and frequency domains are fused, and the feature weights are calculated using an attention mechanism to complete the anomaly classification. In multi-classification tasks on three ICS datasets, the proposed method outperforms three popular time-series models—iTransformer, Crossformer, and TimesNet—across five metrics: accuracy, precision, recall, F1 score, and AUC-ROC, with average improvements of approximately 19%, 37%, 31%, 35%, and 22%, respectively.