Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Rate and Coverage Design for UAV-Enabled Wireless Networks with Underlaid D2D Communications

Unmanned aerial vehicle (UAV) can be utilized as aerial base station to provide emergency coverage in disasters, due to its flexible deployment, potential mobility and low cost. However, the wireless coverage of a UAV is limited because of the constrained transmit power. Therefore, device-to-device (D2D) transmission can be exploited to extend wireless coverage of UAV. In this paper, we aim to jointly maximize the weighted sum rate of the UAV-served users and the total number of users with wireless service for UAV-enabled wireless networks with underlaid D2D communications. The optimal set of UAV-served users are determined, while satisfying the information causality constraint. The formulated problem involving binary variables is non-deterministic polynomial (NP)-hard and extremely difficult to solve. Accordingly, we propose the algorithm to transform the problem into solving linear programming problem with low-complexity, and obtain a suboptimal solution. Simulation results demonstrate that the proposed joint rate and coverage design achieves better system performance.
Real-Time Capture of Holistic Tangible Interactions

When digital applications aim to blend virtual and real worlds, understanding the actual physical actions of users becomes an important task; the precise timing of these tangible interaction events is needed, along with the identity, and possibly location and history, of all involved actors/objects. With multiple actors or objects, it is difficult to identify who touches which object and when. Instrumenting objects for Body Channel Communication (BCC) allows message exchange around the human body between instrumented objects and the user themselves. In this paper we show how BCC can be utilized to perform under real-time conditions so that we can directly notice touch events (and the identity of actors). TangibleID is a framework that unifies tangible interaction capture for objects and users based on wearable BCC. TangibleID provides identification and communication with tagged objects/users in less than 120 ms and supports a variety of tangible interactions, without the need to restrict user (hand) movements or to maintain line-of-sight connection to cameras. When an AR application is combined with TangibleID, a new tangible mixed reality experience is achieved, as demonstrated in the “Haunted Castle” showcase. The paper presents an end-to-end technical evaluation including trade-offs regarding robustness and speed of touch recognition, outlines the breadth of interaction modalities, and reports on an initial user assessment.
Release of natural organic matter sorbed to oxides and a subsoil

Sorption to mineral surfaces is an important process controlling the mobility and stability of natural organic matter (NOM) in soil, yet only knowledge of the reversibility of this process enables the prediction of element cycling and NOM-induced transport in soils. We have elucidated the desorption of mineral-bound NOM in batch experiments with amorphous Al(OH) 3 , goethite, and a subsoil low in organic C. These sorbents were equilibrated with increasing amounts of water-extractable NOM from the Oa horizon of a mor forest-floor layer and then extracted with solutions of different ionic strengths, pH, and concentrations of inorganic anions (Cl - , SO 4 2- , H 2 PO 4 - ). Sorbed NOM was extracted after 24, 48, 72, and 120 h. We investigated structural and functional characteristics of the desorbed NOM by XAD-8 (macroporous resin) fractionation and by 13 C-NMR spectroscopy. Desorption of NOM from minerals and soils was negligible (<3%) under solution conditions similar to those during the sorption (hysteresis). It was not influenced by increasing concentrations of noncompeting inorganic anions such as Cl - . Increased concentrations (≤0.1 M) of competing anions like SO 4 2- or H 2 PO 4 - increased the NOM desorption. Though H 2 PO 4 - was most efficient in desorbing NOM, the extractability was only ≤60% at the highest H 2 PO 4 - concentration. The most significant desorption occurred when solution pH was raised. For goethite, NOM desorption reached a maximum at a pH above the point of zero charge (PZC) of the mineral. With increasing surface coverage of the sorbent by NOM, the proportion of desorbable NOM decreased for all extractants. Increased sorption hysteresis was also observed with an increasing time period between sorption and desorption. The desorption was more pronounced for NOM compounds that exhibit hydrophilic properties and have low contents of aromatic structures and carboxyl groups. The irreversible binding of NOM, especially of the lignin-derived portion, to soil minerals seems to result from its polyelectrolytic nature. This may favor the formation of multi-site coordinative bonds and effective shielding of the binding ligands by other parts of the sorbed molecule.
Field Strength Prediction of 230 MHz Urban Macro Cellular Electric Wireless Private Network Based on Uniform Geometrical Theory of Diffraction

In order to obtain more accurate signal propagation characteristics of 230 MHz electric wireless private network and to better guide network coverage planning, a 230 MHz electric wireless private network field strength prediction method based on uniform geometrical theory of diffraction (UTD) was proposed. The proposed method was applied to the propagation model of 230 MHz urban macro cellular electric wireless private network. The influence of the pitch angle of transmitting antenna to target building on the field strength of 230 MHz electric wireless private network was studied from four aspects: the height of transmitting antenna, the average distance between buildings, the number of buildings and the average height of buildings. The simulation results show that the field strength of the received signal increases with the increase of the pitch angle. Compared with the traditional Okumura-Hata model, the UTD model is more helpful to analyze the local field strength changes and obtain more accurate field strength prediction results.
Effect of Baseline Distance and Corner Consistency on Binocular Visual Locating

Taking baseline distance and corner consistency of checkerboard calibration board as objects and combining stereo matching algorithm, the influence of baseline distance and corner consistency on camera calibration and binocular vision locating error is analyzed through building binocular vision locating system. The results show that the calibration error decreases with the increase of angular consistency and baseline distance, and finally tends to be stable. Under the condition of the same baseline distance, the locating error decreases with the increase of angular consistency. The calibration plate with large angular consistency has better locating error stability. The research has practical value for the design of binocular vision locating system's baseline distance and angular consistency.
Secure Transmission Using Linearly Distributed Virtual Antenna Array With Element Position Perturbations

Physical-layer security (PLS) is a promising alternative or complement to cryptography-based solutions, because security key management and distribution required in such schemes are challenging in future wireless technologies with a distributed nature (e.g., Internet-of-Things (IoT) and massive machine-type communication). Motivated by this fact, in this paper, we investigate a PLS algorithm using linear virtual antenna array (VAA) constructed by multiple single-antenna nodes in mobile ad hoc networks such as unmanned aerial vehicles (UAVs) and mobile robots. Digital cooperative beamforming (CB) has been extensively explored such as cooperative null-steering or jamming. However, in spite of their effectiveness, the digital CB-based schemes incur prohibitive overhead and complexity, which creates difficulty in networks with limited hardware (e.g., IoT). To overcome this issue, we consider an analog CB-based PLS with linearly distributed VAA, which can be implemented in a fully distributed manner. In the proposed scheme, the received signal at the intended receiver is co-phased, while other receivers capture noise-like signals through randomized radiation patterns using the linear VAA. Assuming Rician channels, we derive secrecy rate of the proposed algorithm in a closed-from expression for an arbitrary K-factor, which is not available in previous work on the analog CB-based PLS. Furthermore, we show that the random location perturbations of the VAA elements following Gaussian distributions can enhance the secrecy performance by resolving the angle-of-departure ambiguity issue in linear arrays.
Comprehensive Analysis of Evolutionary Characterization and Expression for Monosaccharide Transporter Family Genes in Nelumbo nucifera

Sugar transporters, an important class of transporters for sugar function, regulate many processes associated with growth, maturation, and senescence processes in plants. In this study, a total of 35 NuMSTs were identified in the Nelumbo nucifera genome and grouped by conserved domains and phylogenetic analysis. Additionally, we identified 316 MST genes in 10 other representative plants and performed a comparative analysis with Nelumbo nucifera genes, including evolutionary trajectory, gene duplication, and expression pattern. A large number of analyses across plants and algae indicated that the MST family could have originated from STP and Glct, expanding to form STP and SFP by dispersed duplication. Finally, a quantitative real-time polymerase chain reaction and cis-element analysis showed that some of them may be regulated by plant hormones (e.g., abscisic acid), biotic stress factors, and abiotic factors (e.g., drought, excessive cold, and light). We found that under the four abiotic stress conditions, only NuSTP5 expression was upregulated, generating a stress response, and ARBE and LTR were present in NuSTP5. In summary, our findings are significant for understanding and exploring the molecular evolution and mechanisms of NuMSTs in plants.
Using eDNA Metabarcoding to Monitor Changes in Fish Community Composition After Barrier Removal

Artificial instream barriers are a major cause of habitat fragmentation that reduce population connectivity and gene flow by limiting fish movements. To mitigate their impacts, obsolete barriers are increasingly been removed worldwide, but few barrier removal projects are monitored. We employed a powerful Before-After-Downstream-Upstream (BADU) approach using environmental DNA (eDNA) metabarcoding to examine the effects on fish community composition of removing a weir in the river Lugg (England) that had been suggested to have a detrimental effect on salmonid migration. We found no change in fish community diversity or relative abundance after the removal above or below the weir, but detected an important effect of sampling season, likely related to the species' life cycles. eDNA detected nine fish species that were also identified by electrofishing sampling and one additional species (Anguilla anguilla) that was missed by traditional surveys. Our results suggest that monitoring of barrier removal projects should be carried out to ensure that any ecological benefits are properly documented and that eDNA metabarcoding is a sensitive technique to monitor the effects of barrier removal.
Combining Multi-Dimensional Convolutional Neural Network (CNN) With Visualization Method for Detection of Aphis gossypii Glover Infection in Cotton Leaves Using Hyperspectral Imaging

Cotton is a significant economic crop. It is vulnerable to aphids (Aphis gossypii Glovers) during the growth period. Rapid and early detection has become an important means to deal with aphids in cotton. In this study, the visible/near-infrared (Vis/NIR) hyperspectral imaging system (376–1044 nm) and machine learning methods were used to identify aphid infection in cotton leaves. Both tall and short cotton plants (Lumianyan 24) were inoculated with aphids, and the corresponding plants without aphids were used as control. The hyperspectral images (HSIs) were acquired five times at an interval of 5 days. The healthy and infected leaves were used to establish the datasets, with each leaf as a sample. The spectra and RGB images of each cotton leaf were extracted from the hyperspectral images for one-dimensional (1D) and two-dimensional (2D) analysis. The hyperspectral images of each leaf were used for three-dimensional (3D) analysis. Convolutional Neural Networks (CNNs) were used for identification and compared with conventional machine learning methods. For the extracted spectra, 1D CNN had a fine classification performance, and the classification accuracy could reach 98%. For RGB images, 2D CNN had a better classification performance. For HSIs, 3D CNN performed moderately and performed better than 2D CNN. On the whole, CNN performed relatively better than conventional machine learning methods. In the process of 1D, 2D, and 3D CNN visualization, the important wavelength ranges were analyzed in 1D and 3D CNN visualization, and the importance of wavelength ranges and spatial regions were analyzed in 2D and 3D CNN visualization. The overall results in this study illustrated the feasibility of using hyperspectral imaging combined with multi-dimensional CNN to detect aphid infection in cotton leaves, providing a new alternative for pest infection detection in plants.
Embracing the Allelopathic Potential of Invasive Aquatic Plants to Manipulate Freshwater Ecosystems

Freshwater ecosystems provide essential resources and vital ecosystem services. These ecosystems exist in a delicate state of balance and are under increasing anthropogenic and climatic pressures. One of the major anthropogenic threats to freshwater ecosystems is eutrophication that often leads to algal blooms, some of which may be extremely harmful. Current chemical and physical interventions to prevent algal blooms can be expensive, ephemeral and disruptive to other aspects of the ecosystem. Therefore, there is interest in utilising biological methods of control. This study aimed to assess the viability of allelopathic repression of nuisance algae species by invasive aquatic plants. The allelopathic effect of Hydrocotyle ranunculoides (Floating Pennywort) and Crassula helmsii (Swamp Stonecrop) were tested in both whole plant and crushed plant states for their ability to affect the average population growth of monocultures and co-cultures of the green algae Chlorella vulgaris and the cyanobacterium Synechocystis sp. PCC6803. Methanol extracts from these species have been identified in the literature to have high allelopathic potential. The key findings of this study are that, for Chlorella: whole H. ranunculoides and crushed C. helmsii had a negative effect on the average population growth; whole H. ranunculoides had a greater negative effect than crushed H. ranunculoides; and crushed C. helmsii had a more negative effect than crushed H. ranunculoides. For Synechocystis: crushed C. helmsii had a greater negative effect on the average population growth than crushed H. ranunculoides; and the presence of Chlorella in co-culture experiments had a universally positive effect on its average population growth. The species-specific nature of these allelopathic interactions suggests that the use of allelopathy for algal bloom control may have to be assessed on a case-by-case basis and the use of combination treatments should be assessed. Moreover, the effects exerted by allelochemicals in open water systems is likely to be direct, indirect and context specific. Although this study explores the possibility of embracing the allelopathic potential of invasive aquatic plants, we do not encourage novel introductions of invasive species into open freshwater systems. However, potential allelopathic effects could be leveraged in already invaded systems, or in closed systems within an invaded range.
Six Weeks of Aerobic Exercise in Untrained Men With Overweight/Obesity Improved Training Adaptations, Performance and Body Composition Independent of Oat/Potato or Milk Based Protein-Carbohydrate Drink Supplementation

Background: Protein availability around aerobic exercise might benefit aerobic capacity and body composition in normal weight adults. However, it is unknown if individuals with overweight/obesity elicit similar adaptations or improve other cardiometabolic/health-related markers in response to different types of protein. Thus, our aim was to study the effect of supplementation of two different protein drinks in conjunction with exercise on aerobic capacity, body composition and blood health markers in untrained subjects with overweight or obesity. Methods: The present study measured training adaptation and health parameters over a 6 week period in untrained men with overweight/obesity (n = 28; BMI 30.4 ± 2.2 kg/m2) ingesting either plant- (Oat/Potato; n = 8) or animal-based (Milk; n = 10) protein-carbohydrate drinks (10 g of protein/serving), or a control carbohydrate drink (n = 10) acutely before and after each training session (average three sessions/week @ 70% HRmax). Pre-post intervention V˙O2peak, muscle biopsies and blood samples were collected, body composition measured (DXA) and two different exercise tests performed. Body weight was controlled with participants remaining weight stable throughout the intervention. Results: For the groups combined, the training intervention significantly increased V˙O2peak (8%; P < 0.001), performance in a time-to-exhaustion trial (~ 100%; P < 0.001), mitochondrial protein content and enzyme activity (~20–200%). Lean body mass increased (1%; P < 0.01) and fat mass decreased (3%; P < 0.01). No significant effects on fasting blood glucose, insulin, lipids or markers of immune function were observed. There were no significant interactions between drink conditions for training adaptation or blood measurements. For body composition, the Oat/Potato and carbohydrate group decreased leg fat mass significantly more than the Milk group (interaction P < 0.05). Conclusions: Aerobic capacity and body composition were improved and a number of mitochondrial, glycolytic and oxidative skeletal muscle proteins and enzyme activities were upregulated by a 6 week training intervention. However, none of the parameters for endurance training adaptation were influenced by protein supplementation before and after each training session.
Migration Aware Virtual Network Function Placing and Routing in Uncertain Environment

Network Function Virtualization (NFV) aims to provide a way to build agile and service-aware networks by building a new paradigm of provisioning network services where physical network functions are deployed as Virtual Network Functions (VNFs). However, how to optimally allocate resources in the uncertain NFV environment where flow rates fluctuate has not been fully resolved. In this paper, we study the cost minimizing problem of VNF placing and routing optimization while two goals are considered: optimizing service migration caused by flow fluctuation and stabilizing queue backlogs in the network. We first formulate the problem as a stochastic optimization programming problem. Then we propose an online algorithm, named Migration Aware VNF plaCing and Routing Online algorithm (MACRO), based on Lyapunov optimization technique. MACRO can make good decisions without knowing any future information. The theoretical analysis suggests that MACRO achieves an optimality gap of $\mathcal{O}(\frac{1}{\mathcal{V}})$ and the queue backlogs are bounded by $\mathcal{O}(\mathcal{V})$, where $\mathcal{V}$ is a tunable parameter that controls the tradeoff between cost and backlogs. The experiment results suggest that MACRO achieves queue stability and outperforms benchmark algorithm by 6% in terms of cost.
Polarization of Macrophages in Insects: Opening Gates for Immuno-Metabolic Research

Insulin resistance and cachexia represent severe metabolic syndromes accompanying a variety of human pathological states, from life-threatening cancer and sepsis to chronic inflammatory states, such as obesity and autoimmune disorders. Although the origin of these metabolic syndromes has not been fully comprehended yet, a growing body of evidence indicates their possible interconnection with the acute and chronic activation of an innate immune response. Current progress in insect immuno-metabolic research reveals that the induction of insulin resistance might represent an adaptive mechanism during the acute phase of bacterial infection. In Drosophila, insulin resistance is induced by signaling factors released by bactericidal macrophages as a reflection of their metabolic polarization toward aerobic glycolysis. Such metabolic adaptation enables them to combat the invading pathogens efficiently but also makes them highly nutritionally demanding. Therefore, systemic metabolism has to be adjusted upon macrophage activation to provide them with nutrients and thus support the immune function. That anticipates the involvement of macrophage-derived systemic factors mediating the inter-organ signaling between macrophages and central energy-storing organs. Although it is crucial to coordinate the macrophage cellular metabolism with systemic metabolic changes during the acute phase of bacterial infection, the action of macrophage-derived factors may become maladaptive if chronic or in case of infection by an intracellular pathogen. We hypothesize that insulin resistance evoked by macrophage-derived signaling factors represents an adaptive mechanism for the mobilization of sources and their preferential delivery toward the activated immune system. We consider here the validity of the presented model for mammals and human medicine. The adoption of aerobic glycolysis by bactericidal macrophages as well as the induction of insulin resistance by macrophage-derived factors are conserved between insects and mammals. Chronic insulin resistance is at the base of many human metabolically conditioned diseases such as non-alcoholic steatohepatitis, atherosclerosis, diabetes, and cachexia. Therefore, revealing the original biological relevance of cytokine-induced insulin resistance may help to develop a suitable strategy for treating these frequent diseases.
Leaf anatomy of two medicinal Croton species: Contribution to plant recognition

Croton L. (Euphorbiaceae) is the second largest genus of the Euphorbiaceae s.l. family, which is composed of about 1,200 taxa. Several species are widely used medicinally in various parts of the world to treat cancer, digestive problems, skin diseases, malaria, ulcers, and obesity. The leaves and stems are sold as dehydrated fragments, or in powder form, and used in the preparation of teas and various types of formulations. The aim of this study was to accurately characterize the leaf anatomy of Croton bonplandianus Baill. and Croton gracilipes Baill. and to perform histochemical analysis of laticifers to provide subsidies for botanical certification. To accomplish this, leaf blade and petiole samples were fixed and prepared following standard techniques for scanning electron and light microscopy. Histochemical tests were carried out on sections to detect the compounds present in the secretions. Results showed neutral and acidic lipids, as well as phenolic compounds, in the latex. In addition, leaf anatomical structures of these two species were studied for specific identification of morphological characters and for quality assessment of these medicinal plants.