The formation and localization of sporadic E (Es) layers predicted by the ion vertical drift velocity and its vertical change in the lower thermosphere during nighttime are shown analytically and by numerical simulations. The consideration of the existence of a minimum negative value of the vertical change of the ion vertical drift velocity as a necessary condition of formation of the Es layer and determining ion convergence rate into this layer is extended in case of the presence of vertical wind. Upward vertical wind can shift the convergence regions upward, while downward vertical wind shifts them downward, unlike the cases of the presence of only meridional and zonal winds. It also changes the ion convergence rate compared to the one with just a horizontal wind. It is shown for the first time that the upward constant wind also causes the convergence of ions with the maximum rate in the region where the ion-neutral collision frequency is equal to their cyclotron frequency. While demonstrating the presented theory by numerical simulations, HWM14 data is used for the meridional and zonal wind velocity profiles and the presence of its vertical component is assumed. In this case, in addition to the estimated ion drift velocity and its vertical changes, their initial distribution and ambipolar diffusion also determine the development of ion convergence/divergence processes. For a small magnitude of vertical wind velocity, its significant influence on the ions/electrons behavior is demonstrated, which shows the importance of both tidal wind and wind changes caused by the propagation of atmospheric gravity waves on the formation of the Es layer. In this theoretical research, it is shown that the realistic profile of the wind velocity, which takes into account the vertical component along with its zonal and meridional ones, is important for the prediction of the Es layers formation, as well as regions of ion depletion. Full article

Inverse Synthetic Aperture Radar (ISAR) is a promising technique for air target imaging and recognition. However, the traditional monostatic ISAR only can provide partial features of the observed target, which is a challenge for high-accuracy recognition. In this paper, to improve the recognition accuracy of air targets, we propose a novel recognition network based on multi-view ISAR imaging and fusion, called Multi-View Fusion Recognition network (MVFRnet). The main structure of MVFRnet consists of two components, the image fusion module and the target recognition module. The fusion module is used for multi-view ISAR data and image preprocessing and mainly performs imaging spatial match, image registration, and weighted fusion. The recognition network consists of the Skip Connect Unit and the Gated Channel Transformation (GCT) attention module, where the Skip Connect Unit ensures the extraction of global depth features of the image and the attention module enhances the perception of shallow contour features of the image. In addition, MVFRnet has a strong perception of image details and suppresses the effect of noise. Finally, simulated and real data are used to verify the effectiveness of the proposed scheme. Multi-view ISAR echoes of six types of aircraft are produced by electromagnetic simulation software. In addition, we also build a millimeter wave ground-based bistatic ISAR experiment system and collect multi-view data from an aircraft model. The simulation and experiment results demonstrate that the proposed scheme can obtain a higher recognition accuracy compared to other state-of-the-art methods. The recognition accuracy can be improved by approximately 30% compared with traditional monostatic recognition. Full article

Ensuring a robust and reliable evaluation of coma deepness and prognostication of neurological outcome is challenging. We propose to develop PET neuroimaging as a new diagnostic and prognosis tool for comatose patients using a recently published methodology to perform functional PET (fPET). This exam permits the quantification of task-specific changes in neuronal metabolism in a single session. The aim of this protocol is to determine whether task-specific changes in glucose metabolism during the acute phase of coma are able to predict recovery at 18 months. Participation will be proposed for all patients coming for a standard PET-CT in our center in order to evaluate global cerebral metabolism during the comatose state. Legally appointed representative consent will be obtained to slightly modify the exam protocol: (1) [18]F-fluorodeoxyglucose ([18]F-FDG) bolus plus continuous infusion instead of a simple bolus and (2) more time under camera to perform dynamic acquisition. Participants will undergo a 55-min fPET session with a 20% bolus + 80% infusion protocol. Two occurrences of three block (5-min rest, 10-min auditory stimulation and 10-min emotional auditory stimulation) will be performed after reaching equilibrium of FDG arterial concentration. We will compare the regional brain metabolism at rest and during the sessions of auditory and emotional auditory stimulation to search for a determinant of coma recovery (18 months of follow-up after the exam). Emotional auditory stimulation should induce an activation of: the auditory cortex, the consciousness areas and the neural circuitry for emotion (function to coma deepness). An activation analysis will be carried out to highlight regional brain activation using dedicated custom-made software based on Python statistical and image processing toolboxes. The association between activation levels and the Coma Recovery Scale-Revisited (CRS-R) will be assessed using multivariate analysis. If successful, the results from this study will help improve coma prognosis evaluation based on the pattern of neuronal metabolism at the onset of the pathology. The study protocol, rationale and methods are described in this paper. Full article

Structured extraction of emergency event information can effectively enhance the ability to respond to emergency events. This article focuses on extracting Chinese document-level emergency events, which entails addressing two key issues in this field: related datasets and the problem of role overlapping between candidate entities, which has been overlooked in existing DEE (document-level event extraction) studies that predominantly employed sequence annotation for candidate entity extraction. To tackle these challenges, we constructed a Chinese document-level emergency extraction dataset (CDEEE) and provides annotations for argument scattering, multiple events, and role overlapping. Additionally, a model named RODEE is proposed to address the role overlapping problem in DEE tasks. RODEE employs two independent modules to represent the head and tail positions of candidate entities, and utilizes a multiplication attention mechanism to interact between the two, generating a scoring matrix. Subsequently, role-overlapping candidate entities are predicted to facilitate the completion of DEE tasks. Experiments were conducted on our manually annotated dataset, CDEEE, and the results show that RODEE effectively solves the problem of role overlapping among candidate entities, resulting in improved performance of the DEE model, with an F1 value of 77.7%. Full article

We conduct a detailed study of the electromagnetic field produced by a massive point particle undergoing hyperbolic (uniformly accelerated) motion in Minkowski space-time. Starting from the Liénard–Wiechert solution and using a covariant notation, we obtain and analyse the main quantities that describe this field. We identify the space-time region to which this solution is restricted and write a solution valid in the whole of space-time. Finally, we verify that this solution satisfies Maxwell’s equations in the sense of distributions. Full article

One-dimensional spin crossover (SCO) solids that convert between the low spin (LS) and the high spin (HS) states are widely studied in the literature due to their diverse thermal and optical characteristics which allow obtaining many original behaviors, such as large thermal hysteresis, incomplete spin transitions, as multi-step spin transitions with self-organized states. In the present work, we investigate the thermal behaviors of a system of two elastically coupled 1D mononuclear chains, using the electro-elastic model, by including an elastic frustration in the nearest neighbors (nn) bond length distances of each chain. The chains are made of SCO sites that are coupled elastically through springs with their nn and next-nearest neighbors. The elastic interchain coupling includes diagonal springs, while the nn inter-chain distance is fixed to that of the high spin state. The model is solved using MC simulations, performed on the spin states and the lattice distortions. When we only frustrate the first chain, we found a strong effect on the thermal dependence of the HS fraction of the second chain, which displays an incomplete spin transition with a significantly lowered transition temperature. In the second step, we frustrate both chains by imposing different frustration rates. Here, we demonstrate that for high frustration values, the thermal dependence of the total HS fraction exhibits multi-step spin transitions. The careful examination of the spin state structures in the plateau regions showed the coexistence of special dimerized ferro–antiferro patterns of type LL-HH-LL-HH along the first chain and HH-LL-HH-LL (H=HS and L=LS) along the second one, revealing that the two chains are antiferro-elastically coupled. This type of spatial modulation of the spin state and bond length distances is very attractive because it anticipates the possible existence of periodic structures in 2D lattices, made of alternate 1D SCO strings with HLHLHL structures, coupled in the ferro-like fashion along the interchain direction. Full article

Battery life management is critical for plug-in hybrid electric vehicles (PHEVs) to prevent dangerous situations such as overcharging and over-discharging, which could cause thermal runaway. PHEVs have more complex operating conditions than EVs due to their dual energy sources. Therefore, the SOH estimation for PHEV vehicles needs to consider the specific operating characteristics of the PHEV and make calibrations accordingly. Firstly, we estimated the initial SOH by combining data-driven and empirical models. The data-driven method used was the incremental state of charge (SOC)-capacity method, and the empirical model was the Arrhenius model. This method can obtain the battery degradation trend and predict the SOH well in realistic applications. Then, according to the multiple characteristics of PHEV, we conducted a correlation analysis and selected the UF as the calibration factor because the UF has the highest correlation with SOH. Finally, we calibrated the parameters of the Arrhenius model using the UF in a fuzzy logic way, so that the calibrated fitting degradation trends could be closer to the true SOH. The proposed calibration method was verified by a PHEV dataset that included 11 vehicles. The experiment results show that the root mean square error (RMSE) of the SOH fitting after UF calibration can be decreased by 0.2–14% and that the coefficient of determination ($R^2$) for the calibrated fitting trends can be improved by 0.5–32%. This provides more reliable guidance for the safe management and operation of PHEV batteries. Full article

Seismic design in building construction is still new in Malaysia. Sabah, a Malaysian state, is situated southeast of the Eurasian Plate, between the highly active Philippine Sea Plate and Indo-Australian Plate, and has a history of earthquakes, with the largest measuring a magnitude of 6.3 (2015). Although small earthquakes occur annually, most old buildings in Sabah were built pre-code and designed without considering earthquake loadings. This study aimed to analyze the potential damage to buildings in Sabah based on their vulnerability to moderate earthquakes. More than 500 buildings in seven districts were evaluated using a quantitative method based on score assignment, within 100 kilometers of the epicenters. According to the findings, more than 160 buildings in the Kota Kinabalu and Kudat districts were assessed as vulnerable to Grade 4 damage. In Ranau, Kota Marudu, Tawau, Semporna, and Lahad Datu, most buildings had a Grade 3 damage potential, with some at Grade 2 or 4. This study’s findings provide a summary of the damage risk for structures in Sabah and offer a starting point for planning and developing safer buildings that can withstand local seismic conditions. The resulting building-grade damage map can be used as a reference for future damage mitigation measures. Full article

This paper proposes a novel WOA-based robust control scheme with two kinds of propagation latencies and external disturbance implemented in Software-Defined Wireless Networks (SDWNs) to maximize overall throughput and enhance the stability of the global network. Firstly, an adjustment model developed using the Additive-Increase Multiplicative-Decrease (AIMD) adjustment scheme with propagation latency in device-to-device paths and a closed-loop congestion control model with propagation latency in device–controller pairs are proposed, and the effect of channel competition from neighboring forwarding devices is analyzed. Subsequently, a robust congestion control model with two kinds of propagation latencies and external disturbance is established. Then, a new WOA-based scheduling strategy that considers each individual whale as a specific scheduling plan to allocate appropriate sending rates at the source side is presented to maximize the global network throughput. Afterward, the sufficient conditions are derived using Lyapunov–Krasovskii functionals and formulated using Linear Matrix Inequalities (LMIs). Finally, a numerical simulation is conducted to verify the effectiveness of this proposed scheme. Full article

Ice melting plays a crucial role in ocean circulation and global climate. Laboratory experiments were used to study the dynamic mechanisms of the influence of waves and currents on ice melting. The results showed that under near stable air temperature and water temperature conditions, the ice melting rate was significantly greater with waves than that without waves, as well as the higher the wave height, the greater the melting rate. This is related to the increase in the contact area between ice and water by waves. Further research was carried out to observe the flow field at different locations on the ice bottom, ice sides, and behind the ice by particle image velocimetry (PIV) and dyeing experiments. At different flow velocities, the changes in the side melting rate and bottom melting rate were not the same. Meltwater is attached to the bottom in the form of plume at low background flow velocity, which leads to the slowness of the heat exchange between the ice with a higher ambient temperature. Therefore, the melting of the ice bottom and the ice side were slower at low flow velocity. At high background flow velocity, there is strong shear instability and vortex at the ice bottom and behind the ice. The dissipation and mixing effects caused by vortices accelerate the melting of the ice bottom and the ice back. The thermodynamic factors, such as air temperature and water temperature, had significant impacts in the experiments. Further research needs to improve the accuracy of temperature control of experimental equipment. Computational fluid dynamics and sensitive tests of numerical simulation may also be carried out on ice melting. Full article

Point cloud registration is a critical problem in 3D vision tasks, and numerous learning-based point cloud registration methods have been proposed in recent years. However, a common issue with most of these methods is that their feature descriptors are rotation-sensitive, which makes them difficult to converge at large rotations. In this paper, we propose a new learning-based pipeline to address this issue, which can also handle partially overlapping 3D point clouds. Specifically, we employ rotation-invariant local features to guide the point matching task, and utilize a cross-attention mechanism to update the feature information between the two point clouds to predict the key points in the overlapping regions. Subsequently, we construct a feature matrix based on the features of the key points to solve the soft correspondences. Finally, we construct a non-learning correspondence constraint module that exploits the spatial geometric invariance of the point clouds after rotation and translation, as well as the compatibility between point pairs, to reject the wrong correspondences. To validate our approach, we conduct extensive experiments on ModelNet40. Our approach achieves better performance compared to other methods, especially in the presence of large rotations. Full article

This paper proposes a novel hardware design for a compact crypto processor devoted to elliptic-curve point multiplication over $GF\left(2^{233}\right)$. We focus on minimizing hardware usage, which we obtain using an iterative bit–serial finite field modular multiplier for polynomial coefficient multiplication. The same multiplier is also used for modular squares and inversion computations, further optimizing the hardware footprint. Our design offers flexibility by permitting users to load different curve parameters and secret keys while keeping a low-area hardware design. To efficiently generate the control signals, we utilize a finite-state-machine-based controller. We have implemented the proposed crypto processor on Virtex-6 and Virtex-7 FPGA devices, and we have evaluated its performance at clock frequencies of 100, 50, and 10 MHz. Specifically, for one point multiplication computation on Virtex-7 FPGA, our crypto processor uses 391 slices, attains a maximum frequency of 161 MHz, has a latency of 4.45 ms, and consumes 77 mW of power. These results, along with a comparison to state-of-the-art designs, clearly demonstrate the practicality of our crypto processor for applications requiring efficient and compact cryptographic computations. Full article

With the increasing utilization of intelligent mobile devices for online inspection of electrical equipment in smart grids, the limited computing power and storage capacity of these devices pose challenges for deploying large algorithm models, and it is also difficult to obtain a substantial number of images of electrical equipment in public. In this paper, we propose a novel distillation method that compresses the knowledge of teacher networks into a compact few-shot classification network, employing a global and local knowledge distillation strategy. Central to our method is exploiting the global and local relationships between the features exacted by the backbone of the teacher network and the student network. We compared our method with recent state-of-the-art (SOTA) methods on three public datasets, and we achieved superior performance. Additionally, we contribute a new dataset, namely, EEI-100, which is specifically designed for electrical equipment image classification. We validated our method on this dataset and demonstrated its exceptional prediction accuracy of 94.12% when utilizing only 5-shot images. Full article

As semi-airborne mineral exploration has a limited budget, it is critical to design experimental procedures that generate data to maximize desired information. We investigated the effects of transmitter–receiver geometries for a variety of anomalies and semi-airborne layouts. Our simulations indicated that flight line spacing of 200 m and a point distance of 100 m provides the optimal trade-off between coverage and survey progress for various targets. Based on the target size and distance between the transmitter and the target, the transmitter length should be at least equal to the length of the target. However, where the distance between the transmitter and the target is more than 1 km, the transmitter length should be at least two or three times the target size. Of similar importance are the location and direction of the transmitter cables, which can have a significant impact on the result of inversion and should be parallel to the target strike. By using more than one transmitter, better results can be obtained. If the strike of the target is known, transmitters should be parallel to each other, and if not, it is better to use perpendicular transmitters. The results of this study showed that the optimal distance between transmitters is 3 km. Our simulations showed that it is even possible to recover targets just below the transmitter in corresponding areas of masked data. Full article

The citrus systemic diseases, including citrus Huanglongbing (caused by Candidatus Liberibacter asiaticus (CLas)), citrus tristeza (caused by citrus tristeza virus (CTV)), citrus tatter leaf (caused by citrus tatter leaf virus (CTLV)), and citrus exocortis (caused by citrus exocortis viroid (CEVd)), are threats to citrus production in Taiwan. Reliable diagnostic methods are important for the management of these systemic diseases. In this study, we developed a multiplex reverse transcription–polymerase chain reaction (RT-PCR) assay to detect four pathogens simultaneously. Herein, the specific amplicons from each pathogen (295 bp for CLas, 468 bp for CTV, 120 bp for CTLV, and 196 bp for CEVd) were successfully produced using the optimized multiplex RT-PCR described here. The sensitivity evaluation showed that low titers of pathogens could be detected using this multiplex RT-PCR. Compared with the published simplex assays, the detection of field samples using the multiplex RT-PCR developed in this study showed a better performance. The detections using multiplex RT-PCR revealed that these four citrus systemic pathogens were commonly found in fields, and 30.0% of field samples were mix-infected. To our knowledge, this is the first study of a survey of the four important citrus systemic diseases in Taiwan, and it provides insights for improving disease management. Therefore, the multiplex RT-PCR assay provides a useful method for routine disease surveying and the production of pathogen-free citrus plants. Full article

Evergreen broad-leaved forests with rich biodiversity play a key role in stabilizing global vegetation productivity and maintaining land carbon sinks. However, quantitative and accurate classification results for humid, evergreen, broad-leaved forests (HEBF) and semi-humid evergreen broad-leaved forests (SEBF) with different vegetation productivity and significant differences in species composition are lacking. Remote sensing technology brings the possibility of vegetation subtype classification. Taking the mountainous evergreen broad-leaved forests distributed in Sichuan Province as an example, this study proposed a hierarchy-based classifier combined with environmental variables to quantitatively classify the two vegetation subtypes with different ecological characteristics but similar image features. Additionally, we applied Sun–Canopy–Sensor and C parameter(SCS + C) topographic correction to preprocess the images, effectively correcting the radiometric distortion and enhancing the accuracy of vegetation classification. Finally, achieving an overall accuracy (OA) of 87.91% and a Kappa coefficient of 0.76, which is higher than that of directly using the classifier to classify the two vegetation subtypes. The study revealed the widespread distribution of evergreen broad-leaved forests in Sichuan, with a clear boundary between the distribution areas of HEBF and SEBF. The HEBF in the east is located in the basin and the low marginal mountains; the SEBF is located in the southwest dry valley. The methods employed in this study offer an effective approach to vegetation classification in mountainous areas. The findings can provide guidance for ecological engineering construction, ecological protection, and agricultural and livestock development. Full article

Polydisperse particles are ubiquitous in both the natural and engineered environment, and the precise prediction of the transport and capture of polydisperse particles in a saturated medium is crucial. Several efforts (Yao model, RT model, TE model, MPFJ model, NG model, MHJ model, and MMS model) were developed to obtain accurate correlation equations for the particle capture probability (single-collector removal efficiency), but the applicability of the existing models to the entire porous medium and the retention characteristic of the polydisperse particles are still unclear. In this study, sand column experiments were undertaken to investigate the transport and capture processes of the polydisperse particles in the saturated medium. The mass density was employed to quantize the effects of particle polydispersity and incorporated into the depth-dependent deposition rate. The experimental results showed that the polydisperse particles formed a hyper-exponential retention profile even under favorable conditions (no repulsion). The excellent agreement between the results obtained from the MMS model and the experimentally observed results of the breakthrough curves (BTCs), as well as the retention profiles demonstrated the validation of the MMS model, as the correlation coefficient and the standard average relative error were 0.99 and 0.005, respectively. The hyper-exponential retention profile is caused by the uneven capture of the polydisperse particles by the porous medium. This study highlights the influences of particle polydispersity on particle transport and capture in a saturated porous medium. Full article

The inhomogeneity of microstructures and mechanical properties in an as-quenched thin specification NM450TP wear-resistant steel plate were quantitatively investigated. The results show that the microstructures exhibit inhomogeneous distribution through the thickness and the area percentage of martensite and ferrite grains varies regularly through the thickness, and the content of ferrite on the top surface of the plate is found to be two times that of ferrite at the core location and more than that of ferrite at the bottom surface. In addition, the steel plate exhibits the obvious anisotropy of tensile properties, the tensile strength paralleling to the rolling direction is lower than that along the transverse direction while the elongation paralleling to the rolling direction is better than that along the transverse direction. The result indicates that the deformation degree of prior austenite grains during hot rolling and the content of martensite after quenching dominate the mechanical properties while the ferrite content is not the main factor affecting the plasticity. The findings provide experimental evidences and lay a theoretical foundation for analyzing the subsequent processing. Full article

Polyampholyte (PA) hydrogels are randomly copolymerized from anionic and cationic monomers, showing good mechanical properties owing to the existence of numerous ionic bonds in the networks. However, relatively tough PA gels can be synthesized successfully only at high monomer concentrations (C_M), where relatively strong chain entanglements exist to stabilize the primary supramolecular networks. This study aims to toughen weak PA gels with relatively weak primary topological entanglements (at relatively low C_M) via a secondary equilibrium approach. According to this approach, an as-prepared PA gel is first dialyzed in a FeCl₃ solution to reach a swelling equilibrium and then dialyzed in sufficient deionized water to remove excess free ions to achieve a new equilibrium, resulting in the modified PA gels. It is proved that the modified PA gels are eventually constructed by both ionic and metal coordination bonds, which could synergistically enhance the chain interactions and enable the network toughening. Systematic studies indicate that both C_M and FeCl₃ concentration () influence the enhancement effectiveness of the modified PA gels, although all the gels could be dramatically enhanced. The mechanical properties of the modified PA gel could be optimized at C_M = 2.0 M and = 0.3 M, where the Young’s modulus, tensile fracture strength, and work of tension are improved by 1800%, 600%, and 820%, respectively, comparing to these of the original PA gel. By selecting a different PA gel system and diverse metal ions (i.e., Al³⁺, Mg²⁺, Ca²⁺), we further prove that the proposed approach is generally appliable. A theoretical model is used to understand the toughening mechanism. This work well extends the simple yet general approach for the toughening of weak PA gels with relatively weak chain entanglements. Full article

This paper presents a benchmarking survey on query expansion techniques for social media information retrieval, with a focus on comparing the performance of methods using semantic web technologies. The study evaluated query expansion techniques such as generative AI models and semantic matching algorithms and how they are integrated in a semantic framework. The evaluation was based on cosine similarity metrics, including the Discounted Cumulative Gain (DCG), Ideal Discounted Cumulative Gain (IDCG), and normalized Discounted Cumulative Gain (nDCG), as well as the Mean Average Precision (MAP). Additionally, the paper discusses the use of semantic web technologies as a component in a pipeline for building thematic knowledge graphs from retrieved social media data with extended ontologies integrated for the refugee crisis. The paper begins by introducing the importance of query expansion in information retrieval and the potential benefits of incorporating semantic web technologies. The study then presents the methodologies and outlines the specific procedures for each query expansion technique. The results of the evaluation are presented, as well as the rest semantic framework, and the best-performing technique was identified, which was the curie-001 generative AI model. Finally, the paper summarizes the main findings and suggests future research directions. Full article

We investigate the possibility of estimating global horizontal irradiance (GHI) in parallel to photovoltaic (PV) power production in India using a radiative transfer model (RTM) called libRadtran fed with satellite information on the cloud and aerosol conditions. For the assessment of PV energy production, we exploited one year’s (January–December 2018) ground-based real-time measurements of solar irradiation GHI via silicon irradiance sensors (Si sensor), along with cloud optical thickness (COT). The data used in this method was taken from two different sources, which are EUMETSAT’s Meteosat Second Generation (MSG) and aerosol optical depth (AOD) from Copernicus Atmospheric Monitoring Services (CAMS). The COT and AOD are used as the main input parameters to the RTM along with other ones (such as solar zenith angle, Ångström exponent, single scattering albedo, etc.) in order to simulate the GHI under all sky, clear (no clouds), and clear-clean (no clouds and no aerosols) conditions. This enabled us to quantify the cloud modification factor (CMF) and aerosol modification factor (AMF), respectively. Subsequently, the whole simulation is compared with the actual recorded data at four solar power plants, i.e., Kazaria Thanagazi, Kazaria Ceramics, Chopanki, and Bhiwadi in the Alwar district of Rajasthan state, India. The maximum monthly average attenuation due to the clouds and aerosols are 24.4% and 11.3%, respectively. The energy and economic impact of clouds and aerosols are presented in terms of energy loss (EL) and financial loss (FL). We found that the maximum EL in the year 2018 due to clouds and aerosols were 458 kWh m⁻² and 230 kWh m⁻², respectively, observed at Thanagazi location. The results of this study highlight the capabilities of Earth observations (EO), in terms not only of accuracy but also resolution, in precise quantification of atmospheric effect parameters. Simulations of PV energy production using EO data and techniques are therefore useful for real-time estimates of PV energy outputs and can improve energy management and production inspection. Success in such important venture, energy management, and production inspections will become much easier and more effective. Full article

A selection of dimeric Cu(II) complexes with bidentate N,N′ ligands with the general formula [Cu(L)(X)(μ-OH)]₂·nH₂O and [Cu(L)(μ-OH)]₂X₂·nH₂O were magneto-structurally analyzed using the Density Functional Theory (DFT). A Broken Symmetry-Density Functional Theory (BS-DFT) study was undertaken for these complexes with relevant decomposition schemes that gave insight into the effect of the nature of the ligand and coordination environment on the DFT-predicted coupling constants (J). The impact of the spin population, which correlates well with the Cu-O-Cu bridging angles and the calculated coupling constant (J) values, was studied. The models were further refined using a complete active space self-consistent field (CASSCF) while expanding the active space from 2 orbitals 2 electrons (2,2) to 10 orbitals 18 electrons (18,10). These models were approximated using multireference methods (n-electron valence state perturbation theory and difference dedicated configuration interaction), and a better approximation of J values was found as expected. Orbitals involved in the superexchange pathway were also visualized. Full article

This paper examines the effects of the Standard and Poor’s 500 (SP500) stock index crash during the global financial crisis and the COVID-19 pandemic periods on the South African top sector indices (basic materials, consumer goods, consumer services, financials, healthcare, industrials, technology, and telecommunication). The results of a copula-based BEKK-GARCH approach technique demonstrate the existence of price and volatility spillover during times of stock crashes. We discover that during a stock crisis, strong shocks and higher volatility spillover effects from the United States (U.S.) SP500 index to the top sector indices of the South African Johannesburg Stock Exchange (JSE) markets are more significant. However, there is no integrated economy, as the results did not show any spillover effects from South Africa to U.S. markets. Furthermore, the Gumbel copulas have higher dependence parameters, implying that extreme co-movements occur in the upper tails, suggesting the possibility of a large transmission of shocks from the SP500 to the eight top sector indices of the JSE and showing an asymmetric dependence between these markets. This result is important for investors willing to invest in the South African sector of equity markets to develop hedging strategies to prevent risk spillover from developed markets. Full article

The current trend in food consumption is toward convenience, i.e., fast food. The present work aims to study the potential of incorporating freeze-dried cooked chickpeas into a complex and traditional dish in Spanish gastronomy, such as Cocido, which has this legume as the main ingredient. Cocido is a two-course meal: a thin-noodle soup and a mix of chickpeas, several vegetables, and meat portions. The textural properties, sensory qualities, and rehydration kinetics of chickpeas of three Spanish varieties were investigated to select the most suitable cooking conditions to obtain freeze-dried chickpeas of easy rehydration whilst maintaining an adequate sensory quality for the preparation of the traditional dish. The sensory quality of various vegetables and meat portions, cooked under different conditions, was evaluated after freeze-drying and rehydration. It was possible to reproduce the sensory quality of the traditional dish after rehydration with water, heating to boiling in a microwave oven for 5 min, and resting for 10 min. Therefore, it is possible to commercialize complex dishes based on pulses and other cooked and freeze-dried ingredients as reconstituted meals with a wide nutrient profile. Nevertheless, additional research is required on the shelf life, together with other economic and marketing issues such as design of a proper packaging, that would allow consumption as a two-course meal. Full article

In this paper, we propose 2D dynamic visual servoing (Dynamic IBVS), where a quadrotor UAV tries to track a moving target using a single facing-down perspective camera. As an application, we propose the tracking of a car-type vehicle. In this case, data related to the altitude and the lateral angles have no importance for the visual system. Indeed, to perform the tracking, we only need to know the longitudinal displacements (along the $x$ and $y$ axes) and the orientation along the $z$-axis. However, those data are necessary for the quadrotor’s guidance problem. Thanks to the concept of differential flatness, we demonstrate that if we manage to extract the displacements according to the three axes and the orientation according to the yaw angle (the vertical axis) of the quadrotor, we can control all the other variables of the system. For this, we consider a camera equipped with a vertical stabilizer that keeps it in a vertical position during its movement (a gimbaled camera). Other specialized sensors measure information regarding altitude and lateral angles. In the case of classic 2D visual servoing, the elaboration of the kinematic torsor of the quadrotor in no way guarantees the physical realization of instructions, given that the quadrotor is an under-actuated system. Indeed, the setpoint has a dimension equal to six, while the quadrotor is controlled only by four inputs. In addition, the dynamics of a quadrotor are generally very fast, which requires a high-frequency control law. Furthermore, the complexity of the image processing stage can cause delays in motion control, which can lead to target loss. A new dynamic 2D visual servoing method (Dynamic IBVS) is proposed. This method makes it possible to generate in real time the necessary movements for the quadrotor in order to carry out the tracking of the target (vehicle) using a single point of this target as visual information. This point can represent the center of gravity of the target or any other part of it. A control by flatness has been proposed, which guarantees the controllability of the system and ensures the asymptotic convergence of the generated trajectory in the image plane. Numerical simulations are presented to show the effectiveness of the proposed control strategy. Full article

In recent years, synthetic aperture radar (SAR) has been a widely used data source in the remote sensing field due to its ability to work all day and in all weather conditions. Among SAR satellites, Sentinel-1 is frequently used to monitor large-scale ground objects. The Mekong Delta is a major agricultural region in Southeast Asia, so monitoring its cropland is of great importance. However, it is a challenge to distinguish cropland from other ground objects, such as aquaculture and wetland, in this region. To address this problem, the study proposes a statistical feature combination from the Sentinel-1 dual-polarimetric (dual-pol) data time series based on the m/χ decomposition method. Then the feature combination is put into the proposed Omni-dimensional Dynamic Convolution Residual Segmentation Model (ODCRS Model) of high fitting speed and classification accuracy to realize the cropland extraction of the Mekong Delta region. Experiments show that the ODCRS model achieves an overall accuracy of 93.85%, a MIoU of 88.04%, and a MPA of 93.70%. The extraction results show that our method can effectively distinguish cropland from aquaculture areas and wetlands. Full article