Channel Order Research Articles

Evaluation of Social Media Tools in Health Tourism Marketing with Multi Criteria Decision Making Methods

Today, countries are turning to alternative tourism types due to the destructive and corrosive effect of mass tourism. Various factors in the global market affect consumer preferences and shape their demands. This situation leads countries to seek alternative tourism types. While developed countries tend to focus more on sports tourism, the rising value of health tourism stands out for developing countries. The high costs of healthcare services in developed countries, waiting times, and the lack of many health services offered by insurance companies lead consumers to outsource their helthcare needs. Relationship marketing plays a key role in health tourism services, and consumers seek first-hand answers to their questions. The health sector's sensitive and complex structure informs and raises consumers' awareness on the axis of relational marketing dimensions. This study aims to via multi criteria decision making to determine and rank the social media channels used in health tourism marketing. While the criteria weights are determined by the Best-Worst Method, the priority order of social media channels is determined by COmbinative Distance-based ASsessment. Expert opinions were consulted during the criteria determination and the ranking of the alternatives. As a result of the analyses, the importance of social media is emphasised once again. Social media tools, especially through mobile application applications, shape the age with the audience it reaches, the distribution between regions, the time spent and the information transmitted. The need for socialisation accelerates the development of technology with the rapid orientation of people towards this field. In this case, youtube, which has gained momentum with the number of users and the use of visual and auditory elements together, holds the leadership in the reflection of relational marketing on health tourism today, as it creates space for all segments, even though information pollution is experienced.

Theoretical MRCI + Q study on electronic structure and spectroscopic properties of the HX+(X = F, Cl, Br, I) cations

In this work, we have systematacially investigated the electronic structure and spectroscopic properties of the HX+ (X = F, Cl, Br, I) cations by using the highly correlated MRCI + Q approach. This is the first comprehensive ab initio study on the electronic states of the HX+ cations. The spin–orbit coupling (SOC) effect is introduced with the state interaction approach. There are total 10 Λ-S and 18 Ω states obtained in the calculation. The results show that the potential energy curves’ (PECs) shapes and structure of the electronic states of different HX+ cations exhibit the significant distinction due to the X-dependent energy-level order of the unique dissociation channel H+(1Sg) + X(2Pu). From PECs, the spectroscopic constants of the bound electronic states are determined, which are in good agreement with available observed values. Regarding HBr+ and HI+, the good agreement has been achieved only when the SOC effect is considered. The predissociation for the first excited A2Σ+ state of HCl+, HBr+, and HI+ is analyzed based on computed spin–orbit matrix elements. Around the equilibrium position, the energy splittings of X2Π are calculated to be 288, 661, 2691, and 5137 cm−1 for HF+, HCl+, HBr+, and HI+, respectively. It has been demonstrated that SOC is substantial for HX+, leading to significant changes in PECs’ shapes as well as in electronic structure. Finally, the transition properties are predicted, including transition dipole moments, Franck–Condon factors, and radiative lifetimes. Both the Ω transitions A2Σ+ 1/2-X2Π3/2 and A2Σ+ 1/2-X2Π1/2 of the HX+ cations are determined to possess the radiative lifetimes at the microsecond (µs) level.

Research on Sound Source Localization of Multiple Fixed Targets Based on Machine Learning and Distributed Arrays

This paper studies a sound source localization method of multiple fixed targets based on machine learning and distributed arrays. In an outdoor open field, a three-line array was applied to collect array data and calculate latency characteristics. Then multiple classification models were established and trained. Finally, the locations of the sound source points were predicted by those models, in which the support vector machine (SVM), the nearest node (KNN), and the naive Bayesian model achieved 100% localization accuracy. Compared to the conventional method, this method has three significant advantages: First, it does not rely on the microphone channel order and does not need to be calibrated in advance, which simplifies the localization process; Second, it can fulfill high accuracy requirements, especially suitable for the scene of multiple fixed targets; Third, it has the advantage of incremental learning, as the times of localization rises, the training set is continuously enriched and the localization results become more precise.

Optimization with genetic algorythms of PVT system global efficiency

Photovoltaic (PV) solar panels generally produce electricity in the 6% to 12% efficiency range, the rest being dissipated in thermal losses. To recover this amount, hybrid photovoltaic thermal systems (PV/T) have been devised. These are devices that simultaneously convert solar energy into electricity and heat. A significant amount of research on PV/T collectors has been carried out over the last decade and water PV/T glazed flat plate collector systems turned out to be the most promising to develop (Zondag[1]). It is thus interesting to study the PV/T system as part of a closed loop single phase water CDU (coolant distribution unit) in laminar forced convection. In particular, the analysis was conducted on the optimal cooling performance of the thermal part, testing polynomial channel profiles of varying order (from zero to fourth) for channels of a real industrial module heat sink, under the following conditions: ideal flux of 1000 W/m² on one side, insulation on the opposite side, periodic conditions on the remaining sides, fully developed thermal and velocity profile in laminar flow of water. Through the use of a genetic algorithm, we have optimized the shape of the channel’s sidewalls in terms of heat transfer maximization. In terms of Nusselt number, results show that fourth order profiles are the most efficient. When limits to allowable pressure loss and module weight are introduced, these bring generally to a lower efficiency of the system than the unconstrained case.

A Deep MIMO Detector Based on MQAM Signal Decomposition and Channel Ordering

A Deep MIMO Detector Based on MQAM Signal Decomposition and Channel Ordering

Blind MMSE Equalization of SISO IIR Channels Using Oversampling and Multichannel Linear Prediction

In this correspondence, a novel linear prediction based blind equalization algorithm for Single Input Single Output (SISO) IIR channels is proposed. The new algorithm is based on second-order statistics and it does not require channel order estimation. By oversampling the channel output, the SISO channel model is converted to a special Single Input Multiple Output (SIMO) model. Two forward linear predictors with consecutive prediction steps are applied to the subchannel outputs of the SIMO model. It is proved that the partial parameters of the SIMO model can be estimated from the difference between the prediction errors when the length of the predictors is sufficiently large. The sufficient filter length for achieving the optimal prediction is also derived. Based on the estimated parameters, both batch and adaptive Minimum-Mean-Square-Error (MMSE) equalizers are developed. The performance of the proposed equalizers is evaluated by computer simulations and compared with existing algorithms.

A Partial Approximate Least Absolute Deviation-Based Identification Algorithm for a Multivariable Closed-Loop System with Spike Noise

Considering the stability and safety of industrial production, the open-loop system cannot meet the requirements of industrial production, so more and more attention has been paid to multivariable closed-loop system identification. This study focuses on the identification of a multivariate closed-loop system with spike noise in which the model order of the feedback channel is lower than that of the forward channel. Combining principal component analysis (PCA), which is used to eliminate the correlation between the data matrix elements, and the derivable approximate least absolute criterion function, partial approximate least absolute deviation (PALAD) for multivariable closed-loop system identification is proposed. By introducing a deterministic function to replace the absolute value in partial approximate least absolute deviation, the non-differentiable problem of the least absolute deviation function can be solved, and the identifiability of the multivariable closed-loop system in the case mentioned above is theoretically verified. Simulation experiments show the validity of the PALAD algorithm. Compared with the partial least squares (PLS) method, PALAD can effectively restrain the spike noise that follows an SαS distribution and shows stronger robustness when white and spike noises exist simultaneously.

Bit-Interleaved Multiple Access: Improved Fairness, Reliability, and Latency for Massive IoT Networks

Internet-of-Things (IoT) networks require massive connections in dense areas. Therefore, a resource efficient multiple access scheme seems inevitable to enable immense connectivity where multiple devices have to share the same resource block. Non-orthogonal multiple access (NOMA) has been considered as the strongest candidate in recent years. However, in this paper, by considering the practical implementation, we first provide a true power allocation (PA) constraint with finite alphabet inputs for conventional downlink NOMA and demonstrate that it cannot support massive connections in practical systems. To this end, we propose bit-interleaved multiple access (BIMA) scheme in downlink IoT networks. The proposed BIMA scheme implements bitwise multiaccess interleaving and deinterleaving at the transceiver ends and there are no strict PA constraints, unlike conventional NOMA, thus allowing a high number of devices in the same resource block. We provide a comprehensive analytical framework for BIMA by investigating all key performance indicators (KPIs) to present both information-theoretic (i.e., ergodic capacity EC and outage probability OP) and finite alphabet inputs (i.e., bit error rate BER) performance metrics with both instantaneous and statistical channel ordering. In addition, we define Jain’s fairness index and proportional fairness index in terms of all KPIs. Based on the extensive computer simulations, we reveal that BIMA outperforms conventional NOMA significantly, with a performance gain of up to 20-30 dB in terms of KPIs in some scenarios. In other words, compared to conventional NOMA schemes, the same KPIs are met in BIMA with 20-30 dB less transmit power, which is quite promising for energy-limited use cases. Moreover, this performance gain becomes greater when more IoT devices are supported. BIMA provides a full diversity order for all IoT devices and enables the implementation of an arbitrary number of devices and modulation orders, which is crucial for IoT networks where a huge number of devices should be supported in a single resource block in dense areas. In addition to the overall performance gain, BIMA guarantees a fairness system where none of the devices gets a severely degraded performance and the sum-rate is shared in a fair manner among devices. It guarantees QoS satisfaction for all devices. Finally, we provide an intense complexity and latency analysis for BIMA and demonstrate that it provides lower latency compared to conventional NOMA receivers, since it allows parallel computation at the receivers and no iterative operations are required. We show that compared to conventional NOMA receivers, BIMA reduces latency by up to 350% for specific IoT devices and 170% on average.

Toward the Advantages of Quantum Trajectories on Entanglement Distribution in Quantum Networks

Quantum mechanics allows an information carrier to traverse through multiple trajectories of communication channels simultaneously, this leads us to quantum trajectories where alternative causal orders of communication channels being traversed are in a superposition, so that the relative orders of communication channels become indefinite. It has been shown that entanglement distribution process over quantum trajectory with an EPR pair being prepared by a sender enables quantum teleportation process to be heralded as a noiseless communication process with probability, the heralded result of which is unachievable in classical trajectory with a definite causal order of communication channels. In this work, we investigate the potential advantage of quantum trajectory on the generation of link-level entanglement, the basic element of quantum networks. To this aim, the performance of entanglement distribution process over multiple quantum trajectories where an EPR pair is prepared by a communication provider is analyzed. Besides, the application of entanglement purification protocol on quantum trajectory is first analyzed to reduce the effect of noise on link-level entanglement. The analysis shows that the link-level entanglement generated over quantum trajectory is more robust to long-distance quantum communication, with respect to classical trajectory, which paves the way for designing effective quantum networks.

As predicted and more: modulated channel occupation in YZn5+x.

Like many complex intermetallic phases, the crystal structures of REZn5+x compounds (RE = lanthanide or Group 3 element) based on the EuMg5 type have gradually unfolded. The original reports described a complex hexagonal structure with an unusual combination of tetrahedrally close-packed regions and open spaces, as well as observations of superstructure reflections. More recently, we reinvestigated the structure of YZn5, reclassifying it as the EuMg5+x-type compound YZn5+x (x ≃ 0.2), in which disordered channels run along c through the spaces formerly considered open. In addition, DFT-chemical pressure (DFT-CP) analysis of ordered models of YZn5+x highlighted paths for communication between neighboring channels setting the stage for superstructure formation. Herein, the experimental elucidation of this effect is presented with the synthesis and structure determination of a modulated form of YZn5+x. By slow-cooling samples of YZn5+x from the annealing temperature, crystals were obtained that exhibit satellite reflections with the modulation wavevector q = {1\over 3}a* + {1\over 3}b* + 0.3041c*. Structure solution and refinement using a (3+1)D model in superspace group P31c({1\over 3}\,\!{1\over 3}σ3)00s reveals incommensurate order in the structure's channels. Here, two Zn sites associated with the channels are present, each with discontinuous atomic domains that are slanted in the x3x4 plane. Their slanting corresponds to adjustments along the c axis for the presence or absence of close neighbors along that axis, while the occupation patterns of neighboring channels are shifted by {1\over 3} of the modulation period. These features follow earlier predictions from CP analysis, highlighting how this approach can be used predictively in search of new phenomena.

Secrecy performance analysis of IRS-NOMA systems

In this paper, we propose to utilize an intelligent reflecting surface (IRS) as a promising technology to enhance the coverage and physical layer security of non-orthogonal multiple access (NOMA) system. In particular, an IRS-assisted NOMA system is considered with the aid of careful channel ordering of the NOMA users, in which the transmitter sends superposed signals to multiple legitimate users by virtue of the IRS in the presence of multiple eavesdroppers. Meanwhile, the secrecy performance of the IRS-assisted NOMA system is investigated under two wiretapping cases: non-colluding and colluding external eavesdroppers. In the non-colluding case, eavesdroppers operate independently, while in the colluding case, every eavesdroppers can combine their observations to decode the messages. To this end, we derive the approximate closed-form expressions for the secrecy outage probability (SOP) and the asymptotic SOP for each wiretapping case. Also, we assume that the phase of the IRS elements is set by using the ON–OFF control method. Based on analytical results, we show that the secrecy diversity order of the IRS-NOMA at legitimate users is in connection with the number of reflecting elements. From the numerical results, it can be seen that the IRS-NOMA can achieve superior secrecy performance with increasing the number of reflecting elements of the IRS. However, we also find out that using the finite ON state reflective elements can improve the secrecy performance. Actually, increasing the number of ON state reflective elements above five has a negative effect on the system’s secrecy performance.

Dual-Channel Feature Enhanced Collaborative Filtering Recommendation Algorithm

The dual-channel graph collaborative filtering recommendation algorithm (DCCF) suppresses the over-smoothing problem and overcomes the problem of expansion in local structures only in graph collaborative filtering. However, DCCF has the following problems: the fixed threshold of transfer probability leads to a decrease in filtering effect of neighborhood information; the K-means clustering algorithm is prone to trapping clustering results into local optima, resulting in incomplete global interaction graphs; and the impact of time factors on the predicted results was not considered. To solve these problems, a dual-channel feature enhanced collaborative filtering recommendation algorithm (DCFECF) is proposed. Firstly, the self-attention mechanism and weighted average method are used to calculate the threshold of neighborhood transition probability for each order in local convolutional channels; secondly, the K-means++ clustering algorithm is used to determine the clustering center in the global convolutional channel, and the fuzzy C-means clustering algorithm is used for clustering to solve the local optimal problem; then, time factor is introduced to further improve predicted results, making them more accurate. Comparative experiments using normalized discounted cumulative gain (NDCG) and recall as evaluation metrics on three publicly available datasets showed that DCFECF improved by up to 2.3% and 4.1% on two metrics compared to DCCF.

A new method to constrain annihilating dark matter

ABSTRACT Recent indirect searches of dark matter using gamma-ray, radio, and cosmic ray data have provided some stringent constraints on annihilating dark matter. In this article, we propose a new indirect method to constrain annihilating dark matter. By using the data of the G2 cloud near the Galactic supermassive black hole Sgr A*, we can get stringent constraints on the parameter space of dark matter mass and the annihilation cross-section, especially for the non-leptophilic annihilation channels $b\bar{b}$ and W±. For the thermal annihilation cross-section, the lower bounds of dark matter mass can be constrained up to TeV order for the non-leptophilic channels with the standard spike index γsp = 7/3.

Semi-Blind structured subspace method for signal estimation in nonlinear convoluted mixture

This work proposes a novel semi-blind structured subspace signal estimation approach for nonlinear single-input multiple-output (SIMO) systems. Here, in this work, a quadratic nonlinearity is considered in the analysis. The proposed approach exploits the Toeplitz structure present in a convolutive communication model together with pilot signals in a semi-blind scheme. It directly estimates the signal with no channel estimation step, which saves the extra computational cost needed when considering a two steps approach (i.e., channel identification then equalization). The simulation findings show that the proposed method is resilient to channel order overestimation errors and allows achieving good signal restoral quality with only few pilot symbols, outperforming the pilot-based approach in such a context.

Multidimensional network of polypyrrole nanotubes loaded with ZIF-67 to construct multiple proton transport channels in composite proton exchange membranes for fuel cells

Due to the designability of their proton transport channels, high-performance long-lasting composite proton exchange membranes (PEMs) are currently the subject of extensive research. However, the compatibility and channel order of the internal components of the composite membranes are still challenging. In this work, hollow polypyrrole (PPy) nanotube structures were obtained to provide a nitrogen source and to act as a skeleton to confine and separate cobalt nanoparticles on the surface of PPy nanotubes. Finally, zeolitic imidazolate framework material-67 (ZIF-67) was attached to the surface. By using this method, PPy@ZIF-67 filler can minimize the particle size and inhibit Co2+ ions from aggregating, thus constructing a reasonably distributed transport channel and improving the proton transport capacity. As a result, the synthesized polymer nanotubes loaded metal-organic framework (MOF) nanofiber network can enhance the physicochemical properties and stability of the membrane by providing a more extensive interfacial interaction. In addition, the composite membrane has excellent ionic conductivity and power density, reaching 233.7 mS cm–1 and 837 mW cm–2 at 80 °C and 100% humidity. It indicates that the nanofibrous MOF structure not only improves the compatibility with the substrate but also provides sufficient leap points for proton transport via the interfacial conduction pathway between the PPy@ZIF-67 filler and the substrate, thus allowing the resulting composite membrane to facilitate proton transfer via the Vehicle and Grotthuss mechanisms synergistically.

Remote creation of quantum coherence via indefinite causal order

Quantum coherence is a prime resource in quantum computing and quantum communication. Quantum coherence of an arbitrary qubit state can be created at a remote location using maximally entangled state, local operation and classical communication. However, if there is a noisy channel acting on one side of the shared resource, then, it is not possible to create perfect quantum coherence remotely. Here, we present a method for the creation of quantum coherence at a remote location via the use of entangled state and indefinite causal order. We show this specifically for the superposition of two completely depolarizing channels, two partially depolarizing channels and one completely depolarizing channel along with a unitary operator. We find that when the indefinite causal order of channels act on one-half of the entangled pair, then the shared state looses entanglement, but can retain non-zero quantum discord. This finding may have some interesting applications on its own where discord can be consumed as a resource. Our results suggest that the indefinite causal order along with a tiny amount of quantum discord can act as a resource in creating non-zero quantum coherence in the absence of entanglement.

Epileptic seizure detection by using interpretable machine learning models

Objective. Accurate detection of epileptic seizures using electroencephalogram (EEG) data is essential for epilepsy diagnosis, but the visual diagnostic process for clinical experts is a time-consuming task. To improve efficiency, some seizure detection methods have been proposed. Regardless of traditional or machine learning methods, the results identify only seizures and non-seizures. Our goal is not only to detect seizures but also to explain the basis for detection and provide reference information to clinical experts. Approach. In this study, we follow the visual diagnosis mechanism used by clinical experts that directly processes plotted EEG image data and apply some commonly used models of LeNet, VGG, deep residual network (ResNet), and vision transformer (ViT) to the EEG image classification task. Before using these models, we propose a data augmentation method using random channel ordering (RCO), which adjusts the channel order to generate new images. The Gradient-weighted class activation mapping (Grad-CAM) and attention layer methods are used to interpret the models. Main results. The RCO method can balance the dataset in seizure and non-seizure classes. The models achieved good performance in the seizure detection task. Moreover, the Grad-CAM and attention layer methods explained the detection basis of the model very well and calculate a value that measures the seizure degree. Significance. Processing EEG data in the form of images can flexibility to use a variety of machine learning models. The imbalance problem that exists widely in clinical practice is well solved by the RCO method. Since the method follows the visual diagnosis mechanism of clinical experts, the model interpretation results can be presented to clinical experts intuitively, and the quantitative information provided by the model is also a good diagnostic reference.

Higgs boson decay into gluons in a 4D regularization: IR cancellation without evanescent fields to NLO

Higgs decay using an effective Higgs–Yang-Mills interaction in terms of a dimension five operator as well as usual QCD interactions is revisited in the context of Implicit Regularization (IReg) and compared with conventional dimensional regularization (CDR), four dimensional helicity (FDH) and dimensional reduction (DRED) schemes. The decay rate for H rightarrow gg(g) is calculated in this strictly four-dimensional set-up to alpha _s^3 order in the strong coupling. Moreover we include joint processes that contribute at the same perturbative order in the real emission channels consisting of 3 gluons as well as gluon quark–antiquark final states with light (zero mass) quarks. Unambiguous identification and separation of UV from IR divergences is achieved putting at work the renormalization group scale relation inherent to the method. UV singularities are removed as usual by renormalization, the IR divergences are cancelled due to the method’s compliance with the Kinoshita–Lee–Nauenberg (KLN) theorem. Most importantly, we verify that no evanescent fields such as epsilon -scalars need be introduced as required by some mixed regularizations that operate partially in the physical dimension.

A Liquid Crystal Ionomer-Type Electrolyte toward Ordering-Induced Regulation for Highly Reversible Zinc Ion Battery.

Novel electrolyte is being pursued toward exploring Zn chemistry in zinc ion batteries. Here, a fluorine-free liquid crystal (LC) ionomer-type zinc electrolyte is presented, achieving simultaneous regulated water activity and long-range ordering of conduction channels and SEI. Distinct from water network or local ordering in current advances, long-range ordering of layered water channels is realized. Via manipulating water activity, conductivities range from ≈0.34 to 15 mS cm-1 , and electrochemical window can be tuned from ≈2.3-4.3V. The Zn|Zn symmetric cell with LC gel exhibits highly reversible Zn stripping/plating at 5mA cm-2 and 5 mAh cm-2 for 800h, with retained ordering of water channels. The capability of gel for inducing in situ formation of long-range ordered layer SEI associated with alkylbenzene sulfonate anion is uncovered. V2 O5 /Zn cell with the gel shows much improved cycling stability comparing to conventional zinc electrolytes, where the preserved structure of V2 O5 is associated with the efficiently stabilized Zn anode by the gel. Via long-range ordering-induced regulation on ion transport, electrochemical stability, and interfacial reaction, the development of LC electrolyte provides a pathway toward advancing aqueous rechargeable batteries.

Joint Identification for Number of Transmit Antennas and Channel Order

Joint Identification for Number of Transmit Antennas and Channel Order