Multichannel Structures Research Articles

Optimizing Multi-Channel Green Supply Chain Dynamics with Renewable Energy Integration and Emissions Reduction

In response to the global imperative of mitigating greenhouse gas emissions (GHGs) and the shifting landscape of business models toward multi-channel structures, this study delves into the intricacies of a green supply chain. Operating through both online and traditional channels with uncertain demands, the producer’s distribution strategy prompts an exploration of supply chain dynamics. Utilizing an integer programming model, this study calculates optimal prices, optimizes total profit, and minimizes transportation costs to curtail carbon dioxide emissions, depending on the transportation mode. Additionally, this study incorporates renewable energy sources into the production and transportation processes to further minimize carbon dioxide emissions. The integration of renewable energy not only supports environmental goals, but also contributes to the overall profitability of the supply chain by reducing energy costs. Employing a theoretical technique for linearization, the model, resolved through the Jimenez and TH methods, demonstrates efficacy in reconciling economic and environmental goals. The Jimenez method enables the transformation of fuzzy parameters into deterministic equivalents, allowing for a more reliable optimization during uncertainty, while the TH method provides an interactive fuzzy multi-objective approach, aligning the model’s dual objectives for both economic and environmental goals. Notably, when transportation costs to both markets are equal, the model prioritizes devices with a lower environmental impact, showcasing adaptability. Furthermore, the proposed solution empowers decision makers to influence pricing and enhance the entire supply chain’s profitability. In conclusion, this research offers nuanced insights, strategically aligning economic viability with environmental sustainability in the discourse on green supply chains.

Enhancing multi-channel electrode structures in ceramic fuel cells: Influence of channel geometry on mass transport and electrochemical performance

Solid oxide fuel cell (SOFC) is a promising electrochemical power generation technology that contributes to the decarbonization of the energy field. The electrode-supported design enables the use of thinner electrolytes, but inevitably brings additional gas diffusion resistance, which in turn leads to higher anodic concentration polarization. Therefore, additional structural design of the electrodes is necessary to ensure efficient operation and mass transport resistance minimization. In this study, anode-supported micro-tubular SOFCs with different multi-channel geometries have been attempted, during which samples with 4 channels exhibit unique elliptical structure due to meticulous manipulation. In addition, the 4-channel samples have been compared to the single-channel and 7-channel counterparts, exploring the influential mechanism of the anode micro-structure on gas transfer and electrochemical performances. The study found that the 4-channel cell has the best geometric controllability, with its minimum gas diffusion path reduced to below 50 μm, such thin electrochemical active region (EAR) accounts for >50 % of the entire external surface. Efficient utilization of the geometric surface area considerably facilitates the fuel transport to reach anode/electrolyte interface. By enhancing the mass transport efficiency, the maximum power density of the 4-channel cell reached 1.40 W cm−2 (750 °C), which is 22.8 % and 102.9 % higher than that of the 7-channel and single-channel cells under the same conditions. The highly efficient anode design additionally exhibited exceptional structural integrity and mechanical robustness, thereby ensuring the steady operation of the cell.

Pulsed Sputtering Epitaxy of Coherent AlN/Al0.5Ga0.5N/AlN Multichannel Structures

In this article, the advantages of AlN low‐temperature interface protective layer (LT‐IPL) prepared by sputtering epitaxy for the fabrication of AlN/Al0.5Ga0.5N/AlN multichannel device structures are discussed. In this study, it is found that the use of LT‐IPL enhances the AlN/AlGaN interface surface morphology and reduces AlN/Al0.5Ga0.5N/AlN multichannel structure misfit dislocations. This approach results in a coherent four‐channel AlN/AlGaN/AlN structure with considerably improved crystallinity. Moreover, consistent with predictions from 1D simulations, reductions in sheet resistance are inversely proportional to the number of channels. These advancements are pivotal for the development of sophisticated AlN/AlGaN/AlN multichannel structures, which are tailored for applications that require low resistance and high voltage.

The best structures of LC columns—A theoretical perspective

The largest peak capacity (n) that LC analysis can generate in isocratic or gradient elution analysis of a given sample in a given time at a given pressure is proportional to the quality factor (qmax) of its column structure. In this study, the multi-channel structures with open pseudo-planar channels (OPPC) and open circular channels (OCC) where compared with PC2 – a typical core-shell column packed with 2 μm particles. These columns have qmax of 1.27, 1.17 and 0.41, respectively. The former two qmax are the highest among all known column structures – about 3 times higher than qmax of PC2. This means that the OPPC and OCC can generate about 3 times higher n compared to what a PC2 can in the same analysis time (tanal) at the same pressure, or they require about 81 times shorter tanal (81 is the 4th power of 3) to generate the same n as a PC2 can at the same pressure. However, while PC2 is a commercially available column, there are substantial challenges in manufacturing the OPPC and OCC that can compete with PC2 in practical applications. In order to be competitive with PC2, the OPPC and OCC should have sub-1μm characteristic dimensions (e.g., the inter-pillar distance, g, in OPPC-based pillar array columns, internal diameters of OCC). Thus, in order to compete with PC2 in one scenario, an OPPC requires g ≤ 0.14 μm. Additionally, to be competitive with PC2, OPPC and OCC should be able to sustain the same high pressure. Highlighting the challenges of their design and manufacturing might help to develop the manufacturable columns substantially superior to the packed ones.

From asymmetry to symmetry: Biomimetic multi-channel structures for enhanced desalination using co-dissolving porogens

The asymmetric structures constructed via traditional methods fundamentally determine the “trade-off” bottleneck of cellulose triacetate reverse osmosis (RO) membrane in desalination. So, based on the structure-determined performance, inspired by the pore mechanism of frozen tofu through “water-ice-water” phase transition, the biomimetic RO membrane with high symmetry and homogeneity was successfully utilized in desalination. The co-solubilized N-methyl-2-pyrrolidone (NMP) narrowed the structural differentiation between Top and Bottom, increasing the number of through-water channels, surface hydrophilicity, and flatness of RO membrane. Typically, the prepared membrane with 0.125% NMP concentration has a salt rejection rate of 98.9% for NaCl, and excellent retention rate for other inorganic salts (MgCl2, LiCl). Notably, the NMP-modified membrane has a water flux of up to 17.2 L m−2 h−1, which is nearly 660% higher than that of No porogen-RO membrane (2.6 L m−2 h−1). Further, the membrane retained its separation capacity even after continued operation for 700 min, owing to the enhanced compression of the ductile pores by NMP. Overall, this novel cellulose-based RO membrane represents a structural mechanism for the design of innovative biomimetic multi-channel structures for long-term and efficient desalination.

Resonance prediction and inverse design of multi-core selective couplers based on neural networks.

Resonance analysis and structural optimization of multi-channel selective fiber couplers currently rely on numerical simulation and manual trial and error, which is very repetitive and time consuming. To realize fast and accurate resonance analysis and calculation, we start with dual-core structures and establish forward classification and regression neural networks to classify and predict different resonance properties, including resonance types, operating wavelength, coupling coefficient, coupling length, 3dB bandwidth, and conversion efficiency. The pre-trained forward neural networks for dual-core fibers can also realize accurate and fast prediction for multi-core fibers if the mode energy exchange occurs only between one surrounding core and the central core. For the inverse design, a tandem neural network has been constructed by cascading the pre-trained forward neural network and the inverse network to solve the non-uniqueness problem and provide an approach to search for appropriate and desired multi-core structures. The proposed forward and inverse neural networks are efficient and accurate, which provides great convenience for resonance analysis and structural optimization of multi-channel fiber structures and devices.

A universal approach to complete control in multi-channel optical structures

The photon spin Hall effect can lead to the more general question of how to control all the light waves in a muti-channel structure (so-called complete control). To address this problem in general and thoroughly, a theoretical approach was established in this paper. Guided by this approach, the complete controls in several complicated structures are realized theoretically or numerically. We also represent this approach in the form of representation theory. The important works reported recently in this area were explained by this theory.

The effect of inside sales and hybrid sales structures on customer value creation

As business-to-business (B2B) sales become digital and multichannel, firms develop inside sales (IS) channels to serve customers and combine such channels with outside sales (OS) and distributor channels in new sales structures. Although IS may offer gains, extant research lacks empirical evidence on their effectiveness in customer value creation when used independently – discrete structures – and in combination with OS and distributors – hybrid structures. This study aims to assess the value creation performance of IS in discrete and hybrid structures. Results from regression estimations of 13,210 B2B customers over two years in a single-firm research design unveil how IS increase value creation when used independently; yet, using IS together with OS or distributors in hybrid structures, decreases performance. IS are more effective in historically less valuable customers and less complex sales processes. This study advances knowledge on multichannel IS structures’ effectiveness in value creation, helping sales managers allocating resources across channels.

Improvement of sciatic nerve regeneration by multichannel nanofibrous membrane-embedded electro-conductive conduits functionalized with laminin

Multichannel structures in the design of nerve conduits offer potential advantages for regeneration of damaged nerves. However, lack of biochemical cues and electrical stimulation could hamper satisfactory nerve regeneration. The aim of this study was to simultaneously evaluate the effects of topographical, biological, and electrical cues on sciatic nerve regeneration. Accordingly, a series of multichannel nerve conduit was made using longitudinally-aligned laminin-coated poly (lactic-co-glycolic acid) (PLGA)/carbon nanotubes (CNT) nanofibers (NF, mean diameter: 455 ± 362 nm) in the lumen and randomly-oriented polycaprolactone (PCL) NF (mean diameter: 340 ± 200 nm) on the outer surface. In vitro studies revealed that the materials were nontoxic and able to promote cell attachment and proliferation on nanofibers and on fibrin gel. To determine the influence of laminin as biological and CNT as electrical cues on nerve regeneration, either of hollow PCL conduits, PLGA NF-embedded, PLGA/CNT NF-embedded or laminin-coated PLGA/CNT NF-embedded PCL conduits were implanted in rats. A new surgery method was utilized and results were compared with an autograft. The results of motor and sensory tests in addition to histopathological examination of the regenerated nerves demonstrated the formation of nerve fibers in laminin-coated PLGA/CNT NF-embedded PCL conduits. Results suggested that these conduits have the potential to improve sciatic nerve regeneration.Graphical abstract

Numerical and experimental study of the size effect on deformation behavior and quality of microembossed multi-channel structures

With an uptrend of product miniaturization and precision manufacturing, the microembossing process of sheet metal has been widely used, and the corresponding researches have been conducted from the aspects of both experimental and theoretical explorations. Many unknowns, however, remain, such as size effect and it-induced scatters of deformation behaviors and product qualities. In this research, the multi-channel structures with various thicknesses made by a microembossing system using pure copper sheets were employed as case studies. The crystal plasticity finite element method simulations and experiments were conducted to study the effects of grain size and sheet thickness on the process and product of microembossing in terms of load-stroke relation, dimensional accuracy, and surface quality. From both the experimental and numerical results, it is revealed that the grain size effect causes deviated load and non-uniformity in deformation. The dimensional accuracy designated by the variations in thickness and height is affected by both grain size and sheet thickness. As grain size increases, the thickness reduction becomes more dramatic, and the thinnest location is at the corner zone. The height of the channel bottom increases with grain size, while the peak height fluctuation due to grain size is relatively minor. In addition, various surface defects on the surface of the embossed multi-channel structure were observed, including microvoids and micro scratches on the sidewalls, and wrinkles and grooves on the peak and bottom surfaces. From the inner to outer of the structures, microvoids gradually change to micro scratches, which become denser and longer as grain size increases in 100 μm thick copper sheets and also more severe with the increasing sheet thickness. All of these enrich the understanding of sheet metal microembossing and facilitate design solution generation in the optimization of the microembossing process and the quality of the embossed structures.

Intrinsic Polarization Super Junctions: Design of Single and Multichannel GaN Structures

Super junctions (SJs) have enabled unprecedented performance in Silicon power devices, which could be further improved by applying this concept to wide bandgap semiconductors like gallium nitride (GaN). Currently, polarization super junctions (PSJs) are the most promising candidates for GaN SJs. Yet, until now, p-type doping of the GaN cap layer was required to ensure the presence of a 2-D hole gas (2DHG), and the proper charge matching between the 2DHG and 2-D electron gas (2DEG), which is fundamental to the operation of SJs. This approach, however, requires precise control of the p-GaN doping level, which is very challenging and has hindered the demonstration of high-performance PSJs. Besides, while PSJs are particularly promising for multichannel structures aimed at reducing the sheet resistance, achieving proper charge matching combined with large electron density for all the buried channels is challenging. Here, we propose a simple and robust platform for intrinsic PSJs (i-PSJs) that enables excellent charge matching for a wide range of structures, without relying on doping. We show that surface donor states are the origin of charge mismatch and provide a strategy to minimize their impact. Simulated devices based on this structure show optimal carrier depletion with a flat electric field profile in the whole drift region. Finally, we extend this concept to multichannel i-PSJ structures. We demonstrate a much-reduced sheet resistance down to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$58 \Omega $ </tex-math></inline-formula> /sq and present a robust strategy to achieve charge balance, which enables reducing the ON-resistance without degrading the OFF-state performance, thus greatly improving the device figure-of-merit.

Lignin-based multi-scale cellular aerogels assembled from co-electrospun nanofibers for oil/water separation and energy storage

Fibrous aerogels have received considerable attentions owing to their high specific surface area and multifunctional applications. However, it remains a huge challenge to prepare multi-structured nanofibrous aerogels using green renewable materials. Herein, lignin-based multi-scale cellular aerogels assembled from co-electrospun nanofibers are successfully constructed. Primarily, by preoxidation of core–shell nanofibers assembled uncrosslinked aerogels, lignin-based pre-oxidized aerogels (LPA) with hierarchically natural wood-tracheid-like structures and porous cell walls are prepared. The LPA exhibits excellent hydrophobicity and high adsorptive capacity for diverse oils and organic solvents (adsorption capacity up to 103 g g−1). Furthermore, benefiting from the existence of nitrogen functional groups, LPA can be carbonized to nitrogen-doped carbon aerogels (NCA). The obtained NCA not only inherits cellular structures from LPA but also creates abundant interior porosity of nanofibers due to the vacancy left by degradation of polymer. The NCA is used as electrodes materials of supercapacitors, the multi-channel structures at micro/nano scales of NCA can serve as “highway” for fast transportation of ions and electrons. The symmetrical supercapacitor assembled from it delivers a high energy density of 32 Wh kg−1. Our study provides a new insight for designing and synthesizing biomass-based multifunctional materials through a low carbon footprint manner.

GaN-based Semiconductor devices with Multichannel Structures

Semiconductor junctions, for example, pn-intersection and Schottky intersection have been very significant as essential components for different semiconductor gadgets. The break-down voltage of the junction PN-intersection relies upon the polluting influence fixations in semiconductors. An opposite voltage is supported in the exhaustion layer thickness which is corresponding to the square of base voltage. The breakdown happens, when the electric field with an intersection arrives at the semiconductor regardless of whether the consumption layer can be extended substantially. To accomplish higher go down voltage, a wide band hole semiconductor, for example, silicon carbide has been examined. The electron gadgets utilizing silicon carbide may undergo serious issues. In any case, the breakdown system of the intersection utilizing silicon carbide is as yet equivalent to that of the conventional junctions. As of late, GaN utilized electron gadgets have developed as cutting-edge high force exchanging gadgets inferable from their high breakdown field. A few papers have revealed that GaN-based HFETs is directly expanded by expanding the junction dividing between the door and channel electrodes. This conduct is unique in relation to that of the customary junctions.

PASSIVE HEATING SYSTEM OF A RESIDENTIAL BUILDING

In this article, we will talk about the passive heating system applied to residential buildings in rural areas. The air heating system is used as a heating surface in buildings with multi-channel structures in the fencing structures (external wall, floor and roof). In these cases, the author considers the issues of the first and second conditions of temperature comfort, taking into account the temperature of the heating surface.

A discrete-time Bayesian network approach for reliability analysis of dynamic systems with common cause failures

The dynamic and dependant behaviors are typical characteristics of modern complex systems, whose reliability is often improved through the design of multichannel parallel structures. The existence of common cause failures (CCFs) has a significant impact on system reliability. A reliability analysis model is proposed for dynamic systems with CCFs based on discrete-time Bayesian networks (DTBNs). The system operating time is dispersed into several time intervals, and the component failures are divided into independent and CCF states. Dynamic systems with cold and warm spare parts are examined to determine the modelling methodology and conditional probability tables (CPTs) of Bayesian network (BN) nodes. The reliability calculation is realised through the Bayesian inference mechanism. The model is applied to the CCF analysis and fault diagnosis of a digital safety-level distributed control system (DCS) of nuclear power plants (NPPs) to prove the effectiveness and feasibility of the method.

Designs, Experiments, and Applications of Multichannel Structures for Hearing Aids

The structures of common multichannel processing for hearing aids include equal bandwidth (BW) finite impulse response (FIR) filter bank, nonuniform BW FIR filter bank, and fast Fourier transform (FFT) plus inverse FFT (IFFT). This paper analyzes their operation principles, indicates the design methods by means of MATLAB R2018b resources, and describes the main characteristics: synthetical ripple, bank filters’ group delays, and individual filter sidelobe attenuation. Three schemes are proposed: equal BW sixteen-filter bank, logarithmic BW eight-filter bank, and 128-point FFT plus IFFT with overlap-add operation. To build the experimental modules, we introduce the settings of spectrum scopes, the acquirement of realistic speech and noises, and the gain enhancing/reducing needs of hearing aid features; the characteristics of synthetical outputs confirm precise control ability of the multichannel modules and differences between the three schemes. Subsequently, this paper illustrates two applications of the multichannel structures in hearing aids, the equal BW sixteen-filter bank with spectral subtraction (SS) for an artificial intelligence (AI) noise reduction (NR) and 128-point FFT plus IFFT spectral distortion removal for a directional microphone (DM). In Amy’s speech mixed with ringing, milk steamer, and strong wind noises separately, the SS processor improves signal-noise-ratio (SNR) by 6.5 to 15.9 dB. By measuring waveforms and spectra at the DM input and output, the DM system seamlessly removes the spectral distortion.

Complementary Feature Extractions for Event Identification in Power Systems Using Multi-Channel Convolutional Neural Network

This paper presents an event identification process in complementary feature extractions via convolutional neural network (CNN)-based event classification. The CNN is a suitable deep learning technique for addressing the two-dimensional power system data as it directly derives information from a measurement signal database instead of modeling transient phenomena, where the measured synchrophasor data in the power systems are allocated by time and space domains. The dynamic signatures in phasor measurement unit (PMU) signals are analyzed based on the starting point of the subtransient signals, as well as the fluctuation signature in the transient signal. For fast decision and protective operations, the use of narrow band time window is recommended to reduce the acquisition delay, where a wide time window provides high accuracy due to the use of large amounts of data. In this study, two separate data preprocessing methods and multichannel CNN structures are constructed to provide validation, as well as the fast decision in successive event conditions. The decision result includes information pertaining to various event types and locations based on various time delays for the protective operation. Finally, this work verifies the event identification method through a case study and analyzes the effects of successive events in addition to classification accuracy.

Three-dimensional breast tumor segmentation on DCE-MRI with a multilabel attention-guided joint-phase-learning network.

Accurate breast and tumor segmentations from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is vital in breast disease diagnosis. Here, we propose a novel attention-guided joint-phase-learning network for multilabel segmentation including the breast and tumors simultaneously and automatically. Instead of common multichannel inputs, our novel network consists of five separated streams designed for extracting comprehensive features for each DCE-MRI phase to fully use the dynamic intensity of enhanced images. A new time-signal intensity map was designed based on the DCE-MRI pixel-by-pixel values and added as an additional stream to reflect breast tumor dynamic variations. The multiple streams were fused in a fully connected layer to integrate the comprehensive tumor information. Weighted-loss was applied to the multilabel strategy to highlight breast tumor segmentation. In addition, the net applies the self-attention module with grid-based attention coefficients based on a global feature vector to emphasize breast regions and suppress irrelevant non-breast tissue features. We trained our method on 144 DCE-MRI datasets acquired from Philips and achieved mean Dice coefficients of 0.92 and 0.86 for breast and tumor segmentations that were superior to common networks with multichannel structures. The model was extended to an independent test set with 59 cases from two different MRI machines and achieved a Dice coefficient of 0.83 for breast tumor segmentation, which illustrates the robustness of our framework. The automatically generated masks can improve the accuracy and time of diagnosis of malignant and benign breast tumors. Qualitative comparisons illustrate that the proposed method has high precision and generalizability.

Effective suppression of conductance in multichannel molecular wires

Although quantum effects have introduced fascinating properties to molecular circuits, exploring new features for molecular circuits remains a challenge. In this work, effective suppression of conductance in the parallel intramolecular circuit is identified, going beyond the well-known superposition effect. By introducing oligo(phenylene ethynylene) (OPE) as a high-conductivity channel (HCC) and [cis-Pt(PEt3)2]2+ coordinated with m-phenylethynyl as a low-conductivity channel (LCC), the single-molecule conductance measured by the scanning tunneling microscopy break junction (STM-BJ) technique demonstrates that, in the multichannel molecular wires, the conductance decreases progressively with the addition of the LCC. A theoretical investigation indicates that all channels participate in electron transport. The conductance suppression in the multichannel structures is ascribable to the higher contribution of the LCC than of the HCC to the conductive orbitals. This finding improves the understanding of electron transport theory at the molecular scale and provides a strategy to design parallel intramolecular circuits.

Predictable interfacial mass transfer intensification of Sn–N doped multichannel hollow carbon nanofibers for the CO2 electro-reduction reaction

Construction of controllable multichannel and mesoporous structures on Sn and N co-doped carbon nanofibers enables intensified interfacial CO2 transfer and stabilized high-loading CO2,ads, enhancing CO2 electro-reduction performance.