Multiple Input Research Articles

Inset-fed quad-port hexagonal-shaped MIMO T-shaped slots on patch with slots on ground plane antenna for IoT and X-band applications

A compact and novel inset-fed hexagonal-shaped multiple input multiple output patch (IHMP) antenna with T-shaped slot is designed for IoT and X-band applications. The IHMP antenna comprises with four identical hexagonal-shaped radiating elements. In order to achieve better diversity performance and low mutual coupling, radiating elements P1, P4 are positioned side by side with ideal distance and P2, P3 are positioned opposite to P1, P4. The proposed hexagonal-shaped MIMO antenna is prototyped on low-cost Fr-4 substrate with the dimensions of 28 × 28 × 1.6 mm3. It achieves dual bandwidths (S11 < −10 dB) of 1.37 GHz (2.12–3.49 GHz) and 1.69 GHz (8.07–9.76 GHz) at 2.5 and 8.7 GHz resonating frequencies. The IHMP antenna having gain of 5.68dBi, and 5.51dBi at 2.5 and 8.7 GHz, respectively. At operating frequencies, surface current distribution and radiation patterns are examined. To calculate the diversity performance of the IHMP antenna, MIMO parameters envelope correlation coefficient < 0.05 within the band, diversity gain > 9.9 within the band, total active reflection coefficient, channel capacity loss < 0.2 bits/sec/Hz, and mean effective gain −3 dB < MEG < −6 dB are also investigated. The simulated and experimentally obtained results are presented and they are in fine agreement.

A systematic review of design and performance enhancement techniques for the reduction of radar cross section in multiple input multiple output antennas

multiple-input multiple-output technology has emerged as a pivotal solution for addressing the capacity constraints of high-speed broadband wireless networks. By employing multiple antennas at both the transmitter and receiver, multiple-input multiple-output significantly enhances wireless communication efficiency. This paper presents a comprehensive review of multiple-input multiple-output antenna design strategies tailored for fifth-generation (5 G) networks and beyond. Specifically, it critically examines methodologies aimed at mitigating radar cross section in multiple-input multiple-output antennas, a crucial aspect of antenna engineering, especially in scenarios requiring reduced detectability. Various design and performance enhancement techniques proposed in the literature for RCS reduction in multiple-input multiple-output antennas are systematically analyzed, focusing on aspects such as antenna geometry and materials methods. Synthesizing these findings provides valuable insights into the current state of research, highlighting advancements, challenges, and future directions in developing efficient multiple-input multiple-output antennas with minimized radar cross section. This review contributes to antenna technology by aiding researchers and practitioners in informed decision-making regarding radar cross section reduction techniques for multiple-input multiple-output systems, and it identifies innovative approaches pertinent to multiple-input multiple-output applications. Finally, key considerations in multiple-input multiple-output antenna design are addressed, proposing potential directions for future research and development initiatives.

Accelerating large-scale DEA computation using sequential categorization and dynamic reference set selection

Data envelopment analysis (DEA) is a well-known data-enabled analytic tool for evaluating relative efficiency of units with multiple inputs and multiple outputs. The DEA computation increases substantially in the presence of large samples. In this study, we first recall two lemmas to distinguish efficient units using arithmetic operations without solving linear programming (LP). Using the used lemmas, the total sample of units is partitioned into several sequential blocks, where units in the preceding blocks are relatively efficient to those in the subsequent blocks. A novel reference set selection procedure is then formulated. We implement the proposed approach into one of the fastest existing methods and demonstrate a significant improvement in elapsed time. We conduct simulation experiments and illustrate the outcomes across varying dimensions, cardinality, and density.

Broadband self-isolating MIMO antenna array for 6G IoT systems

A novel antenna array with improved radiation characteristics using a series-feed technique is presented in this article. The antenna array has a size of 132 × 80 μm and is developed using Polytetrafluoroethylene (PTFE) with graphene as conductive material. The proposed antenna comprises a central microstrip line loaded with short slant radiating stubs. The bandwidth characteristics of the antenna are enhanced by loading the slant stubs with octagonal ring elements. The number of radiating stubs is increased to enhance the overall radiation characteristics. The proposed THz antenna operates from 3.8 THz to 5.3 THz offering a fractional bandwidth of 31 % with reference |S11| ≤ −10 dB. In addition, a 1 × 2 antenna array with differential feeding is explored to improve the overall directionality of the antenna making it a viable solution for directional IoT systems. The estimated theoretical directivity is above 11 dBi and the total efficiency is greater than 75 % throughout the operating bandwidth. Furthermore, the multiple input and multiple output (MIMO) performance of the THz antenna is discussed. The proposed two-element has an intrinsic isolation of more than 40 dB. Owing to the enhanced bandwidth and radiation property, the proposed THz antenna is suitable for high data-rate 6 G Internet-of-things (IoT) communications.

High gain MIMO antenna with multiband characterization for terahertz applications

Recently, the relevance of multiple input multiple output (MIMO) technology has increased due to the growing user base and the surge in demand for high data rates. It provides an essential answer to the pressing requirement for high-capacity communication systems compliant with new standards. In this study, a four-element MIMO antenna with specific properties—a thickness of 0.0224 mm and a relative permittivity of 4.3 —is presented on a polyamide substrate. It is intended to operate in terahertz (THz) applications at 0.395 and 0.629 THz. The configuration of the antenna is based on a traditional fork antenna. At the resonant frequencies, this MIMO antenna can achieve bandwidths of 9.53 GHz and 24.19 GHz, with reflection coefficients of -16.19 dB and -24.27 dB, respectively. Moreover, a gain of 5.17 dB and 3.19 dB are obtained at the second and first resonant frequencies, respectively. In the effort to maintain the operational band and enhance gain, a significant challenge arises from the insufficient isolation between the four antenna elements within the limited space. A Defected Ground Structure (DGS) is constructed and the inter-element distance is carefully investigated in order to effectively address this issue. Mutual coupling effects are effectively reduced with this antenna, and an amazing MIMO diversity response is shown. Additionally, it achieves a mean effective gain of less than -3, envelope correlation coefficients below 0.0125, diversity gain of around 10 dB, and total active reflection coefficients below -4 dB. The suggested design was simulated using the ansys High-Frequency Structure Simulator program. These enhancements show that the recommended MIMO antenna is a suitable choice for terahertz applications like 5 G networks of the future and terahertz imaging systems used in medical settings for tissue imaging and cancer diagnosis.

Identification of Hamiltonian systems using neural networks and first integrals approaches

This research introduces a class of non-parametric identifiers based on differential neural networks represented by Hamiltonian dynamics. The structure of the identifier corresponds to the form of a canonical Hamiltonian system that uses the evolution of generalized coordinates and momentums. The learning laws of the identifier come from applying the first integrals approach, which justifies the design of an exact identifier considering the time invariance of the Hamiltonian, with a finite number of activation functions in the identifier structure. The first integrals approach derives several learning laws for the proposed class of identifiers. The learning laws design uses the estimated derivative of the generalized momentum assessed via a super-twisting differentiator with multiple inputs and outputs. All proposed laws require the solution of differential continuous-time Riccati equations and nonlinear differential equations for the learning laws, which depend on the identification error and state constraints. The developed identifier was evaluated compared to an identifier that did not consider the Hamiltonian constraint using first integrals. This comparison included a numerical evaluation of the identifier considering its application to a classical Hamiltonian system associated with the Kepler dynamics representing satellite orbital evolution. This evaluation confirmed that the identification results were improved with the proposed learning laws regarding the class of Hamiltonian structures, and the quality indicators based on the mean square error were several times lower.

StarTracer is an accelerated approach for precise marker gene identification in single-cell RNA-Seq analysis

Revealing the heterogeneity among tissues is the greatest advantage of single-cell-sequencing. Marker genes not only act as the key to correctly identify cell types, but also the bio-markers for cell-status under certain experimental imputations. Current analysis methods such as Seurat and Monocle employ algorithms which compares one cluster to all the rest and select markers according to statistical tests. This pattern brings redundant calculations and thus, results in low calculation efficiency, specificity and accuracy. To address these issues, we introduce starTracer, a novel algorithm designed to enhance the efficiency, specificity and accuracy of marker gene identification in single-cell RNA-seq data analysis. starTracer operates as an independent pipeline, which exhibits great flexibility by accepting multiple input file types. The primary output is a marker matrix, where genes are sorted by the potential to function as markers, with those exhibiting the greatest potential positioned at the top. The speed improvement ranges by 2 ~ 3 orders of magnitude compared to Seurat, as observed across three independent datasets with lower false positive rate as observed in a simulated testing dataset with ground-truth. It’s worth noting that starTracer exhibits increasing speed improvement with larger data volumes. It also excels in identifying markers in smaller clusters. These advantages solidify starTracer as an important tool for single-cell RNA-seq data, merging robust accuracy with exceptional speed.

Collaborative graph neural networks for augmented graphs: A local-to-global perspective

In the field of graph neural networks (GNNs) for representation learning, a noteworthy highlight is the potential of embedding fusion architectures for augmented graphs. However, prevalent GNN embedding fusion architectures mainly focus on handling graph combinations from a global perspective, often ignoring their collaboration with the information of local graph combinations. This inherent limitation constrains the ability of the constructed models to handle multiple input graphs, particularly when dealing with noisy input graphs collected from error-prone sources or those resulting from deficiencies in graph augmentation methods. In this paper, we propose an effective and robust embedding fusion architecture from a local-to-global perspective termed collaborative graph neural networks for augmented graphs (LoGo-GNN). Essentially, LoGo-GNN leverages a pairwise graph combination scheme to generate local perspective inputs. Together with the global graph combination, this serves as the basis to generate a local-to-global perspective. Specifically, LoGo-GNN employs a perturbation augmentation strategy to generate multiple augmentation graphs, thereby facilitating collaboration and embedding fusion from a local-to-global perspective through the use of graph combinations. In addition, LoGo-GNN incorporates a novel loss function for learning complementary information between different perspectives. We also conduct theoretical analysis to assess its expressive power under ideal conditions, demonstrating the effectiveness of LoGo-GNN. Our experiments, focusing on node classification and clustering tasks, highlight the superior performance of LoGo-GNN compared to state-of-the-art methods. Additionally, robustness analysis further confirms its effectiveness in addressing uncertainty challenges.

Multiple Slotted Quad-Band Two Element Multiple Input Multiple Output Antenna with Defected Ground Structure for UMTS, WLAN, and 5G Sub-6 GHz Applications

Multiple Slotted Quad-Band Two Element Multiple Input Multiple Output Antenna with Defected Ground Structure for UMTS, WLAN, and 5G Sub-6 GHz Applications

Investigation of circularly polarized MIMO antenna with enhanced isolation for sub-6 GHz application

This article demonstrates the design process for a unique, two-port, circularly polarized (CP) multiple input multiple outputs (MIMO) antenna for fifth-generation (5G) applications operating at sub-6 GHz. The proposed 2-port CP MIMO antenna comprises a patch antenna with a distinctive slot and a circular split ring resonator (SRR) at the ground plane. The circular patch antenna’s resonant frequency is moved to a lower frequency by the addition of a circular SRR at the ground plane. Investigating a special slot at the patch antenna can help in the enhancement of circular polarization and bandwidth. A sequence of parasitic elements in the shape of ‘h’ is placed in the space between the two antenna elements for decoupling. The minimum 20 dB inter isolation among the antenna elements in the proposed 2-port CP MIMO antenna’s operating band is made possible by these h-shaped parasitic elements. In addition, to ensure the proposed antenna is more reliable for 5G applications at sub-6 GHz, other diversity parameters including Envelop Correlation Co-efficient (ECC), Mean Effective Gain (MEG), Diversity Gain (DG), TARC (Total Active Reflection Co-efficient) and CCL (channel capacity loss) are also calculated . In comparison to existing MIMO systems of the same order, the proposed 2-port CP MIMO antenna appears to have the minimum channel capacity loss and excellent diversity performance. The proposed 2-port CP MIMO antenna achieves an impedance bandwidth of 3.3 to 3.6 GHz with impedance matching better than 10 dB and has an axial ratio bandwidth of 3.35 to 3.51 GHz with a steady radiation pattern in both planes over the operating frequency bands.

Fading model due to scintillation and pointing error on an optical wireless MISO channel

Optical wireless communication (OWC) yields high data rates and a license-free spectrum. However, OWC is drastically affected by scintillation owing to atmospheric turbulence and pointing errors because of misalignment. To alleviate the ramifications of scintillation and pointing error, multiple input single output (MISO) optical wireless channels are studied by several researchers. In this paper, it is proposed to study the performance of a MISO system in terms of the probability of outage and normalized threshold. Gamma–Gamma distribution is used to model scintillation because it is suitable for modeling both weak and strong turbulence conditions. The analytical technique is used to derive the closed-form expressions for the probability of outage of a 2×1 MISO channel. Moreover, under numerous scintillation conditions, the Mellin transform is applied to derive the closed-form expressions for the probability of outage of an optical wireless MISO channel. The closed-form results are validated through simulations.

Blockchain-enabled cooperative spectrum sensing in 5G and B5G cognitive radio via massive multiple-input multiple-output nonorthogonal multiple access

One of the greatest ways to guarantee that networks designed for fifth generation (5G) and beyond reach the required levels of spectrum efficiency (SE) is through nonorthogonal multiple access (NOMA). This work presents two new blockchain-based techniques that take advantage of massive multiple input multiple output in a single-cell network to improve performance. NOMA power domain is used in the 5G cooperative cognitive radio network (CCRN) to improve the SE of the downlink. This study investigates a novel cooperative NOMA-based CCRN for underlay spectrum sharing. A cooperative NOMA technique is proposed by considering the access modes of relays and secondary users (SUs) on the secondary network. The proposed system's performance is assessed with respect to random channel characteristics, frequency-selective Rayleigh fading and perfect successive interference cancellation (SIC). The first signal decoded by SU identifies the relay states with the best channel quality between users and the destination users to offset the bit error rate. The throughput dropped during the period because of the perfect SIC. The precise closed-form expressions for the system throughput of the secondary network are derived under the interference constraint of the primary network to evaluate the efficacy of the suggested cooperative strategy. The suggested approaches are assessed under various conditions using the MATLAB application by considering varying transmit power levels, power location coefficients and lengths. In every case, four users are assumed to be using a 90 MHz bandwidth and M-ary Quadrature Amplitude Modulation technology.

Oscillatory contractile forces refine endothelial cell-cell interactions for continuous lumen formation governed by Heg1/Ccm1.

The formation and organization of complex blood vessel networks rely on various biophysical forces, yet the mechanisms governing endothelial cell-cell interactions under different mechanical inputs are not well understood. Using the dorsal longitudinal anastomotic vessel (DLAV) in zebrafish as a model, we studied the roles of multiple biophysical inputs and cerebral cavernous malformation (CCM)-related genes in angiogenesis. Our research identifies heg1 and krit1 (ccm1) as crucial for the formation of endothelial cell-cell interfaces during anastomosis. In mutants of these genes, cell-cell interfaces are entangled with fragmented apical domains. A Heg1 live reporter demonstrated that Heg1 is dynamically involved in the oscillatory constrictions along cell-cell junctions, whilst a Myosin live reporter indicated that heg1 and krit1 mutants lack actomyosin contractility along these junctions. In wild-type embryos, the oscillatory contractile forces at junctions refine endothelial cell-cell interactions by straightening junctions and eliminating excessive cell-cell interfaces. Conversely, in the absence of junctional contractility, the cell-cell interfaces become entangled and prone to collapse in both mutants, preventing the formation of a continuous luminal space. By restoring junctional contractility via optogenetic activation of RhoA, contorted junctions are straightened and disentangled. Additionally, haemodynamic forces complement actomyosin contractile forces in resolving entangled cell-cell interfaces in both wild-type and mutant embryos. Overall, our study reveals that oscillatory contractile forces governed by Heg1 and Krit1 are essential for maintaining proper endothelial cell-cell interfaces and thus for the formation of a continuous luminal space, which is essential to generate a functional vasculature.

MixTrain: accelerating DNN training via input mixing.

Training Deep Neural Networks (DNNs) places immense compute requirements on the underlying hardware platforms, expending large amounts of time and energy. An important factor contributing to the long training times is the increasing dataset complexity required to reach state-of-the-art performance in real-world applications. To address this challenge, we explore the use of input mixing, where multiple inputs are combined into a single composite input with an associated composite label for training. The goal is for training on the mixed input to achieve a similar effect as training separately on each the constituent inputs that it represents. This results in a lower number of inputs (or mini-batches) to be processed in each epoch, proportionally reducing training time. We find that naive input mixing leads to a considerable drop in learning performance and model accuracy due to interference between the forward/backward propagation of the mixed inputs. We propose two strategies to address this challenge and realize training speedups from input mixing with minimal impact on accuracy. First, we reduce the impact of inter-input interference by exploiting the spatial separation between the features of the constituent inputs in the network's intermediate representations. We also adaptively vary the mixing ratio of constituent inputs based on their loss in previous epochs. Second, we propose heuristics to automatically identify the subset of the training dataset that is subject to mixing in each epoch. Across ResNets of varying depth, MobileNetV2 and two Vision Transformer networks, we obtain upto 1.6 × and 1.8 × speedups in training for the ImageNet and Cifar10 datasets, respectively, on an Nvidia RTX 2080Ti GPU, with negligible loss in classification accuracy.

Joint Optimization-Based QoS and PAPR Reduction Technique for Energy-Efficient Massive MIMO System

Massive multiple input multiple output (MIMO) is an innovative wireless communication technology that significantly enhances the capacity and efficiency of data transmission in modern networks. Massive multiple input multiple output, despite benefits, faces difficulties in managing numerous antennas affecting signal quality, peak-to-average power ratio (PAPR), and energy efficiency due to its scale and complexity. However, overcoming these complexities is vital for unlocking massive multiple input multiple output’s potential in high-quality, energy-efficient wireless communication. A proposed joint optimization-based technique aims to address these issues in massive MIMO systems. In phase 1, joint optimization with quality of service maximizes capacity via power distribution, employing the hybrid spider wasp Fick’s law algorithm. In phase 2, zero-forcing with symbol-level linear precoding, especially the stacked convolutional sparse bidirectional long short-term memory autoencoder (SCS–BiLSTMAE) network, efficiently reduces peak-to-average power ratio, collaborating for reduced bit rate error and peak-to-average power ratio through adaptive mapping. Refining the model involves precise hyperparameter tuning using the enhanced influencer buddy optimization algorithm. The proposed framework not only demonstrates exceptional performance, but the metrics also underscore the model's effectiveness in addressing diverse challenges, establishing it as a robust solution for quality enhancement, peak-to-average power ratio reduction, and energy efficiency. Additionally, the proposed model attains 28.14% greater system capacity than existing methods.

Stage stochastic incremental data envelopment analysis models and applications

Data envelopment analysis (DEA) is a mathematical programming method that can evaluate the relative efficiency of multiple inputs and multiple outputs of a decision-making unit (DMU). The classical DEA model assumes that inputs and outputs are determined. However, there are some applications where the inputs–outputs are stochastic. In practice, it is important to evaluate stage performance. It is essential to eliminate the effect of preceding stage inputs (outputs) on stage performance in order to accurately assess stage performance. In this paper, we propose stage stochastic incremental DEA models that integrate two different kinds of inputs and outputs. The first kind of model takes into account the assessment of stage efficiency when determinate incremental inputs and stochastic incremental outputs are applied at the beginning and end of the stage. The second kind of model uses stochastic incremental inputs–outputs to evaluate stage efficiency. To verify the efficacy of the suggested models, the first kind of model is applied to assess the stage financing efficiency of 15 energy-saving and environmental protection clean enterprises (ESEPCEs). The second kind of model is applied in assessing the stage investment efficiency of 15 ESEPCEs. The empirical results show that the proposed models not only eliminate the effect of prior performance but also more accurately assess stage efficiency in a stochastic environment.

Textile omnidirectional antenna for wearable WiFi-7 applications using higher order modes

In WiFi 7 (IEEE 802.11be), Multiple Input Multiple Output (MIMO) technology plays a crucial role in enhancing throughput. This combination enables numerous applications for wearable WiFi devices. For instance, WiFi can be utilized for tracking and fall detection purposes. Additionally, wearable devices used in gaming, vital signal monitoring, and tracking can benefit from the implementation of wearable MIMO antennas. Despite the demand for wearable WiFi antennas, specifically in the new 6 GHz band, a significant gap exists in the investigation of antennas that are completely made of textile, are low profile, and provide vertical polarization and omnidirectional patterns. Addressing this gap, in this paper, a wearable planar antenna is introduced, which is specifically designed to offer an omnidirectional pattern and linear polarization. To ensure its practicality and ease of use, the antenna utilizes lightweight and wearable textile material. The design details and performance of the proposed antenna are presented. The antenna covers all three bands of WiFi, namely, 2.4 GHz, 5 GHz, and 6 GHz bands. The 10-dB return loss bandwidth is 2.4 GHz–2.5 GHz, and 4.6 GHz–7.2 GHz. The maximum Specific Absorption Rate (SAR) for an input power of 500 mW is calculated at 1.053 W/Kg for a 2 mm air gap between the antenna and tissue surface. The size of the antenna is given by a circular area of π × (50 mm)2, and a thickness of 6 mm.

Bone metastasis scintigram generation using generative adversarial learning with multi-receptive field learning and two-stage training.

Deep learning is the primary method for conducting automated analysis of SPECT bone scintigrams. The lack of available large-scale data significantly hinders the development of well-performing deep learning models, as the performance of a deep learning model is positively correlated with the size of the dataset used. Therefore, there is an urgent demand for an automated data generation method to enlarge the dataset of SPECT bone scintigrams. We introduce a deep learning-based generation model that can generate realistic but not identical samples from the original SPECT bone scintigrams. Following the generative adversarial learning architecture, a bone metastasis scintigram generation model christened BMS-Gen is proposed. First, BMS-Gen takes multiple input conditions and employs multi-receptive field learning to ensure that the generated samples are as realistic as possible. Second, BMS-Gen adopts generative adversarial learning to retain the diversity of the generated samples. Last, BMS-Gen uses a two-stage training strategy to improve the quality of the generated samples. Experimental evaluation conducted on a set of clinical data of SPECT BM scintigrams has shown the performance of the proposed BMS-Gen, achieving the best overall scores of 1678.0, 69.33, and 19.51 for FID (Fréchet Inception Distance), MSE (Mean Square Error), and PSNR (Peak Signal-to-Noise Ratio) metrics. The introduction of samples generated by BMS-Gen contributes a maximum (minimum) increase of 3.01% (0.15%) on the F-1 score and a maximum (minimum) increase of 6.83% (2.21%) on the DSC score for the image classification and segmentation tasks, respectively. The proposed BMS-Gen model can be used as a promising tool for augmenting the data of bone scintigrams, greatly facilitating the development of deep learning-based automated analysis of SPECT bone scintigrams.

Predictors of length of postoperative stay following endoscopic skull base surgery with intraoperative CSF leak.

Establishing benchmarks for length of stay (LOS) may inform strategies to improve resource efficiency, decrease costs, and advance care quality. In this study, the authors characterize postoperative LOS in endoscopic skull base surgery (ESBS) and elucidate prolonging factors. A retrospective chart review was conducted at a tertiary academic center including consecutive adult patients who underwent intradural ESBS with intraoperative CSF leak during primary repair between July 2018 and March 2024. LOS, calculated as the time between the end of anesthesia until discharge from the hospital, comprised the primary outcome. Categorical and continuous independent study variables were assessed for univariate LOS association via the Mann-Whitney U-test and Kendall's tau-b correlation, respectively, and those with significant associations were included as multiple linear regression inputs. One hundred sixty-three patients were included, with a median LOS of 4.0 (interquartile range [IQR] 2.8-5.8) days. LOS was significantly prolonged in high-flow (n = 82) compared with low-flow (n = 81) CSF leak cohorts (median 4.5 [IQR 3.9-6.5] vs 2.9 [IQR 2.1-4.7] days, p = 0.002). Defects involving the anterior cranial fossa (n = 16, median 4.6 [IQR 3.3-7.5)] days), suprasellar region (n = 94, median 4.4 [IQR 3.2-6.4] days), sella (n = 138, median 3.9 [IQR 2.8-5.8] days), or posterior cranial fossa (n = 17, median 4.5 [IQR 3.9-6.5] days) had variable LOSs. On multiple linear regression, after controlling for numerous patient, surgical, and postoperative factors, lesion diameter (B = 0.16, 95% CI 0.048-0.26), bone defect area (B = 0.008, 95% CI 0.001-0.014), anesthesia time (B = 0.015, 95% CI 0.004-0.026), bed rest length (B = 2.34, 95% CI 1.12-3.56), postoperative CSF leak (B = 11.06, 95% CI 4.11-18.01), postoperative meningitis (B = 11.79, 95% CI 4.83-18.74), postoperative stroke/hemorrhage (B = 25.25, 95% CI 18.43-32.06), and postoperative pneumonia (B = 5.59, 95% CI 0.79-10.38) independently predicted overall prolonged LOS. With healthcare utilization receiving increased attention, mitigating factors that extend LOS are important. Extent of surgery and certain postoperative complications may constitute key factors prolonging LOS following intradural ESBS with intraoperative CSF leak.

Insights on CDI parametric controls and dependencies using gloabal sensitivity analysis

Capacitive deionization (CDI) emerges as a promising desalination technology due to its high energy efficiency and potential for energy recovery. Its performance improvement and application extension highly require understanding how variations in multiple input parameters, as well as their interactions, lead to variations in output, which is not straightforward in the complex system of CDI. In this work we use the extended Fourier amplitude sensitivity test (eFAST) method to conduct a global sensitivity analysis (GSA) in order to examine the effects of several parameters, including material properties, operating conditions, and ionic properties, on the energy consumption and salt removal efficiency of CDIs. The research reveals that high capacitance and efficient mass transfer are crucial for CDI advancement, with a trade-off between desalination rate and energy efficiency. The interplay between input parameters is revealed by factorial design and GSA, which show that no single parameter alone determines salt adsorption capacity but that feedwater salt concentration and current density are important when considering both individual and coupled effects. The study suggests that the specific application and feedwater characteristics should be taken into consideration when deciding between the CC and CV modes of desalination. While CC mode excels in water production and energy efficiency for high salinity feedwater pretreatment, CV mode is better at treating low salinity waterr. The comprehensive parametric analysis provides valuable insights into optimizing CDI design and operating conditions to balance desalination capacity and energy efficiency.