Intrinsic Characteristics Research Articles

Charging behavior of ZnMn2O4 and LiMn2O4 in a zinc- and lithium-ion battery: an ab initio study

In the field of sustainable energy storage systems, zinc-ion batteries (ZIB) employing aqueous electrolytes have emerged as viable successors to the widely used lithium-ion batteries, attributed to their cost-effectiveness, environmental friendliness, and intrinsic safety features. Despite these advantages, the performance of ZIBs is significantly hindered by the scarcity of suitable cathode materials, positioning manganese zinc oxide (ZnMn2O4) as a potential solution. In this study, we describe the ZnMn2O4 (ZMO) compound focusing on its properties variations during Zn extraction and potential battery applications. For the sake of comparison, we also analyze the same properties of the LiMn2O4 in its tetragonal phase (TLMO), for the first time, motivated by a recent discovery that the substitution of Zn ions by Li in ZMO forms isostructural TLMO compound at room temperature. The study was conducted within the density functional theory (DFT) framework, where the structural, electronic, magnetic, electrochemical, and spectroscopic properties of ZMO and TLMO are investigated under various conditions. Although both systems crystallize in tetragonal structures, they demonstrate distinct electronic and magnetic properties due to different oxidation states of the Mn. Computationally optimized lattice parameters align closely with experimental values. The TLMO exhibits a narrower band gap compared to ZMO, indicating enhanced electrical conductivity. In addition, TLMO presented a lower diffusion energy barrier than ZMO, indicating better ionic conductivity. To evaluate the potential application of these materials in battery technologies, we further explored their volume changes during charging/discharging cycles, simulating Zn or Li ions extraction. TLMO underwent a significant volume contraction of 5.8% upon complete Li removal, while ZMO experienced a more pronounced contraction of 12.5% with full Zn removal. By adjusting ion extraction levels, it is possible to reduce these contractions, thereby approaching more viable battery applications. Voltage profiles, constructed from DFT-based simulation results, unveiled an average voltage of 4.05 V for TLMO, closely matching experimental values. Furthermore, spectroscopy results provide insights into the electronic transitions and validate the computational findings, consolidating our understanding of the intrinsic properties of ZMO and TLMO.

Dual-Graph Attention Convolution Network for 3-D Point Cloud Classification.

Three-dimensional point cloud classification is fundamental but still challenging in 3-D vision. Existing graph-based deep learning methods fail to learn both low-level extrinsic and high-level intrinsic features together. These two levels of features are critical to improving classification accuracy. To this end, we propose a dual-graph attention convolution network (DGACN). The idea of DGACN is to use two types of graph attention convolution operations with a feedback graph feature fusion mechanism. Specifically, we exploit graph geometric attention convolution to capture low-level extrinsic features in 3-D space. Furthermore, we apply graph embedding attention convolution to learn multiscale low-level extrinsic and high-level intrinsic fused graph features together. Moreover, the points belonging to different parts in real-world 3-D point cloud objects are distinguished, which results in more robust performance for 3-D point cloud classification tasks than other competitive methods, in practice. Our extensive experimental results show that the proposed network achieves state-of-the-art performance on both the synthetic ModelNet40 and real-world ScanObjectNN datasets.

Effect of Magnetic Flow and Convective Heat Transfer Enhancement Using Hybrid Nanofluid: A Structured Review

Convective heat transfer is vital in a variety of engineering applications, including thermal management systems, electronic refrigeration, and energy conversion devices. Improving the rate of heat transfer in these systems is of the utmost significance for increasing their efficiency. This review focuses on the single and combined effects of these parameters on improving heat transmission in such systems. The heat and mass transfer of nanofluid is greatly influenced by various factors, including the intrinsic features of the nanofluid, the process used for synthesising the nanofluid, the impact of magnetic force, the concentration and size of nanoparticles, and the Reynolds number (Re). Furthermore, it is important to note that the material characteristics, thermal properties, and performance of magnetic nanofluids are significantly influenced by slight variations in the magnetic force and magnetic field gradient. Multiple research projects have reached the agreement that the inclusion of a magnetic field within magnetic nanoparticles enhances the convective heat transfer capabilities of a nanofluid, resulting in an improvement ranging from around 13% to 75%. Moreover, several applications of hybrid nanofluids in thermal systems have been introduced.

Lipidomic Profiling of Kidney Cortical Tubule Segments Identifies Lipotypes with Physiological Implications.

A detailed knowledge of the lipid composition of components of nephrons is crucial for understanding physiological processes and the development of kidney diseases. However, the lipidomic composition of kidney tubular segments is unknown. We manually isolated the proximal convoluted tubule (PCT), the cortical thick ascending limb of Henle's loop, and the cortical collecting duct from 5 lean and obese mice and subjected the samples to shotgun lipidomics analysis by high-resolution mass spectrometry acquisition. Across all samples, more than 500 lipid species were identified, quantified, and compared. We observed significant compositional differences among the 3 tubular segments, which serve as true signatures. These intrinsic lipidomic features are associated with a distinct proteomic program that regulates highly specific physiological functions. The distinctive lipidomic features of each of the 3 segments are mostly based on the relative composition of neutral lipids, long-chain polyunsaturated fatty acids, sphingolipids, and ether phospholipids. These features support the hypothesis of a lipotype assigned to specific tubular segments. Obesity profoundly impacts the lipotype of PCT. In conclusion, we present a comprehensive lipidomic analysis of 3 cortical segments of mouse kidney tubules. This valuable resource provides unparalleled detail that enhances our understanding of tubular physiology and the potential impact of pathological conditions.

SiG: A Siamese-Based Graph Convolutional Network to Align Knowledge in Autonomous Transportation Systems

Domain knowledge is gradually renovating its attributes to exhibit distinct features in autonomy, propelled by the shift of modern transportation systems (TS) toward autonomous TS (ATS) comprising three progressive generations. The knowledge graph (KG) and its corresponding versions can help depict the evolving TS. Given that KG versions exhibit asymmetry primarily due to variations in evolved knowledge, it is imperative to harmonize the evolved knowledge embodied by the entity across disparate KG versions. Hence, this article proposes a siamese-based graph convolutional network (GCN) model, namely SiG , to address unresolved issues of low accuracy, efficiency, and effectiveness in aligning asymmetric KGs. SiG can optimize entity alignment in ATS and support the analysis of future-stage ATS development. Such a goal is attained through (a) generating unified KGs to enhance data quality, (b) defining graph split to facilitate entire-graph computation, (c) enhancing a GCN to extract intrinsic features, and (d) designing a siamese network to train asymmetric KGs. The evaluation results suggest that SiG surpasses other commonly employed models, resulting in average improvements of 23.90% and 37.89% in accuracy and efficiency, respectively. These findings have significant implications for TS evolution analysis and offer a novel perspective for research on complex systems limited by continuously updated knowledge.

Machine-learning-assisted hydrogen adsorption descriptor design for bilayer MXenes

Currently, most of the MXene hydrogen storage materials with excellent performances are screened by empirical trial-and-error methods. All of them are single-layer materials, and they have difficulty meeting actual demands. Herein, we report the accurate prediction of hydrogen adsorption energies for three adsorption modes inside M12X1–M22X2 bilayer MXenes using only physical intrinsic features (no density functional theory computational variables). The gradient boosting regression and random forest regression algorithms achieved R2 of 0.957/0.946 and 0.952/0.935 for chemisorption and physical adsorption models on the training/test set, respectively. In particular, the presence of a nanopump effect mechanism in the MXenes with a small layer spacing ensured that the system had a strong Kubas adsorption of H2. Symbolic regression was used to guide the design of hydrogen adsorption descriptors, and two simple descriptors, (χ/M1)×(r/M2)2 and (r/M2)3(m/X1), were identified to be applied to chemisorption and physical adsorption, respectively. The results could provide a theoretical basis for the subsequent synthesis of MXene materials with excellent hydrogen storage properties.

THC and sperm: Impact on fertilization capability, pre-implantation in vitro development and epigenetic modifications.

Global cannabis use has risen 23% since 2010, with 209 million reported users, most of whom are males of reproductive age. Delta-9-tetrahydrocannabinol (THC), the main psychoactive phytocannabinoid in cannabis, disrupts pro-homeostatic functions of the endocannabinoid system (ECS) within the male reproductive system. The ECS is highly involved in regulating morpho-functional and intrinsic sperm features that are required for fertilization and pre-implantation embryo development. Previous work by our group demonstrated that THC altered sperm capacitation and the transcriptome, including several fertility-associated microRNAs (miRs). Despite the prevalent use of cannabis among males of reproductive age, clinical and pre-clinical research investigating the impact of paternal cannabis on sperm function and the outcomes of artificial reproductive technologies (ARTs) remains inconclusive. Therefore, the present study investigates the impact of in vitro THC exposure on morpho-functional and intrinsic sperm functions, including contributions to embryo development following IVF. Bovine sperm were used as a translational model for human and treated with concentrations of THC that reflect plasma levels after therapeutic (0.032μM), and low (0.32μM)-high (4.8μM) recreational cannabis use. After 6-hours of treatment, THC did not alter the acrosomal reaction, but 4.8μM significantly reduced mitochondrial membrane potential (MMP) (p<0.05), primarily through agonistic interactions with CB-receptors. Fertilization of bovine oocytes with THC-treated sperm did not alter developmental rates, but blastocysts generated from sperm treated with 0.32-4.8μM THC had fewer trophoblasts (p<0.05), while blastocysts generated from sperm exposed to any concentration of THC had fewer cells in the inner cell mass (ICM), particularly within the 0.032μM group (p<0.001). Fertility associated miRs, including miR-346, miR-324, miR-33b, and miR-34c were analyzed in THC-exposed sperm and associated blastocysts generated by IVF, with lower levels of miRs-346, -324, and -33b found in sperm treated with 0.32μM THC, while miR-34c levels were higher in sperm treated with 0.032μM THC (p<0.05). Levels of miR-346 were also lower in sperm treated with 0.032μM THC, but higher in blastocysts generated from sperm exposed to 0.32μM THC (p<0.05). Our findings suggest that THC may alter key morpho-functional and epigenetic sperm factors involved in fertilization and embryo development. This is the first study to demonstrate that sperm exposed to THC in vitro negatively affects embryo quality following IVF.

Multidecadal Monsoon Variations during the Early Last Deglaciation Revealed by Speleothem Record from Southwestern China

The Asian monsoon (AM) has direct and profound effects on the livelihoods of residents in South Asia and East Asia. Modern observations have shown multi-decadal alternations of flood and drought periods in these regions, likely influenced by climatic processes such as the Atlantic Multidecadal Oscillation and the Pacific Decadal Oscillation. However, our understanding of the multi-decadal variability of the AM under different climatic conditions remains uncertain. In this study, we collected an annually laminated and 780-mm stalagmite (sample number: BJ7) from Binjia (BJ) Cave in southwestern China, which is deeply influenced by the Asian monsoon system. Based on this sample, we established 6-year resolution and multi-proxy records for the Asian summer monsoon (ASM) variabilities during the early last termination, spanning from 18.2 to 16.1 ka BP. Measurements of five pairs of uranium and thorium solutions for 230Th dating were conducted using a multi-collector inductively coupled plasma mass spectrometer (MC-ICP- MS), and 374 pairs of stable isotope (δ18O and δ13C) analyses were run on a Kiel Carbonate Device connected with Finnigan MAT-253 at Nanjing Normal University. The chronology for this sample was established by annual layer counting anchored with 230Th dating results. Our BJ7 δ18O record replicates well with other Chinese δ18O records on the general trend, all of which are superimposed by frequent multidecadal-scale fluctuations at approximately 60 years periodicity. Inspection of the 60-year band in BJ7 δ18O and δ13C records and results of the cross-wavelet analysis indicate coherent changes in the ASM and biomass production/karst processes during most of the studied period. In addition, the 60-year band of BJ7 and NGRIP δ18O records are consistent, implying the impacts of the high-latitude North Atlantic or Atlantic Multidecadal Oscillation on the ASM. Our study suggests that the 60-year variability should be an intrinsic feature of the climate system regardless of glacial or interglacial backgrounds.

Electric field dependence of spin qubit in a Si-MOS quantum dot

Abstract Valley, the intrinsic feature of silicon, is an inescapable subject in silicon-based quantum computing. At the spin–valley hotspot, both Rabi frequency and state relaxation rate are significantly enhanced. With protection against charge noise, the valley degree of freedom is also conceived to encode a qubit to realize noise-resistant quantum computing. Here, based on the spin qubit composed of one or three electrons, we characterize the intrinsic properties of valley in an isotopically enriched silicon quantum dot (QD) device. For one-electron qubit, we measure two electric-dipole spin resonance (EDSR) signals which are attributed to partial occupation of two valley states. The resonance frequencies of two EDSR signals have opposite electric field dependences. Moreover, we characterize the electric field dependence of the upper valley state based on three-electron qubit experiments. The difference of electric field dependences of the two valleys is 52.02 MHz/V, which is beneficial for tuning qubit frequency to meet different experimental requirements. As an extension of electrical control spin qubits, the opposite electric field dependence is crucial for qubit addressability, individual single-qubit control and two-qubit gate approaches in scalable quantum computing.

Gamification in nutrition education: the impact and the acceptance of digital game-based intervention for improving nutritional habits

AbstractAs school-based nutrition education interventions have become increasingly popular in recent years, they have proven effective in raising children awareness and responsibility toward good eating habits as well as improving their knowledge, skills, and attitudes. The aim of this work is to evaluate whether a gamification approach, using a digital application developed in AdobeXD, could be an appropriate strategy for increasing attention span toward nutrition education messages when compared to a classical didactic approach. The study involved 126 children aged 7 to 8 years, divided into control group (lesson with nutrition expert supported by slides) and intervention group (interactive lesson via application). A questionnaire was then administered to all participants to assess the knowledge they gained regarding basic nutrition education concepts. An additional questionnaire was distributed to the intervention group for the prototype digital evaluation based on the Technology Acceptance Model (TAM) framework. The results show that the digital application has the potential to be an effective tool for producing significant improvements in nutrition knowledge. The greater rating on the usefulness of the content, rather than on other intrinsic features of the prototype, demonstrates that the use of a digital approach can play a key role in capturing new concepts of nutrition education.

Enhancing the sensitivity of 3D printed sensors via ironing and void reduction

ABSTRACT Material Extrusion (MEX) Additive Manufacturing (AM) has risen as a promising technology to monolithically manufacture smart structures with embedded sensors. Despite all the benefits of MEX AM, 3D printed piezoresistive sensors still suffer from low sensitivity, making them unsuitable for the detection of low values of force, displacement and bending angle. In the present paper, a simple, effective, and inexpensive method to increase the sensitivity in 3D printed sensors is proposed, based on the leveraging of the ironing strategy, which resulted in an improvement in the sensitivity of 83% compared to traditional process parameter selection. The ironing strategy reduced intralayer porosity by 59%, as verified by X-Ray CT. Additionally, the ironing strategy involves an increased healing time, which promotes the polymer chain diffusion between layers, which translated into a greater stability of the sensor when cyclically stressed. Smart structures capable of detecting small forces (0.19 N of resolution against 1.96 N for traditional MEX scenario) and smart auxetic devices have been manufactured, demonstrating the potential of the proposed approach. The present research demonstrates the ability to reduce interlayer voids by using an intrinsic feature of the MEX process and consequently improve electrical performance of 3D printed sensors.

Exploring a Solvent Dependent Strategy to Control Self-Assembling Behavior and Cellular Interaction in Laminin-Mimetic Short Peptide based Supramolecular Hydrogels.

Self-assembled hydrogels, fabricated through diverse non-covalent interactions, have been extensively studied in regenerative medicines. Inspired from bioactive functional motifs of ECM protein, short peptide sequences have shown remarkable abilities to replicate the intrinsic features of the natural extracellular milieu. In this direction, we have fabricated two short hydrophobic bioactive sequences derived from the laminin protein i. e., IKVAV and YIGSR. Based on the substantial hydrophobicity of these peptides, we selected a co-solvent approach as a suitable gelation technique that included different concentrations of DMSO as an organic phase along with an aqueous solution containing 0.1 % TFA. These hydrophobic laminin-based bioactive peptides with limited solubility in aqueous physiological environment showed significantly enhanced solubility with higher DMSO content in water. The enhanced solubility resulted in extensive intermolecular interactions that led to the formation of hydrogels with a higher-order entangled network along with improved mechanical properties. Interestingly, by simply modulating DMSO content, highly tunable gels were accessed in the same gelator domain that displayed differential physicochemical properties. Further, the cellular studies substantiated the potential of these laminin-derived hydrogels in enhancing cell-matrix interactions, thereby reinforcing their applications in tissue engineering.

Immunotherapeutic Strategies for the Treatment of Glioblastoma: Current Challenges and Future Perspectives.

Despite decades of research and the best up-to-date treatments, grade 4 Glioblastoma (GBM) remains uniformly fatal with a patient median overall survival of less than 2 years. Recent advances in immunotherapy have reignited interest in utilizing immunological approaches to fight cancer. However, current immunotherapies have so far not met the anticipated expectations, achieving modest results in their journey from bench to bedside for the treatment of GBM. Understanding the intrinsic features of GBM is of crucial importance for the development of effective antitumoral strategies to improve patient life expectancy and conditions. In this review, we provide a comprehensive overview of the distinctive characteristics of GBM that significantly influence current conventional therapies and immune-based approaches. Moreover, we present an overview of the immunotherapeutic strategies currently undergoing clinical evaluation for GBM treatment, with a specific emphasis on those advancing to phase 3 clinical studies. These encompass immune checkpoint inhibitors, adoptive T cell therapies, vaccination strategies (i.e., RNA-, DNA-, and peptide-based vaccines), and virus-based approaches. Finally, we explore novel innovative strategies and future prospects in the field of immunotherapy for GBM.

The Influence of Sharia Financial Literacy and Sharia Financial Inclusion Through Islamic Fintech on Sharia Financial Transactions on BUS in Indonesia

This research proposes a model to explain the influence of the role of Islamic financial technology and inclusion Islamic finance in moderate influence literacy Islamic finance towards transaction sharia finance at Sharia Commercial Banks in Indonesia. Theresults of the research state that: 1) literacy Islamic finance has a significant positive effect on transactions sharia finance; 2) Islamic financial technology moderates influence literacy Islamic finance towards transaction sharia finance; 3) inclusionIslamic finance moderates influence literacy Islamic finance towards transaction Islamic finance. In implementing digital transformation, Sharia BPRs in Indonesia must seriously consider information transparency, financial stability, and innovation capabilities, and choose different paths based on intrinsic characteristics such as property rights, scale, and growth.

Relevant Intrinsic Feature Enhancement Network for Few-Shot Semantic Segmentation

For few-shot semantic segmentation, the primary task is to extract class-specific intrinsic information from limited labeled data. However, the semantic ambiguity and inter-class similarity of previous methods limit the accuracy of pixel-level foreground-background classification. To alleviate these issues, we propose the Relevant Intrinsic Feature Enhancement Network (RiFeNet). To improve the semantic consistency of foreground instances, we propose an unlabeled branch as an efficient data utilization method, which teaches the model how to extract intrinsic features robust to intra-class differences. Notably, during testing, the proposed unlabeled branch is excluded without extra unlabeled data and computation. Furthermore, we extend the inter-class variability between foreground and background by proposing a novel multi-level prototype generation and interaction module. The different-grained complementarity between global and local prototypes allows for better distinction between similar categories. The qualitative and quantitative performance of RiFeNet surpasses the state-of-the-art methods on PASCAL-5i and COCO benchmarks.

Homophily-Related: Adaptive Hybrid Graph Filter for Multi-View Graph Clustering

Recently there is a growing focus on graph data, and multi-view graph clustering has become a popular area of research interest. Most of the existing methods are only applicable to homophilous graphs, yet the extensive real-world graph data can hardly fulfill the homophily assumption, where the connected nodes tend to belong to the same class. Several studies have pointed out that the poor performance on heterophilous graphs is actually due to the fact that conventional graph neural networks (GNNs), which are essentially low-pass filters, discard information other than the low-frequency information on the graph. Nevertheless, on certain graphs, particularly heterophilous ones, neglecting high-frequency information and focusing solely on low-frequency information impedes the learning of node representations. To break this limitation, our motivation is to perform graph filtering that is closely related to the homophily degree of the given graph, with the aim of fully leveraging both low-frequency and high-frequency signals to learn distinguishable node embedding. In this work, we propose Adaptive Hybrid Graph Filter for Multi-View Graph Clustering (AHGFC). Specifically, a graph joint process and graph joint aggregation matrix are first designed by using the intrinsic node features and adjacency relationship, which makes the low and high-frequency signals on the graph more distinguishable. Then we design an adaptive hybrid graph filter that is related to the homophily degree, which learns the node embedding based on the graph joint aggregation matrix. After that, the node embedding of each view is weighted and fused into a consensus embedding for the downstream task. Experimental results show that our proposed model performs well on six datasets containing homophilous and heterophilous graphs.

Self-Prompt Mechanism for Few-Shot Image Recognition

Few-shot learning poses a formidable challenge as it necessitates effective recognition of novel classes based on a limited set of examples. Recent studies have sought to address the challenge of rare samples by tuning visual features through the utilization of external text prompts. However, the performance of these methods is constrained due to the inherent modality gap between the prompt text and image features. Instead of naively utilizing the external semantic information generated from text to guide the training of the image encoder, we propose a novel self-prompt mechanism (SPM) to adaptively adjust the neural network according to unseen data. Specifically, SPM involves a systematic selection of intrinsic semantic features generated by the image encoder across spatial and channel dimensions, thereby engendering self-prompt information. Subsequently, upon backpropagation of this self-prompt information to the deeper layers of the neural network, it effectively steers the network toward the learning and adaptation of new samples. Meanwhile, we propose a novel parameter-efficient tuning method that exclusively fine-tunes the parameters relevant to self-prompt (prompts are no more than 2% of the total parameters), and the incorporation of additional learnable parameters as self-prompt ensures the retention of prior knowledge through frozen encoder weights. Therefore, our method is highly suited for few-shot recognition tasks that require both information retention and adaptive adjustment of network parameters with limited labeling data constraints. Extensive experiments demonstrate the effectiveness of the proposed SPM in both 5-way 1-shot and 5-way 5-shot settings for standard single-domain and cross-domain few-shot recognition datasets, respectively. Our code is available at https://github.com/codeshop715/SPM.

Higher-Order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexes

Despite the recent successes of vanilla Graph Neural Networks (GNNs) on various tasks, their foundation on pairwise networks inherently limits their capacity to discern latent higher-order interactions in complex systems. To bridge this capability gap, we propose a novel approach exploiting the rich mathematical theory of simplicial complexes (SCs) - a robust tool for modeling higher-order interactions. Current SC-based GNNs are burdened by high complexity and rigidity, and quantifying higher-order interaction strengths remains challenging. Innovatively, we present a higher-order Flower-Petals (FP) model, incorporating FP Laplacians into SCs. Further, we introduce a Higher-order Graph Convolutional Network (HiGCN) grounded in FP Laplacians, capable of discerning intrinsic features across varying topological scales. By employing learnable graph filters, a parameter group within each FP Laplacian domain, we can identify diverse patterns where the filters' weights serve as a quantifiable measure of higher-order interaction strengths. The theoretical underpinnings of HiGCN's advanced expressiveness are rigorously demonstrated. Additionally, our empirical investigations reveal that the proposed model accomplishes state-of-the-art performance on a range of graph tasks and provides a scalable and flexible solution to explore higher-order interactions in graphs. Codes and datasets are available at https://github.com/Yiminghh/HiGCN.

Harnessing the Power of SVD: An SVA Module for Enhanced Signal Classification

Deep learning methods have achieved outstanding performance in various signal tasks. However, due to degraded signals in real electromagnetic environment, it is crucial to seek methods that can improve the representation of signal features. In this paper, a Singular Value decomposition-based Attention, SVA is proposed to explore structure of signal data for adaptively enhancing intrinsic feature. Using a deep neural network as a base model, SVA performs feature semantic subspace learning through a decomposition layer and combines it with an attention layer to achieve adaptive enhancement of signal features. Moreover, we consider the gradient explosion problem brought by SVA and optimize SVA to improve the stability of training. Extensive experimental results demon-strate that applying SVA to a generalized classification model can significantly improve its ability in representations, making its recognition performance competitive with, or even better than, the state-of-the-art task-specific models.

Deep Unfolded Network with Intrinsic Supervision for Pan-Sharpening

Existing deep pan-sharpening methods lack the learning of complementary information between PAN and MS modalities in the intermediate layers, and exhibit low interpretability due to their black-box designs. To this end, an interpretable deep unfolded network with intrinsic supervision for pan-sharpening is proposed. Building upon the observation degradation process, it formulates the pan-sharpening task as a variational model minimization with spatial consistency prior and spectral projection prior. The former prior requires a joint component decomposition of PAN and MS images to extract intrinsic features. By being supervised in the intermediate layers, it can selectively provide high-frequency information for spatial enhancement. The latter prior constrains the intensity correlation between MS and PAN images derived from physical observations, so as to improve spectral fidelity. To further enhance the transparency of network design, we develop an iterative solution algorithm following the half-quadratic splitting to unfold the deep model. It rigorously adheres to the variational model, significantly enhancing the interpretability behind network design and efficiently alternating the optimization of the network. Extensive experiments demonstrate the advantages of our method compared to state-of-the-arts, showcasing its remarkable generalization capability to real-world scenes. Our code is publicly available at https://github.com/Baixuzx7/DISPNet.