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This article employs security dilemma theory to probe the geopolitical implications of state intervention in the digital realm. Its central argument is that with cross-border data flows being conducive to subversive actions, governments have grown wary of rival states leveraging control over data flows to advance strategic objectives. Therefore, when a government tightens its domestic regulation over data flows, its actions could trigger a spiral of suspicions and countermeasures with other states. Such a security dilemma fosters the technology rivalry between China and the United States. As Beijing became sensitive to unrestricted flows of information and data, it set out to exert tighter control over data flows within and across Chinese borders. But Beijing’s move aggravated US perceptions of subversive threats, prompting Washington to try to drive Chinese entities out of the US-centric technology ecosystem. Not surprisingly, Washington’s actions signaled hostile intent to China, which in turn decided to build alternative digital infrastructures. Given that state intervention in the digital realm could exacerbate great power rivalry, Web 3.0 will likely perpetuate security dilemma dynamics by shifting the battlefield from corporate platforms to protocol layers, from data ownership to infrastructure sovereignty.

HighlightsThe theoretical knowledge in the field of microwave absorption is summarized in detail.The recent progress of metal–organic frameworks-derived porous carbon-based nanocomposites as microwave absorption materials is reviewed.The development of microwave absorption materials (MAMs) is a considerable important topic because our living space is crowed with electromagnetic wave which threatens human’s health. And MAMs are also used in radar stealth for protecting the weapons from being detected. Many nanomaterials were studied as MAMs, but not all of them have the satisfactory performance. Recently, metal–organic frameworks (MOFs) have attracted tremendous attention owing to their tunable chemical structures, diverse properties, large specific surface area and uniform pore distribution. MOF can transform to porous carbon (PC) which is decorated with metal species at appropriate pyrolysis temperature. However, the loss mechanism of pure MOF-derived PC is often relatively simple. In order to further improve the MA performance, the MOFs coupled with other loss materials are a widely studied method. In this review, we summarize the theories of MA, the progress of different MOF-derived PC‑based MAMs, tunable chemical structures incorporated with dielectric loss or magnetic loss materials. The different MA performance and mechanisms are discussed in detail. Finally, the shortcomings, challenges and perspectives of MOF-derived PC‑based MAMs are also presented. We hope this review could provide a new insight to design and fabricate MOF-derived PC-based MAMs with better fundamental understanding and practical application.

Gastrointestinal cancer (GIC) is the most prevalent and highly metastatic malignant tumor and has a significant impact on mortality rates. Nevertheless, the swift advancement of contemporary technology has not seamlessly aligned with the evolution of detection methodologies, resulting in a deficit of innovative and efficient clinical assays for GIC. Given that exosomes are preferentially released by a myriad of cellular entities, predominantly originating from neoplastic cells, this confers exosomes with a composition enriched in cancer-specific constituents. Furthermore, exosomes exhibit ubiquitous presence across diverse biological fluids, endowing them with the inherent advantages of non-invasiveness, real-time monitoring, and tumor specificity. The unparalleled advantages inherent in exosomes render them as an ideal liquid biopsy biomarker for early diagnosis, prognosticating the potential development of GIC metastasis. In this review, we summarized the latest research progress and possible potential targets on cancer-derived exosomes (CDEs) in GIC with an emphasis on the mechanisms of exosome promoting cancer metastasis, highlighting the potential roles of CDEs as the biomarker and treatment in metastatic GIC.

We propose a Bregman inertial forward-reflected-backward (BiFRB) method for nonconvex composite problems. Assuming the generalized concave Kurdyka-Łojasiewicz property, we obtain sequential convergence of BiFRB, as well as convergence rates on both the function value and actual sequence. One distinguishing feature in our analysis is that we utilize a careful treatment of merit function parameters, circumventing the usual restrictive assumption on the inertial parameters. We also present formulae for the Bregman subproblem, supplementing not only BiFRB but also the work of Boţ-Csetnek-László and Boţ-Csetnek. Numerical simulations are conducted to evaluate the performance of our proposed algorithm.

Crystalline thermoelectrics have been developed to be potential candidates for power generation and electronic cooling, among which SnSe crystals are becoming the most representative. Herein, we realize high-performance SnSe crystals with promising efficiency through a structural modulation strategy. By alloying strontium at Sn sites, we modify the crystal structure and facilitate the multiband synglisis in p-type SnSe, favoring the optimization of interactive parameters μ and m * . Resultantly, we obtain a significantly enhanced PF ~85 μW cm −1 K −2 , with an ultrahigh ZT ~1.4 at 300 K and ZT ave ~2.0 among 300–673 K. Moreover, the excellent properties lead to single-leg device efficiency of ~8.9% under a temperature difference ΔT ~300 K, showing superiority among the current low- to mid-temperature thermoelectrics, with an enhanced cooling Δ T max of ~50.4 K in the 7-pair thermoelectric device. Our study further advances p-type SnSe crystals for practical waste heat recovery and electronic cooling. Thermoelectric technology directly enables both power generation and electronic cooling. Here, the authors realize high-performance SnSe crystals with promising device efficiencies by modulating crystal and band structures.

Ensuring data integrity in cloud-edge environments is critical for IoT ecosystems but is challenged by dynamic data and resource constraints. This paper proposes a certificateless auditing scheme harmonizing cloud security with edge efficiency. By integrating online/offline cryptography and sparse Merkle trees, our approach achieves (1) significant user-side computation reduction via offline or edge-side tag generation, (2) dynamic update complexity versus traditional approaches, and (3) 75% communication overhead savings through pre-download mechanism. The scheme eliminates certificate management and mitigates Key Generation Centre (KGC) risks via decentralized trust mechanisms. Security proofs demonstrate resilience against KGC collusion and tag forgery under the Inv-CDH assumption. Experiments show our scheme audits faster than prior schemes, supporting 500k+ operations at sub-second latency. This work bridges scalability and real-time demands for smart cities and Industry 4.0 while enabling future extensions in ML-optimized caching and blockchain trust models.

We extend the Malitsky-Tam forward-reflected-backward (FRB) splitting method for inclusion problems of monotone operators to nonconvex minimization problems. By assuming the generalized concave Kurdyka-Łojasiewicz (KL) property of a quadratic regularization of the objective, we show that the FRB method converges globally to a stationary point of the objective and enjoys the finite length property. Convergence rates are also given. The sharpness of our approach is guaranteed by virtue of the exact modulus associated with the generalized concave KL property. Numerical experiments suggest that FRB is competitive compared to the Douglas-Rachford method and the Boţ-Csetnek inertial Tseng’s method.

Orally administered probiotics encounter various challenges on their journey through the mouth, stomach, intestine and colon. The health benefits of probiotics are diminished mainly due to the substantial reduction of viable probiotic bacteria under the harsh conditions in the gastrointestinal tract and the colonization resistance caused by commensal bacteria. In this review, we illustrate the factors affecting probiotic viability and their mucoadhesive properties through their journey in the gastrointestinal tract, including a discussion on various mucosadhesion-related proteins on the probiotic cell surface which facilitate colonization.

Parkinson's disease (PD)-related cognitive impairment (PD-CI) is a common and impactful complication of PD, yet current predictive models often rely on specialized resources, lack interpretability, or have limited cross-population validation. This study aimed to develop an interpretable machine learning framework for PD-CI detection using only routine clinical data, addressing unmet needs in accessible and generalizable PD care. We analyzed 1,279 participants from the Parkinson's Progression Markers Initiative (PPMI) as the discovery cohort and 197 patients from an independent validation cohort. PD-CI was defined by a Montreal Cognitive Assessment (MoCA) score ≤26 and Unified Parkinson's Disease Rating Scale Part I (UPDRS-I) score ≥1. Twenty-one clinical features-encompassing hematological parameters, metabolic markers, and demographics-were preprocessed with synthetic minority over-sampling. Four machine learning models were trained and optimized via nested 5-fold cross-validation. The Random Forest algorithm achieved superior performance in the discovery cohort (AUC = 0.83), outperforming CatBoost (AUC = 0.82), XGBoost (AUC = 0.79), and neural networks (AUC = 0.66). External validation of the framework preserved 71.57% accuracy. SHAP interpretability analysis identified age, neutrophil-to-lymphocyte ratio (NLR), and serum uric acid as critical predictors, revealing synergistic risk effects between elevated inflammation markers and reduced antioxidant levels. This framework demonstrates diagnostic accuracy comparable to advanced neuroimaging while utilizing readily available clinical data, enhancing accessibility in resource-limited settings. It highlights neuroinflammation and oxidative stress as key mechanistic drivers of PD-CI, advancing pathophysiological understanding. Multicenter validation confirms the model's robustness across ethnic populations, supporting its utility as a clinically actionable tool for PD-CI screening and monitoring.

Chlorogenic acid (CGA), a natural phenolic compound, is an important bioactive compound, and its antibacterial activity has been widely concerned, but its antibacterial mechanism remains largely unknown. Protein leakage and the solution exosmosis conductivity of Bacillus subtilis 24434 (B. subtilis) reportedly display no noticeable differences before and after CGA treatment. The bacterial cells treated with CGA displayed a consistently smooth surface under the electron microscope, indicating that CGA cannot directly disrupt bacterial membranes. However, CGA induced a significant decrease in the intracellular adenosine triphosphate (ATP) concentration, possibly by affecting the material and energy metabolism or cell-signaling transduction. Furthermore, metabolomic results indicated that CGA stress had a bacteriostatic effect by inducing the intracellular metabolic imbalance of the tricarboxylic acid (TCA) cycle and glycolysis, leading to metabolic disorder and death of B. subtilis. These findings improve the understanding of the complex action mechanisms of CGA antimicrobial activity and provide theoretical support for the application of CGA as a natural antibacterial agent.