Search Results (14,738)

Multi-tenancy is essential for scalable IoT–Cloud systems; however, it introduces complex security vulnerabilities at the intersection of shared cloud infrastructures and resource-constrained IoT environments. This systematic review evaluates next-generation security frameworks designed to enforce tenant isolation without violating the strict latency (<10 ms) and energy bounds of lightweight sensors. Adhering to PRISMA guidelines, we analyze selected high-quality studies to categorize intersectional threats, including cross-tenant data leakage, side-channel attacks, and privilege escalation. Our analysis identifies a critical, unresolved conflict: existing mitigation strategies often incur a 12% computational and communication overhead, creating a significant barrier for real-time applications. Furthermore, we critically analyze emerging technologies, including Zero Trust Architectures (ZTA), adaptive Artificial Intelligence (AI), blockchain, and Post-Quantum Cryptography (PQC). We find that direct PQC deployment is currently infeasible for LPWAN protocols due to key-size constraints (1.6 KB) that exceed typical payload limits. To address these challenges, we propose a novel multi-layer security design principle that offloads heavy isolation and cryptographic workloads to hardware-accelerated edge gateways, thereby maintaining tenant isolation without compromising real-time performance. Finally, this review serves as a roadmap for future research, highlighting federated learning and hardware enclaves as essential pathways for securing next-generation multi-tenant IoT ecosystems. Full article

The neuropharmacology of psychedelics has traditionally focused on serotonergic mechanisms, particularly 5-HT2A receptor activation. However, this paradigm incompletely explains the diversity of neurobiological and therapeutic effects observed across psychedelic compounds. Non-classical psychedelics such as salvinorin A, the primary active constituent of Salvia divinorum, challenge this framework through direct kappa opioid receptor (KOR) agonism, representing a serotonin-independent pathway to altered consciousness. This review systematically examines the role of the endogenous opioid system in mediating psychedelic effects, with emphasis on salvinorin A’s unique KOR-dependent mechanisms. We synthesized preclinical and clinical evidence from in vitro studies, genetically modified animal models, optogenetic circuit dissection, and human neuroimaging trials. Salvinorin A’s selective KOR activation is characterized by pronounced β-arrestin-biased signaling, distinguishing it from endogenous dynorphins and classical KOR agonists. This produces rapid receptor desensitization, transient functional plasticity, and profound dissociative effects mediated through thalamocortical disruption, mesolimbic dopaminergic suppression, and fragmentation of large-scale brain networks. Classical serotonergic psychedelics indirectly engage opioid systems through downstream 5-HT2A signaling, contributing to analgesic and mood-regulatory effects via secondary MOR/DOR modulation. Despite being a potent opioid agonist, salvinorin A exhibits low abuse potential due to aversive phenomenology, dopaminergic suppression, and absence of positive reinforcement in animal models. Incorporating opioid receptor pharmacology into psychedelic neuroscience expands mechanistic understanding beyond serotonin-centric models, revealing multiple neurochemical pathways capable of inducing therapeutically relevant altered states. This framework enables rational development of biased KOR ligands and establishes salvinorin A as a paradigmatic model for non-serotonergic psychedelia with applications in treatment-resistant depression, addiction, and chronic pain. Full article

Modulation of the gut microbiota represents a promising approach to counteract diet-induced metabolic alterations, with microalgae emerging as potential interventions. Building on our previous in vivo evidence that dietary supplementation with the marine microalga Tisochrysis lutea F&M-M36 (T. lutea) positively modulates selected metabolic alterations under high-fat feeding, the present study aimed to identify potential associations between these metabolic changes and coordinated modifications of the gut microbiota. Animals were fed normal-fat (NF), high-fat (HF), or HF supplemented with 5% T. lutea (HFTiso) diets for three months. Gut microbial profiles were analyzed by 16S rRNA sequencing and correlated with plasma lipids, glucose, blood pressure, fecal lipid excretion, and adiponectin levels. T. lutea supplementation was associated with significant modulation of selected metabolic parameters and coherent alterations in gut microbial communities. Multivariate analyses revealed treatment-dependent clustering of metabolic profiles, with HFTiso forming an intermediate group between HF and NF diets. Beta-diversity analyses showed marked treatment-specific shifts, while alpha-diversity remained stable. Linear discriminant analysis identified 31 discriminative genera, with the HFTiso group enriched in taxa associated with fermentative metabolism and lipid-related metabolic pathways including Anaerotruncus, Marvinbryantia, and Eubacterium coprostanoligenes, while the HF group was linked to Clostridium sensu stricto 1 and Terrisporobacter. Positive correlations between HFTiso-associated taxa and adiponectin levels were consistent with microbiota-associated metabolic signatures. In parallel, T. lutea supplementation was associated with downregulation of colonic Niemann-Pick C1-like 1 (NPC1L1) mRNA expression, a key mediator of intestinal cholesterol uptake. The bioactivity of T. lutea likely reflects its content of polyunsaturated fatty acids, oleic acid, phytosterols, and fucoxanthin; however, whether these components act synergistically or whether specific bioactive compounds are primarily responsible remains to be clarified. Together, these findings indicate that T. lutea supplementation is associated with coordinated changes in gut microbiota composition and transcriptional modulation of the intestinal cholesterol transporter NPC1L1 in the context of selected early-stage metabolic alterations under high-fat feeding. While direct extrapolation to humans remains limited, these results suggest potential translational relevance of T. lutea as a nutraceutical approach targeting early-stage metabolic dysregulation. Future studies will be required to determine the mechanistic contribution of individual bioactive components and to assess whether microbiota- and gene expression-associated changes play a causal role in mediating the observed metabolic outcomes, thereby informing the rational development of T. lutea-derived interventions. Full article

The proliferation of Unmanned Aerial Vehicles (UAVs) equipped with multispectral imaging sensors has revolutionized data collection in precision agriculture. These platforms provide high-resolution, temporally dense data crucial for monitoring crop health, optimizing resource management, and predicting yield. However, the development and validation of robust data-driven algorithms, from vegetation index analysis to complex deep learning models, are contingent upon the availability of high-quality, standardized, and publicly accessible datasets. This review systematically surveys and characterizes the current landscape of available datasets containing multispectral imagery acquired by UAVs in agricultural contexts. Following guidelines for reporting systematic reviews and meta-analyses (PRISMA methodology), 39 studies were selected and analyzed, categorizing them based on key attributes including spectral bands (e.g., RGB, Red Edge, Near-Infrared), spatial and temporal resolution, types of crops studied, presence of complementary ground-truth data (e.g., biomass, nitrogen content, yield maps), and the specific agricultural tasks they support (e.g., disease detection, weed mapping, water stress assessment). However, the review underscores a critical gap in standardization, with significant variability in data formats, annotation quality, and metadata completeness, which hampers reproducibility and comparative analysis. Furthermore, we identify a need for more datasets targeting specific challenges like early-stage disease identification and anomaly detection in complex crop canopies. Finally, we discuss future directions for the creation of more comprehensive, benchmark-ready open datasets that will be instrumental in accelerating research, fostering collaboration, and bridging the gap between algorithmic innovation and practical agricultural deployment. This work serves as a foundational guide for researchers and practitioners seeking suitable data for their work and contributes to the ongoing effort of standardizing open data practices in agricultural remote sensing. Full article

Hydrogen gas sensors are essential for ensuring safety and efficient operation in the expanding hydrogen energy economy and its infrastructure. However, real-world sensor performance is frequently compromised by interference from coexisting gases, environmental fluctuations, and physical disturbances, leading to false alarms, missed detections, and increased recalibration or maintenance burden. This comprehensive review systematically summarizes recent advances in anti-interference technologies for hydrogen gas sensors across material-, signal-processing-, and system-level domains. At the material level, strategies such as noble-metal doping, nanostructure engineering, and selective membrane coatings improve selectivity and long-term stability. At the signal level, advanced noise reduction, dynamic calibration, drift compensation, and machine-learning-based pattern recognition enhance detection accuracy and robustness under varying conditions. At the system level, sensor arrays, optimized packaging, structural isolation, and adaptive redundancy mitigate interference in realistic deployments. By critically evaluating these multi-scale strategies, this review highlights progress, identifies key performance trade-offs, and outlines research directions toward interference-resilient sensing that supports scalable, low-maintenance, and energy-efficient hydrogen infrastructure. Full article

Accurate determination of in situ stress is fundamental for the safe and efficient design of underground construction projects such as tunnels, caverns, and deep mining excavations. Conventional techniques—particularly overcoring and hydraulic fracturing—have been widely adopted for decades, but their practical use is often constrained by high operational cost, rigorous field requirements, and logistical limitations at depth. As engineering projects advance into deeper and more complex geological environments, these constraints have prompted growing interest in laboratory-based, core-derived stress measurement approaches. Such methods utilize the stress-relief deformation that occurs when drill cores are extracted, enabling stress estimation without extensive downhole instrumentation. This paper presents a critical review of core-based stress measurement techniques based on a structured survey of peer-reviewed literature retrieved from major scientific databases (Web of Science, Scopus, and Google Scholar), covering studies published from the 1960s to 2025. The review examines Anelastic Strain Recovery (ASR), Differential Strain Curve Analysis (DSCA), Deformation Rate Analysis (DRA), acoustic-emission-based Kaiser effect approaches, and the emerging Diametrical Core Deformation Technique (DCDT). Recent studies show that DCDT, which measures instantaneous elastic diametrical deformation of cores, provides a more direct and physically transparent link to differential in situ stress, with reduced sensitivity to time-dependent effects. The DCDT, based on precise measurement of instantaneous elastic deformation upon coring, offers high-resolution stress estimation with minimal disruption to field operations. Its compatibility with optical scanning, laser micrometers, and CT imaging highlights its potential as a practical alternative to conventional techniques. A comparative synthesis of assumptions, accuracy, and applicability is provided, and key limitations and future research needs of core-based stress measurement methods are identified. The findings of this review provide practical guidance for selecting stress measurement techniques and support the application of core-based methods, particularly DCDT, in deep underground engineering, where cost-effective and reliable stress characterization is required. Full article

The endo-lysosomal channel TRPML2 regulates key processes like membrane trafficking and autophagy, which are hijacked by many RNA viruses during endocytic entry. However, the development of TRPML2-targeted therapeutics has been hindered by a notable lack of high-affinity and selective peptide-based activators. Scorpion venom peptides, honed by evolution for exceptional specificity toward diverse membrane ion channels, represent a promising, underexplored natural library for discovering novel pharmacological probes and drug leads. Here, we screened and identified seven candidate peptides interacting with TRPML2 using co-immunoprecipitation combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis of the Mesobuthus martensii venom. Based on molecular docking analysis, the top four candidates—MMTX, BmP05, BmTX1, and BmKK12—were selected for chemical synthesis, oxidatively cyclized to form their native disulfide-bridged conformations, and subsequently purified and characterized by analytical HPLC and MS. Calcium imaging confirmed that two of the four oxidized peptides, BmP05 and BmKK12, exhibited superior potency in inducing a sharp increase in Ca²⁺ influx. Crucially, BmP05 and BmKK12 demonstrated potent, concentration-dependent inhibition of Zika virus (ZIKV) replication at the RNA level at non-cytotoxic concentrations, whereas the weaker activators MMTX and BmTX1 did not. The current study first reports animal venom-derived peptides that function as specific TRPML2 agonists with concomitant antiviral activity. Together, our findings provide not only new molecular probes for dissecting TRPML2 biology but also a pioneering strategy for developing host-directed, broad-spectrum therapeutics against viruses dependent on endo-lysosomal entry. Full article

Objective: The aim of this thesis was to externally validate a predictive model of suboptimal surgery in advanced ovarian cancer, developed by doctors Escrig and Llueca. The model classifies patients pre-surgically to estimate the likelihood of incomplete cytoreductive surgery. Methods: A retrospective cohort comparison between two time periods was performed. Validation used a new cohort of 83 patients with advanced ovarian cancer, prospectively collected between 2017 and 2023 across five hospitals (experimental group). This group was compared with the original control cohort (2013–2016), which had served for model development. The predictive models (R3 and R4) are based on the Peritoneal Carcinomatosis Index (PCI) assessed by CT, laparoscopic PCI, and the presence of intestinal sub-obstruction. For model R4, intraoperative PCI was also included. Results: The experimental group had a lower rate of suboptimal cytoreduction compared with the control group (4.8% vs. 13.8%; p = 0.049). Significant differences were observed in ascites (49.4% vs. 27.5%; p = 0.002), and no patient in the experimental group presented intestinal sub-obstruction (0% vs. 8%; p = 0.002). Although at least 13 suboptimal surgeries were expected for validation, only four occurred. The predictive models did not classify any of these four cases as high risk, instead categorizing them as low or intermediate risk. Conclusions: Statistical external validation could not be performed due to event scarcity. This reduced incidence is attributed to selection bias: highly experienced surgical teams from participating centres likely applied criteria similar to those of the model, referring high risk patients (e.g., with intestinal sub-obstruction) to neoadjuvant therapy and thus avoiding suboptimal primary surgeries. Although direct validation was not possible, the findings indirectly suggest that the model is effective in guiding patient selection and improving surgical outcomes. Full article

In-tube solid-phase microextraction (IT-SPME) is an advanced microextraction technique in which a sample solution flows through a capillary containing an internal stationary phase, enabling efficient extraction and preconcentration of target analytes. The online coupling to liquid chromatography is a key advantage of this technique, enabling full automation and high analytical throughput, both of which are significant for food analysis. Recent advances have focused on developing novel sorbent materials that respond to external stimuli (e.g., magnetic, electrical, or thermal) and on integrating them into emerging chromatographic platforms. Moreover, key operational parameters, including sample volume, pH, phase thickness, and the capillary’s dimensions (length and inner diameter), must be optimized to achieve enhanced selectivity, speed, and sensitivity. Despite this, the literature still lacks updated reviews of SPME concepts and their innovations for versatile applications in food matrices. Hence, this review outlines the fundamental principles of IT-SPME while highlighting key parameters that affect analytical performance. Finally, we provide a literature review of SPME applications in food analysis over the past 6 years, while exploring current trends and future directions for SPME development and enhanced applications in food science. Full article

The global expansion of genetically modified (GM) crop cultivation has increased the demand for analytical platforms that can provide rapid, reliable, and cost-effective detec-tion of GM-derived ingredients to support traceability, regulatory compliance, and accu-rate labeling. Conventional molecular assays such as polymerase chain reaction (PCR) and isothermal amplification are highly sensitive and specific but depend on sophisticated instrumentation and trained personnel, limiting their applicability in field settings. Here, we present a label-free and amplification-free nanobiosensor based on citrate-capped gold nanoparticles (AuNPs) for the direct colorimetric detection of the Cry1Ac gene associated with the MON87701 soybean event, without the use of polymerase chain reaction (PCR) or any enzymatic nucleic acid amplification step. The assay relies on the localized surface plasmon resonance (LSPR) of AuNPs, which induces a red-to-purple color transition upon hybridization between complementary DNA strands. Critical reaction parameters, including NaCl concentration, AuNP size, and ionic strength, were optimized to enable selective and reproducible aggregation. Integration with a Support Vector Machine (SVM) algorithm enabled automated spectral classification and semi-quantitative discrimination of GM content levels. The optimized AuNP–SVM system achieved high sensitivity (limit of detection ≈ 2.5 ng μL⁻¹, depending on nanoparticle batch), strong specificity toward Cry1Ac-positive sequences, and reproducible classification accuracies exceeding 90%. By eliminating enzymatic amplification steps, the proposed platform significantly reduces assay time, operational complexity, and instrumentation requirements, making it suitable for rapid on-site GMO screening. Full article

A significant bottleneck for the practical deployment of fluid antenna systems (FASs) in 6G high-mobility scenarios is the conflicting demands of low outage probability and the high overhead of full port channel estimation. To resolve this problem, a novel “prediction-axiom” dual-driven paradigm is introduced that fundamentally differs from pure data-driven approaches. The core innovation lies in using an enhanced unified adaptive modeling algorithm (UAMA) not for direct decision-making but as a computational foundation to enable information-theoretic axioms under sparse observation conditions (30% of ports). The UAMA predictor, leveraging spatiotemporal correlations, accurately reconstructs the full channel state from limited measurements. This prediction then empowers an information-theoretic scoring mechanism, which synergizes Fisher information, curvature metrics, and port entropy to transform optimal port selection into a tractable maximization problem. Consequently, the system outage probability remains close to the ideal performance limit achievable under full observability. Tests on diverse antenna systems confirm the algorithm’s high accuracy and robust adaptive capability. This work delivers a reliable, low-cost implementation strategy for 6G dynamic networks, effectively bridging the gap between mathematical theory and practical FAS deployment. Full article

Owing to the superior reduction kinetics of limonite and goethite relative to silicates, coupled with the poor beneficiation performance of saprolite-type laterite, the direct carbothermal reduction of saprolite-type laterite exhibits limited nickel selectivity. This study leverages the selective oxidation effect of CO-CO₂ atmosphere on the metallic iron of pre-reduced minerals, as well as its suppression of Fe²⁺ reduction, to promote iron migration from oxides to the silicate phase, achieving homogenization and thereby negating its kinetic advantage in reduction. Parameter optimization experiments revealed that treating pre-reduced minerals with a 30 vol% CO atmosphere at 1200 °C for 20 min achieves complete iron homogenization within the silicate phase. Compared with the nickel–iron alloy (containing less than 10 wt% Ni) obtained via the RKEF process, the combination of pre-reduction, CO-CO₂ treatment, and the melting reduction process yielded nickel–iron alloys with nickel contents of 52.1 wt% (FeNi50 alloy) and 64.2 wt% at carbon consumptions of 4.0 wt% and 3.83 wt%, respectively, accompanied by nickel recovery rates of 95.5% and 91.2%. Furthermore, the enrichment of Fe²⁺ in the slag significantly reduces its melting point to approximately 1450 °C, enabling complete slag–metal separation after smelting at 1550 °C for 10 min. Full article

Colorectal cancer (CRC) is a significant global health challenge, characterized by an increasing incidence rate and high mortality rate. Early detection and effective treatment are crucial to improving patients’ quality of life. Cytokines and chemokines are key modulators of the tumor microenvironment, influencing the recruitment of immune cells, angiogenesis, proliferation, and metastasis. This narrative review summarizes the current knowledge regarding the potential diagnostic and therapeutic applications of selected cytokines and chemokines in CRC. We discuss their potential as biomarkers for early detection, prognosis, and prediction of treatment response. We also highlight emerging therapeutic strategies targeting cytokine and chemokine pathways, including immune checkpoint inhibitors, modulation of chemokine signaling, and the direct use of cytokines to enhance antitumor immunity, with particular emphasis on interleukin-6 (IL-6), C-X-C motif chemokine ligand 8 (CXCL8), C-C motif chemokine ligand 2 (CCL2), and the C-X-C motif chemokine ligand 12 (CXCL12)–C-X-C chemokine receptor type 4 (CXCR4) axis, which show consistent associations with tumor stage, metastasis, and treatment response. Integrating cytokine- and chemokine-based approaches with combination therapies could lead to more effective conventional treatments. In summary, this review emphasizes the potential of cytokines and chemokines as diagnostic tools and therapeutic targets, paving the way for more personalized and effective treatment strategies for colorectal cancer. Full article

Software-Defined Networking (SDN) has emerged as a fundamental architecture for future Internet systems by enabling centralized control, programmability, and fine-grained traffic management. However, the logical centralization of the SDN control plane also introduces critical vulnerabilities, particularly to Distributed Denial-of-Service (DDoS) attacks that can severely disrupt network availability and performance. To address these challenges, machine-learning (ML) techniques have been increasingly adopted to enable intelligent, adaptive, and data-driven DDoS detection mechanisms within SDN environments. This study presents a PRISMA-guided systematic literature review of recent ML-based approaches for DDoS detection in SDN-based networks. A comprehensive search of IEEE Xplore, ACM Digital Library, ScienceDirect, and Google Scholar identified 38 primary studies published between 2021 and 2025. The selected studies were systematically analyzed to examine learning paradigms, experimental environments, evaluation metrics, datasets, and emerging architectural trends. The synthesis reveals that while single machine-learning classifiers remain dominant in the literature, hybrid and ensemble-based approaches are increasingly adopted to improve detection robustness under dynamic and high-volume traffic conditions. Experimental evaluations are predominantly conducted using SDN emulation platforms such as Mininet integrated with controllers, including Ryu and OpenDaylight, with performance commonly measured using accuracy, precision, recall, and F1 score, alongside emerging system-level metrics such as detection latency and controller resource utilization. Public datasets, including CICIDS2017, CICDDoS2019, and InSDN, are widely used, although a significant portion of studies rely on custom SDN-generated datasets to capture control-plane-specific behaviors. Despite notable advances in detection accuracy, several challenges persist, including limited generalization to low-rate and unknown attacks, dependency on synthetic traffic, and insufficient validation under real-time operational conditions. Based on the synthesized findings, this review highlights key research directions toward intelligent, scalable, and resilient DDoS defense mechanisms for future Internet architectures, emphasizing adaptive learning, lightweight deployment, and integration with programmable networking infrastructures. Full article

An experimental investigation was conducted to evaluate the thermal conductivity (TC) and local heat-transfer coefficients (LHTCs) of nanofluids containing alumina (Al₂O₃), hematite (Fe₂O₃), and copper oxide (CuO) nanoparticles dispersed in deionized water. A newly developed non-invasive LHTC probe was integrated into the inner wall of the test section to enable direct quantification of interfacial heat-transfer performance. The measurements were conducted under laminar and turbulent flow conditons across Reynolds numbers ranging from 1000 to 10,000. The selected nanoparticles were chosen based on their high intrinsic thermal conductivity, cost effectiveness, and, in the case of Fe₂O₃, magnetic recoverability. The nanoparticles enhanced both TC and LHTCs through improved thermophysical propoerties and possible interfacial effects. Maximum TC enhancements of 19%, 21%, and 25% were achieved for Al₂O₃/distilled water (DW), Fe₂O₃/DW, and CuO/DW nanofluids, respectively, at 0.05 vol% and 55 °C, while the corresponding LHTC enhancements reached 44%, 50%, and 53%. Under turbulent flow, CuO/DW exhibited the highest heat-transfer performance, attributed to a 25% increase in TC and corresponding improvement in connective heat transfer. Since the boundary-layer thickness exceeded the nanoparticle diameter (30 nm), nanoparticles penetrated the interfacial film, inducing localized micro-convection and catalytic micro-mixing, which intensified interfacial heat transport. The experimentally determined Nusselt numbers showed strong agreement with the Xuan–Qiang correlation at 55 °C, suggesting that the nanoparticle volume fraction governs the catalytic interfacial heat-transfer mechanism. Full article