Search Results (44,567)

Background and Objectives: Systemic inflammatory biomarkers have emerged as potential prognostic indicators in metastatic colorectal cancer (mCRC). However, the prognostic robustness of inflammatory indices such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), C-reactive protein (CRP), C-reactive protein-to-albumin ratio (CAR), and Glasgow Prognostic Score (GPS) remains incompletely characterized. In this study, we aimed to evaluate the prognostic significance of NLR, PLR, CRP, CAR, and GPS for progression-free survival in metastatic colorectal cancer in a cohort of patients from Romania. Materials and Methods: This retrospective observational study included 148 patients diagnosed with mCRC. Inflammatory biomarkers were determined from baseline laboratory parameters. Progression-free survival (PFS) was the primary endpoint. Statistical analyses included correlation testing, Kaplan–Meier survival analysis, Cox proportional hazards regression, Firth penalized Cox regression, restricted cubic spline modeling, time-dependent receiver operating characteristic (ROC) analysis, LASSO penalized regression, multiple imputation, and parsimonious multivariable Cox models adjusted for major clinicopathologic confounders. Results: Median PFS was 21 months (95% CI 19–24). In univariable Cox analyses, elevated NLR (HR 1.98, 95% CI 1.11–3.51, p = 0.020), PLR (HR 1.89, 95% CI 1.25–2.85, p = 0.002), CRP (HR 1.45, 95% CI 1.15–1.83, p = 0.002), and CAR (HR 1.44, 95% CI 1.05–1.98, p = 0.022) were associated with shorter PFS. Restricted cubic spline analysis demonstrated a significant nonlinear association between NLR and PFS (p = 0.0025). After multiple imputation, NLR remained associated with shorter PFS (HR 2.04, 95% CI 1.13–3.68, p = 0.018). However, in a multivariable model adjusted for major clinicopathologic confounders, this association was not retained (HR 1.41, 95% CI 0.81–2.43, p = 0.221) and time-dependent ROC analyses demonstrated its limited discriminatory performance. Conclusions: Although some inflammatory markers were associated with shorter PFS in univariable analyses, the prognostic effect of NLR was attenuated after adjustment and was not consistently confirmed across all analyses. Full article

Background: Evidence on powered wearable exoskeleton gait training in patients with chronic hypoxic-ischemic encephalopathy (HIE) is virtually absent, and existing studies have focused on macroscopic functional outcomes while neglecting joint-level neuromuscular force-generation characteristics such as rate of force development (RFD). Objective: To examine the effects of a six-week powered exoskeleton gait training program on isometric hip strength and RFD, sit-to-stand (STS) performance, frontal-plane hip strength, and center-of-pressure (CoP) dynamics in a patient with chronic HIE-induced quadriparesis. Methods: A case report with pre- and post-intervention evaluation was conducted. A 47-year-old male with chronic HIE-induced quadriparesis (onset 2017) completed 18 sessions (three per week, six weeks) of powered lower-limb exoskeleton gait training. Outcomes included isometric hip peak force and RFD (DynaMo, Vald Performance), STS peak force and body mass-normalized RFD (ForceDecks, Vald Performance), frontal-plane hip strength (ForceFrame, Vald Performance), and CoP path length and mean velocity. Results: Hip extension peak force increased by 247–256% bilaterally, and hip extension RFD increased by 174–188%, whereas hip flexion peak force showed minimal change (+3.3–5.2%). Body mass-normalized STS RFD increased by 250% (10 to 35 N·s⁻¹·kg⁻¹), representing the largest relative gain. Hip abduction strength increased by 27.1–36.8% with improved bilateral symmetry; hip adduction imbalance reversed from right to left dominance. CoP path length and mean velocity each decreased by 3.7%. Conclusions: Six weeks of powered exoskeleton gait training selectively enhanced time-dependent neuromuscular output—particularly RFD—beyond maximal strength gains, with meaningful improvements in functional weight acceptance during STS. These findings support exoskeleton-based training as a promising rehabilitation strategy for patients with chronic CNS injury. Full article

This study systematically reviews the application of machine learning (ML) in emergency resource allocation across Europe, with the aim of synthesizing current evidence and identifying future research directions. A systematic literature review (SLR) was conducted following PRISMA guidelines. Data were collected from major academic databases (2018–2025) using predefined inclusion and exclusion criteria. A total of 52 relevant studies were analyzed through qualitative thematic synthesis. The review finds that ML significantly enhances predictive analytics, enabling accurate forecasting of emergency demand and proactive resource allocation. ML-driven optimization improves ambulance dispatch, hospital resource management, and logistics efficiency, while real-time decision support systems strengthen situational awareness and coordination. However, challenges persist, including data quality issues, system fragmentation, ethical concerns (bias, transparency), and limited interoperability across European systems. ML has transformative potential in shifting emergency resource allocation from reactive to data-driven, predictive systems. Its effectiveness, however, depends on robust data infrastructure, ethical governance, and system integration. The study recommends strengthening data systems, adopting hybrid ML-optimization models, enhancing ethical frameworks, investing in human capacity, and promoting cross-border collaboration. Full article

With the rapid advancement of digital technology, the self-service model has emerged, introducing a new work model known as digital shadow work (DSW). In this model, consumers perform tasks traditionally performed by employees, such as item scanning and self-checkout, without compensation. While this optimizes service processes and reduces business costs, it raises concerns about consumer rights, work value, and business sustainability. This study explores the psychological factors that affect consumer participation in DSW within self-service environments. Using a grounded theory approach and semi-structured interviews, the study reveals key psychological drivers under the dual-system framework. This study’s findings indicate that habitual behavior, impulsivity, time pressure, technological dependence, social identification, and delayed gratification significantly affect participation in DSW. Notably, the intuitive system (System 1) plays a dominant role in decision-making, leading consumers to make quick, automatic choices, often leaving them unaware of the work involved. By identifying these psychological factors, this research increases consumer awareness of DSW, promoting self-protection in self-service contexts. Additionally, understanding decision-making psychology provides essential insights for companies in non-face-to-face self-service technologies, supporting sustainable business practices. Full article

Accurate thermal management is crucial for ensuring the safety, longevity, and performance of lithium-ion batteries, especially in compact embedded systems like USB chargers, power banks, and IoT nodes. Despite extensive research on predictive thermal models and intelligent control frameworks, their implementation in resource-constrained microcontroller-class devices has been limited. Existing strategies in the literature, such as threshold-based or PID logic, cloud-enabled analytics, machine learning models, and observer-based estimators, are often reactive, computationally intensive, or dependent on external infrastructure, making them unsuitable for low-power, standalone applications. This study introduces a novel Scalable Embedded Thermal Intelligence architecture designed for real-time battery thermal regulation in locally executable, without cloud dependency, low-cost platforms. Unlike conventional methods, the proposed system operates entirely on-device using closed-form models implemented on an ESP32 microcontroller. It combines two synergistic algorithms: a static preemptive model that calculates a safe C-rate at startup based solely on ambient and initial battery temperature, and a dynamic disturbance-aware model that monitors temperature rise per SOC step and adjusts airflow or current adaptively without requiring high memory, floating-point units, or supervisory control. The architecture achieves sub-second response times, <7% RAM, and <25% Flash usage, and does not need cloud connectivity, simulation backend, or complex thermal-management infrastructures such as liquid cooling circuits, phase-change systems, or cloud-supervised architectures. The significant contribution of this work is not the introduction of a new electrochemical–thermal formulation, but the effective integration and application of previously validated closed-form thermal predictors on low-cost microcontroller-class hardware, designed for anticipatory battery thermal regulation while adhering to strict computational limitations. Compared to traditional battery thermal management systems using PCM, liquid-cooling circuits, or cloud-based predictive estimators, the proposed approach eliminates the need for complex thermal hardware, fluidic systems, external computing infrastructure and resource-efficient edge operation. This makes the system suitable for deployment in real-world embedded applications like USB-C smart charging cables, compact IoT power banks, and portable medical devices, where form factors, energy efficiency, and cost are critical. The proposed SETI framework offers a firmware-integrated architecture and a firmware-integrated solution that provides a lightweight embedded alternative for predictive thermal regulation for distributed energy systems and miniaturized electronics. Full article

This study evaluates the corrosion inhibition performance of α-pinene, β-pinene, and caryophyllene, constituents of black pine (Pinus nigra) essential oil, on carbon steel in 1 M HCl. At concentrations reflecting their natural abundance in 100 ppm essential oil, α-pinene (66.5 ppm) showed the highest efficiency among individual compounds (up to 94.99%), while β-pinene and caryophyllene exhibited lower efficiencies due to their minor natural content. At an identical concentration (80 ppm), caryophyllene displayed the highest inhibition efficiency after 4 h immersion (96.16%), exceeding α-pinene (92.46%), β-pinene (89.75%), and slightly surpassing the essential oil (95.26%). Electrochemical measurements revealed time-dependent enhancement of protection for all inhibitors. Potentiodynamic polarization indicated mixed-type inhibition with predominant cathodic control and a decrease in corrosion current density from 173.33 μA cm⁻² (blank) to 7.03 μA cm⁻² (caryophyllene). SEM confirmed reduced surface degradation in inhibited systems, while contact angle measurements showed increased hydrophobicity after prolonged exposure to caryophyllene, indicating formation of a stable adsorbed film. DFT calculations corroborated experimental trends, identifying caryophyllene as the most efficient inhibitor due to favorable electronic properties. The results highlight individual phytochemicals as promising sustainable corrosion inhibitors. Full article

Additive manufacturing delivers internal substructures that alter the mechanical performance, yet their exploitation is still limited in structural part design, to a certain degree due to the absence of comparative studies. All slicer software solutions can exchange the infill with predefined infill patterns. Often their performance properties are unknown, and engineers make choices that depend on the printing time or material use. We conduct an experimental campaign to understand infill patterns’ effect on the mechanical performance. This work is inspired by biomimicry and studies honeycomb-, gyroid-, and Archimedean chords-type infill patterns in order to determine their performance. Experimental analysis via the three-point bending test has been conducted by using samples from PolyLactic Acid (PLA) with infill densities of 50, 60, 70, 80, 90 and 100% for these infill patterns. An additional set of samples was printed with Acrylonitrile Butadiene Styrene (ABS) for additional evaluation of Archimedean chords. We characterize the mechanical performance by comparing strength properties and observe that a mass-normalized flexural strength measure is meaningful when selecting an adequate infill pattern. Honeycomb showed the highest absolute flexural strength; strength per mass peaked at 90% infill. Mass reduction effected by infill density reduction fails to be linear; lowering infill down to 50% decreases mass marginally by up to 17% only. The performance of each infill pattern and comparisons between mass, strength, and print time are described to serve as a guide for designers. Full article

Transient instability during the low-voltage ride-through (LVRT) recovery of permanent magnet synchronous generator (PMSG) wind turbines is strongly influenced by weak-grid interactions, while the quantitative relationship among grid strength, control parameters, and recovery performance remains insufficiently understood. This paper develops a small-signal transient recovery characteristic matrix for a grid-connected PMSG system by incorporating the dynamic interactions among the phase-locked loop (PLL), inner current loop, DC-link voltage loop, and grid-side inductance. Dominant-mode and root-locus analyses are employed to investigate how variations in the short-circuit ratio (SCR) affect dominant eigenvalue trajectories and the sensitivities of six PI control parameters. Based on the identified dynamic mechanisms, an SCR-adaptive sensitivity-guided particle swarm optimization (SG-PSO) method is proposed for coordinated PI parameter tuning. The proposed approach introduces SCR-dependent damping constraints and physical feasibility constraints, while normalized real-part eigenvalue sensitivities are utilized to guide the optimization process toward the most influential control parameters. Comparative simulation results demonstrate that, under SCR = 1.5, SG-PSO reduces the point of common coupling (PCC) voltage overshoot to 1.4% and shortens the recovery time to 58 ms, achieving better transient recovery indices than conventional PSO and OOBO-PI under the same simulation and constraint settings. Under SCR = 2.5, the recovery time is further reduced to 46 ms while maintaining a low overshoot of 0.9%. Additional robustness tests under parameter uncertainties and fault-condition variations further support the effectiveness and adaptability of the proposed method. The results indicate that the proposed SG-PSO framework provides an effective solution for enhancing LVRT recovery performance of PMSG wind turbines operating in weak-grid environments. Full article

Audio deepfake and vocoder fingerprint detectors are increasingly used to identify synthetic speech and attribute it to its generating model. However, their robustness against adversarial perturbations remains unclear across attack algorithms, perturbation domains, detector representations, and vocoder types. This paper presents a focused, quality-aware evaluation of four representative adversarial attacks, namely the Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), Projected Gradient Descent (PGD), and Carlini–Wagner (CW) attack, against audio deepfake and vocoder fingerprint detectors. Each attack is implemented in both the waveform domain and the short-time Fourier transform (STFT) magnitude domain. All attacks are optimized against Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks (AASIST) under a targeted fake-to-real objective and are evaluated on synthetic speech generated by HiFi-GAN, Fullband MelGAN, StyleMelGAN, and Parallel WaveGAN. Attack performance is first measured on the source AASIST detector, after which black-box transferability is assessed on three target detector families: ResNet with Linear Frequency Cepstral Coefficient (LFCC) features, LCNN with Constant-Q Cepstral Coefficient (CQCC) features, and a bidirectional long short-term memory (BiLSTM) detector. The results show that adversarial effectiveness depends strongly on perturbation domain and detector representation. STFT-magnitude PGD transfers strongly to LFCC-based ResNet detectors but has limited effect on CQCC-based and recurrent detectors. In contrast, waveform-domain attacks produce broader transferability across feature-based detectors, with different attacks showing distinct ASR–quality trade-offs. Under the chosen waveform-domain budget, FGSM and BIM preserve transcription-level intelligibility while retaining meaningful black-box transferability, whereas CW provides the strongest overall source-detector and black-box attack performance. To distinguish effective adversarial perturbations from destructive signal degradation, we evaluate audio quality and intelligibility using word error rate (WER) and signal-to-noise ratio (SNR). Overall, the findings show that robustness claims in audio deepfake and vocoder fingerprint detection are limited when adversarial perturbations, black-box transferability, and audio quality are jointly considered. Full article

The soybean aphid (Aphis glycines) and soybean cyst nematode (Heterodera glycines) are major aboveground and belowground pests of soybean (Glycine max) in the U.S. Midwest, but the molecular basis of their combined effects on soybean defense remains poorly understood. This study examines how soybean genotypes influence demographic and root-transcriptomic responses to single and combined pest infestation. Soybean cyst nematode reproduction increased under combined infestation in the susceptible cultivar but remained unchanged in the resistant cultivar, whereas soybean aphid populations declined when plants were also infested with nematodes. Root RNA-seq revealed strong time-dependent transcriptional responses, with substantially more differentially expressed genes at 30 days post-infestation than at 5 days post-infestation. Co-expression and enrichment analyses showed that early responses were associated with defense signaling, plant–pathogen interaction, and cutin, suberin, and wax biosynthesis, whereas later responses involved redox processes, isoflavonoid biosynthesis, phenylpropanoid metabolism, and one-carbon metabolism. Several differentially expressed soybean genes co-localized with known soybean cyst nematode resistance quantitative trait loci, including genes near the rhg1 region. Together, these results suggest that soybean genotypes strongly influence soybean aphid–soybean cyst nematode interactions and identify candidate genes and pathways that may contribute to durable resistance against interacting aboveground and belowground pests. Full article

Early diagnosis of Diabetic Retinopathy (DR) is critical for preventing irreversible vision loss, but precise lesion annotation by ophthalmologists is the dominant cost in building any clinical-grade DR detection model. The structural problem in real hospital settings is not labeling cost per se, but expert availability: ophthalmologists’ time is bounded by clinical duties, so the active-learning (AL) cycle can iterate only a handful of times in practice. We frame this constraint explicitly and ask which AL designs work best under a tight expert budget. We propose Virtuous Cycle, a Human-in-the-Loop (HITL) pipeline that integrates (i) a YOLOv8x-based object detector for microaneurysms, hemorrhages, and exudates, (ii) four AL sampling strategies (Average Confidence, Random, Hybrid-Diversity, Monte Carlo Dropout), and (iii) an in-hospital annotation platform (Diavision Studio) in which clinicians refine AI pre-labels rather than draw from scratch. We evaluate Virtuous Cycle on a real-world fundus dataset from the National Medical Center (NMC) across eight AL rounds, expanding the labeled pool from 81 images (R0) to 481 images (R8) within the actual expert-time budget of two ophthalmologists. Across three independent random seeds, random sampling dominates at cold start (mean mAP@50 $0.14\to 0.25$ over R0–R1), whereas Hybrid-Diversity converges to the highest mAP@50, Precision, and Recall by R7 (431 images; mAP@50 $0.40$, Precision $0.55$, Recall $0.41$), with MC Dropout close behind; by R8, the labeled pool is exhausted and all strategies converge to the same final model. A clinician crossover analysis of 36 paired clinical images, controlling for per-clinician speed bias and per-image difficulty bias, shows no statistically significant difference in overall per-image labeling time between AI-assisted and manual annotation ($p=0.52$), but a statistically significant increase in confirmed lesion detections under AI assistance ($p=0.0058$), driven predominantly (84–$100\%$ of the net increase) by microaneurysms, the lesion type most prone to being missed unaided. The results indicate that, under expert-budget constraints, AL strategy choice should be staged: random sampling for cold start, uncertainty-and-diversity sampling once the model has matured, and that AI assistance trades a modest, lesion-burden-dependent time cost for a measurable gain in the sensitivity of microaneurysm detection. Full article

Virtually all spaceborne topographic lidars to date have used a single beam, with the exception of the ATLAS lidar on NASA’s ICESat-2 satellite, which split the beam into 3 “strong” and 3 “weak” beamlets distributed perpendicular to the along-track path of the satellite. This approach has provided high-resolution along-track surface measurements but relatively poor resolution cross-track measurementswithin a given surface area. The present paper attempts to resolve this discrepancy by (1) transmitting and scanning a single Gaussian beam and (2) imaging the return onto a 14 × 14 pixelated, single-photon sensitive, detector array, thereby providing between 100 and 196 measurements per pulse, depending on the solar background. Besides enhancing the lidar’s capability to penetrate tree canopies and water bodies, the proposed single-beam approach provides one to two orders of magnitude more measurements per pulse with equal spatial resolution in boththe along-track and cross-track directions. At the 10 kHz pulse rate of the ATLAS laser on NASA’s ICESat-2 satellite, this implies between 1 and 2 million topographic measurements per second. The maximum surface area observable by a single pulse increases with the laser peak power defined by the ratio of the pulse energy to the temporal pulsewidth. Larger surface areas per pulse result in more time for cross-track scanning while still maintaining contiguous along-track mapping. Two scanning methods appear to be feasible: (1) circular scans using individual but temporally coordinated wedge scanners for the transmitted and received beams, and (2) unidirectional linear scans utilizing Acousto-Optic Deflectors. The circular scan approach is probably easier to implement, but it also requires additional post-processing to obtain an accurate contiguous 3D image of the planetary terrain. Full article

Type 2 diabetes (T2D) is a chronic metabolic disorder leading to increased cardiovascular risk and vascular dysfunction. Hyperglycemia, a hallmark of T2D, drives hypercontractility, thereby compromising vascular function. Heat shock protein 70 (HSP70) has emerged as an important player in vascular reactivity under physiological conditions via its interaction with calcium mobilization, and in T2D, blocking this protein prevents hypercontractility. Circulating extracellular HSP70 (eHSP70) has also been proposed as a biomarker in chronic diseases, as it can function as a damage-associated molecular pattern (DAMP) to activate the innate immune system and promote low-grade inflammation. Berberine (BBR), a natural alkaloid with anti-inflammatory properties, has been shown to attenuate vascular contraction by modulating intracellular calcium handling. Yet the link between HSP70 and BBR in modulating vascular contraction in T2D remains unknown. Therefore, we investigated whether acute and/or chronic BBR treatment modulates HSP70 to prevent vascular hypercontractility in the T2D mouse model. For acute ex vivo treatment, db/+ and db/db aortic rings were incubated for 30 min with or without the HSP70 inhibitor VER155008, in the presence or absence of BBR or vehicle. For chronic in vivo treatment, db/+ and db/db mice received intraperitoneal BBR injections (10 mg/kg, 3 times per week) and BBR in their drinking water (0.5 mg/mL) for 28 days. Following chronic (4 weeks, in vivo) or acute ex vivo (30 min) BBR treatment, vascular function was assessed in aortic rings isolated from male T2D (db/db) and age-matched non-diabetic (db/+) mice using wire myography. Rings were incubated with or without the HSP70 inhibitor VER155008, in the presence or absence of BBR or vehicle. Overt hyperglycemia and hypercontractility were observed in diabetic animals compared with non-diabetic controls. While acute BBR treatment attenuated vasoconstriction in both diabetic and nondiabetic groups, the combination of BBR and VER155008 produced a stronger inhibitory effect only in the diabetic group. Chronic BBR treatment prevented aortic hypercontractility in diabetic mice; however, the synergistic effect with VER155008 was no longer observed. Additionally, BBR reduced systemic HSP70 levels. Collectively, these findings indicate that BBR improves vascular smooth muscle cells’ function in T2D, at least in part, through HSP70-dependent mechanisms during chronic treatment. Full article

Effective thoracic surgery requires timely, predictable operative lung collapse. During one-lung ventilation (OLV), lung collapse is not merely a mechanical consequence of nonventilated lumen opening but a phase-dependent physiological process. Rapid phase I collapse is driven by elastic recoil and passive gas venting, whereas slower phase II collapse depends on residual alveolar gas absorption. Communication between the operative-side airway and the atmosphere before pleural opening may permit tidal gas movement, ambient air entrainment, and nitrogen re-entry during the closed-chest period, delaying subsequent absorption collapse. This narrative review reorganizes lung collapse strategies, including denitrogenation, operative-side airway occlusion, preemptive OLV, disconnection, bronchial suction, and the open-clamp airway technique, according to timing and physiological target. Before pleural opening, alveolar nitrogen should be reduced and ambient air entrainment prevented. Around the pleural opening, airway patency and brief suspension of positive-pressure ventilation may preserve elastic recoil venting. During OLV maintenance, re-clamping or limiting atmospheric communication may support residual gas absorption. This phase-based framework interprets recent clinical findings as interventions acting before, during, and after pleural opening. This may help clinicians select strategies according to the lung isolation device, oxygenation reserve, and surgical environment, although standardized endpoints and component-level validation remain necessary. Full article

Automatic modulation classification (AMC) is a critical enabler of cognitive radio (CR) systems, allowing secondary users to identify primary user modulation schemes and adapt transmission parameters in real time. Traditional AMC approaches, based on likelihood functions or hand-crafted features, suffer from degraded performance under low signal-to-noise ratio (SNR) conditions and realistic channel impairments. In this paper, we propose MS-SENet (Multi-Scale Squeeze–Excitation Network), a novel deep-learning architecture that integrates multi-scale convolutional feature extraction, squeeze-and-excitation channel attention, residual learning, bidirectional long short-term memory (BiLSTM) temporal modelling, and global attention pooling into a unified framework for robust AMC. The multi-scale convolution module employs parallel branches with kernel sizes of 3, 5, and 7 to capture both fine-grained phase transitions and coarse envelope patterns from raw in-phase/quadrature (I/Q) signal samples. Squeeze–excitation residual blocks perform channel-wise feature recalibration, enabling the network to emphasize informative feature maps while suppressing less relevant ones. A bidirectional LSTM layer models temporal dependencies across the signal sequence, and a global attention pooling mechanism performs weighted temporal aggregation prior to classification. We present a comprehensive taxonomy of deep-learning architectures for AMC organised along five axes—input representation, feature extraction, temporal modelling, regularization strategy, and architectural complexity—and conduct a rigorous comparative evaluation against ten baseline architectures on a RadioML-style synthetic dataset (110,000 samples, 11 modulation classes, and 20 SNR levels from −20 to +18 dB). The experimental results demonstrate that MS-SENet achieves a mean classification accuracy of 87.9% at SNR ≥ 0 dB (the average of the medium and high SNR regime averages: 86.06% for 0 ≤ SNR < 10 dB and 89.68% for SNR ≥ 10 dB) while maintaining a compact footprint of approximately 406 K parameters, making it suitable for deployment on resource-constrained edge devices. We further analyze the robustness of the proposed architecture to multipath fading, carrier frequency offset, and sample rate offset, confirming its resilience under practical operating conditions. MS-SENet is an architecture designed for automatic modulation classification of I/Q signals and is not related to the homonymous architecture for speech emotion recognition. Full article