Search Results (64)

As Large Language Models (LLMs) become increasingly integrated into web environments, they introduce complex microarchitectural noise that challenges existing hardware security mechanisms. This paper investigates the impact of concurrent web-based LLM workloads on the detection accuracy of Spectre attacks. Firstly, we constructed a representative dataset by executing multiple web-accessible LLMs (e.g., DeepSeek, Kimi, Doubao and Qwen) alongside Spectre attacks, capturing the specific interference patterns introduced by these AI workloads. Experimental analysis reveals that traditional Hardware Performance Counter (HPC)-based detectors, relying primarily on branch prediction and Last-Level Cache (LLC) events, suffer significant accuracy degradation due to the masking effects of LLM-induced noise. To address this limitation, we then propose a novel Spectre attack detector Spec-LAMP via augmenting conventional HPC feature sets with the L1D Miss Pending event. This new metric specifically captures unresolved speculative memory dependencies, a distinctive characteristic of Spectre attacks that remains discernible even under web-accessible LLM interference. Comparative statistical analysis demonstrates that incorporating this event significantly enhances the separability between malicious and benign executions. Finally, experimental results show that our proposed feature augmentation effectively restores detection performance, increasing average accuracy from 85.15% to 98.43% and demonstrating superior robustness compared to traditional approaches in realistic web-based LLM scenarios. Full article

An enhanced dynamic bias comparator with a reference-compensated offset calibration technique is implemented in a 180 nm CMOS process. In order to reduce the delay time of the comparator, an enhanced structure is used. To reduce the power consumption, a dynamic bias technique is applied to the comparator. A novel reference-compensated offset calibration technique is introduced to achieve offset calibration. Spectre simulation results indicate that the proposed comparator achieves a delay time of 190.3 ps and an energy consumption of 324.2 fJ/comparison under operating conditions of 150 MHz and an input differential amplitude of 0.1 V, compared to a delay time of 235.5 ps and an energy consumption of 636.6 fJ/comparison for the conventional comparator. Furthermore, the application of a reference-compensated offset calibration technique facilitates a reduction in the offset voltage of the comparator from 18.1 mV to 6.3 mV. Full article

Addressing the limitations of existing power amplifiers, particularly in terms of accuracy and noise performance, a high-voltage and high-current power amplifier has been developed. The input stage utilizes a rail-to-rail circuit structure, allowing the amplifier to deal with the full swing of input signals from the negative to the positive power supply. The output stage features an innovative class AB configuration with a bias structure, effectively reducing the crossover distortion typically associated with traditional circuits. This design improves linearity, achieving an output range that extends to the rails, while also enhancing the power supply rejection ratio and optimizing noise performance. Furthermore, over-temperature protection and current limiting circuits have been integrated to safeguard the system against permanent damage under extreme conditions. The power amplifier circuit was simulated and validated using Cadence 61 Spectre software. With a power supply of ±30 V, the amplifier achieved an output current of 560 mA, a low-frequency gain of 138 dB, a bandwidth of 24 MHz, and a noise level of 4.8 nV/$\sqrt{\mathrm{H}\mathrm{z}}$. The slew rate was measured at 14.2 V/μs. Compared to existing literature, significant advancements have been achieved in terms of gain, bandwidth, and noise performance. Full article

Background/Objectives. ICD–11 classifies burnout as a work-related issue arising from chronic workplace stress that has not been successfully managed. According to the Job Demands/Resources Model, job demands represent sources of stress and job resources may buffer the impact of job demands on job strain. Since every profession has its specific spectre of work demands/resources related to stress development, the aim of this study was to examine a model predicting workplace burnout dimensions (emotional exhaustion—EE, depersonalisation—DP, personal accomplishment—PA) in special educational needs (SEN) and general education (GE) teachers, with job demands representing potential “risk factors” and job resources potential “protective factors”. Methods. The study involved 116 SEN teachers from eight primary schools for children with learning difficulties and a sample of 145 teachers from general primary schools in the Belgrade region, which was balanced according to the representation of the main demographic variables in the SEN group. The Maslach Burnout Inventory and Job Characteristics Questionnaire were the instruments employed. Results. No difference was found between SEN and GE teachers in the intensity of burnout dimensions. In the SEN group, Changes were the predictors of all three burnout dimensions, Work environment for EE and DP, Emotional demands and Support from colleagues for EE, Cognitive/Quantitative for PA, and Job control for PA. Concerning the GE group, Support from colleagues predicted all three dimensions, Job control EE and DP, Cognitive/Quantitative DP and PA, Changes DP, and Role conflict and Seniority EE. Conclusions. The results of the study provide a foundation for further testing of a hypothetical predictive model of burnout with job demands as direct predictor and job resources as mediators of this relation. Full article

Digital evidence custody requires consensus protocols that guarantee immediate and deterministic finality. Legal admissibility depends on proof that no party can alter or delay confirmation of evidence transfers. Conventional Byzantine fault tolerance protocols scale poorly because of quadratic communication overhead, while probabilistic ledger systems such as IOTA and SPECTRE produce confirmation uncertainty that weakens custody verification. This paper introduces SelectVote Byzantine Fault Tolerance, a deterministic consensus protocol that infers virtual votes from graph structure instead of exchanging explicit messages. The protocol operates in permissioned forensic networks and assigns validation witnesses through a fixed, hash-based selection process. Empirical evaluation demonstrates sub-quadratic communication scaling ($O\left(n^{1.7}\right)$) compared to traditional $O\left(n^2\right)$ Byzantine protocols and maintains Byzantine resilience. To ensure physical integrity, the paper also presents an environmental compensation framework for precision weight verification. The framework models temperature, humidity, and pressure effects on load cells and corrects measurement drift to preserve sub-gram accuracy across normal storage conditions. Experimental evaluation confirms that the integrated system sustains high throughput with deterministic finality and maintains consistent measurement precision under environmental variation. The combined result supports reliable, legally defensible custody of digital evidence across distributed institutions. Full article

Aiming at the problems of fixed trigger patterns that are prone to detection in existing invisible backdoor attacks, this paper proposes a backdoor attack method that integrates local binary pattern (LBP) with dynamic randomized least significant bit (LSB) steganography. The multi-bit coding characteristic of LBP is leveraged to enrich the representational expressiveness of trigger information within the embedding budget, combined with LSB steganography to maintain visual imperceptibility, and a pseudo-random number generator (PRNG) is introduced to randomize embedding locations to mitigate detectors that rely on fixed-position patterns. Experiments show that the proposed method demonstrates potential advantages in terms of steganography, attack success rate, and anti-detection capability on both CIFAR-10 and Tiny-ImageNet datasets. Among them, the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) reach up to 0.98 and above 36 dB in terms of covertness, respectively. In anti-detection experiments, the attack method maintains high attack success rates under D-BR defense (CIFAR-10: Test_ASR > 85%; Tiny-ImageNet: Test_ASR > 95%), while under SPECTRE defense—a spectral-based statistical method—the defender’s leakage detection rate of poisoned samples remains low (CIFAR-10: 5.96%; Tiny-ImageNet: 10.56%). This clearly validates the proposed attack’s robustness against mainstream defense mechanisms. Full article

The Bilbao parish, located on the slopes of the Tungurahua volcano (Ecuador), was heavily impacted by ashfall during eruptions between 1999 and 2016. Volcanic ash may contain toxic metals such as Pb, Cd, Hg, As, and Se, which are linked to neurological, renal, skeletal, pulmonary, and dermatological disorders. This study evaluated metal concentrations in soil (40–50 cm depth, corresponding to the rooting zone of forage grasses), forage (English ryegrass and Kikuyu grass), and raw milk to assess potential risks to livestock and human health. Sixteen georeferenced sites were selected using a simple random probabilistic sampling method considering geological variability, vegetation cover, accessibility, and cattle presence. Samples were digested and analyzed with a SpectrAA 220 atomic absorption spectrophotometer (Varian Inc., Victoria, Australia). Soils (Andisols) contained Hg (1.82 mg/kg), Cd (0.36 mg/kg), As (1.36 mg/kg), Pb (1.62 mg/kg), and Se (1.39 mg/kg); all were below the Ecuadorian limits, except for Hg and Se. Forage exceeded FAO thresholds for Pb, Cd, As, Hg, and Se. Milk contained Pb, Cd, and Hg below detection limits, while Se averaged 0.047 mg/kg, exceeding water safety guidelines. Findings suggest soils act as sources with significant bioaccumulation in forage but limited transfer to milk. Although immediate consumer risk is low, forage contamination highlights long-term hazards, emphasizing the need for monitoring, soil management, and farmer guidance. Full article

In this cohort study, we evaluated the cognitive and learning difficulties of school-age children perinatally infected with Chikungunya virus (CHIKV) on Reunion Island using the Evaluation of Cognitive Functions and Learning in Children (EDA) battery screening test compared to the healthy children cohort used for EDA development. Of the 19 infected children, 11 (57.9%) exhibited subnormal or abnormal scores, of whom 3 were classified as high risk, and 8 were classified as at risk for cognitive and learning difficulties. Children who had encephalopathy were at higher risk for displaying at least one difficulty than non-encephalopathic children (relative risk 2.13; 95% CI 1.05–4.33). The difficulties observed affected verbal functions, non-verbal functions, and learning abilities, such as phonology, lexical evocation and comprehension, graphism, selective visual attention, planning, visual–spatial reasoning, dictation and mathematics, as well as core executive functions, such as inhibitory control, shifting, and working memory. Neurocognitive dysfunctions could be linked to severe brain damage, as evidenced by severe white matter reduction mainly in the frontal lobes and corpus callosum and potentially in all functional networks involved in difficulties. These results should motivate further investigation of intellectual and adaptive functioning to diagnose intellectual deficiency and severe maladaptive behaviour in children perinatally infected with Chikungunya virus. Full article

This paper investigates the impact of different Large Language Models (DeepSeek, Kimi and Doubao) on the attack detection success rate of evasive Spectre attacks while accessing text, image, and code tasks. By running different Large Language Models (LLMs) tasks concurrently with evasive Spectre attacks, a unique dataset with LLMs noise was constructed. Subsequently, clustering algorithms were employed to reduce the dimension of the data and filter out representative samples for the test set. Finally, based on a random forest detection model, the study systematically evaluated the impact of different task types on the attack detection success rate. The experimental results indicate that the attack detection success rate follows the pattern of “code > text > image” in both the evasive Spectre memory attack and the evasive Spectre nop attack. To further assess the influence of different architectures on evasive Spectre attacks, additional experiments were conducted on an NVIDIA RTX 3060 GPU. The results reveal that, on the RTX 3060, the attack detection success rate for code tasks decreased, while those for text and image tasks increased compared to the 2080 Ti. This finding suggests that architectural differences impact the manifestation of Hardware Performance Counters (HPCs), influencing the attack detection success rate. Full article

This preliminary study investigates the potential relationship between electrophysiological profiles measured by quantitative electroencephalography (QEEG) and attentional performance in 34 university American football players. QEEG data revealed patterns associated with burnout, chronic pain, and insomnia among the athletes. Attentional performance was generally average, but players exhibited faster reaction times in the alertness task without warning, fewer errors in the sustained attention task, and lower scores in the divided attention task, favoring visual information over auditory information. Significant negative correlations emerged between QEEG profiles associated with burnout, ADHD, depression, and anxiety and specific attentional subcomponents. These findings suggest a link between mental health-related brain activity and attentional performance. In a clinical context, they emphasize the need for early detection and intervention in mental health problems. This might improve cognitive performance and well-being in athletes. However, due to the small sample size and the lack of a control group, these results are considered preliminary, and further research is required to confirm and expand on these associations. Full article

Spectre variants 1 and 2 pose grave security threats to dynamic branch predictors in modern CPUs. While extensive research has focused on mitigating these attacks, little attention has been given to their energy and power implications. This study presents an empirical analysis of how compiler-based Spectre mitigation techniques influence energy consumption. We collect fine-grained energy readings from an HPC-class CPU via embedded sensors, allowing us to quantify the trade-offs between security and power efficiency. By utilizing a standard suite of microbenchmarks, we evaluate the impact of Spectre mitigations across three widely used compilers, comparing them to a no-mitigation baseline. The results show that energy consumption varies significantly depending on the compiler and workload characteristics. Loop unrolling influences power consumption by altering branch distribution, while speculative execution, when unrestricted, plays a role in conserving energy. Since Spectre mitigations inherently limit speculative execution, they should be applied selectively to vulnerable code patterns to optimize both security and power efficiency. Unlike previous studies that primarily focus on security effectiveness, this work uniquely evaluates the energy costs associated with Spectre mitigations at the compiler level, offering insights for power-efficient security strategies. Our findings underscore the importance of tailoring mitigation techniques to application needs, balancing performance, energy consumption, and security. The study provides practical recommendations for compiler developers to build more secure and energy-efficient software. Full article

Neutrophils and neutrophil extracellular traps (NETs) contribute to thrombosis and hyperinflammation in myeloproliferative neoplasms (MPN). High-density neutrophils (HDNs) and low-density neutrophils (LDNs) have recently been characterized as distinct neutrophil sub-populations with distinct morphological and functional properties. We aim to study the kinetics of NET formation and inhibition with interferon-α (IFNα) in neutrophils derived from patients with MPN as compared to matched healthy controls. Ex vivo NET formation was assessed by neutrophil-elastase activity, neutrophil-associated nucleosomes, myeloperoxidase (MPO), and citrullinated histone H3 content. IFNα significantly inhibited NET formation in neutrophils derived from MPN patients. Neutrophil sub-population analysis demonstrated that HDNs drive the increase in NET formation as compared to LDNs in patients and in healthy controls and are effectively inhibited by IFNα, an effect that is lost in LDNs. In conclusion, we demonstrate that in MPN, HDNs drive excess NET formation and are more sensitive to IFNα inhibition. These observations uncover unique neutrophil sub-population biology and dynamics in MPN. Full article

This paper presents the CMOS circuit realization of a low-pass Inverse Chebyshev fractional-order filter (FOF) of order (1 + α) using the inverse-follow-the-leader feedback (IFLF) topology. A nonlinear least squares optimization routine is used to determine the coefficients of the fractional-order transfer function to approximate the stop-band characteristics. The Inverse Chebyshev FOF of orders 1.3, 1.6, and 1.9 using cross-coupled operational transconductance amplifier (OTA) was designed in united microelectronics corporation (UMC) 180 nm complementary metal–oxide–semiconductor process. The MATLAB and Cadence Spectre simulations are used to validate the implementation of the fractional-order filter of orders 1.3, 1.6 and 1.9. The dynamic range (DR) of the filter is found to be 83.04 dB, 86.13 dB, and 84.71 dB, respectively, for order of 1.3, 1.6, and 1.9. The simulation results such as magnitude response, transient plot, Monte Carlo, and PVT plots, have justified the design accuracy. Full article

In 1978, the American hard rock band Blue Öyster Cult released the song “Godzilla” as the first single from the fifth studio album Spectres. Despite not registering on popular charts, it would eventually evolve into an iconic song of its era. “Godzilla” continues to receive airplay on classic rock stations, and it remains a staple of the band’s touring performances. In 2019, a cover of the song, more than forty years after its release, made its film debut in Godzilla: King of the Monsters. Though the song is primarily a tribute to the Japanese monster from which it gets its name, “Godzilla” also reflects the nuclear fear and paranoia of the 1970s Cold War era. “Godzilla’s” cultural impact, the song’s lyrics, the Cold War context in which it was written, and its connection to the kaiju films featuring the famous monster are examined. While this is the most popular and well-known song dedicated to Godzilla, it is not the only one. Other compositions have, but they have failed to achieve the iconic status that Blue Öyster Cult’s version has attained. This song has evolved into an unofficial anthem for the great monster. Full article

Evasive Spectre attacks have used additional nop or memory delay instructions to make effective hardware performance counter based detectors with lower attack detection successful rate. Interestingly, the detection performance gets worse under different workloads. For example, the attack detection successful rate is only 59.8% for realistic applications, while it is much lower 27.52% for memory stress test. Therefore, this paper proposes a two-stage smart detector T-Smade designed for evasive Spectre attacks (e.g., evasive Spectre nop and evasive Spectre memory) under various workloads. T-Smade uses the first-stage detector to identify the type of workloads and then selects the appropriate second-stage detector, which uses four hardware performance counter events to characterize the high cache miss rate and low branch miss rate of Spectre attacks. More importantly, the second stage detector adds one dimension of reusing cache miss rate and branch miss rate to exploit the characteristics of various workloads to detect evasive Spectre attacks effectively. Furthermore, to achieve the good generalization for more unseen evasive Spectre attacks, the proposed classification detector T-Smade is trained by the raw data of Spectre attacks and non-attacks in different workloads using simple Multi-Layer Perception models. The comprehensive results demonstrate that T-Smade makes the average attack detection successful rate of evasive Spectre nop under different workload return from 27.52% to 95.42%, and that of evasive Spectre memory from 59.8% up to 100%. Full article