Search Results (11,211)

Background/Objectives: Persistent symptoms following SARS-CoV-2 infection pose a substantial burden in occupational settings. This study aimed to characterize symptoms following work-related SARS-CoV-2 infection and to assess their associations with sociodemographic and clinical factors. Methods: Data were obtained from a multicenter clinical registry of insured individuals referred for persistent symptoms 12 weeks after laboratory-confirmed work-related SARS-CoV-2 infection. Participants were assessed within a standardized post-COVID diagnostic program at six specialized clinics for occupational accident insurance in Germany. Persistent symptoms reported by ≥50% of participants were analyzed using generalized linear mixed models with random intercepts for center. Results: A total of 1511 participants (76.7% women; median age 54 years) were included, with a median interval of 16 months between infection and assessment. On average, participants reported ten persistent symptoms. The most frequent complaints were limited physical capacity (95.6%), concentration difficulties (78.8%), dyspnea (70.5%), exhaustion/tiredness (68.9%), and memory difficulties (67.5%). Individuals reporting more than ten acute symptoms had increased odds of persistent complaints (ORs between 2.1 and 4.66). Hospitalization was independently associated with persistent dyspnea (OR 1.62; 95%CI 1.17–2.25). Reinfections were linked to exhaustion and cognitive fatigue. Compared with Omicron, wild-type infection was associated with higher odds of concentration difficulties (OR 1.65; 95%CI 1.17–2.33). Comorbidities demonstrated symptom-specific associations. Conclusions: Among individuals with work-related SARS-CoV-2 infection, limited physical capacity and cognitive impairments were the most frequently reported symptoms, and higher acute symptom burden was strongly associated with the development of persistent symptoms. These findings support course-oriented evaluation and symptom-specific approaches in occupational disease assessment and management. Full article

This study presents an integrated approach for producing antioxidant-rich polar fractions from Inula salicina L. via pressurised ethanol/water extraction (PLE-EtOH/H₂O), optimised by coupling a central composite design and response surface methodology (CCD-RSM) with Aspen Plus^® simulation. The effects of PLE temperature, extraction time, and EtOH/H₂O ratio for yield, total phenolic (TPC) and flavonoid (TFC) content, and Trolox equivalent antioxidant capacity (TEAC) measured in ABTS^•+-scavenging, cupric ion reducing antioxidant (CUPRAC) and oxygen radical absorbance (ORAC) assays were assessed via a multi-response optimisation approach. Optimal conditions were set at 82 °C, 27 min, and 60% EtOH (v/v), yielding ~29 g extract per 100 g plant material, characterised by high TPC (227 mg GAE/g), TFC (34 mg QE/g), and TEAC values in the CUPRAC (1473 mg TE/g), ABTS (869 mg TE/g), and ORAC assays (1165 mg TE/g). The TPC and TEAC values of the post-extraction residue were >92% lower than those of unextracted I. salicina, confirming efficient recovery of the major portion of antioxidant-active constituents by PLE-EtOH/H₂O. The high in vitro radical scavenging capacity, reducing power, and photoprotective potential (sun protection factor ~50 at 0.5 mg/mL) of the I. salicina extract are consistent with its phenolic-rich composition, with chlorogenic acid (~97 mg/g extract) and its derivatives being the major constituents. The validated Aspen Plus^® model closely aligned with the CCD-RSM predictions, supporting process scale-up and energy feasibility and demonstrating an industry-relevant, green-solvent PLE process for producing higher value-added I. salicina fractions with potential applications in the food, pharmaceutical, nutraceutical, and cosmetic sectors. Full article

Dark current represents a fundamental limiting factor in photodiode performance, establishing the noise floor and constraining detectivity in low-light applications. This comprehensive literature review examines publications covering the physical mechanisms underlying dark current generation and diverse techniques employed for its reduction. Covered mechanisms include diffusion current, Shockley–Read–Hall (SRH) generation–recombination, trap-assisted tunneling, band-to-band tunneling, and surface leakage, each examined with respect to its physical origin and characteristic signatures. Reduction strategies are categorized into thermal management approaches, surface passivation techniques including atomic-layer-deposited aluminum oxide (ALD Al₂O₃), guard ring architectures (attached, floating, and combined configurations), gettering and defect engineering methods, doping profile optimization, bias voltage management, and advanced device architectures such as pinned photodiodes and black silicon structures. A classification table organizes all the reviewed literature by material system, reduction technique, and key findings. Special emphasis is placed on silicon, germanium, III–V compounds, and emerging material photodiodes relevant to near-infrared detection, CMOS imaging, single-photon avalanche diodes (SPADs), and Time-of-Flight (ToF) applications. Full article

The use of nanoparticles (NPs) synthesized from plant extracts is an alternative to conventional pesticides for the control of agricultural pests. This study aimed to optimize the conditions of synthesis of silver–zinc nanoparticles (Ag-ZnNPs) using extracts of Ocimum basilicum L. and Crotalaria longirostrata Hook. & Arn. and to evaluate their phytotoxic impact on maize seedlings. The Ag-ZnNPs (Ag-Zn nanoparticles) were synthesized by redox reaction between metal ions and reducing metabolites present in the extracts. A response surface methodology (RSM) with three factors (extract concentration, heating time and pressure) was applied to determine the optimal synthesis conditions. The phytotoxicity of nanoparticles (NPs) on maize seedlings was subsequently evaluated on root growth, oxidative stress enzymes (CAT, POD, and APX), and physiology of seedlings. Nanoparticles synthesized from C. longirostrata extract demonstrated superior properties, with an optimization of synthesis (R² = 95.3%) where the extract concentration (1:4 v/v; p < 0.01) was the critical factor influencing the reduction of metallic ions to nanoparticles. These NPs exhibited superior stability, smaller size (<100 nm), and zeta potential greater than 30 mV compared with O. basilicum extracts. Their NPs exhibited poorer optimization of synthesis (R² = 43.8%) without the effect of any of the variables evaluated. Essentially, C. longirostrata NPs showed no phytotoxic effects on maize seedlings’ physiological parameters and enhanced root growth (117.2 mm) without negatively affecting photosynthesis (PSII 70-81 FvFm). Ag-ZnNPs synthesized with C. longirostrata exhibited optimal stability and size, along with no observed possible phytotoxicity effects, unlike O. basilicum NPs, which cause stress on maize seedlings. Therefore, Crotalaria longirostrata NPs could represent a promising material for agricultural pest control, with no apparent adverse effect on maize crops. Full article

With the growing interest in personalized medicine, alternative biological matrices to blood are increasingly explored as sources of diagnostic information. Saliva has emerged as a promising diagnostic matrix due to its non-invasive collection, suitability for home sampling, and minimal requirements for personnel training. Numerous studies have demonstrated the presence of metabolites in saliva that enable disease diagnosis and monitoring. However, the influence of pre-analytical and analytical factors on salivary metabolomics outcomes remains insufficiently characterized. In this study, we investigated factors potentially affecting the number and abundance of detected metabolites in untargeted salivary metabolomics using liquid chromatography coupled with mass spectrometry (LC–MS). The impact of chromatographic column type, extraction protocol, and saliva type (stimulated versus resting) was evaluated. Additionally, the effect of swab type on analyte recovery was assessed. The use of a synthetic swab for saliva collection yielded results most comparable to those obtained without swabs, for both resting and stimulated saliva samples, indicating minimal pre-analytical interference. The greatest metabolite coverage was obtained using ACN:MeOH (1:1, v/v), with a ZIC-HILIC column for polar metabolites and a C18 column for non-polar metabolite separation. These findings demonstrate that swab type, chromatographic column, extraction solvent, and saliva type critically shape metabolite coverage in untargeted salivary metabolomics. Importantly, the distinct metabolic profiles of resting and stimulated saliva suggest that these matrices may provide complementary clinical insights, underscoring the need for saliva type selection tailored to specific diagnostic and biomarker discovery objectives. Full article

A 0.3 V nanowatt CCII⁻ is presented in 0.18 $\mathsf{\mu }$m TSMC CMOS, targeting ultra-low-power current-mode interfaces. Post-layout extracted simulations demonstrate correct conveying operation with a total DC power consumption of less than 2.40 nW. The low-frequency tracking factors evaluated at 1 Hz are ${\beta }_0=0.9452$ (−0.48 dB) and ${\alpha }_0=0.9609$ (≈−0.35 dB), with $-3$ dB bandwidths of 22.95 kHz and 63.95 kHz for the voltage and current transfers, respectively. Small-signal extraction confirms the intended impedance profile, yielding $R_X=46.73$ M$\mathsf{\Omega }$, $R_Z=1.204$ G$\mathsf{\Omega }$, and a very high input resistance $R_Y=392$ G$\mathsf{\Omega }$. Robustness is verified through full PVT and mismatch analyses, showing stable functionality across process corners, a 0–80 °C temperature range, and 270–330 mV supply variations while maintaining nanowatt-level dissipation. Full article

Non-convex stochastic optimization presents fundamental mathematical challenges across machine learning, wireless networks, data center resource allocation, and optical wireless communication systems, where complex loss landscapes with multiple local minima and saddle points impede classical variational inference methods. This paper introduces the Quantum-Inspired Variational Inference (QIVI) framework, which systematically integrates quantum mechanical principles (superposition, entanglement, and measurement operators) into classical variational inference through rigorous mathematical formulations grounded in Hilbert space theory and operator algebras. We develop a unified optimization framework that encodes classical parameters as quantum-inspired states within finite-dimensional complex Hilbert spaces, employing unitary evolution operators and adaptive basis selection governed by gradient covariance eigendecomposition. The core mathematical contribution establishes that QIVI achieves a convergence rate of $\mathit{O}({log}^{2}T/T^{1/2})$ for $\sigma$-strongly non-convex functions, provably improving upon the classical $\mathit{O}\left(T^{-1/4}\right)$ rate, yielding a theoretical speedup factor of $1.85$–$1.96\times$. Comprehensive experiments across synthetic benchmarks, Bayesian neural networks, and real-world applications in network optimization and financial portfolio management demonstrate 23–$47\%$ faster convergence, 15–$35\%$ superior objective values, and 28–$46\%$ improved uncertainty calibration. The principal contributions include: (i) a rigorous Hilbert space-based mathematical framework for quantum-inspired variational inference grounded in operator algebras, (ii) a novel hybrid quantum–classical algorithm (QIVI) with adaptive basis selection via gradient covariance eigendecomposition, (iii) formal convergence proofs establishing provable improvement over classical methods, (iv) comprehensive empirical validation across diverse problem domains relevant to machine learning and network optimization, and (v) demonstration of the framework’s applicability to optimization problems arising in wireless networks, data center resource allocation, and network system design. Statistical validation using the Friedman test (${\chi }^2=847.3$, $p<0.001$) and post hoc Wilcoxon signed-rank tests with Holm–Bonferroni correction confirm that QIVI’s improvements over all baseline methods are statistically significant at the $\alpha =0.05$ level across all benchmark categories. The framework discovers $18.1$ out of 20 true modes in multimodal distributions versus $9.1$ for classical methods, demonstrating the potential of quantum-inspired optimization approaches for challenging stochastic problems arising in machine learning, wireless communication, and network optimization. Full article

This study presents a statistically optimized protocol for the green synthesis of gold nanoparticles (Au NPs) using aqueous Kalanchoe pinnata leaf extract (AKPLE). An integrated experimental strategy, transitioning from preliminary one-factor-at-a-time (OFAT) screening to a five-factor Box–Behnken Design, was employed to model and simultaneously optimize two critical optical responses derived from surface plasmon resonance: the peak position (λ_max) and its absorbance intensity. Highly predictive quadratic models (R² > 0.97) revealed that synthesis outcomes are governed by significant nonlinear curvature, with minimal interaction effects. Multi-response optimization via a desirability function identified a harmonized set of conditions (HAuCl₄: 0.44 mM, AKPLE: 3.50% v/v, temperature: 80.6 °C, pH: 7.2, time: 66.7 min) predicted to minimize λ_max at 540 nm while maximizing absorbance to 0.61. Synthesis under these optimized conditions successfully produced spherical, crystalline Au NPs, as confirmed by characterization (average TEM size: 26.3 ± 4.1 nm; zeta potential: –30.45 mV). This work demonstrates that a hybrid OFAT-RSM approach is superior for the precise, multivariate optimization of plant-mediated Au NP synthesis, providing a validated and scalable framework to balance nanoparticle size and plasmonic intensity—an outcome unattainable through conventional OFAT methods. Full article

The increasing utilization of radiation in medicine, industry, and water purification highlights the need for efficient radiation-protection materials. This study investigates lead-free polymer composites based on high-density polyethylene (HDPE) filled with four metallic fillers: tungsten carbide (WC), molybdenum carbide (MoC), tungsten (W), and molybdenum (Mo) at 15 wt%. The objective is to evaluate their potential as alternatives to lead for shielding ionizing radiation. Mechanical performance was examined through tensile testing, while thermal stability was assessed based on the residual mass. Radiation-attenuation behavior was analyzed through linear and mass attenuation coefficients (µ and µₘ), radiation protection efficiency (RPE), half-value layer (HVL), mean free path (MFP), buildup factors (B), and effective atomic number (Z_eff) within the 47.9–248 keV energy range. The HDPE/W composite exhibited the greatest enhancement, with a mass attenuation coefficient (µₘ) 82.5% higher than that of pure HDPE, along with the highest linear attenuation coefficient (µ). Furthermore, tungsten-loaded samples achieved an RPE of 98.05% at 47.9 keV. The increased density, low B, and high Z_eff values collectively contribute to superior shielding performance. These findings indicate that HDPE filled with WC, MoC, W, and Mo are promising lead-free candidates for low-energy X-ray shielding applications. Full article

Residential air conditioning systems are a major contributor to household electricity consumption in tropical regions, where environmental factors such as climate variability and particulate pollution (PM10) can further increase cooling demand and accelerate equipment degradation. This study proposes an Enhanced Deep Q-Network (Enhanced DQN) for energy-efficient and maintenance-aware control of residential split-type air conditioners under dynamic environmental conditions. The proposed method integrates several stability-oriented reinforcement learning mechanisms, including Double Q-learning, a dueling architecture, prioritized experience replay, multi-step returns, Bayesian-style regularization via Monte Carlo dropout, and entropy-aware exploration. The framework is evaluated through a two-stage process consisting of a diagnostic benchmark on LunarLander-v3 to assess learning stability, followed by a realistic 365-day simulation driven by Thai weather and PM10 data. Compared with a fixed 25 °C baseline, the proposed controller reduced annual electricity consumption from 5116.22 kWh to as low as 4440.03 kWh, corresponding to a saving of 13.22%. The learned policy also exhibited environmentally adaptive behavior under high PM10 conditions, indicating maintenance-aware characteristics. These findings demonstrate that reinforcement learning can provide robust, adaptive, and sustainable control strategies for residential cooling systems in tropical environments. Full article

This study provides a theoretical assessment of the structural, electronic, and thermal properties of MgMH₃ (M = Mn and Ni) compounds using the full-potential linearized augmented plane wave (FP-LAPW) method, with a range of modern functionals. The thermoelectric properties that are surveyed here relate to the power factor, the dimensionless thermoelectric figure of merit, the thermal conductivity, and the electrical conductivity that are associated with these compounds. The study finds that MgNiH₃ has superior thermoelectric properties compared to MgMnH₃. The analysis of the band structure reveals that both materials conduct electricity like metals, as there is no energy gap (0 eV), indicating that the conduction and valence bands overlap. The thermal conductivity was found to be linearly related to an increase in temperature, whereas the electrical conductivity varied with temperature. At elevated temperatures, the maximum power factor values reach 1.45 × 10⁻³ W/(K².m) for MgMnH₃ and 1.96 × 10⁻³ W/(K².m) for MgNiH₃ at 900 K. Upon examination of the electronic states, the contributions to the metallic nature of these hydrides come largely from the Ni and Mn orbitals. This type of prospective information on the potential of MgNiH₃ and MgMnH₃ in industrial applications, especially thermoelectric applications, is a valuable contribution. Understanding their thermal and electronic structure will demonstrate their potential for industry. Full article

High-frequency endoscopic ultrasound is crucial for the early diagnosis of gastrointestinal tumors. However, achieving high axial resolution, deep tissue signal-to-noise ratio, and real-time data processing simultaneously remains a significant challenge in hardware implementation. This paper proposes a miniaturized real-time high-frequency imaging system based on the Xilinx Artix-7 FPGA. To overcome attenuation limitations of high-frequency signals, we employ a 4-bit Barker code-encoded excitation scheme coupled with a programmable ±100 V high-voltage transmission circuit. This effectively enhances echo energy without exceeding peak voltage safety thresholds. At the receiver end, the system utilizes a multi-channel analog front end integrated with mixed-signal time-gain compensation technology. Furthermore, to address transmission bottlenecks for massive echo data, we designed a Low-Voltage Differential Signaling (LVDS) interface logic based on dynamic phase calibration, ensuring stable, high-speed data transfer to the host computer via USB 3.0. Experimental results with a 20 MHz transducer demonstrate that the system achieves real-time B-mode imaging at 30 frames per second. Phantom testing revealed an axial resolution of 0.13 mm, enabling clear differentiation of 0.1 mm microstructures. Compared to conventional single-pulse excitation, coded excitation technology improved signal-to-noise ratio (SNR) by approximately 4.5 dB at a depth of 40 mm. These results validate the system’s capability for high-precision deep imaging suitable for clinical endoscopy applications, delivered in a compact, low-power form factor. Full article

SARS-CoV-2 is the etiological agent responsible for COVID-19. While most research has focused on structural proteins, the accessory protein Open Reading Frame 8 (ORF8) has attracted attention for its role in immune evasion and the induction of a cytokine storm. Although the exact mechanisms underlying viral pathogenicity remain to be elucidated, oxidative stress has been proposed as a key contributing factor. In this study, we evaluated the effect of intranasal administration of ORF8 on arterial blood pressure and the antioxidant system in different organs of male BALB/c mice at 2- or 8 weeks post-administration. A significant increase in blood pressure and renal total antioxidant capacity was observed in the 8-week group, and decreased catalase activity in the prefrontal cortex was observed in the 2-week group. These findings suggest that ORF8 may contribute to long-term renal alterations and potentially to mechanism relevant to cognitive dysfunction associated with COVID-19. Full article

Klebsiella pneumoniae has been associated with reproductive infections in equines. The detection of β-lactam resistance determinants, especially extended-spectrum β-lactamase (ESBL) genes, within genomic regions linked to horizontal gene transfer (HGT), is of a particular concern. In this study, we characterize the whole-genome sequences (WGS) of three K. pneumoniae equine isolates harboring multiple antimicrobial resistance genes. Two isolates were recovered from uterine washes of mares: one with endometritis (YAH-KPEM1) and one clinically normal (YAH-KPSE1), and a third from the feces of a diarrheic foal (YAH-KPF132). WGS was performed using the Illumina MiSeq platform, and the reads were subsequently processed through hybrid assembly in Unicycler v0.5.1. Genome annotation was completed using PROKKA v1.14.5. Strain YAH-KPEM1 was classified as ST127, whereas YAH-KPSE1 and YAH-KPF132 belonged to ST1630 and ST224, respectively. Notably, K. pneumoniae ST1630 and ST224 have not been reported before in equines. All three genomes encoded multiple antimicrobial resistance (AMR) determinants, including two encoding ESBL genes (CTX-M-15), as well as virulence factors and regions associated with HGT. Additionally, two (YAH-KPEM1 and YAH-KPSE1) isolates were found to be multidrug resistant (MDR), harboring an IncFIB(K) plasmid replicon, and another isolate, YAH-KPF132, carried an IncFII replicon. The detection of AMR and virulence genes in equine Klebsiella isolates has important clinical implications for guiding antimicrobial selection and improving treatment success. Full article

Background: Although multiple pomalidomide-based combinations are active in relapsed and/or refractory multiple myeloma (RRMM), comparative data to guide regimen selection remain limited. Methods: A total of 230 patients with RRMM from 12 centers in China who received pomalidomide-based regimens were included in this retrospective analysis. Overall response rate (ORR) and progression-free survival (PFS) were compared across regimens incorporating bortezomib or ixazomib (V/IPD), carfilzomib (KPD), or daratumumab (DPD), and multivariable analyses were performed to identify prognostic factors. Results: The overall ORR was 73.9%, with rates of 63%, 79%, and 85% in the V/IPD (n = 66), KPD (n = 69), and DPD (n = 95) cohorts, respectively. ORR differed significantly between V/IPD and DPD (p = 0.0165), driven by a higher proportion of ≥VGPR in the DPD group. The median PFS for the entire cohort was 17.4 months (95% CI: 13.7–20.1), compared with 15.4 months (95% CI: 12.8–20.5), 14.2 months (95% CI: 6.9–not estimable), and 19.2 months (95% CI: 15.1–24.9) for V/IPD, KPD, and DPD, respectively, without significant differences. In multivariable analysis, DPD was associated with improved ORR (HR 4.83, p < 0.001) but not with PFS. R-ISS stage III predicted inferior response (HR 0.35, p = 0.04), whereas ≥3 prior lines of therapy correlated with shorter PFS (HR 1.77, p = 0.012). Adverse events were predominantly hematologic, with limited grade 3–4 toxicity and no treatment-related mortality. Conclusions: This multicenter real-world analysis clarifies the relative positioning of commonly used pomalidomide-based regimens in RRMM and underscores the importance of treatment timing and disease stage in optimizing outcomes. Full article