Search Results (1,052)

Cloud computing is one of the essential computing platforms for modern enterprises. A total of 84 percent of large businesses use cloud computing services in 2025 to enable remote working and higher flexibility of operation with reduction in the cost of operation. Cloud environments are dynamic and multitenant, often demanding high computational resources for real-time processing. However, the cloud system’s behavior is subjected to various kinds of anomalies in which patterns of data deviate from the normal traffic. The varieties of anomalies that exist are performance anomalies, security anomalies, resource anomalies, and network anomalies. These anomalies disrupt the normal operation of cloud systems by increasing the latency, reducing throughput, frequently violating service level agreements (SLAs), and experiencing the failure of virtual machines. Among all anomalies, virtual machine failures are one of the potential anomalies in which the normal operation of the virtual machine is interrupted, resulting in the degradation of services. Virtual machine failure happens because of resource exhaustion, malware access, packet loss, Distributed Denial of Service attacks, etc. Hence, there is a need to detect the chances of virtual machine failures and prevent it through proactive measures. Traditional machine learning techniques often struggle with high-dimensional data and nonlinear correlations, ending up with poor real-time adaptation. Hence, quantum machine learning is found to be a promising solution which effectively deals with combinatorially complex and high-dimensional data. In this paper, a novel quantum-enhanced support vector machine (QSVM) is designed as an optimized binary classifier which combines the principles of both quantum computing and support vector machine. It encodes the classical data into quantum states. Feature mapping is performed to transform the data into the high-dimensional form of Hilbert space. Quantum kernel evaluation is performed to evaluate similarities. Through effective optimization, optimal hyperplanes are designed to detect the anomalous behavior of virtual machines. This results in the exponential speed-up of operation and prevents the local minima through entanglement and superposition operation. The performance of the proposed QSVM is analyzed using the QuCloudSim 1.0 simulator and further validated using expected value analysis methodology. Full article

In near-infrared optical breast lesion screening and diagnosis systems, high-speed four-dimensional scanners can dynamically acquire tens of thousands of lesion images within a five-minute period. Currently, manual computer annotation is required to generate standard samples from these scanned breast lesion images, a process that depends heavily on physicians with clinical expertise. On average, a single physician can annotate only approximately ten samples per working day. As a result, this process is time-consuming and labor-intensive, and the collected samples often suffer from low accuracy, large variability, and limited diagnostic reliability. Several AI-based annotation tools, such as QuPath, HALO AI™, and X-AnyLabeling, have been developed to assist this process. However, these tools are primarily manual or semi-automated and are unable to provide rapid and high-precision recognition. To address these limitations, this study proposes a new AI-based method for the rapid, accurate, and fully automated detection and diagnosis of breast lesions. The proposed approach complements existing AI-based annotation and diagnostic methods by enabling automated detection and classification of breast lesion samples. The proposed system employs a deep learning–based classification framework to construct a professional-level AI diagnostic model. The system automatically generates diagnostic outputs based on the annotation criteria used by professional physicians, including positive/negative classification and accuracy metrics. Compared with conventional manual diagnostic methods, the proposed approach provides faster and more reliable diagnostic estimates for new patients. These results demonstrate the potential of the proposed AI-based method to advance automated breast lesion screening and diagnosis and to contribute to future research and clinical applications in this field. Full article

Highly sensitive methods for trace-level aflatoxin determination are indispensable for cereal food safety and public health protection. This study developed a ZrO₂-assisted QuEChERS-UHPLC-MS/MS method for the simultaneous determination of AFB₁, AFB₂, AFG₁, and AFG₂ in maize, wheat, rice, and soybean. Systematic optimization identified acetonitrile as the optimal extraction solvent and 10 mg ZrO₂ in combination with PSA, C₁₈, and GCB as the optimal cleanup formulation, providing recoveries of 107.33–111.60%. Chromatographic baseline separation was achieved within 8.0 min using a moderate gradient program. The method exhibited excellent linearity (R² > 0.999) with LODs of 0.15–0.25 µg/kg and LOQs of 0.50–0.75 µg/kg. Negligible matrix effects (0.85–1.02) validated the efficacy of ZrO₂-assisted cleanup in eliminating co-extractive interferences in maize. Satisfactory accuracy (recoveries of 86.66–111.04%) and precision (RSDs < 14%) were obtained across all matrices. The method demonstrated consistent performance across diverse cereal and soybean matrices, fulfilling international regulatory requirements for routine aflatoxin monitoring in agricultural commodities. Full article

To explore microbial resources for ester production in sub-high-temperature Daqu, this study first established that the esterifying enzyme activity in Daqu predominantly originated from fungi, with Rhizopus being the dominant fungal genus. Six Rhizopus strains capable of decomposing esters were isolated and purified from Daqu. Following secondary screening, strain M1 exhibited the highest esterification activity (40.26 U/mL) and was identified as Rhizopus oryzae based on morphological characteristics and molecular biological analyses. This strain was subsequently designated as Rhizopus oryzae M1 (R. oryzae M1). Using mycelial powder of strain M1 as the inoculum and sterilized wheat bran as the substrate, a pure-culture Fuqu was prepared. Orthogonal array design experiments were conducted to optimize the preparation process of this Fuqu, using esterifying enzyme activity as the evaluation index. Under the optimal conditions, the spore count and esterification activity of the pure-culture Fuqu reached 1.73 × 10⁹ CFU/g and 80.13 U/g, respectively. This pure-culture Fuqu was subsequently used as an inoculum to produce fortified Daqu. Following orthogonal optimization of the Daqu preparation process, the esterification activity of the fortified Daqu reached 103.22 U/g, and its key physicochemical indices met the requirements for high-quality sub-high-temperature Daqu. Analysis of the microbial community structure revealed that Rhizopus was the dominant fungal genus in the fortified Daqu, with its relative abundance increased by 35% compared to the non-fortified Daqu. Consistent with this, the esterifying enzyme activity of the fortified Daqu was 51.79% higher, suggesting that Rhizopus may have been largely responsible for the increase in esterification capacity. In laboratory-scale Baijiu brewing trials, this fortified Daqu produced a base Baijiu with a total ester content of 2.74 g/L, representing a 40.5% increase over the non-fortified Daqu and further confirming the pivotal role of Rhizopus in driving the esterifying enzyme activity. This study successfully screened a high esterifying enzyme-producing strain, R. oryzae M1, systematically optimized its enzyme production and Qu-making processes, and provides an excellent microbial strain and process reference for the preparation of fortified Daqu and the enhancement of Baijiu flavor. Full article

In this study, a comprehensive assessment of pesticide residues in fruits, vegetables, and derived products collected between 2022 and 2025 within the Apulia and Basilicata regions (Southern Italy) is reported. The analytical workflow combined QuEChERS extraction with Gas Chromatography coupled to Triple Quadrupole Mass Spectrometry (GC-QqQ-MS/MS) and Ultra-High-Performance Liquid Chromatography coupled to Heated Electrospray Ionization-Quadrupole Orbitrap Mass Spectrometry (UHPLC-HESI-Q-Orbitrap-MS). A total of 198 samples were analyzed, including fruits (51%), vegetables (35%), and processed products (14%). Approximately 60% of the samples originated from large-scale distribution networks (EU and non-EU imports), while 40% were derived from local production in Apulia and Basilicata. Validation parameters for both methods met the SANTE/11312/2021 and Commission Implementing Regulation (EU) 2021/808 performance requirements. Results showed that 76.8% of samples were free of quantifiable pesticides, while 23.2% contained residues below EU maximum limits, confirming high compliance and effective regional agronomic management in Apulia and Basilicata. The estimated daily intake and chronic hazard index values were below 100%, across all population groups, confirming the absence of chronic dietary risk. The integration of GC-QqQ-MS/MS and UHPLC-HESI-Q-Orbitrap-MS platforms ensured comprehensive chemical coverage and high selectivity, providing an effective regional surveillance model aligned with EU “Farm to Fork” food safety purposes. Full article

Background/Objectives: Children and adolescents undergoing Haematopoietic Stem Cell Transplantation (HSCT) experience complex symptoms, often under-reported by patients and undetected by clinicians, which cause distress. Patient-Reported Outcome Measures (PROMs) offer a way to capture symptom experiences directly from patients, with the potential of supporting effective symptom assessment and management, yet their routine use in paediatric HSCT remains unclear. This systematic review synthesises evidence on PROMs used during inpatient paediatric HSCT care, examining their role in symptom monitoring and clinical decision-making, and identifying gaps to strengthen person-centred, developmentally appropriate care. Methods: We searched the MEDLINE, CINAHL, Embase, APA PsychINFO, and Cochrane Library in October 2024 for studies published in English between 2014 and 2025 describing the use of PROMs during inpatient paediatric (0–18 years) HSCT admission (up to Day +100 post HSCT). In March 2025, prior to data extraction, we added additional studies published by authors of included studies. Two-stage independent screening and data extraction were conducted, and the Quality Assessment with Diverse Studies (QuADS) tool was used to appraise each study. Narrative syntheses informed by Symptom Management Theory were used to compare PROM use, clinical integration, and reported impacts. Results: Seventeen studies met inclusion criteria, describing 20 PROMs used during paediatric HSCT hospitalisation. PROMs captured a wide range of physical and psychological symptoms, with pain and nausea most frequently reported. While PROMs reportedly improve symptom detection and communication, integration into routine paediatric HSCT clinical care was rare; and only two studies systematically used PROMs data to guide symptom management. Evidence of PROMs-driven improvements in HSCT clinical outcomes was scarce, and longitudinal data on symptom trajectories were limited. Conclusions: PROMs are not routinely used to inform clinical practice in paediatric HSCT, and current evidence provides only a partial understanding of symptom trajectories and lived symptom experiences during the paediatric acute transplant admission. To realise the full potential of PROMs in enhancing symptom assessment and management, systematic PROMs integration into clinical workflows is required, supported by electronic health record integration, clinician training, and longitudinal research designs that capture symptom evolution across the transplant continuum. Full article

Background/Objectives: Breast cancer remains the leading cause of cancer-related death among women worldwide. While tools such as Adjuvant Online, PREDICT, OncotypeDx and Mammoprint identify patients at higher risk of relapse who should therefore be offered chemotherapy, there are currently no tools to accurately predict response to chemotherapy, with varied response rates (regardless of subtypes, etc.) of 8–70% reported. Accurately stratifying patients based on their likelihood of benefiting from SoC chemotherapy is therefore critical to guide personalised treatment decisions. Methods: A retrospective cohort of 293 breast cancer patients treated with SoC adjuvant anthracycline-based regimen was analysed. Five biomarkers (TOP2A, PTEN, EGFR, IGF1R, and phospho-mTOR), selected for their prognostic and therapeutic relevance, were assessed using immunohistochemistry (IHC) combined with digital pathology. Results: Biomarker expression was quantified using the digital pathology platform, QuPath, with each marker, when stratified based on high/low expression, demonstrating a significant association with relapse-free survival following SoC chemotherapy in specific subtypes of breast cancer. A composite five-biomarker signature was then generated by integrating the individual biomarker scores to improve prognostic precision. Patients with a five-biomarker signature score greater than zero exhibited a significantly higher likelihood of favourable outcomes following anthracycline-based chemotherapy compared with those with a score of zero or below. Conclusions: This study establishes a novel IHC-based five-biomarker signature capable of predicting patient outcome in the context of SoC chemotherapy. As the signature relies exclusively on IHC, it is simple, cost-effective and readily integratable into routine diagnostic workflows. In addition to its prognostic value, several biomarkers within the panel are potentially actionable, offering opportunities to guide targeted therapies in patients predicted to have poor response to conventional chemotherapy. Full article

Jiaomian base baijiu is an important seasoning liquor used in the blending of sauce-flavor baijiu, yet the mechanism underlying its flavor formation remains insufficiently understood. Moreover, the specific contribution of high-temperature Daqu (HTDQ) to the flavor profile of Jiaomian base baijiu has not been clearly defined. Therefore, this study compared the aroma profiles and flavor compounds of Jiaomian and Chuntian base baijiu. Jiaomian base baijiu displayed stronger Qu-aroma and floral–fruity notes, with six differential flavor markers (VIP > 1) identified, including ethyl acetate, acetaldehyde, and n-propanol. Further analysis showed that yellow HTDQ exhibited greater inner–outer heterogeneity in aroma and flavor profiles than white and black HTDQ. It also contained the highest concentration of flavor compounds and exerted the strongest influence on the flavor of Jiaomian base baijiu. By comparing the flavor compounds of HTDQ and base baijiu, 14 key compounds were identified that mediate the influence of HTDQ on the flavor of Jiaomian base baijiu. These compounds were primarily formed during the early to middle stages of HTDQ fermentation. Correlation analysis further indicated that microorganisms during HTDQ fermentation were predominantly positively correlated with the key flavor compounds. Among them, Thermoactinomyces, Byssochlamys, Kazachstania, Leiothecium, and Trichothecium showed the closest associations—positively correlated with compounds such as 1-nonanol and furfuryl alcohol, and negatively correlated with isovaleric acid. Finally, KEGG enrichment analysis of the flavor compounds suggested that Beta Oxidation of Very Long-Chain Fatty Acids, Mitochondrial Beta-Oxidation of Short-Chain Saturated Fatty Acids, and Fatty Acid Biosynthesis are key pathways involved in the formation of these flavor substances. In summary, this study clarifies the key flavor compounds through which different HTDQ types influence base baijiu flavor, reveals the microbial origins and metabolic pathways of these key flavor compounds, and provides a theoretical basis for regulating HTDQ production and improving the quality of base baijiu. Full article

Background/Objectives: Nomophobia, the irrational fear of being without a mobile phone, is increasingly prevalent among university students and has emerged as a concerning form of digital dependence. Among nursing students, this condition is particularly relevant due to the emotional demands and cognitive challenges of healthcare education. Nomophobia has been linked with adverse psychological outcomes, sleep disturbances, and impaired academic and clinical performance. However, existing evidence remains fragmented and lacks an integrated conceptual synthesis. This review aimed to synthesize current evidence on the prevalence, correlates, and outcomes of nomophobia among nursing students. Methods: An integrative review was conducted following Whittemore and Knafl’s methodology and PRISMA guidelines. A systematic search was performed in PubMed, CINAHL, PsycINFO, PsycArticles, and Medline (between 2015 and 2025), supplemented by Google Scholar. Cross-sectional studies and literature focusing on nomophobia in nursing students were included. The primary studies and selected review articles were considered when no overlap with the included primary evidence was identified. Methodological quality appraisal was assessed using validated tools (QuADS and JBI). Results: Twenty-two studies were included (19 cross-sectional and 3 reviews). Four thematic areas emerged: prevalence and severity (50–90% moderate to severe); psychological correlates (anxiety, depression, stress, insomnia, alexithymia, fear of missing out); academic and cognitive outcomes (impaired performance, procrastination, reduced decision-making); and behavioural predictors (excessive smartphone use and emotional dysregulation). The Nomophobia Questionnaire (NMP-Q) was the most frequently used instrument. Conclusions: Nomophobia represents a relevant dimension of the mind–technology relationship in nursing education, with implications for students’ mental health, academic engagement, and clinical readiness. Addressing nomophobia may support healthier learning environments and contribute to the development of emotionally competent and safe future healthcare professionals. However, significant gaps remain, particularly regarding longitudinal evidence and intervention-based approaches. Full article

Jimo Huangjiu (JMHJ), a Chinese geographical indication product from Shandong Province, is characterized by distinctive burnt-like and smoky aromas. However, the specific odorants responsible for these sensory attributes remain uncharacterized. In this study, the flavor characteristics of Jimo Huangjiu are characterized through static and dynamic sensory evaluation during the drinking process. This study identified the essential odorants of JMHJ through integrated sensomics analysis. Results revealed pyrazines and phenolic compounds as the characteristic aroma markers responsible for the unique smoky and burnt-like aroma of JMHJ. Ethyl 2-methylpropionate, 4-methylphenol, 4-ethyl-2-methoxyphenol, β-phenylethyl alcohol, 2-ethyl-6-methylpyrazine, 2-ethyl-3-methylpyrazine, 2-methylpyrazine, 2-methoxyphenol, 2-methylphenol, 2,3-dimethylpyrazine, and 2-hydroxy-3-methyl-2-cyclopentenone were confirmed as key odorants in JMHJ. Furthermore, the synergistic interactions between nonanoic acid and phenolic compounds were found to contribute to a Qu-like aroma, representing a novel mechanism for this characteristic sensory attribute in Huangjiu. Full article

Background and Objectives: Bone fragility in people living with HIV (PLWH) reflects both reduced bone mineral density (BMD) and impaired microarchitecture, while functional decline may further amplify fracture vulnerability. This study evaluated whether adding a pragmatic sarcopenia screen improves bone fragility characterization beyond DXA-BMD, trabecular bone score (TBS), calcaneal quantitative ultrasound (QUS), and biomarkers, and explored the relationship between tenofovir disoproxil fumarate (TDF) exposure and microarchitectural impairment. Materials and Methods: In this single-center cross-sectional study at Victor Babeș University of Medicine and Pharmacy Timișoara, 98 adults on stable ART underwent DXA (T-scores), lumbar TBS (reported as TBS × 100), calcaneal QUS (SOS/BUA), and bone turnover markers (CTX, P1NP, 25(OH)D). Sarcopenia screening used handgrip strength and 4 m gait speed. Associations were tested using group comparisons, correlations, and multivariable modeling for degraded TBS (TBS × 100 < 124.0). Results: Sarcopenia screen-positive participants (n = 28) had lower TBS (123.8 vs. 127.7, p = 0.02), lower lumbar T-score (−1.7 vs. −1.2, p = 0.014), lower SOS (1523.3 vs. 1548.8 m/s, p = 0.002), and higher CTX (0.6 vs. 0.4 ng/mL, p < 0.001), with less frequent viral suppression (60.7% vs. 85.7%, p = 0.006). With >5 years TDF exposure (n = 28), degraded TBS prevalence was 82.1% vs. 40.0% in never-exposed (p = 0.001), alongside lower TBS (123.1 vs. 129.8, p < 0.001) and higher CTX (0.6 vs. 0.4 ng/mL, p < 0.001). Viral suppression independently reduced odds of degraded TBS (aOR 0.3, 95% CI 0.1–0.9; p = 0.034). Conclusions: In PLWH, prolonged TDF exposure and functional impairment co-occur with worse densitometric and microarchitectural profiles; viral suppression shows an independent protective association with microarchitecture. Full article

Real-time quaking-induced conversion (RT-QuIC) is highly sensitive for prion detection; however, inhibitory factors present in tissue homogenates readily interfere with the assay. We previously reported that recombinant cervid prion protein (rCerPrP) enabled the establishment of practical RT-QuIC for detecting chronic wasting disease and atypical bovine spongiform encephalopathy (BSE) prions, i.e., detecting low levels of prions in high concentration of brain tissue homogenates. Accordingly, the present study aimed to establish RT-QuIC for detecting classical BSE (C-BSE) and classical and atypical scrapie (C- and A-scrapie, respectively). A single-step lipid extraction using a 3:1 mixture of 2-butanol and methanol was effective as a pretreatment to remove inhibitors from brain homogenates. Among three rPrPs extensively evaluated, recombinant sheep PrP (rShPrP) was the most suitable substrate for practical detection of C-BSE prions. rCerPrP-173S/177N and rCerPrP-98S/173S/177N, which carry sheep-type amino acid substations at codons 173 and 177 and at codons 98, 173, and 177, showed excellent performance for detecting C-scrapie prions. Moreover, rCerPrP-98S/173S/177N, but not rCerPrP-173S/177N, was identified as an optimal substrate for detecting A-scrapie prions. These results suggested that combining inhibitor-removal pretreatment with the optimization of rPrP substrate for each animal prions further enhanced of RT-QuIC performance. Full article

Background: Global dementia policies advocate for early or timely diagnosis, yet evidence on benefits or harms remains limited. This systematic review evaluates quantitative evidence comparing outcomes of early versus late and timely versus untimely dementia diagnoses. Methods: Following PRISMA guidelines, the protocol was registered on PROSPERO. Comprehensive searches of PsycINFO, Medline, Embase, SCOPUS, and CINAHL were conducted without date restrictions. Eligible studies defined diagnostic timing and examined associations with outcomes for people with dementia and/or carers. Quality was appraised using the QuADS tool, and data were narratively synthesised. Results: Four studies (2018–2021) met inclusion criteria, encompassing 37,341 individuals with dementia and 1409 carers across Europe and the United States. Three studies investigated early versus late diagnosis; one assessed perceived timeliness. Definitions varied. Evidence of benefit was limited: one study reported a 9–23% reduction in mortality risk for early diagnosis. Another found that carers perceiving the diagnosis as untimely experienced greater and more persistent emotional distress. No significant associations were observed for cognitive or functional decline, hospitalisation, or emergency department attendance. Conclusions: Despite strong policy endorsement, empirical evidence on benefits of early or timely dementia diagnosis remains scarce, geographically narrow, and methodologically constrained. Future longitudinal studies explicitly defining diagnostic timing and incorporating psychosocial and contextual factors are needed to clarify potential benefits or harms for people with dementia and their carers. Full article

Credit default prediction from firm-level accounting statements is central to risk management, yet the underlying financial data are highly sensitive and often siloed across banks, auditors, and platforms. Federated learning (FL) offers a practical route to collaborative modeling without centralizing raw records, but standard FL optimization can converge slowly under severe client heterogeneity, heavy-tailed accounting features, and label imbalance typical of default events. This paper proposes FedQuAD, a novel fast-converging FL algorithm that couples (i) quasi-Newton curvature aggregation on the server with a lightweight limited-memory update to accelerate global progress, (ii) a proximal variance-reduced local solver that stabilizes client drift under non-IID accounting distributions, and (iii) federated robust standardization of tabular financial ratios via secure aggregated quantile statistics to mitigate scale instability and outliers. FedQuAD is communication-efficient by design: It transmits compact gradient and curvature sketches and adapts local computation to each client’s stochasticity and drift. We provide convergence guarantees for strongly convex default-risk objectives (logistic and calibrated GLM losses) under bounded heterogeneity, and extend the analysis to nonconvex deep tabular models via expected stationarity bounds. Experiments on public credit-risk benchmarks with simulated cross-silo (institutional) partitions demonstrate that FedQuAD reaches target AUC and calibration error with substantially fewer communication rounds than representative baselines while maintaining privacy constraints compatible with secure aggregation and optional client-level differential privacy accounting. Full article

This study assessed the level of agreement between quantitative ultrasound (QUS) feature estimates derived from ultrasound images of breast tumors in women with locally advanced breast cancer (LABC) produced using a cart-based and a handheld ultrasound system. Thirty LABC patients receiving neoadjuvant chemotherapy were imaged at two separate times: a pre-treatment ‘baseline’ time point, and four weeks after the start of chemotherapy. Three sets of QUS features were produced using the reference phantom technique, one for each imaging time and a third set calculated by taking the differences in feature estimates between times. Cross-system statistical testing using the Wilcoxon signed-rank test was performed for each feature set to assess the level of feature estimate agreement between ultrasound systems. The Bland–Altman method was employed to graphically assess feature sets for systematic skew. The range of p-values was 4.50 × 10⁻¹¹ to 0.277 for the baseline features, 2.77 × 10⁻⁵ to 0.865 for the week 4 features, and 2.03 × 10⁻⁹ to 1 for the feature differences. For the feature differences, all five of the primary QUS features (MBF, SS, SI, ASD, AAC) were found to be in agreement between the two scanner types at the 5% confidence level. For the baseline feature set and week 4 feature set, 0 out of 5 and 3 out of 5 of the primary features were found to be in agreement, respectively. Of the 20 QUS texture features examined, the number and proportion of the total for each feature set which were found to have statistically significant similarity in their sample medians at the 5% confidence level were as follows: 2 out of 20 (10%) for the baseline features; 17 out of 20 (85%) for the week 4 features; and 12 out of 20 (60%) for the feature differences. The specific texture features found to be in agreement varied between QUS-specific feature sets. Overall, a moderate level of agreement between sets of feature differences produced using the two systems was demonstrated. Full article