Search Results (149)

Accurate prediction of photovoltaic (PV) power is crucial for ensuring grid stability. The belief rule base (BRB) is a rule-based expert system capable of effectively handling nonlinear causal relationships. Therefore, it can be applied to PV power prediction. In practical prediction scenarios, a high-quality initial model can produce more accurate predictions. However, obtaining sufficient expert knowledge to determine the structure and parameters of the BRB is usually difficult. To address this issue, a PV power prediction method is proposed based on a data-driven interval construction belief rule base (DD-IBRB), which reduces the reliance on expert knowledge during model construction. First, a fuzzy clustering algorithm is designed to construct reference intervals. Then, a Gaussian membership interval function (GIBM) strategy is proposed to initialize the belief degrees. Next, a representative point selection mechanism is designed within the reference intervals. Model inference is subsequently performed based on evidential reasoning (ER) rules. Finally, a multi-population evolution animated oat optimization with parameter constraints (MEAOO) is used to optimize the DD-IBRB model. Taking the PV power output as a case study, the mean squared error is 0.00056, indicating that the proposed DD-IBRB method can effectively complete modeling and obtain accurate prediction results. Full article

The Soil and Water Assessment Tool (SWAT) model has been widely used to simulate ecohydrological processes in watersheds. However, the SWAT model uses a simplified Environmental Policy Impact Climate (EPIC) model to simulate the leaf area index (LAI), creating a critical gap in accurately simulating evapotranspiration (ET) and runoff in semi-arid regions. This work aims to fill this gap by modifying the SWAT source code to integrate high-resolution Global Land Surface Satellite (GLASS) leaf area index (LAI) data. The modified version was applied to the semi-arid Wuding River Basin and calibrated using a Fortran-based dynamic dimension search (DDS) algorithm. The results show a relatively significant improvement in the accuracy of the daily-scale runoff simulation (R² from 0.52 to 0.71 and NSE from 0.52 to 0.7 for the calibration period, and R² from 0.21 to 0.58 and NSE from 0.2 to 0.51 for the validation period). The improved version also corrects the unrealistic default LAI peak (from >5.0 to 1.5–3.0), correcting the multi-year average ET from 251.7 mm to 341.8 mm. The improved vegetation growth module of the SWAT model effectively improved the accuracy of hydrologic simulation in the semi-arid region and enhanced the structural robustness of SWAT for water management. Full article

Background: In acute myeloid leukemia (AML), the most sensitive measurable residual disease (MRD) methods are single-gene approaches, but these are applicable only in ~60% of AML cases. Methods: We applied multi-omics single-cell analysis on diagnostic and first remission samples to identify leukemia-specific molecular markers for subsequent MRD monitoring in six AML patients lacking AML-defining variants. Results: Five selection criteria were defined to identify suitable MRD markers. Markers of primordial leukemic clones were identified by combining data from single-cell sequencing and immunophenotyping. Specific markers suitable for use in MRD follow-up were identified in 6/6 patients, in some cases in myelodysplasia-related genes and clonal hematopoiesis-related genes usually not recommended for use in MRD determinations. Patient-specific ddPCR (limits of detection: 0.06–0.0011%) or EC-NGS assays correlated with therapeutic responses: 0/4 markers displayed molecular relapses in three non-relapsing patients, contrary to 4/4 markers of three relapsing patients. Of these, 3/4 and 1/4 markers detected molecular relapses earlier than or simultaneous with conventional methods, respectively (−115 to −338 days). Conclusions: Our results demonstrate that single-cell subclonal mapping at diagnosis and during first remission enables selection of reliable MRD targets for personalized disease surveillance in patients lacking conventional MRD markers. Full article

Human iPSC-derived cardiomyocytes (iPSC-CMs) exhibit fetal-like mitochondrial networks and limited oxidative metabolism, constraining their translational utility. The key bottleneck is mitochondrial immaturity, resulting from blunted PGC-1α–NRF1/2–TFAM axis activation and insufficient nuclear–mitochondrial coordination, rather than sarcomeric or electrophysiological immaturity alone. This review synthesizes genome-guided interventions (CRISPRa and mtDNA editing) and complementary environmental strategies—including metabolic substrate switching, electromechanical stimulation, and extracellular vesicle (EV)-mediated mitochondrial transfer—to drive mitochondrial biogenesis and maturation in iPSC-CMs. We systematically reviewed studies (2005–2025) targeting (1) key regulators of mitochondrial biogenesis (PGC-1α, NRF1/2, TFAM), (2) CRISPR-based transcriptional activators/repressors and mtDNA editors (DdCBE, mitoTALENs), and (3) maturation approaches such as metabolic conditioning, electromechanical stimulation, 3D tissue culture, and EV-mediated mitochondrial transfer. CRISPRa-mediated activation of PGC-1α, NRF1, and GATA4, combined with mtDNA base editors, enhances mitochondrial mass and OXPHOS function, while integration with environmental maturation strategies further promotes adult-like phenotypes. Integrative approaches that combine genome-guided interventions (CRISPRa, mtDNA editing) with environmental maturation cues yield the most adult-like iPSC-CM phenotypes reported to date. CRISPR-guided mitochondrial biogenesis thus represents a frontier for producing metabolically competent, structurally mature iPSC-CMs for disease modeling and therapy. Remaining translational challenges include efficient mitochondrial delivery, metabolic homeostasis, and multi-omics validation. We propose standardized workflows to couple nuclear and mitochondrial editing with maturation strategies. Full article

In this study, thermoplastic polyurethane (TPU) parts were fabricated using fused filament fabrication (FFF) by varying key process parameters, namely extruder temperature (210–230 °C), layer thickness (200–400 µm), and printing speed (30–50 mm/s). A Box–Behnken experimental design was used to systematically evaluate the combined influence of these parameters on surface roughness (R_a), dimensional deviation (DD), and ultimate tensile strength (UTS). After fabrication, all specimens were subjected to a Tetrahydrofuran (THF)-based chemical smoothing process to modify surface characteristics. Surface roughness measurements showed a substantial reduction after chemical smoothing, with values decreasing from an initial range of 13.17 ± 0.21–15.87 ± 0.23 µm to 4.01 ± 0.18–7.35 ± 0.16 µm, corresponding to an average decrease of approximately 50–72%. Dimensional deviation improved moderately, from 260–420 µm in the as-printed condition to 160–310 µm after post-processing, representing a reduction of about 20–38%. Mechanical testing revealed a consistent increase in UTS following chemical smoothing, with values improving from 30.24–40.30 ± 0.52 MPa to 33.97–47.94 ± 0.36 MPa, yielding an average increase of approximately 10–24%. Then, the experimental data were used for multi-objective optimization of the FFF process parameters, using a non-dominated sorting genetic algorithm (NSGA-II) implemented in Python 3.11, to identify best parameter combinations that provide a balanced surface quality, dimensional accuracy, and mechanical performance. Full article

Peaches are a fruit crop with global importance due to their economic value. Fruit quality (e.g., weight, soluble solids content (SSC)) and phenology traits (e.g., maturity date) are essential for generating novel varieties. Nevertheless, modern germplasm’s narrow genetic diversity hampers breeding efforts to enhance these traits. To identify genetic markers helpful for marker-assisted breeding, this work leveraged a diverse panel of 140 peach commercial cultivars and advanced breeding lines phenotyped across three harvest seasons for the maturity date (MD), chlorophyll absorbance (IAD), SSC, and fruit weight (FW). Genotypic data were generated via ddRADseq, identifying 5861 SNPs. A rapid linkage disequilibrium decay (critical r² = 0.308 at 950 kb) was determined, and a population structure analysis revealed two admixed genetic clusters, with phenotypic distributions influenced by seasonal environmental factors. A total of 599 marker–trait associations were detected by using single and multi-year analysis, and for each trait the surrounding genomic regions explored to identify potential candidate genes annotated with functions related to the trait under study, and expressed in peach fruits. This study highlights multiple loci potentially responsible for phenotypic variations in plant phenology and fruit quality, and provides molecular markers to assist peach breeding for fruit quality. Full article

Norovirus is the leading cause of acute gastroenteritis worldwide, responsible for up to 90% of viral gastroenteritis outbreaks and an estimated 10.6 billion USD in annual economic losses in the U.S. Despite its well-documented seasonality, wastewater surveillance in the Eastern Upper Peninsula of Michigan reveals persistent GI norovirus detection year-round, diverging from national clinical trends that consistently show far greater GII prevalence. To characterize norovirus dynamics in this region, 250 mL wastewater influent grab samples were collected once per week across 14 sites, concentrated using a PEG-based method, and analyzed via digital droplet PCR (ddPCR) for GI and GII concentrations. Across the study period, the rate of positive sites per month ranged from 57 to 100% for GI and 74 to 97% for GII, with mean positivity rates of 85.4% (GI) and 88.7% (GII), indicating that both genogroups were detected frequently at comparable levels. GI was more prevalent in winter and spring (December–May), whereas GII was more prevalent during spring and summer (March–August). Mean GI gene copies per 100 mL ranged from 12,898 (October) to 532,792 (February), while mean GII concentrations ranged from 29,806 (December) to 1,100,215 (May). These patterns contrast with national clinical data, where GI contributes to a small minority of reported norovirus cases. This study explores potential environmental and behavioral factors contributing to this regional pattern. GI norovirus demonstrates greater resistance to wastewater treatment and environmental stability, which may facilitate its persistence in the region. Additionally, congregate living settings, such as college campuses and correctional facilities, may contribute to sustained GI prevalence through foodborne transmission and asymptomatic viral shedding. Overall, these findings suggest that environmental and social factors influence norovirus seasonality and genogroup distribution in this region, underscoring the need for improved monitoring and expanded multi-site wastewater and epidemiological research to better understand norovirus persistence in similar communities. Full article

Background/Objectives: Diverticular disease (DD) affects a significant portion of the aging population and is increasingly linked to gut microbiota alterations. Probiotics have emerged as a potential adjunct therapy, particularly in managing symptoms and inflammation. The evidence for the recommended use of probiotics in clinical practice for management of diverticular disease is still a matter of controversy. Methods: A comprehensive literature search was conducted across five major databases up to October 2024. Eligible studies included randomized controlled trials (RCTs) and observational studies assessing probiotic use in adult patients with diverticular disease. Results: Thirteen studies met the eligibility criteria. Probiotic therapy was associated with improvement in abdominal pain (SMD 0.63; 95% CI: 0.38–0.88). For bloating, probiotics demonstrated a small trend toward improvement (SMD 0.158; 95% CI: −0.107 to 0.422), although this did not reach statistical significance. C-reactive protein (CRP) outcomes were reported in three studies conducted in acute uncomplicated diverticulitis. All showed reductions in CRP following probiotic therapy; however, substantial variability in baseline levels and assessment timepoints prevented a reliable pooled estimate, and findings were summarized descriptively. Long-term outcomes from two RCTs showed a reduced risk of recurrence (RR 0.22; 95% CI: 0.095–0.510), with multi-strain and longer-duration regimens appearing more beneficial. Conclusions: Probiotics, particularly multi strain formulations administered over longer durations, may help improve symptoms and reduce inflammatory activity in diverticular disease; however, the certainty of evidence remains low to very low due to heterogeneity and methodological limitations. Larger, high-quality randomized trials are needed to clarify the long-term clinical impact of probiotic therapy. Full article

Cancer pain seriously damages the quality of life of patients, and its management urgently needs new strategies with both efficacy and safety. This review deeply analyzes the clinical limitations of WHO’s third-order analgesic strategy in cancer pain management, especially emphasizes the unique value of integrated traditional Chinese and Western medicine in synergy and reduction in adverse reactions, and summarizes the network interaction of related drugs through the regulation of multi-target analgesic mechanisms such as inflammatory factors, ion channels, neurotransmitters, and even glial cells and osteoclast activity in the tumor microenvironment. Building on this foundation, the article systematically analyzed the clinical advantages and limitations of drug delivery systems (DDS): oral sustained and controlled drug delivery system, mucosal drug delivery system (MDDS), transdermal drug delivery system (TDDS), and intrathecal targeted drug delivery (ITDD) in the treatment of cancer pain for the first time. The development prospects of new DDS: microneedles, disposable intrathecal drug delivery, and nano-drug delivery system (NDDS) in cancer pain were summarized in detail. Looking ahead, research into the analgesic mechanisms of drugs holds promise for providing a theoretical foundation for cancer pain management. Collaborative strategies integrating Chinese and Western medicine, coupled with precision delivery technologies, are expected to advance more efficient and safer pain control, offering new approaches and methods for achieving superior pain management outcomes. Full article

Assumptions about environmental and operational conditions play a key role in the design of sensor-driven and cyber–physical systems. When these assumptions later change or prove incorrect, they can cause rework, inconsistency, and other forms of requirements technical debt (RTD). Although prior studies have highlighted this problem conceptually, there has been limited quantitative evidence showing how assumptions volatility contributes to RTD during early system modeling. Objective: This work introduces the concept of assumptions volatility, the degree to which environmental assumptions evolve or become invalid, and provides the first empirical assessment of how these measures relate to RTD indicators in model-based development. Methods: We analyzed 89 environmental assumptions curated from a prior controlled modeling study. For assumptions volatility, we identified three metrics, i.e., assumption change (ACR), invalidation ratio (IR), and dependence density (DD). These measures were compared against three RTD indicators, i.e., rework ratio, inconsistency density, and correction count. Correlation and regression analyses with robustness checks were used to evaluate the strength and consistency of the observed relationships. Results: Our results showed that assumptions with higher volatility were consistently linked to a greater level of RTD, with dependency density showing the most stable associations among the three volatility measures. Conclusions: The findings provide initial quantitative evidence that environmental assumption volatility is associated with RTD during conceptual design and motivate future multi-domain validation in broader Model-based Systems Engineering settings. Full article

The dissipative Kerr soliton (DKS) microcomb has been regarded as a highly promising multi-wavelength laser source for optical fiber communication, due to its excellent frequency and phase stability. However, in some specific application scenarios, such as direct modulation and direct detection (DM/DD), the relative intensity noise (RIN) performance of Kerr optical combs still fails to meet the requirements. Here, we systematically investigate the key factors that contribute to the power fluctuations in DKS combs. By exploiting the gain saturation effect of the semiconductor optical amplifier (SOA), the RIN of an on-chip DKS microcomb is effectively suppressed, achieving a maximum reduction of about 30 dB (@600 kHz offset frequency) for all comb lines. Moreover, such DKS comb RIN suppression technology based on an SOA chip can eliminate the need for additional complex feedback control circuits, showcasing the potential for further chip integration of the ultra-low-RIN DKS microcomb system. Full article

When performing the milling process, it is essential to consider the life estimation and availability of the milling tool to achieve a reliable and optimized result at a lower cost. It is necessary to monitor the tool’s condition during the milling process due to its inherent wear nature. In earlier times, visual inspection was used to assess the condition of the milling tool, and it was considered a complex and specialized task. Due to this issue, the milling process requires further investigation. In the manufacturing and automation industry, deteriorated milling tools have led to several challenges, including a decline in product quality, reduced equipment utilization, and increased costs. The tool wear prediction is a challenging and complex task, as it includes several variables. The existing framework for tool condition monitoring, in terms of the degree, typically falls short in terms of real-time prediction and accuracy. Hence, in this research, a tool-life estimation model is developed to minimize unexpected failures during the milling process using deep learning techniques. Initially, the data are collected from benchmark sources. The statistical features, deep features via fuzzy autoencoders (FAEs), and t-Distributed Stochastic Neighbor Embedding (t-SNE)-based features are extracted from the input data to capture various information related to the machine. These features are passed to the proposed multi-head cross-covariance attention fusion-based dilated dense bi-directional gated recurrent unit (MCF-DD-BiGRU) for accurate prediction of tool life. The input features are fused using a multi-head cross-covariance attention mechanism to enhance the representation of interdependencies among features. The DBi-GRU network processes the fused features to improve the accuracy of tool-life prediction for milling machines. The prediction efficiency of the implemented model is compared with the existing models to ensure its effectiveness. Full article

This study examines dual-area load–frequency control (LFC) in the context of significant renewable energy integration, energy storage systems (ESSs), and collective electric vehicle (EV) involvement. We propose a RBBMO-FO-FuzzyPID+PI(1+DD) hybrid controller in which fractional-order fuzzy regulation shapes the ACE, while an auxiliary PI(1+DD) path adds damping without steady-state penalty. Across ideal linear plants, 3% governor-rate constraints (GRC), and stressed conditions that include contract violations in Area-2, renewable power variations, and partial EV State of Charge (SoC 50–70%), EV participation yields systematic gains for all controller families, and the magnitude depends on the control architecture. Baseline methods improve by 15–25% with EVs, whereas advanced designs—especially the proposed controller—benefit by 25–45%, revealing a clear synergy between controller intelligence and EV flexibility. With EV support, the proposed controller limits frequency overshoot to 0.055 Hz (a 20–55% reduction versus peers), caps the nadir at −0.065 Hz (15–41% better undershoot), and attains 3.5–4.5 s settling (25–61% faster than competitors), while holding tie-line deviations within ±0.02 Hz. Optimization studies confirm the algorithmic advantage: RBBMO achieves 30% lower cost than BBOA and converges 25% faster; EV integration further reduces cost by 15% and speeds convergence by 12%. A strong correlation between optimization cost and closed-loop performance (r² ≈ 0.95) validates the tuning strategy. Collectively, the results establish the proposed hybrid controller with EV participation as a new benchmark for robust, system-wide frequency regulation in renewable-rich multi-area grids. Full article

Since the beginning of the 21st century, major epidemics and pandemics such as SARS, H1N1pdm09, Ebola, and COVID-19 have repeatedly challenged global systems of disease diagnostics and control. These crises exposed the weaknesses of traditional diagnostic models, including long turnaround times, uneven resource distribution, and supply chain bottlenecks. As a result, there is an urgent need for more advanced diagnostic technologies and integrated diagnostics strategies. Our review summarizes key lessons learned from four recent major outbreaks and highlights advances in diagnostic technologies. Among these, molecular techniques such as loop-mediated isothermal amplification (LAMP), transcription-mediated amplification (TMA), recombinase polymerase amplification (RPA), and droplet digital polymerase chain reaction (ddPCR) have demonstrated significant advantages and are increasingly becoming core components of the detection framework. Antigen testing plays a critical role in rapid screening, particularly in settings such as schools, workplaces, and communities. Serological assays provide unique value for retrospective outbreak analysis and assessing population immunity. Next-generation sequencing (NGS) has become a powerful tool for identifying novel pathogens and monitoring viral mutations. Furthermore, point-of-care testing (POCT), enhanced by miniaturization, biosensing, and artificial intelligence (AI), has extended diagnostic capacity to the front lines of epidemic control. In summary, the future of epidemic and pandemic response will not depend on a single technology, but rather on a multi-layered and complementary system. By combining laboratory diagnostics, distributed screening, and real-time monitoring, this system will form a global diagnostic network capable of rapid response, ensuring preparedness for the next global health crisis. Full article

Genetic variability in terrestrial mammals is essential for understanding population and evolutionary dynamics, as well as for establishing effective strategies in conservation biology. This comprehensive review aimed to critically analyze invasive and non-invasive techniques used to assess genetic variability in wild terrestrial mammals. Using the PICO (Population, Intervention, Comparison, Outcome) format and following PRISMA guidelines, a comprehensive literature search was conducted in Web of Science, Scopus and Science Direct databases, including articles published in English from January 2015 to April 2025. Thirty-one experimental studies were selected that met specific criteria related to genetic evaluation using invasive (direct blood or tissue collection) and non-invasive (stool, hair and saliva collection) techniques. The results indicate that invasive techniques provide samples of high genetic quality, albeit with important ethical and animal welfare considerations. In contrast, non-invasive techniques offer less disruptive methods, although they present significant challenges in terms of quantity and purity of DNA obtained, potentially affecting the accuracy and confidence of genetic analysis. Detailed analysis of selected studies showed diverse patterns of heterozygosity and inbreeding coefficients between different taxonomic orders (Carnivora, Artiodactyla, Proboscidea, Primates and Rodentia). In addition, the main anthropogenic threats and current conservation strategies implemented in different species were identified. An overall genetic variability ranging from high to moderate was observed, with large species being more vulnerable to genetic reduction due to changes in habitat and human activities. Rather than a static comparison, our synthesis traces a clear methodological arc from small short tandem repeats (STR, or microsatellites) panels towards SNP-based approaches enabled by next-generation sequencing, including reduced representation (ddRAD), amplicon panels (GT-seq), and hybridisation capture tailored to degraded DNA from hair, faeces, and environmental substrates. Over 2015–2025, study designs shifted from presence/absence and coarse diversity estimates to robust inference of relatedness, assignment, effective population size, and gene flow using hundreds–thousands of SNPs and genotype-likelihood frameworks tolerant of allelic dropout and low coverage. Laboratory practice converged on multi-tube replication, synthetic blocking oligos, and capture-based enrichment; bioinformatics adopted probabilistic genotype calling, error-aware filtering, and replication-based consensus. This review provides a solid basis for optimizing genetic sampling methods, allowing for more ethical and efficient studies. Furthermore, it contributes to strengthening conservation strategies by underlining the importance of adapting the sampling method to the biological and ecological particularities of each species studied. Ultimately, these findings can significantly improve genetic conservation decision-making, benefiting the sustainability and resilience of wild land mammal populations. Full article