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Harmonic oscillator based particle swarm optimization
Numerical optimization techniques are widely applied across various fields of science and technology, ranging from determining the minimal energy of systems in physics and chemistry to identifying optimal routes in logistics or strategies for high-speed trading. Here, we present a novel method that integrates particle swarm optimization (PSO), a highly effective and widely used algorithm inspired by the collective behavior of bird flocks searching for food, with the physical principle of conserving energy and damping in harmonic oscillators. This physics-based approach allows smoother convergence throughout the optimization process and wider tunability options. We evaluated our method on a standard set of test functions and demonstrated that, in most cases, it outperforms its natural competitors, including the original PSO, as well as commonly used optimization methods such as COBYLA and Differential Evolution.
The impact of fiscal pressure on education expenditure: Evidence from China
Adjusting the tax distribution relationship among governments at all levels in the reform of the fiscal and taxation system will inevitably trigger changes in local government fiscal revenue. Will the fiscal pressures accompanying such changes have a significant impact on the expenditure decisions of local governments? Drawing on the 2002 Income Tax Sharing Reform in China as a quasi-natural experiment, we apply an intensity difference-in-difference methodology to evaluate how fiscal pressure influence county-level education provision. The empirical evidence indicates that counties most exposed to the reform experienced a marked reduction in the proportion of fiscal expenditure devoted to education, with the impact exhibiting a lagged and persistent pattern. The heterogeneity analysis reveals that fiscal pressure has a more pronounced negative impact on education expenditure in counties with developed economies, lower pre-reform education expenditure ratios, and outflow of transfer payments, while special transfer payments can better alleviate this negative effect. In addition, we also found that county governments will give priority to cutting education expenditure after suffering a fiscal shock, and intergovernmental competition further amplifies the adverse effect of fiscal pressure on county education expenditure.The analysis and conclusions of this article help to explain the reasons for insufficient education expenditure at the county level across China, thereby providing effective suggestions for the local government’s fiscal expenditure decision-making choices under fiscal pressure, and also providing important inspiration for advancing modern fiscal and tax system reforms.
Level of satisfaction to clinical learning environment and its associated factors among nursing students of public universities in Central Ethiopia
Background Students’ satisfaction with clinical learning environment is a vital to evaluate teaching-learning process. However, there is a scarcity of information regarding the nursing students’ satisfaction with clinical learning environment and associated factors in Ethiopia. The purpose of this study was aimed to assess level of satisfaction to clinical learning environment and its associated factors among nursing students of public universities in Central Ethiopia, 2022. Méthodes An institution based cross-sectional study was conducted among 245 undergraduate nursing students found in Universities of central Ethiopia from September 1–30, 2022. A simple random sampling technique was applied to recruit study participants. Data were collected via pretested self-administered questionnaire. The data were entered into Epi data version 3.1 and exported to SPSS version 25 for analysis. Descriptive statistics like frequency, percentage, median and IQR were computed. Binary and multivariable logistic regression analysis was done. For measuring the strength of the association between the outcome and independent variables, adjusted odds ratios (AOR) along with 95% confidence interval (CI) was used. Finally, statistical significance was declared at p-value <0.05. Results In this study, nearly half of nursing students, 49% [95% CI (42.6–55.4)] were satisfied with clinical learning environment. Students who did met their clinical learning outcome [AOR = 2.742 (95%CI: 1.407–5.343)], Students those who met 3 times per week with clinical preceptors [AOR: 2.829 (95% CI: 1.428–5.602)] and students who perceived inadequate supporting staffs [(AOR: 0.136 (95%CI: (0.073–0.254)] were associated factors. Conclusion Meeting clinical learning outcome, students’ perception of staff supports and having a frequent contact of supervisor were an association with satisfaction statistically.
Innovation and valuation of Chinese born-global firms
With the advancement of corporate globalization, an increasing number of small and medium-sized enterprises (SMEs) have leveraged globalized resources to achieve accelerated growth mode that significantly depart from the traditional gradual development trajectories of large enterprises. Notably, the emergence and evolution of born-global (BG) firms have attracted substantial scholarly attention in international business research. This paper studies the innovation and valuation of Chinese born-global (BG) firms, based on dynamic capabilities theory and resource-based view, explores the determinant factors of becoming BG firms, and explores an empirical analysis of changes in the value of BG firms. This paper utilizes the OLS model and panel model, as well as Heckman two-stage, propensity score matching (PSM), and heterogeneity analyzes. We conducted some empirical tests on financial data from 2007 to 2021. The empirical results show that the implementation of the BG mode by enterprises contributes to the growth of corporate value and innovation plays a positive moderating role in this relationship. In addition, the determinant factors for a company to adopt the BG mode are total assets, ownership, and the rate of the largest shareholder. Heterogeneity analysis indicates greater impact on private, foreign, and eastern regional firms. The Heckman two-stage selection model effectively addressed the identification requirements for exclusion restriction variables, while the PSM methodology demonstrated improved covariate balance distributions across matched groups. This dual approach collectively mitigated endogeneity concerns and enhanced the robustness of estimation outcomes. Finally, this study provides business managers with a valuation model for enterprise internationalization, which helps small and medium-sized enterprises choose BG mode to start the internationalization process in their initial stage. Furthermore, this study has significantly enriched the existing literature concerning innovation, corporate value, and equity characteristics of BG firms, while establishing novel theoretical perspectives and methodological avenues for subsequent research investigations.
Real-time human-robot interaction and service provision using hybrid intelligent computing framework
Human-robot interaction has gained significant attention in various domains, including healthcare, customer service, and industrial automation. High computational cost, inefficient service matching, and elevated failure rates in dynamic service contexts are some primary disadvantages of existing query-processing systems. This research introduces a Hybrid Intelligent Computing Model (HICM) to improve robots’ ability to process inquiries autonomously. The goal is to make robots better at responding to human questions in real time with efficient, personalized, and context-specific solutions. Using self-organized computing approaches, robotic agents can reliably provide end-users with services suited to their demands. Due to their autonomous nature, robots must be able to calculate quickly and accurately to provide timely services. To meet these needs, the proposed HICM incorporates a sophisticated decision-support system to handle human questions and find the appropriate services. Within this decision-making framework, the model evaluates the characteristics and relevance of questions about accessible services by combining annealing and Tabu Search approaches. To avoid addressing queries incompatibly, the Tabu Search technique approaches query resolution as a non-convergent optimization issue. Comparing HICM’s performance to other models reveals significant improvements over CDS, DGTA, and CCS. In particular, HICM reduced calculation time by 8.67%, service time by 15.09%, and failure rates by 7.87%. In terms of important metrics, HICM fared better than the competing models. Its success factor was 11.8% higher, its matching ratio was 14.88% higher, and its failure rates were 6.22% lower. These findings demonstrate the model’s efficiency and reliability in terms of robotic query processing and real-time service delivery.
One-year survival after critical care as a decision basis for advance care directives in general medicine: Real word data analysis of 149,144 patients
Providing counsel on advance care directives is challenging for general practitioners. Counselling is done on unknown future circumstances of possible critical illness and critical care in intensive care units. Following the principles of evidence-based medicine, the physician’s task is to communicate evidence and elucidate the patient’s position on it. However, suitable evidence of chances of survival in case of critical illness is lacking. Aim of this study was to generate long-term survival rates of patients receiving critical care as evidence for general practitioners who provide counselling for patients on advance care directives. We conducted a retrospective cohort study analysing one-year survival rates of critical care using German health insurance claims data from an anonymised nationwide health claims data pool of over five million German patients. All patients over 18 years of age receiving critical care for the first time were included.Main outcome of our study were one-year survival probabilities depending on age and on acute life prolonging procedures. Procedures analysed were non-invasive and invasive mechanical ventilation (nMV, iMV), renal replacement therapy (RRT), their combinations (nMV + RRT, iMV + RRT), and cardiopulmonary resuscitation (CPR). A total of 149,144 datasets was analysed. One-year survival probability of all patients was 77.5%. Survival rates ranged from 94.5% in patients under 50 without any further acute life prolonging procedures to 16.4% in those older than 80 who received iMV + RRT. The application of at least one procedure was associated with an increased risk of death (HR 3.06, 95% CI 2.99 to 3.12) as was CPR (HR 4.22, 95% CI 4.07 to 4.37). Differences between pre- and COVID periods were modest. To enable patient’s decision-making in creating advance care directives, our results provide easily applicable external evidence for general practitioners counselling on advance care directives by providing probabilities of survival in critical care.
Climate is stronger than you think: Exploring functional planting and TRIAD zoning for increased forest resilience to extreme disturbances
In the face of global changes, forest management must now consider adapting forests to novel and uncertain conditions alongside objectives of conservation and production. In this perspective, we modified the TRIAD zoning approach to add a resilience component through functionally diverse plantations following harvesting in the extensive areas. We then assessed the capacity of this new “TRIAD+” zoning approach for improving the resilience of the mature forest biomass to climate change and three potential extreme pulse disturbances: a large fire, a severe drought, and an insect outbreak. We used the forest landscape simulation model LANDIS-II on a management unit in Mauricie (Quebec, Canada) to simulate and compare the TRIAD+ scenario with a classic TRIAD zoning scenario, and two business-as-usual harvesting scenarios with and without functional enrichment planting. We also simulated three different climate change scenarios (Baseline, RCP 4.5 and RCP 8.5) in which these management and extreme disturbance scenarios took place. We monitored the changes in three variables: the mature wood biomass across the landscape, the mature biomass of each functional group, and the functional diversity of stands in the landscape. Resilience was measured according to three indicators: resistance, net change and recovery time of mature biomass. TRIAD+ management resulted in a good compromise, harvesting the same amount of wood as other scenarios while increasing the surface of protected forests by around 240% compared to BAU scenarios, and improving the mean functional diversity of stands by around 15% compared to the classic TRIAD and BAU without plantations. Following the pulse disturbance events, TRIAD+ also increased the resilience of the mature biomass across the landscape. However, this increase was limited, depended on the resilience indicator and the event considered, and was negligible in terms of tree biomass recovered in the long term. It’s uncertain whether these results stemmed from the relative lack of small-scale interactions in LANDIS-II through which the effect of functional diversity on stand resilience should occur, or if this effect is small to begin with. Overall, our study reveals that an adaptation component can be included in current or future management strategies, but that increasing functional diversity via plantations will likely be insufficient to significantly boost forest resilience. Future research should therefore explore other (combined) means of increasing forest resilience, and improve the representation of small-scale interactions in landscape-scale models.
ChunkUIE: Chunked instruction-based unified information extraction
Large language models (LLMs) have demonstrated remarkable performance across various linguistic tasks. However, existing LLMs perform inadequately in information extraction tasks for both Chinese and English. Numerous studies attempt to enhance model performance by increasing the scale of training data. However, discrepancies in the number and type of schemas used during training and evaluation can harm model effectiveness. To tackle this challenge, we propose ChunkUIE, a unified information extraction model that supports Chinese and English. We design a chunked instruction construction strategy that randomly and reproducibly divides all schemas into chunks containing an identical number of schemas. This approach ensures that the union of schemas across all chunks encompasses all schemas. By limiting the number of schemas in each instruction, this strategy effectively addresses the performance degradation caused by inconsistencies in schema counts between training and evaluation. Additionally, we construct some challenging negative schemas using a predefined hard schema dictionary, which mitigates the model’s semantic confusion regarding similar schemas. Experimental results demonstrate that ChunkUIE enhances zero-shot performance in information extraction.
In vitro assessment of berberine-loaded carboxymethyl chitosan hydrogel: A promising antimicrobial candidate for S. aureus-induced bovine mastitis treatment
Bovine mastitis poses significant challenges to the global dairy industry, leading to substantial economic losses and public health concerns. Staphylococcus aureus, a prevalent causative agent of bovine mastitis, depends on effective adhesion and biofilm formation to establish infections. Berberine (BER), a naturally occurring phytochemical, demonstrates broad-spectrum antibacterial activity but suffers from poor bioavailability. This study developed a composite berberine-carboxymethyl chitosan/sodium alginate hydrogel to address these limitations. The hydrogel was characterized using scanning electron microscopy, Fourier-transform infrared spectroscopy, and X-ray diffraction. In vitro assessments revealed that the BER hydrogel eradicated S. aureus biofilms (42% eradication at 156.26 μg/mL), inhibited bacterial adhesion, and reduced inflammatory cytokines (IL-6 and TNF-α) in S. aureus-infected MAC-T cells, with compliant biosafety biocompatibility (hemolysis rate <5%) and sustained drug release (100% over 6 h), though pH-dependent release kinetics necessitate microenvironment-specific formulation refinement. In conclusion, the BER hydrogel represents a potential therapeutic candidate for S. aureus-induced bovine mastitis.
3Mont: A multi-omics integrative tool for breast cancer subtype stratification
Breast Cancer (BRCA) is a heterogeneous disease, and it is one of the most prevalent cancer types among women. Developing effective treatment strategies that address diverse types of BRCA is crucial. Notably, among different BRCA molecular sub-types, Hormone Receptor negative (HR-) BRCA cases, especially Basal-like BRCA sub-types, lack estrogen and progesterone hormone receptors and they exhibit a higher tumor growth rate compared to HR+ cases. Improving survival time and predicting prognosis for distinct molecular profiles is substantial. In this study, we propose a novel approach called 3-Multi-Omics Network and Integration Tool (3Mont), which integrates various -omics data by applying a grouping function, detecting pro-groups, and assigning scores to each pro-group using Feature importance scoring (FIS) component. Following that, machine learning (ML) models are constructed based on the prominent pro-groups, which enable the extraction of promising biomarkers for distinguishing BRCA sub-types. Our tool allows users to analyze the collective behavior of features in each pro-group (biological groups) utilizing ML algorithms. In addition, by constructing the pro-groups and equalizing the feature numbers in each pro-group using the FIS component, this process achieves a significant 20% speedup over the 3Mint tool. Contrary to conventional methods, 3Mont generates networks that illustrate the interplay of the prominent biomarkers of different -omics data. Accordingly, exploring the concerted actions of features in pro-groups facilitates understanding the dynamics of the biomarkers within the generated networks and developing effective strategies for better cancer sub-type stratification. The 3Mont tool, along with all supporting materials, can be found at https://github.com/malikyousef/3Mont.git.
Potential risk factors associated with diabetic mellitus in patients with Febrile upper urinary tract calculi with infection
Purpose Patients with febrile upper urinary tract calculi with infection (FUUTCI) are prone to develop or have developed severe infection. This research aimed to evaluate the potential risk factors associated with diabetes mellitus (DM) in patients with FUUTCI. Materials and methods From September 2018 to December 2023, patients with FUUTCI admitted to our hospital were retrospectively studied. The patients were divided into a diabetic group (n=52) and a non-diabetic group (n=148), and the differences in demographics, etiology, infection indicators on admission, treatment, and outcome between the two groups were compared. Then regression analysis was performed for gender, stone location, occurrence of urinary sepsis, septic shock, use of pressors, bacterial multiresistance, positive fungal culture, and use of two or more antibiotics. Results Compared with non-diabetic patients (148 cases, 74.0%), diabetic patients (52 cases, 26.0%) had a higher prevalence of cardiovascular and cerebrovascular diseases (P=0.031), the rate of using two or more antibiotics (P=0.029), the positive rate of yeast culture (P=0.037), the procalcitonin value of admission or emergency (P=0.022). There was a significant difference in stone location (P=0.039). Regression analysis showed that DM was an independent risk factor for febrile urinary tract infection in patients with kidney stones compared to patients with ureteral stones (P=0.032). Conclusions In patients with FUUTCI, the risk factors associated with DM made treatment more complicated. In patients with FUUTCI and under the premise of active treatments, DM was not a risk factor for urosepsis, septic shock, use of vasoactive drugs, and infection of multi-drug resistant bacteria. Compared to patients with ureteral calculi, DM was an independent risk factor for febrile urinary tract infection in patients with kidney calculi.
Characterization of anti-canine CD20 antibody 4E1-7-B_f and comparison with commercially available anti-human CD20 antibodies
This study characterizes the previously reported anti-canine CD20 antibody 4E1-7-B_f and compares this with commercially available anti-human CD20 antibodies, rituximab and an obinutuzumab biosimilar. While the obinutuzumab biosimilar exhibited binding to canine CD20 in a CD20-transduced cell line, canine B-cell lymphoma cell line (CLBL-1/luc), and canine CD21 + B cells from healthy dogs, functional assays revealed the superiority of 4E1-7-B_f in antibody-dependent cellular cytotoxicity and complement-dependent cytotoxicity activities over those of the obinutuzumab biosimilar. Epitope analysis suggested an extracellular region on canine CD20 targeted by 4E1-7-B_f. Furthermore, the lipid raft localization of CD20 in CLBL-1/luc cells by treatment with 4E1-7-B_f classified this antibody as a type II anti-CD20 antibody which works with strong ADCC activity, similar to the obinutuzumab biosimilar, unlike rituximab, a type I anti-CD20 antibody, whose main action is CDC activity. These findings underscore the potential clinical utility of 4E1-7-B_f, emphasizing the specificity, potency, and therapeutic promise in canine lymphoma treatment.
Proteomic and phosphoproteomic analysis of rabies pathogenesis in the clinical canine brain and identification of a kinase inhibitor as a potential repurposed antiviral agent
Rabies is a fatal zoonosis caused by the rabies virus (RABV) that has afflicted humans for thousands of years. RABV infection leads to neurological symptoms and death; however, its pathogenesis in the brain is unclear, which complicates patient care. Given that no treatment exists for symptomatic cases, there is an urgent need for effective antiviral drugs. In this study, we aimed to investigate the pathogenic mechanism of RABV in the brain and screen for potential anti-RABV drugs. Protein samples were extracted from the brains of RABV-positive and RABV-negative dogs, and proteomic and phosphoproteomic analyses were conducted. The results showed that the synaptic vesicle cycle is critical to RABV pathogenesis. The kinases involved in the phosphorylation of proteins in the synaptic vesicle cycle were identified and examined as potential drug targets. Casein kinase 2 and protein kinase C were found to be key kinases for RABV replication, and five inhibitors of these enzymes were tested for their anti-RABV properties. Pretreating cells with the kinase inhibitor sunitinib significantly reduced the viral yield after RABV infection. Our findings suggest that RABV interferes with synaptic communication, which leads to rabies, and that inhibiting a vital kinase can reduce viral production. Hence, our findings have implications for the development of rabies treatment regimes.
Machine learning application to predict binding affinity between peptide containing non-canonical amino acids and HLA-A0201
Class Ι major histocompatibility complexes (MHC-Ι), encoded by the highly polymorphic HLA-A, HLA-B, and HLA-C genes in humans, are expressed on all nucleated cells. Both self and foreign proteins are processed to peptides of 8–10 amino acids, loaded into MHC-Ι, within the endoplasmic reticulum and then presented on the cell surface. Foreign peptides presented in this fashion activate CD8 + T cells and their immunogenicity correlates with their affinity for the MHC-Ι binding groove. Thus, predicting antigen binding affinity for MHC-Ι is a valuable tool for identifying potentially immunogenic antigens. While quite a few predictors for MHC-Ι binding exist, there are no currently available tools that can predict antigen/MHC-Ι binding affinity for antigens with explicitly labeled post-translational modifications or unusual/non-canonical amino acids (NCAAs). However, such modifications are increasingly recognized as critical mediators of peptide immunogenicity. In this work, we propose a machine learning application that quantifies the binding affinity of epitopes containing NCAAs to MHC-Ι and compares its performance with other commonly used regressors. Our model demonstrates robust performance, with 5-fold cross-validation yielding an R2 value of 0.477 and a root-mean-square error (RMSE) of 0.735, indicating strong predictive capability for peptides with NCAAs. This work provides a valuable tool for the computational design and optimization of peptides incorporating NCAAs, potentially accelerating the development of novel peptide-based therapeutics with enhanced properties and efficacy.
Needs assessment and preparedness of the primary health care network for scaling-up preventive tuberculosis treatment in 5 Brazilian capitals
This study aims to conduct a needs and preparedness assessment of public primary care units to scale up tuberculosis infection diagnosis and tuberculosis preventive treatment in 5 Brazilian capitals. This observational operational study was carried out across five Brazilian high tuberculosis-burden cities. Clinics with at least one monthly new tuberculosis case were included. Data on Purified Protein Derivative (PPD) storage, tuberculin skin testing (TST) and interferon-gamma release assay (IGRA) availability, personnel qualified for performing TST, radiological facilities and tuberculosis preventive treatment drug availability, were gathered between August 2023 and January 2024. Out of 285 clinics included, 78% (CI95%: 73%−82%) did not offer TST on-site, with only 28% (CI95%: 22%3%) having staff qualified to perform TST, and 35% (CI95%: 29%−40%) lacking dedicated refrigerators for PPD storage. Most (97%, CI95%: 94%−99%) clinics did not collect IGRA testing, with an average distance of 6.7 km (CI95%: 5%−7%) to IGRA labs and a turnaround time of 11.7 days (CI95%: 9%13%) for results. Most (87%, CI95%: 83%−91%) do not offer on-site radiological testing. The primary care network was unprepared to scaling up tuberculosis infection testing. Key issues include unavailability of TST mainly because of insufficient qualified personnel. Without accelerated qualification of staff for TST, scaling up tuberculosis preventive treatment will be impossible.
Harnessing hybrid perception on multi-scale features for hand-foot-mouth disease multi-region prediction based on Seq2Seq
Accurate prediction of Hand, Foot, and Mouth Disease (HFMD) is crucial for effective epidemic prevention and control. Existing prediction models often overlook the cross-regional transmission dynamics of HFMD, limiting their applicability to single regions. Furthermore, their ability to perceive spatio-temporal features holistically remains limited, hindering the precise modeling of epidemic trends. To address these limitations, a novel HFMD prediction model named Seq2Seq-HMF is proposed, which is based on the Sequence-to-Sequence(Seq2Seq) framework. This model leverages hybrid perception of multi-scale features. First, the model utilizes graph structure modeling for multi-regional epidemic-related features. Secondly, a novel Spatio-Temporal Parallel Encoding(STPE) Cell is designed; multiple STPE Cells constitute an encoder capable of hybrid perception across multi-scale spatio-temporal features. Within this encoder, graph-based feature representation and iterative convolution operations enable the capture of cumulative influence of neighboring regions across temporal and spatial dimensions, facilitating efficient extraction of spatio-temporal dependencies between multiple regions. Finally, the decoder incorporates a frequency-enhanced channel attention mechanism(FECAM) to improve the model’s comprehension of temporal correlations and periodic features, further refining prediction accuracy and multi-step forecasting capabilities. Experimental results, utilizing multi-regional data from Japan to predict HFMD cases one to four weeks ahead, demonstrate that our proposed Seq2Seq-HMF model outperforms baseline models. Additionally, the model performs well on single-region data from a city in southern China, confirming its strong generalization ability.
Depression, anxiety and change in eating habits during the COVID-19 pandemic in Brazilian university students
This cross-sectional study aims to evaluate the association between anxiety and depression with changes in the consumption of hyperpalatable foods and meal patterns in a sample of 771 Brazilian university students during the social isolation period in the COVID-19 pandemic. More than half of the subjects self-reported clinically significant symptoms of anxiety (53.8%) and depression (62.5%), with 47.6% having both. Most individuals who showed increased consumption of hyperpalatable foods were also part of the group that reported clinically significant symptoms of anxiety or depression. Statistical analysis was performed using exploratory structural equations. The latent variable “symptoms of anxiety and depression” was created using the anxiety and depression scores. Symptoms of anxiety and depression had a positive correlation with the increased consumption of hyperpalatable foods and meal substitution (standardized coefficient = 0.212), after analysing their total direct and indirect effects. It was concluded that higher scores of anxiety and depression negatively affects the eating habits of university students.
Effects of chemotherapy on skeletal muscle mitochondrial oxidative capacity using near-infrared spectroscopy (NIRS): Protocol paper for an observational mixed model repeated measures design in patients with breast and gynecological cancer
Mitochondrial dysfunction, a hallmark of metabolic disturbances in the skeletal muscle, has previously been studied in health participants using invasive muscle biopsy and/or time consuming, high-cost magnetic resonance spectroscopy. However, less is understood regarding mitochondrial dysfunction in patients with cancer. Near infrared spectroscopy (NIRS) is a non-invasive and cost-effective approach to assessing mitochondrial function of skeletal muscle by measuring oxygenated and deoxygenated hemoglobin and calculating the resulting tissue saturation index. NIRS has not yet been utilized to evaluate skeletal muscle change in cancer patients throughout chemotherapy. Therefore, we plan to conduct a single center clinical trial, using an observational mixed model repeated measures design to evaluate the change in mitochondrial oxidative capacity from baseline and throughout progression of an oncologist-prescribed chemotherapy regimen. Evaluation of mitochondrial function will be performed by taking real-time, NIRS in-situ measurements within the working muscle during stationary cycling exercise and subsequent recovery periods (e.g., “on” kinetics and “off” kinetics). We also plan to observe if there is a difference in mitochondrial oxidative capacity between different chemotherapy regimens across different patients. This pilot study will provide information on the feasibility of capturing on/off kinetics mitochondrial function data longitudinally using NIRS in patients newly diagnosed with breast or gynecological cancer, provide preliminary data for future extramural funding, as well as inform the scientific community of results through dissemination via conferences and peer-reviewed journal publications. This clinical trial has been registered with clinicaltrials.gov (Identifier: NCT006672497). Data collection started on July 26, 2021 and is ongoing through March 27, 2026. The authors confirm that all ongoing and related trials for this observation trial (no drug or intervention) are registered.
RETRACTED: Wind energy resource assessment based on joint wolf pack intelligent optimization algorithm
Wind energy is a clean and renewable energy source with great potential for development, but the intermittent and stochastic characteristics of wind speed have brought great challenges to the effective development and utilisation of wind energy resources, resulting in high development costs. Therefore, how to accurately assess the wind energy resources and effectively predict the wind speed has become a key issue to be solved in the current wind energy field. In view of this, the study proposes the Weibull model to model the wind speed data, and then introduces the wolf pack intelligent optimisation algorithm and improves it through the pollination mechanism to improve the accuracy of wind energy resource assessment. Secondly, considering the complexity and diversity of wind speed data characteristics, data decomposition technique, autoregressive moving average (ARIMA) model and cuckoo search algorithm are used to achieve data preprocessing, serial data modelling and hybrid prediction. The experimental results show that the Weibull model has good fitting accuracy for wind speed data, with residual sum of squares, RMSE, and average coefficient of determination of 0.05, 0.014, and 0.96, respectively, accurately reflecting the statistical characteristics of wind speed data. The wind speed prediction performance of the hybrid prediction model is good, with a maximum deviation of no more than 3% from the true value, which is significantly better than the compared VMD-ISOA-KELM model and CNN-BLSTM model, and its prediction error is relatively small. The hybrid prediction model has a smaller relative error value compared to a single algorithm, with a maximum value of less than 0.2. It has better prediction performance than the combination model, with a coefficient of determination approaching 1.0, a fitting accuracy of 0.994, a mean square error of 0.1947, a root mean square error of 0.3847, and an average absolute percentage error of 15.23%. And the research method can effectively evaluate the status of wind energy resources, with low time complexity at different data scales, taking no more than 5 seconds, and improving operational efficiency. This research method can provide strong technical support and reference basis for the development and utilisation of wind energy resources, and help to promote the sustainable development of wind energy industry.
Stories that bridge us: A mixed methods study to understand the impact of a hospital-wide storytelling event
Oral storytelling events for healthcare professionals are gaining in popularity, yet evaluation of these initiatives is scarce. We designed and assessed the impact of a hospital-wide storytelling event at an academic medical center in New England. This study was grounded in social constructivism, which posits that knowledge and collaborative meaning-making are socially constructed through interpersonal interactions and shared language. Stories were solicited from interdisciplinary staff on a theme, and six selected storytellers were paired with coaches. The hybrid in-person/virtual event was held in 2021. Attendees were invited to complete a post-event survey, as well as a semi-structured interview or written response. Storytellers were invited to reflect via a post-event focus group or written responses. Qualitative data were coded using a mixed inductive and deductive content analytic approach. Survey data were analyzed using descriptive statistics. The storytellers included representation from internal and emergency medicine, nursing, infrastructure project management, and research administration. The 155 attendees included 25 in-person/130 virtual. Qualitative data (nine participants) revealed that sharing stories fostered interpersonal connection and a sense of common humanity, enhanced by the storytellers’ vulnerability and diversity. Storytellers valued coaches’ emotional and creative support in co-creating stories with them. Lastly, the event was felt to strengthen the hospital community. These themes were echoed in the survey data (30 participants): > 75% of respondents indicated that the event helped them reflect on their values, connect with others, and access a sense of purpose. A multidisciplinary hospital-wide oral storytelling event is one way to enhance self-reflection, interpersonal connection, and a sense of community among healthcare professionals.