Browse Articles

Effectiveness of Mellow Parenting on parental mental health and parenting outcomes on a vulnerable parent sample in Moldova

Introduction Poor parental mental health is a risk factor for reduced mental health outcomes and increased behavioural problems in children. High quality parenting interventions are important in minimising the risks associated with poor outcomes for children. Aims and research question This study assessed the implementation of Mellow Parenting, which provides a range of attachment-based parenting programmes, designed to help parents improve their capacity for sensitive and responsive caregiving and develop their relationship with their child, in Moldova. Research questions were 1) do parents who attend Mellow Parenting experience improvements in their wellbeing? 2) do parents demonstrate improvements in parenting confidence? and 3) do parents experience improvements in parenting daily stress?. Methods Secondary data analysis was used for measures collected pre- and post-intervention from groups run in Moldova between 2016 and 2020 of n = 244 mothers. Outcomes were parenting wellbeing, parenting daily stress, parenting confidence, and children’s behaviour. The study focused on two of the current MP programmes; Mellow Babies and Mellow Toddlers. Results T-tests were performed to assess effectiveness of treatment. Correlations and ANCOVAs explored the interactions between variables. Mothers who participated in the group showed improvements in their self-reported wellbeing, parenting confidence, and child behaviour. A reduction was seen in parental stress. Urban and rural intervention groups showed significant differences in terms of pre- and post-scores for anxiety, outward irritability, and parenting confidence. Conclusion Mellow Parenting appears to be an effective intervention for mothers in Moldova in terms of improving parenting wellbeing and parenting confidence and reducing stress. The results of this study suggest that Mellow Parenting is an effective parenting intervention with potential for scaling up across Moldova, as well as other culturally similar countries across the Eastern European and West Asian regions.

Microstructural evolution and phase transitions in porous Ta/Cu alloys under high strain rates

Variable selection methods for descriptive modeling

Variable selection methods are widely used in observational studies. While many penalty-based statistical methods introduced in recent decades have primarily focused on prediction, classical statistical methods remain the standard approach in applied research and education. In this study, we evaluated the variable selection performance of several widely used classical and modern methods for descriptive modeling, using both simulated and real data. A novel aspect of our research is the incorporation of a statistical approach inspired by the supersaturated design-based factor screening method in an observational setting. The methods were evaluated based on Type I and Type II error rates, the average number of predictors selected, variable inclusion frequency, absolute bias, and root mean square error. The detailed results of these evaluations are presented, and the methods’ performance is discussed across various simulation scenarios and in application to real data.

Synergistic integration of refined pelican optimization algorithm and deep neural networks for autonomous vehicle control in edge computing architectures

The effect of D-cycloserine on brain connectivity over a course of pulmonary rehabilitation – A randomised control trial with neuroimaging endpoints

Combining traditional therapies such as pulmonary rehabilitation with brain-targeted drugs may offer new therapeutic opportunities for the treatment of chronic breathlessness. Recently, we asked whether D-cycloserine, a partial NMDA-receptor agonist which may enhance behavioural therapies, modifies the relationship between breathlessness related brain activity and breathlessness anxiety over pulmonary rehabilitation. However, whether any changes are supported by alterations to underlying brain structure remains unknown. Here we examine the effect of D-cycloserine over a course of pulmonary rehabilitation on the connectivity between key brain regions associated with the processing of breathlessness anxiety. 72 participants with mild-to-moderate COPD took part in a longitudinal study in parallel to their pulmonary rehabilitation course. Diffusion tensor brain imaging and clinical measures of respiratory function were collected at three time points (before, during and after pulmonary rehabilitation). Participants were assigned to 250mg of D-cycloserine or placebo, which they were administered with on four occasions in a randomised, double-blind procedure. Following the first four sessions of pulmonary rehabilitation (visit 2), during which D-cycloserine was administered, improvements in breathlessness anxiety were linked with increased insula-hippocampal structural connectivity in the D-cycloserine group when compared to the placebo group. No differences were found between the two groups following the completion of the full pulmonary rehabilitation course 4–6 weeks later (visit 3). The action of D-cycloserine on brain connectivity appears to be restricted to within a short time-window of its administration. This temporary boost of the brain connectivity of two key regions associated with the evaluation of how unpleasant an experience is may support the re-evaluation of breathlessness cues, illustrated improvements in breathlessness anxiety. Trial registration ClinicalTrials.gov (NCT01985750).

Histone deacetylase 6 and programmed death ligand-1 expressions after neoadjuvant chemotherapy are upregulated in patients with ovarian high-grade serous carcinoma

AD-GCN: A novel graph convolutional network integrating multi-omics data for enhanced Alzheimer’s disease diagnosis

Alzheimer’s disease (AD) etiology is complex, influenced by demographic risk factors such as age, sex, and educational level, alongside multi-omics factors derived from genomics, transcriptomics, and epigenomics. Advancements in multi-omics technology present both challenges and opportunities for AD diagnosis, enabling a more comprehensive understanding of the complex interactions among contributing factors, with the goal of improving diagnostic accuracy. To address this challenge, we propose a novel feature fusion approach in this study, AD-GCN, which integrates multi-omics data and their interaction networks to achieve more precise diagnosis and analysis of AD. In this study, we applied polygenic risk score and random forest algorithms for feature selection on genetic variation and methylation data. We then developed an AD-GCN for both multi-omics and single-omics classification tasks and compared its performance with that of machine learning ensemble methods. The experimental results demonstrated that multi-omics classification significantly outperformed single-omics classification, with AD-GCN surpassing the machine-learning ensembles. These findings highlight AD-GCN’s strong potential to enhance AD diagnosis and improve accuracy in differentiating disease stages by integrating interactions across omics data, laying a solid foundation for the development of more precise and personalized AD diagnostic models.

Synthesis and characterization of kaolin glass cullet ceramics modified with transition metal oxides for enhanced mechanical and optical properties

Abstract A range of ceramic materials was developed using Egyptian Kaolin combined with varying amounts of glass cullet waste (0–50 wt%) through uniaxial pressing and sintering at temperatures between 900 and 1200 °C. The study further examined the effects of adding transition metal oxides, Co3O4 or CuO, into a mix of 70% kaolin and 30% cullet, sintered at 1000 °C. Phase identification and chemical composition analysis were carried out using X-ray diffraction (XRD) and dispersive X-ray fluorescence (XRF), while physical properties such as bulk density, apparent porosity, hardness, and microstructure were evaluated through scanning electron microscopy (SEM). The results revealed that increasing the cullet content up to 50 wt% resulted in higher apparent porosity. The sintered ceramics exhibited a hardness of 7.9 GPa, with the lowest bulk density (2.75 g/cm3) and highest apparent porosity (13%). Adding Co3O4 or CuO up to 30 wt% increased the density of the material and reduced porosity, with Co3O4 achieving the highest density (2.44 g/cm3) and lowest porosity (13%). CuO slightly increased porosity to around 4%, with a density of 2.46 g/cm3. Co3O4-based ceramics exhibited superior hardness compared to CuO, as the latter encouraged the formation of anorthite. Optical tests showed that Co3O4 caused a color change from light to dark, while CuO samples turned dark brown to black. CuO-containing ceramics had reflectance values below 40%, indicating their potential application in antireflection coatings for solar cells.

UICD: A new dataset and approach for urdu image captioning

Advancements in deep learning have revolutionized numerous real-world applications, including image recognition, visual question answering, and image captioning. Among these, image captioning has emerged as a critical area of research, with substantial progress achieved in Arabic, Chinese, Uyghur, Hindi, and predominantly English. However, despite Urdu being a morphologically rich and widely spoken language, research in Urdu image captioning remains underexplored due to a lack of resources. This study creates a new Urdu Image Captioning Dataset (UCID) called UC-23-RY to fill in the gaps in Urdu image captioning. The Flickr30k dataset inspired the 159,816 Urdu captions in the dataset. Additionally, it suggests deep learning architectures designed especially for Urdu image captioning, including NASNetLarge-LSTM and ResNet-50-LSTM. The NASNetLarge-LSTM and ResNet-50-LSTM models achieved notable BLEU-1 scores of 0.86 and 0.84 respectively, as demonstrated through evaluation in this study accessing the model’s impact on caption quality. Additionally, it provides useful datasets and shows how well-suited sophisticated deep learning models are for improving automatic Urdu image captioning.

A deep learning and IoT-driven framework for real-time adaptive resource allocation and grid optimization in smart energy systems

Towards using Tweet sentiment for infectious disease detection

Social media data has shown potential for identifying infectious disease outbreaks faster than official records of disease incidence. We examine spatial, temporal, and spatiotemporal relationships between COVID-19-related microblog sentiment and COVID-19 cases over space and time to investigate whether microblog-derived sentiment can be used for local infectious disease outbreak early warning. Therefore, we measure the sentiment of 56,755,894 COVID-19 related microblogs (tweets) from the microblogging platform X. We group these tweets by county and by calendar week to investigate spatial and temporal correlation between sentiment and observed cases (in the corresponding county and week). Our temporal analysis shows a significant negative correlation between sentiment and cases between June and September 2020. During this time, tweet sentiment could have served as an early warning for new COVID-19 outbreaks. Our spatial analysis shows that the East of the United States exhibits a significant negative correlation between Sentiment and Cases while the West exhibits a significant positive correlation. In these regions, Tweet Sentiment could have been used as an early warning signal for new outbreaks. Our spatiotemporal analysis discovers even stronger correlations in certain regions during certain time periods. If we could understand when, where, and why this correlation is strong, then we may be able to leverage social media as a successful early warning system.

A nomogram combining clinical features, O-RADS US, and radiomics based on ultrasound imaging for diagnosing ovarian cancer

Incidence of and factors associated with brimonidine allergy

Purpose This study examined the incidence of and the factors associated with brimonidine allergy, as well as its clinical characteristics and management strategies. Methods We conducted a retrospective review of brimonidine prescriptions and the medical charts of patients who were administered brimonidine between 2019 and 2020. The participants were divided into two groups according to the presence or absence of brimonidine allergy. Data on the demographic and clinical variables were collected for comparative analyses between the two groups. Results A total of 12,024 brimonidine prescriptions were administered to 2,850 patients. Brimonidine allergy’s incidence was 5.5% (157 out of 2,850 patients). The median time from usage to the onset of allergic signs and symptoms was 32 weeks (interquartile range, 15–72 weeks). Conditional multivariable logistic regression analysis showed that brimonidine allergy was associated with concurrent topical steroid use (odds ratio [OR] = 0.18; 95% confidence interval [CI], 0.04 to 0.87; p = 0.033), concurrent artificial tear use (OR = 3.07, 95%, CI, 1.36 to 6.93; p = 0.007), and concurrent tafluprost use (OR = 3.63, 95% CI, 1.04 to 12.63; p = 0.043. The patients mostly experienced redness of the eyes (73.8%) and itching (50.0%). Most patients discontinued brimonidine usage (98.7%). The symptoms and signs improved after a median of 5.5 weeks of treatment. Conclusions Approximately 5.5% of brimonidine users developed brimonidine allergy, which typically manifested at 32 weeks. Concurrent artificial tear and tafluprost use increased brimonidine allergy risk, whereas topical steroids’ concomitant use reduced the risk.

New online in-air signature recognition dataset and embodied cognition inspired feature selection

Abstract In this study, we introduce MIAS-427, one of the largest and most comprehensive inertial datasets for in-air signature recognition, comprising 4270 multivariate signals. This dataset addresses a critical gap in the field by providing a robust foundation for advancing research in cognitive computation and biometric authentication. Leveraging embodied cognition theory, we propose a novel feature selection approach using dimension-wise Shapley Value analysis, which uncovers the intrinsic relationship between human motoric preferences and device-specific sensor data. Our methodology includes a thorough statistical analysis with domain descriptors and DTW algorithms, alongside a comparative evaluation of seven deep-learning models on both the MIAS-427 and smartwatch datasets. The FCN and InceptionTime models achieved remarkable accuracies of 98% and 97.73% on MIAS-427 and smartwatch data, respectively. Notably, our analysis revealed that $$gyr_y$$ and $$acc_x$$ contributed the most (12.82%) and least (8.71%) for the smartwatch, while $$att_y$$ and $$att_x$$ contributed the most (15.63%) and least (7.26%) for MIAS-427, highlighting significant dimension compatibility variations across devices. This research not only provides a valuable dataset for the community but also offers novel insights into human motoric behavior, paving the way for the development of more effective cognitive computation models.

Optimization of multi-AGV task allocation based on an improved PSO algorithm

Research on task allocation for multiple automated guided vehicles (AGVs) in factory environments is a key topic in intelligent manufacturing. Existing studies often struggle to balance fairness and priority in task allocation, leading to low AGV utilization and high no-load distances. Moreover, the stability and applicability of task allocation algorithms in real-world production environments face significant challenges. To address these issues, a mathematical model is formulated with the objective of minimizing the no-load distances of all AGVs in material delivery tasks. The model is subsequently enhanced by incorporating task allocation balance and priority. To solve the optimization model, an improved particle swarm optimization algorithm is proposed, and extensive simulation experiments are conducted based on a real factory environment. By comparing the optimization results of the proposed algorithm with those of the latest multi-population genetic algorithm (MGA) and the market-based bundle task allocation method (MBTA), it is evident that both the proposed algorithm and MGA achieve higher AGV utilization and shorter total task completion times than MBTA, while also optimizing no-load distances. Although the running time of the proposed algorithm is slightly higher than that of MBTA, it is significantly lower than that of MGA, and its overall performance in reducing no-load distances and enhancing AGV utilization is superior to that of MGA. The proposed method can be applied to guide multiple AGVs in multi-material delivery tasks in real factory environments.

A sequence to formula tree model for solving electrical text problems

Intravenous methadone for perioperative acute and chronic pain management in Chinese adult cardiac surgical patients: A protocol for pilot randomized controlled trial

Background Postoperative pain is significant in cardiac surgical patients. Perioperative analgesia with intermittent administration of opioids can result in significant fluctuations in serum opioid concentrations. Methadone should provide a rapid onset and long-term pain relief upon a single intravenous dose at induction of anesthesia, and may reduce chronic postsurgical pain (CPSP) in cardiac surgical patients. The feasibility of using intravenous methadone in Chinese cardiac surgical patients, and its effect on acute and chronic pain management after cardiac surgery will be evaluated. Methods A single-center, prospective, randomized-controlled pilot trial. Adult cardiac surgical patients will be randomized to receive 0.2 mg/kg methadone or morphine at induction of anesthesia. Patient-controlled analgesia morphine protocol, oral paracetamol and dihydrocodeine will be given for postoperative analgesia. Venous blood sampling for plasma methadone concentration will be obtained at regular intervals from study drug infusion to 96 hours after administration. The primary outcome will be a description of study feasibility, encompassing recruitment and retention, protocol adherence and stakeholder acceptability. Secondary outcomes include the time of ventilator weaning to spontaneous breathing, time of extubation, morphine requirements within 24 hours and 72 hours after surgery, time to first morphine rescue, postoperative pain scores, patient satisfaction, and length of stay in ICU and hospital. Opioid-related side effects including sedation, nausea and vomiting, and time to first bowel opening will be recorded. CPSP will be assessed with Neuropathic Pain Scale and Pain Catastrophizing Scale at 3 and 6 months after surgery. Discussion Randomized controlled trials on intravenous methadone in cardiac surgical patients are scarce, with none in Chinese populations. This study, supported by plasma methadone concentration analysis, will establish a basis for future large-scale research aimed at improving recovery through optimized pain management. Clinical trial registration ClinicalTrials.gov NCT05913284.

Diagnosis and classification of neuromuscular disorders using Bi-LSTM optimized with grey Wolf optimizer for EMG signals

The prospective acceptability of preventative IV bisphosphonate therapy prior to fracture: Perspectives of young people with Duchenne muscular dystrophy, parents and health professionals

Young people with Duchenne muscular dystrophy (DMD) commonly experience osteoporosis and fractures which can lead to chronic pain and reduced quality of life. Initiating IV bisphosphonate therapy prior to first fracture may be a logical primary preventative approach given the extent and related morbidity of osteoporosis, although there is limited evidence for this. This qualitative study using semi-structured interviews and focus groups, aimed to explore the opinions and prospective acceptability of young people with DMD, parents and health professionals in the UK on initiating bisphosphonate therapy prior to first fracture. Four boys with DMD (aged 15–17 years) and 20 parents participated in semi-structured interviews. Twenty-seven health professionals involved in the care of young people with DMD participated in focus groups. A framework analysis was conducted. Three categories were identified which represented a continuum of opinions on the endorsement of preventative bisphosphonate therapy: 1) “It buys them time”, endorsement of preventative bisphosphonate therapy; 2) Uncertainty and the importance of “choice”; and 3) “Worry about... starting bisphosphonates even earlier”, not endorsing the use of preventative bisphosphonate therapy. Young people with DMD and parents discussed a range of opinions about the prospective acceptability of IV bisphosphonate as preventative therapy, highlighting the importance of family choice before initiation of therapy. Health professionals called for future research exploring the risks and benefits of preventative IV bisphosphonate therapy for young people with DMD to inform clinical practice.

Milk NIR spectroscopy and Aquaphotomics novel diagnostic approach to Paratuberculosis in dairy cattle

Abstract Mycobacterium Avium subspecies Paratuberculosis (MAP) causes Johne’s disease or Paratuberculosis, a chronic, progressive intestinal disease in ruminants. The incidence and prevalence of Johne’s disease are higher in dairy cattle herds because of intensive breeding and high production. Developing non-destructive diagnostic methods for early detection of this disease by simple sampling is paramount for breeding, economic, and health programs. Conventional methods are almost entirely destructive, have low accuracy, and are time-consuming. Near- infrared spectroscopy (NIRS) and Aquaphotomics can detect changes in biofluids and thus have the potential to diagnose the disease. This study aimed to investigate the diagnostic ability of NIRS and Aquaphotomics for Paratuberculosis in dairy cattle by milk sample. Milk samples from dairy cattle were collected in the NIR range (1300–1600 nm) 60 days before and 100–200 days after calving in two groups, positive and negative, using the three same consecutive ELISA test results of blood plasma and milk, as a reference test. The NIRS and Aquaphotomics methods in quadratic discriminant analysis (QDA) and support vector machine (SVM) models achieved high accuracy in detecting negative and positive groups. In internal validation, SVM and QDA models in 12 water absorbance bands had 100% accuracy. In external validation, milk samples with blood plasma ELISA reference test achieved 100% sensitivity, which is more accurate than milk ELISA as a reference test. The current study found that monitoring milk with NIR spectra provides an opportunity to analyze antibody levels indirectly via changes in water spectral patterns caused by complex physiological changes, such as the amount of antibodies related to Paratuberculosis by aquagram.