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MOSRS: An engineering multi-objective optimization through Einsteinian concept
Multi-objective optimization stands at the intersection of mathematics, engineering, and decision-making, and metaheuristics offer a promising avenue for tackling such challenges. The literature shows they are the best, and there is space for new algorithms to deliver Pareto Fronts (PFs) with more convergence and coverage at lower computational costs. This paper presents the Multi-objective Special Relativity Search (MOSRS) for the first time. It relies on principles inspired by the theory of special relativity physics, which iteratively refines solutions toward optimality and self-adapts its parameters using these laws. Unlike most algorithms in the literature today, the user sets only the number of iterations and particles (or population). To test the performance, MOSRS is applied to the most challenging test functions set (CEC 2009) and 21 real and constrained world problems, being compared with a total of eleven metaheuristics: NSGA-II, NSGA-III, MOEA/D, MOPSO, MOGWO, ARMOEA, TiGE2, CCMO, ToP, and AnD. Inverted Generational Distance, Spacing, Maximum Spread, and Hypervolume are used to identify the best algorithm. MOSRS was robust in finding the best PF in most studied problems. The source codes of the MOSRS algorithm are publicly available at https://nimakhodadadi.com/algorithms-%2B-codes.
Comparing non-machine learning vs. machine learning methods for Ki67 scoring in gastrointestinal neuroendocrine tumors
Abstract The Ki67 score is a crucial prognostic biomarker for neuroendocrine tumors, but its manual assessment is labor-intensive, requiring the counting of 500-2,000 cells in hotspots. Digital image analysis could streamline this process, yet few comprehensive comparisons exist between different tools. We compared a non-machine learning (non-ML) tool (ImageScope, Leica Biosystems) with a machine learning (ML) tool (Aiforia Create, Aiforia Technologies) on Ki67-stained slides from 10 low proliferative neuroendocrine tumor cases (Ki67 score < 5%, eight regions per slide). Performance metrics based on the coordinates of detected cells were used to assess the capability of image analysis tools to detect (i) total and (ii) Ki67 positive tumor cells, and consequently calculate the (iii) Ki67 score. Manual scoring by an experienced pathologist was used as the reference standard. The ML compared to the non-ML tool showed better performance metrics (F-score 0.90 vs. 0.74) in detecting the tumor cells. Also, the ML tool had a higher agreement with the reference standard in detecting tumor cells (ICC 0.91 vs. 0.62), Ki67 positive tumor cells (ICC 0.70 vs. 0.24), and the Ki67 score (ICC 0.86 vs. 0.45). Our findings highlight the enhanced accuracy of ML-based image analysis in detecting the correct tumor cells, outperforming traditional methods.
Usability of a hearing test mobile app across generations
Introduction Traditional diagnostic methods of hearing assessment, such as pure tone audiometry, may not be equally accessible to everyone due to geographical or mobility limitations. Utilizing a mobile application (app) for self-assessment of hearing is a promising alternative. However, the effectiveness of apps, as well as their usability across different age groups, remains largely unexplored. The objective of the present study was to assess, across different age groups, the usability of the “Hearing Test” app which allows self-testing of hearing on a mobile phone. Materials and methods The study was conducted on 77 participants from three age groups (16–39 years, 40–59 years, 60 years and older) who self-tested their hearing thresholds using the mobile app and who later underwent pure tone audiometry with an audiologist. The usability of the app was evaluated using a questionnaire based on the Mobile App Rating Scale (MARS), which was complemented by participant observation and interview. Results The app generally yielded results comparable to pure tone audiometry. However, older age groups tended to report higher levels of difficulty across several usability dimensions. Specifically, the oldest group rated the app lower in terms of functionality (M = 2.30; SD = 1.27) and engagement-customization (M = 2.11; SD = 1.28). For the oldest participants, the greatest difficulties related to installation (48%), and interpretation of results (26%). None of the participants aged 60 or older were able to complete the test independently, in contrast to 67% of the youngest participants and 28% of the middle-aged who did not require assistance. All age groups expressed a preference for a conventional hearing test over an app-based assessment, although the youngest group showed the greatest openness to using mobile apps. Conclusions The “Hearing Test” app has demonstrated its potential as a tool for initial hearing assessment, particularly among younger users. However, older individuals often encounter difficulties with installation, interpretation of results, and overall usability. Adapting the interface to meet the specific needs of older users, including user-friendly tutorials and clear presentation of results, is crucial for enhancing its usability.
The albumin-globulin ratio is associated with periodontitis in American adults: results from the NHANES 2009–2014
Indigenous wood species classification using a multi-stage deep learning with grad-CAM explainability and an ensemble technique for Northern Bangladesh
Wood species recognition has recently emerged as a vital field in the realm of forestry and ecological conservation. Early studies in this domain have offered various methods for classifying distinct wood species found worldwide using data collected from a particular region. An image dataset has been developed for wood species classification of Bangladeshi forest. Our aim is to address the gaps by comparing and contrasting our developed sequential Convolutional Neural Network based BdWood model with several deep learning, ensemble technique, Machine learning classification models on specific wood species identification for Bangladeshi forests. Using our own dataset, comprising more than 7119 high-quality captured images representing seven types of wood species of Bangladesh. It is found that DenseNet121 is the clear winner in our thorough evaluation among seven pre-trained models. The highest accuracy of DenseNet121 is achecived 97.09%. In addition, our customized BdWood model, which is adapted to the desired outcome, produced results that are excellent. BdWood model achieves a training accuracy of 99.80%, validation accuracy of 97.93%, an F1-score of 97.94%, and an outstanding ROC-AUC of 99.85%, demonstrating its effectiveness in wood species classification. Gradient-weighted Class Activation Mapping (Grad CAM) is used to interpret the model’s predictions, providing insights into the features contributing to the classification decisions. Finally, to make our research practically applicable, we have also developed an Android application as a tangible outcome of this work.
Correction: Influence of fermented whey protein fractions on the growth performance, haematological traits, serum biochemistry, faecal and caeca microbiota of broiler chickens
Expression of Concern: Menstrual hygiene practices among high school girls in urban areas in Northeastern Ethiopia: A neglected issue in water, sanitation, and hygiene research
Improvement of anti-swelling property and anticorrosion performance of epoxy coatings by the introduction of nanocrystalline cellulose composite
Guiding principles for accelerating change through health inequities research and practice: A modified Delphi consensus process
Despite a preponderance of evidence and considerable resources, health and social inequities persist, and in many cases, are widening. These inequities are not simply the result of passive structural and economic conditions but are actively maintained through institutional processes, norms, and ideologies that uphold the status quo. Reform within health inequities research, policy, and health and social care practice is therefore critical to disrupting these entrenched systems and catalysing both bottom-up and top-down change. We aimed to develop agreement for an iterative set of guiding principles underpinning ways of working for a newly formed Health and Social Equity Collective comprising researchers, community leaders, policymakers, and health and care professionals, seeking to address inequity by identifying and engaging the levers of change within and across institutions. The principles aim to inform a more inclusive and translational knowledge base through research practices, tackling entrenched inequalities in education, training, and capacity-building; and centring communities affected by health inequities through engagement and advocacy. We carried out a modified Delphi consensus process between March and September 2022 with Collective members and networks through online workshops and surveys. Out of 24 consensus statements developed and refined over a workshop and three successive survey rounds, we identified eleven key principles agreed upon by a majority of respondents. Two of these were rated high priority by over 75% of respondents, four by over 60% and five by over 50%. These could be grouped into three main topics detailing ways of working and change needed within: ‘Knowledge and framing of health and social inequities, and incorporation into practice’, ‘Community engagement, involvement and peer research’, and ‘Organisational culture change’, respectively. Given the pressing need to address inequities, these principles offer a grounding for future consensus building initiatives which also incorporate a wider diversity of perspectives, and which should be iteratively updated with ongoing learning from health equity initiatives nationally and internationally.
Enhancing grid connected wind energy conversion systems through fuzzy logic control optimization with PSO and GA techniques
Plasma-activated water modulates taxanes production and phenylalanineammonia-lyase activity in Taxus baccata cell culture
Paclitaxel, an anti-cancer compound from the Taxus baccata L. (yew tree), is limited in availability from natural sources. This study explores the use of plasma-activated water (PAW) as an elicitor in T. baccata suspension cell cultures to enhance the production of paclitaxel and its precursor, 10-Deacetylbaccatin III (10-DAB III). The effects of PAW on various factors, including fresh and dry weight, cell viability, and phenylalanine ammonia-lyase (PAL) activity were investigated. The PAW treatment was conducted at different concentrations (200, 300, and 400 μL), with a pH of 5.6 to 5.8, and was applied at different time points (0, 7, 14, and 21 days). The results revealed that 10-DAB III was increased (14.04 µg/g) significantly at a concentration of 400 μL of PAW on day 21. In contrast, the highest paclitaxel content (3.342 µg/g) was achieved in the control group on day 21. The PAW treatment reduced cell viability by 32.25% compared to day 0 (86.25%), and PAL activity increased initially before declining, but remained higher than in the control group. This study is the first to demonstrate the potential of PAW to enhance taxanes production in T. baccata cell cultures, warranting further investigation into the underlying mechanisms.
A network analysis of carbapenem-resistant Klebsiella pneumoniae among healthcare facilities
Correction: Predicting wildlife corridors for multiple species in an East African ungulate community
Complement activation by the artificial surface of cardiopulmonary bypass is a persistent clinical problem
Protopanaxadiol stimulates glucose consumption by modulating the AMP-activated protein kinase pathway in myotubes, hepatoma cells, and adipocytes
Ginsenosides, the main active constituents of Panax ginseng, possess potent anti-diabetic and anti-obesity properties. In this study, we investigated the molecular and cellular mechanisms underlying the effects of protopanaxadiol (PPD), Rg3, Rb2, Re, Rc, Rh2, Rb1, Rg1, and compound K on palmitic acid (PA)-induced lipid accumulation in HepG2 hepatoma cells and glucose consumption (GC) in C2C12 myotubes and 3T3-L1 adipocytes. PA-induced lipid accumulation was determined using lipid (Oil Red O) staining. GC was performed using a 2-deoxy glucose based colorimetric GC kit. Protein expression was examined by western blot analysis. PPD, Rg1, Rb2, and Rg3 inhibited lipid accumulation in PA-treated HepG2 cells. PA significantly decreased lipid levels in HepG2 cells, which was prevented by PPD, Rg1, Rb2, and Rg3. PPD, Re, Rb1, and compound K enhanced PA-induced GC inhibition in 3T3-L1 cells, while PPD, Rg3, Rc, and Rh2 enhanced PA-induced GC inhibition in C2C12 cells. PA also significantly decreased the phospho-phosphoinositide 3-kinase, phospho-Akt, phospho- AMP-activated protein kinase α, and phospho-glycogen synthase kinase-3β levels as well as increased glycogen synthase, glucose-6-phosphatase, and phosphoenolpyruvate carboxykinase phosphorylation in all three cell lines, which were prevented by PPD. PPD may be a potential drug candidate that can stimulate GC in key insulin-sensitive tissues, such as the skeletal muscle, liver, and adipose tissue.
Sustainable RCCI engine operation with an ANN based novel tri-fuel approach
Abstract This research delves into a novel tri-fuel RCCI engine strategy that uses diesel as the base fuel, biodiesel from Andropogon narudus, and hydrogen as a reactivity promoter to enhance combustion efficiency and enhance environmental sustainability. The tested blends, BD80H20 and BD70H30, showed a 3–5% improvement in Brake Thermal Efficiency (BTE) compared to conventional diesel at full load operation. Hydrogen-rich blends recorded a 5–8% brake-specific fuel economy improvement over diesel and B20 at both 75% and 100% engine loads. The biodiesel has made a substantial reduction in hydrocarbon (HC) and carbon monoxide (CO) emissions. Specifically, B20 recorded a 15% decrease in HC and 12% decrease in CO emission compared to straight diesel, while hydrogen blends register another decrease of 20–25% in CO emissions. The addition of hydrogen resulted in a rough estimate of 10–15% increase in the emissions of carbon dioxide (CO2) and nitrogen oxides (NOx), reflecting an emission trade-off. The smoke opacity decrease varied between 18 and 25% for hydrogen–biodiesel blends, reflecting an increased combustion efficiency level. The increase in in-cylinder peak pressure by 5–10% with hydrogen reflects an accelerated and efficient combustion process. A sustainability analysis by means of a Pugh matrix and Kiviat plot showed that BD80H20 was the most sustainable mixture. The ANN validation proved to be of excellent prediction quality, with RMSE values ranging from 0.9965 to 0.9996, and MPAE less than 4%.
Evaluation of the gastrotolerability of ketoprofen, lysine, and gabapentin co-crystal administration in an in vitro model of gastric epithelium: a proteomic update
Chronic pain is a distressful condition that impacts strongly on people’s health, and, to date, no cure has been found. However, several strategies against pain have been proposed. Promising data regarding the usage of nonsteroidal anti-inflammatory drugs (NSAIDs) in combination with gabapentin in pain management laid the foundations for more complex approaches. A recently published study proposed a multimodal approach based on ketoprofen lysine salt (KLS) combined with gabapentin (GABA) in the context of chronic pain. Experiments on in vitro models showed supra-additive effects in modulating key pathways involved in neuropathic pain and gastric mucosal damage. Thus, the co-crystallization of ketoprofen, lysine, and gabapentin led to a new ternary drug-drug co-crystal (KLS-GABA co-crystal) to better take advantage of such effects. The new compound showed positive features in in vitro and in vivo pain models, particularly at the gastrointestinal level. To better understand the gastric impact of the co-crystal we chose to analyze proteomic fluctuations that occur in an in vitro model of gastric epithelium upon ethanol injury, aiming to observe the gastric effects of KLS-GABA co-crystal’s administration in comparison with KLS or GABA alone or co-administered as in the multimodal approach. Thus, we performed a 2-dimensional gel electrophoresis (2DE) to compare proteomes from lysates of NCI-N87 cells, chosen as model of gastric epithelium. Among all the localized spots (n = 117), the differentially abundant ones have been filtered and excised (n = 24) to perform mass spectrometry. A total of 414 non-redundant proteins have been found in the excised spots analyzed. A Gene Ontology-based enrichment analysis identified the proteins involved in biological processes, cellular components, and pathways. We then compared the 2DE findings with the western blot analysis confirming the differential proteomic fluctuations in the model. The methodology described here provides a broader picture of the effects of KLS, GABA, and KLS-GABA co-crystal administration in the ethanol-gastric injury model, identifying processes not revealed by other studies by showing proteomic changes and related mechanisms in detail, particularly via modulation of the oxidative stress-related GSTP1 which suggests the higher gastric tolerability of KLS-GABA co-crystal in the analyzed model highlighting its clinical reliability.
Equivalent method for calculating internal blast loads in cylindrical lattice shell structure
Abstract It is crucial to improve the calculation efficiency of internal blast loads of a long-span spatial steel structure. This study develops an equivalent model for such loads using a one-way inclined single-layer cylindrical lattice shell structure as a case study. First, ANSYS/LS-DYNA was used to simulate free-air blasts and benchmark against experimental data, with peak overpressure errors below 8%, confirming the modeling approach and material parameters. Next, a numerical model of the cylindrical lattice shell structure under internal explosion was generated via the same modelling method and material parameters. The simulation results indicated that the internal explosion overpressure differed from that of free-air blasts, exhibited pronounced reflection and convergence effects, and was no longer related to the scaled distance. On this basis, an equivalent model combining a standard overpressure distribution with correction factors for reflection and convergence was formulated. Validation against two additional case studies demonstrated that the model provides conservative predictions, with average errors of 9.38% and 7.47% relative to detailed simulations. The proposed equivalent model therefore offers a rapid, reliable tool for the preliminary assessment of internal blast loads in similar spatial structures.