Clinical characterisation of patients diagnosed with cancer following emergency self-referral.

BACKGROUND: Despite their frequency and potential impact on prognosis, cancers diagnosed via self-referral to the emergency department are poorly documented. We conducted a detailed analysis of cancer patients diagnosed following emergency self-referral and compared them with those diagnosed following emergency referral from primary care. Given the challenges associated with measuring intervals in the emergency self-referral pathway, we also aimed to provide a definition of the diagnostic interval for these cancers. METHODS: A retrospective observational analysis was performed on patients diagnosed with 13 cancers, either following emergency self-referral or emergency referral from primary care. We analysed demographics, tumour stage, clinical data (including 28 presenting symptoms categorised by body systems), and diagnostic intervals by cancer site, then testing for differences between pathways. RESULTS: Out of 3624 patients, 37 % were diagnosed following emergency self-referral and 63 % via emergency referral from primary care. Emergency self-referrals were associated with a higher likelihood of being diagnosed with cancers manifesting with localising symptoms (e.g., breast and endometrial cancer), whereas the likelihood of being diagnosed with cancers featuring nonspecific symptoms and abdominal pain (e.g., pancreatic and ovarian cancer) was higher among patients referred from primary care. Diagnostic intervals in self-referred patients were half as long as those in patients referred from primary care, with most significant differences for pancreatic cancer (28 [95 % CI -34 to -23] days shorter, respectively). CONCLUSION: These findings enrich the best available evidence on cancer diagnosis through emergency self-referral and showed that, compared with the emergency referral pathway from primary care, these patients had a significantly increased likelihood of presenting with symptoms that are strongly predictive of cancer. Since the starting point for the diagnostic interval in these patients is their emergency presentation, comparing it with that of those referred from primary care as an emergency is likely to result in biased data.

Dynamic changes in mRNA nucleocytoplasmic localization in the nitrate response of Arabidopsis roots.

Nitrate is a nutrient and signal that regulates gene expression. The nitrate response has been extensively characterized at the organism, organ, and cell-type-specific levels, but intracellular mRNA dynamics remain unexplored. To characterize nuclear and cytoplasmic transcriptome dynamics in response to nitrate, we performed a time-course expression analysis after nitrate treatment in isolated nuclei, cytoplasm, and whole roots. We identified 402 differentially localized transcripts (DLTs) in response to nitrate treatment. Induced DLT genes showed rapid and transient recruitment of the RNA polymerase II, together with an increase in the mRNA turnover rates. DLTs code for genes involved in metabolic processes, localization, and response to stimulus indicating DLTs include genes with relevant functions for the nitrate response that have not been previously identified. Using single-molecule RNA FISH, we observed early nuclear accumulation of the NITRATE REDUCTASE 1 (NIA1) transcripts in their transcription sites. We found that transcription of NIA1, a gene showing delayed cytoplasmic accumulation, is rapidly and transiently activated; however, its transcripts become unstable when they reach the cytoplasm. Our study reveals the dynamic localization of mRNAs between the nucleus and cytoplasm as an emerging feature in the temporal control of gene expression in response to nitrate treatment in Arabidopsis roots.

Dataset of the effect of the number of injector holes on the heat transfer coefficient and the pressure in the combustion chamber of a hydrogen-diesel engine.

There are several methods for simulating internal combustion engines. The computational fluid dynamics method is the best way to simulate these engines because it can simulate the combustion process, which is a microscopic process. In this study, the simulation of the combustion process in a closed cycle in a diesel engine with a mixture of diesel and hydrogen is done by AVL Fire software. In order to simulate the combustion in the Species and chemical transmission section, a chemkin mechanism is coupled with AVL Fire software. In this study, the effect of 10 % hydrogen fuel and 90 % diesel fuel as well as the effect of nozzle holes (1, 3 and 6 holes) on the engine performance were directly investigated. In order to validate the results of the pressure simulation and the temperature inside the cylinder in the diesel fuel combustion mode, at 2800 rpm and 100 % load, the data were compared with the experimental data. The research also included verification of the heat transfer coefficient (HTC) results with theoretical data obtained by Woschni and Hohenberg. To accurately simulate the combustion process, the simulation data was validated by comparing the pressure and temperature inside the cylinder at a specific operating condition with experimental data. The results indicate that the maximum heat transfer coefficient is achieved at the angle of maximum pressure, with the exhaust valve having the highest coefficient. The addition of hydrogen to diesel fuel results in a 1.72 % increase in the heat transfer coefficient due to increased collisions. In addition, the introduction of hydrogen fuel increases cylinder pressure and engine power, while increasing the number of fuel nozzle holes decreases the coefficient and pressure, which affects fuel penetration and evaporation rate.

Avirulent UG10 Entamoeba histolytica mutant derived from HM-1:IMSS strain shows limited genome variability and aberrant 5-methyl cytosine genomic distribution.

Entamoeba histolytica, an intestinal parasite of global significance, poses substantial health risks with its associated high morbidity and mortality rates. Despite the current repertoire of molecular tools for the study of gene function in, the regulatory mechanisms governing its pathogenicity remain largely unexplored. This knowledge gap underscores the need to elucidate key genetic determinants orchestrating cellular functions critical to its virulence. Previously, our group generated an avirulent strain, termed UG10, with the same genetic background as the HM1:IMSS strain. UG10 strain, despite showing normal expression levels of well-known virulence factors, was unable to perform in-vitro and in-vivo activities related to amoebic virulence. In this study, we aimed to uncover the genome-wide modifications that rendered the avirulent phenotype of the UG10 strain through whole-genome sequencing. As a complementary approach, we conducted Methylated DNA Immunoprecipitation coupled with sequencing (MeDIP-seq) analysis on both the highly virulent HM1:IMSS strain and the low-virulence UG10 strain to uncover the genome-wide methylation profile. These dual methodologies revealed two aspects of the UG10 avirulent strain. One is the random integration of fragments from the ribosomal gene cluster and tRNA genes, ranging from 120 to 400bp; and secondly, a clear, enriched methylation profile in the coding and non-coding strand relative to the start codon sequence in genes encoding small GTPases, which is associated with the previously described avirulent phenotype. This study provides the foundation to explore other genetic and epigenetic regulatory circuitries in E. histolytica and novel targets to understand the pathogenic mechanism of this parasite.

Projecting the economic burden of chronic kidney disease at the patient level (Inside CKD): a microsimulation modelling study.

Background: The growing burden of chronic kidney disease (CKD) places substantial financial pressures on patients, healthcare systems, and society. An understanding of the costs attributed to CKD and kidney replacement therapy (KRT) is essential for evidence-based policy making. Inside CKD maps and projects the economic burden of CKD across 31 countries/regions from 2022 to 2027. Methods: A microsimulation model was developed that generated virtual populations using national demographics, relevant literature, and renal registries for the 31 countries/regions included. Patient-level country/region-specific cost data were extracted via a pragmatic local literature review and under advisement from local experts. Direct cost projections were generated for diagnosed CKD (by age, stage 3a-5), KRT (by modality), cardiovascular complications (heart failure, myocardial infarction, stroke), and comorbidities (hypertension, type 2 diabetes). Findings: For the 31 countries/regions, Inside CKD projected that annual direct costs (US$) of diagnosed CKD and KRT would increase by 9.3% between 2022 and 2027, from $372.0 billion to $406.7 billion. Annual KRT-associated costs were projected to increase by 10.0% from $169.6 billion to $186.6 billion between 2022 and 2027. By 2027, patients receiving KRT are projected to constitute 5.3% of the diagnosed CKD population but contribute 45.9% of the total costs. Interpretation: The economic burden of CKD is projected to increase from 2022 to 2027. KRT contributes disproportionately to this burden. Earlier diagnosis and proactive management could slow disease progression, potentially alleviating the substantial costs associated with later CKD stages. Data presented here can be used to inform healthcare resource allocation and shape future policy. Funding: AstraZeneca.

Nutritional Strategies for Optimizing Health, Sports Performance, and Recovery for Female Athletes and Other Physically Active Women: A Systematic Review.

CONTEXT: Despite the progress toward gender equality in events like the Olympic Games and other institutionalized competitions, and the rising number of women engaging in physical exercise programs, scientific studies focused on establishing specific nutritional recommendations for female athletes and other physically active women are scarce. OBJECTIVE: This systematic review aimed to compile the scientific evidence available for addressing the question "What dietary strategies, including dietary and supplementation approaches, can improve sports performance, recovery, and health status in female athletes and other physically active women?" DATA SOURCES: The Pubmed, Web of Science, and Scopus databases were searched. DATA EXTRACTION: The review process involved a comprehensive search strategy using keywords connected by Boolean connectors. Data extracted from the selected studies included information on the number of participants and their characteristics related to sport practice, age, and menstrual function. DATA ANALYSIS: A total of 71 studies were included in this review: 17 focused on the analysis of dietary manipulation, and 54 focused on the effects of dietary supplementation. The total sample size was 1654 participants (32.5% categorized as competitive athletes, 30.7% as highly/moderately trained, and 37.2% as physically active/recreational athletes). The risk of bias was considered moderate, mainly for reasons such as a lack of access to the study protocol, insufficient description of how the hormonal phase during the menstrual cycle was controlled for, inadequate dietary control during the intervention, or a lack of blinding of the researchers. CONCLUSION: Diets with high carbohydrate (CHO) content enhance performance in activities that induce muscle glycogen depletion. In addition, pre-exercise meals with a high glycemic index or rich in CHOs increase CHO metabolism. Ingestion of 5-6 protein meals interspersed throughout the day, with each intake exceeding 25 g of protein favors anabolism of muscle proteins. Dietary supplements taken to enhance performance, such as caffeine, nitric oxide precursors, ß-alanine, and certain sport foods supplements (such as CHOs, proteins, or their combination, and micronutrients in cases of nutritional deficiencies), may positively influence sports performance and/or the health status of female athletes and other physically active women. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registration no. CRD480674.

Persistence of COVID-19 Symptoms and Quality of Life at Three and Twelve Months after Hospital Discharge.

Background and Objectives: Medical and public recognition of "long-COVID or post-COVID syndrome", as well as its impact on the quality of life (QoL), is required to better address the disease burden. Objectives: We aimed to describe the persistence of COVID-19 symptoms and QoL among patients at three and twelve months after their discharge from the hospital. Materials and Methods: We conducted an observational, prospective, and longitudinal analytic study from September 2021 to April 2022. To measure QoL, we used a validated version of the 36-item Short-Form Health Survey (SF-36). Results: We included 68 patients in the study. A total of 54 (79.4%) patients reported at least one persistent symptom at three months vs. 52 (76.4%) at twelve months (p = 0.804). Some persistent symptoms (myalgia, alopecia, and cough) decreased significantly at twelve months (50% vs. 30.9%, 29.4% vs. 13.2%, and 23.5% vs. 7.4%; respectively, p = 0.007); in contrast, other persistent symptoms (sleep-wake and memory disorders) were more frequent (5.9% vs. 32.4% and 4.4% vs. 20.6%; respectively, p = ≤0.001). Regarding QoL, a statistically significant improvement was observed in some scores over time, p = ≤0.037. At twelve months, dyspnea, myalgia, and depression were risk factors associated with a poor physical component summary (PCS), p = ≤0.027, whereas anxiety, depression, and fatigue were associated with a poor mental component summary (MCS), p = ≤0.015. Conclusion: As the proportion of persistent symptoms at twelve months is high, we suggest that patients must continue under long-term follow up to reclassify, diagnose, and treat new onset symptoms/diseases.

The inorganic pyrophosphatases of microorganisms: a structural and functional review.

Pyrophosphatases (PPases) are enzymes that catalyze the hydrolysis of pyrophosphate (PPi), a byproduct of the synthesis and degradation of diverse biomolecules. The accumulation of PPi in the cell can result in cell death. Although the substrate is the same, there are variations in the catalysis and features of these enzymes. Two enzyme forms have been identified in bacteria: cytoplasmic or soluble pyrophosphatases and membrane-bound pyrophosphatases, which play major roles in cell bioenergetics. In eukaryotic cells, cytoplasmic enzymes are the predominant form of PPases (c-PPases), while membrane enzymes (m-PPases) are found only in protists and plants. The study of bacterial cytoplasmic and membrane-bound pyrophosphatases has slowed in recent years. These enzymes are central to cell metabolism and physiology since phospholipid and nucleic acid synthesis release important amounts of PPi that must be removed to allow biosynthesis to continue. In this review, two aims were pursued: first, to provide insight into the structural features of PPases known to date and that are well characterized, and to provide examples of enzymes with novel features. Second, the scientific community should continue studying these enzymes because they have many biotechnological applications. Additionally, in this review, we provide evidence that there are m-PPases present in fungi; to date, no examples have been characterized. Therefore, the diversity of PPase enzymes is still a fruitful field of research. Additionally, we focused on the roles of H+/Na+ pumps and m-PPases in cell bioenergetics. Finally, we provide some examples of the applications of these enzymes in molecular biology and biotechnology, especially in plants. This review is valuable for professionals in the biochemistry field of protein structure-function relationships and experts in other fields, such as chemistry, nanotechnology, and plant sciences.

Readability and Comprehension of Anesthesia Informed Consent Forms in a Spanish County Hospital: An Observational Study.

(1) Background: The wording of informed consent forms could hinder their comprehension and hinder patients' autonomous choice. The objective of this study was to analyze the readability and comprehension of anesthesia informed consent forms in a Spanish county hospital. (2) Methods: Descriptive and cross-sectional study carried out on patients who were going to undergo anesthetic techniques. The readability of the forms was analyzed using the INFLESZ tool and their subjective comprehension using an ad hoc questionnaire. (3) Results: The analyzed forms presented a "somewhat difficult" legibility. A total of 44.2% of the patients decided not to read the form, mainly because they had previously undergone surgery with the same anesthetic technique. The language used in the forms was considered inadequate by 49.5% of the patients and 53.3% did not comprehend it in its entirety. A statistically significant negative correlation of age and INFLESZ readability score with the overall questionnaire score was found. A statistically significant association was observed as a function of age and educational level with the different criteria of the questionnaire. (4) Conclusions: The anesthesia informed consent forms presented low readability with limited comprehension. It would be necessary to improve their wording to favor comprehension and to guarantee patients' freedom of choice.

Filling the gaps on root hair development under salt stress and phosphate starvation using current evidence coupled with a meta-analysis approach.

Population expansion is a global issue, especially for food production. Meanwhile, global climate change is damaging our soils, making it difficult for crops to thrive and lowering both production and quality. Poor nutrition and salinity stress affect plant growth and development. Although the impact of individual plant stresses has been studied for decades, the real stress scenario is more complex due to the exposure to multiple stresses at the same time. Here we investigate using existing evidence and a meta-analysis approach to determine molecular linkages between two contemporaneous abiotic stimuli, phosphate (Pi) deficiency and salinity, on a single plant cell model, the root hairs (RHs), which is the first plant cell exposed to them. Understanding how these two stresses work molecularly in RHs may help us build super-adaptable crops and sustainable agriculture in the face of global climate change.

Additive Manufacturing of Head Surrogates for Evaluation of Protection in Sports.

Head impacts are a major concern in contact sports and sports with high-speed mobility due to the prevalence of head trauma events and their dire consequences. Surrogates of human heads are required in laboratory testing to safely explore the efficacy of impact-mitigating mechanisms. This work proposes using polymer additive manufacturing technologies to obtain a substitute for the human skull to be filled with a silicone-based brain surrogate. This assembly was instrumentalized with an Inertial Measurement Unit. Its performance was compared to a standard Hybrid III head form in validation tests using commercial headgear. The tests involved impact velocities in a range centered around 5 m/s. The results show a reasonable homology between the head substitutes, with a disparity in the impact response within 20% between the proposed surrogate and the standard head form. The head surrogate herein developed can be easily adapted to other morphologies and will significantly decrease the cost of the laboratory testing of head protection equipment, all while ensuring the safety of the testing process.

Silencing of Sporothrix schenckii GP70 Reveals Its Contribution to Fungal Adhesion, Virulence, and the Host-Fungus Interaction.

Sporothrix schenckii is one of the etiological agents of sporotrichosis, a cutaneous and subcutaneous infection distributed worldwide. Like other medically relevant fungi, its cell wall is a molecular scaffold to display virulence factors, such as protective pigments, hydrolytic enzymes, and adhesins. Cell wall proteins with adhesive properties have been previously reported, but only a handful of them have been identified and characterized. One of them is Gp70, an abundant cell wall protein mainly found on the surface of yeast-like cells. Since the protein also has a role in the activity of 3-carboxy-cis,cis-muconate cyclase and its abundance is low in highly virulent strains, its role in the Sporothrix-host interaction remains unclear. Here, a set of GP70-silenced strains was generated, and the molecular and phenotypical characterization was performed. The results showed that mutants with high silencing levels showed a significant reduction in the adhesion to laminin and fibrinogen, enzyme activity, and defects in the cell wall composition, which included reduced mannose, rhamnose, and protein content, accompanied by an increment in ß-1,3-glucans levels. The cell wall N-linked glycan content was significantly reduced. These strains induced poor TNFα and IL-6 levels when interacting with human peripheral blood mononuclear cells in a dectin-1-, TLR2-, and TLR4-dependent stimulation. The IL-1ß and IL-10 levels were significantly higher and were stimulated via dectin-1. Phagocytosis and stimulation of neutrophil extracellular traps by human granulocytes were increased in highly GP70-silenced strains. Furthermore, these mutants showed virulence attenuation in the invertebrate model Galleria mellonella. Our results demonstrate that Gp70 is a versatile protein with adhesin properties, is responsible for the activity of 3-carboxy-cis,cis-muconate cyclase, and is relevant for the S. schenckii-host interaction.

Heart Failure With Reduced Ejection Fraction in a Patient With Erdheim-Chester Disease on Dabrafenib.

A 54-year-old woman with a diagnosis of Erdheim-Chester disease under therapy with dabrafenib presents with clinical signs of heart failure. After discontinuing the offending medication and initiating guideline-directed medical therapy for heart failure with reduced ejection fraction, the clinical picture improved.

Deep convolutional neural network for weld defect classification in radiographic images.

The quality of welds is critical to the safety of structures in construction, so early detection of irregularities is crucial. Advances in machine vision inspection technologies, such as deep learning models, have improved the detection of weld defects. This paper presents a new CNN model based on ResNet50 to classify four types of weld defects in radiographic images: crack, pore, non-penetration, and no defect. Stratified cross-validation, data augmentation, and regularization were used to improve generalization and avoid over-fitting. The model was tested on three datasets, RIAWELC, GDXray, and a private dataset of low image quality, obtaining an accuracy of 98.75 %, 90.255 %, and 75.83 %, respectively. The model proposed in this paper achieves high accuracies on different datasets and constitutes a valuable tool to improve the efficiency and effectiveness of quality control processes in the welding industry. Moreover, experimental tests show that the proposed approach performs well on even low-resolution images.

Biochemical parameters, oxidative stress biomarkers, and anatomopathological changes in Wistar rats treated with 3'-demethoxy-6-O-demethylisoguaiacin and norisoguaiacin.

Artemisia cina (Ac) is a plant with anthelmintic compounds such as 3'-demethoxy-6-O-demethylisoguaiacin (D) and norisoguaiacin (N). Three major objectives were proposed: (1) To evaluate biochemical parameters in blood (2) to determine the tissue oxidative stress by biomarkers as TBARS and glutathione peroxidase activity, and (3) to evaluate anatomopathological changes in organs such as the brain, liver, kidney, and lung after oral administration of n-hexane extract of Ac and D and N. D and N were administrated following the OECD guides for acute oral toxicity evaluation (Guide 420). Fifty Wistar rats were distributed into ten groups as follows: Group 1 (G1): 4 mg/Kg; G2: 40 mg/Kg; G3: 240 mg/Kg; G4: 1600 mg/Kg of n-hexane extract of Ac. G5: 2 mg/Kg; G6: 20 mg/Kg; G7: 120 mg/Kg; G8: 800 mg/Kg of D and N, G9: water and G10: polyvinylpyrrolidone at 2000 mg/Kg. At 14 days, the rats were euthanized, and the blood, liver, brain, kidney, and lung were taken for biochemical analysis, anatomopathological changes, and TBARS and GSH evaluation. Glucose, cholesterol, and phosphorus were altered. Histopathological analysis showed multifocal neuronal degeneration in the brain (G2). The kidney and lungs had changes in G7. The GSH and TBARS increased in G6 and G7. The TBARS activity was higher in G1 and G2. In conclusion, extract and D and N of Ac did not have damage at therapeutic doses. D, N, and n-hexane extract of A. cina do not cause histopathological damage at pharmaceutical doses. Still, the brain, kidney, and liver are related to biochemical parameters at higher doses. However, compounds are proposed as antioxidant agents.

Optimal distribution grid allocation of reactive power with a focus on the particle swarm optimization technique and voltage stability.

A structured approach to managing reactive power is imperative within the context of power systems. Among the restructuring initiatives in the electrical sector, power systems have undergone delineation into three principal categories: generation, transmission, and distribution entities, each of which is overseen by an independent system operator. Notably, active power emerges as the predominant commodity transacted within the electrical market, with the autonomous grid operator assuming the responsibility of ensuring conducive conditions for the execution of energy contracts across the transmission infrastructure. Ancillary services, comprising essential frameworks for energy generation and delivery to end-users, encompass reactive power services pivotal in the regulation of bus voltage. Of particular significance among the array of ancillary services requisite in a competitive market milieu is the provision of adequate reactive power to uphold grid safety and voltage stability. A salient impediment to the realization of energy contracts lies in the inadequacy of reactive power within the grid, which poses potential risks to its operational safety and voltage equilibrium. The optimal allocation of the reactive power load is predicated upon presumptions of consistent outcomes within the active power market. Under this conceptual framework, generators are afforded continual compensation for the provision of reactive power indispensable for sustaining their active energy production endeavors.

Fortification of goat milk yogurts with date palm (Phoenix dactylifera L.) coproducts: Impact on their quality during cold storage.

The aim of this study was to investigate the impact of different concentrations (3% and 6%) of two ingredients (paste and flour) obtained from the valorization of date fruit coproducts on the nutritional (proximate composition and mineral profile), technological (coagulation curve, pH, acidity, sugar and organic acid content and syneresis), physicochemical (color, water activity and texture), microbiological and sensory properties of goat's yogurt during 21 days of refrigerated storage. Both ingredients enhanced the growth and stability of the yogurt starter culture, thereby improving the probiotic potential of date-added yogurts. Physicochemically, the addition of date flour (at both concentrations) induces stronger modifications (texture, color and syneresis) in yogurts than the date paste. During storage, date paste reduced the syneresis and hence maintained yogurts' physical quality. Consumers preferred the yogurts with date paste (3% and 6%) rather than with date flour, because its addition led to a more brownish color and granular texture.

High-Throughput Screening Method Using Escherichia coli Keio Mutants for Assessing Primary Damage Mechanism of Antimicrobials.

The Escherichia coli Keio mutant collection has been a tool for assessing the role of specific genes and determining their role in E. coli physiology and uncovering novel functions. In this work, specific mutants in the DNA repair pathways and oxidative stress response were evaluated to identify the primary targets of silver nanoparticles (NPs) and their mechanism of action. The results presented in this work suggest that NPs mainly target DNA via double-strand breaks and base modifications since the recA, uvrC, mutL, and nfo mutants rendered the most susceptible phenotype, rather than involving the oxidative stress response. Concomitantly, during the establishment of the control conditions for each mutant, the katG and sodA mutants showed a hypersensitive phenotype to mitomycin C, an alkylating agent. Thus, we propose that KatG catalase plays a key role as a cellular chaperone, as reported previously for the filamentous fungus Neurospora crassa, a large subunit catalase. The Keio collection mutants may also be a key tool for assessing the resistance mechanism to metallic NPs by using their potential to identify novel pathways involved in the resistance to NPs.

Study on the Interaction of Polymeric Chemical Additives with Phase Change Materials in Air Lime Renders.

The interaction of microencapsulated phase change materials (PCMs) with polymeric chemical additives in an air lime binding matrix was studied. These polymer-based additives included an adhesion booster (derived from starch) and a superplasticizer (polycarboxylate ether). Two different PCMs with melting points of 18 °C and 24 °C were assayed. The microcapsules were composed of melamine, with paraffin-based PCM cores. Measurements of zeta potential, particle size distribution, adsorption isotherms, and viscosity analyses were performed to comprehend the behavior of the polymer-based additives within the air lime matrix and their compatibility with PCMs. Zeta potential experiments pointed to the absence of a strong interaction between the lime particles and the microcapsules of PCMs. At the alkaline pH of the lime mortar, the negative charge resulting from the deprotonation of the melamine shell of the microcapsules was shielded by cations, yielding high positive zeta potential values and stable dispersions of lime with PCMs. The polycarboxylate ether demonstrated the ability to counteract the increase in mixing water demand caused by the PCM addition in the lime matrix. The dispersing action of the superplasticizer on the lime particles was seen to exert a collateral dispersion of the PCMs. Conversely, despite the positive values of zeta potential, the addition of the starch-based additive resulted in the formation of large PCM-lime clumps. Air lime renders incorporating 5, 10, and 20% PCMs by weight with various dosages of these chemical additives were experimented with until the optimal formulation for the specific application of the mortars as renderings was achieved. This fine-tuned formulation effectively tackled issues commonly associated with the addition of PCMs to mortars, such as poor adhesion, crack formation, and reduced fluidity.

Machine learning regression algorithms to predict emissions from steam boilers.

Currently, the modeling of complex chemical-physical processes is drastically influencing industrial development. Therefore, the analysis and study of the combustion process of the boilers using machine learning (ML) techniques are vital to increase the efficiency with which this equipment operates and reduce the pollution load they contribute to the environment. This work aims to predict the emissions of CO, CO2, NOx, and the temperature of the exhaust gases of industrial boilers from real data. Different ML algorithms for regression analysis are discussed. The following are input variables: ambient temperature, working pressure, steam production, and the type of fuel used in around 20 industrial boilers. Each boiler's emission data was collected using a TESTO 350 Combustion Gas Analyzer. The modeling, with a machine learning approach using the Gradient Boosting Regression algorithm, showed better performance in the predictions made on the test data, outperforming all other models studied. It was achieved with predicted values showing a mean absolute error of 0.51 and a coefficient of determination of 99.80%. Different regression models (DNN, MLR, RFR, GBR) were compared to select the most optimal. Compared to models based on Linear Regression, the DNN model has better prediction performance. The proposed model provides a new method to predict CO2, CO, NOx emissions, and exhaust gas outlet temperature.