Reliable Method Research Articles

An innovative synergistic combination using eudragit-coated galactosylated PLGA-pluronic nanoparticles for addressing colorectal cancer

The goal of this research was to find a way to enhance the pharmacological activity of Capecitabine (CAP) and Thymoquinone (TQ) while also decreasing the likelihood of drug resistance by studying the synergistic activity of TQ, formulating the functionalized polymeric nanoparticles (PNPs) as carriers for effectively targeting the colon to manage colorectal cancer (CRC). CAP and TQ-loaded galactosylated PLGA nanoparticles coated with Eudragit-S-100 were created and optimized to target the colon's pH for drug release after oral delivery using Box-Behnken design. The standard study revealed that they had a high stability of normal pH, a tiny size of spherical shape droplets, and a unique zeta potential that was non-toxic and promoted faster cell migration. Cancer cells treated with PNPs that had a protective effect on intracellular reactive oxygen species (ROS), oxidative stress, and mitochondrial membrane potential had their apoptotic morphology altered to promote cell survival. Moreover, the investigation of pharmacodynamics on male Wistar rats unveiled a notable reduction in the number of tumors, aberrant crypt foci, accompanied by a discernible amelioration in dysplastic characteristics and enhancement in various blood parameters, and its toxic effects were mitigated proving Galactosylation to be a reliable method which has shown to be useful in targeting CRC through galectin-mediated endocytosis. When compared to free CAP, the targeted approach increases cytotoxicity and cell death. The data demonstrated that the targeted method maximized the efficacy of CAP in regulating tumor development, enhancing apoptosis and decreasing drug resistance. This demonstrated that the engineered PNP showed encouraging anticancer activity with less toxicity and may be employed as an efficient carrier against cancer. Docking research stated that TQ had the maximum binding affinity to Mitogen-activated protein kinase kinase molecule which is inextricably linked to colon cancer (binding energy −6.07 kcal/mol). This is the first work to our knowledge to demonstrate that eudragit S-100 coated CAP and TQ-loaded Galactosylated PLGANP can induce apoptosis, which could be used to kill off cancer cells.

Scalable Moment Propagation and Analysis of Variational Distributions for Practical Bayesian Deep Learning.

Bayesian deep learning is one of the key frameworks employed in handling predictive uncertainty. Variational inference (VI), an extensively used inference method, derives the predictive distributions by Monte Carlo (MC) sampling. The drawback of MC sampling is its extremely high computational cost compared to that of ordinary deep learning. In contrast, the moment propagation (MP)-based approach propagates the output moments of each layer to derive predictive distributions instead of MC sampling. Because of this computational property, it is expected to realize faster inference than MC-based approaches. However, the applicability of the MP-based method in deep models has not been explored sufficiently, even though some studies have demonstrated the effectiveness of MP only in small toy models. One of the reasons is that it is difficult to train deep models by MP because of the large variance in activations. To realize MP in deep models, some normalization layers are required but have not yet been studied. In addition, it is still difficult to design well-calibrated MP-based models, because the effectiveness of MP-based methods under various variational distributions has also not been investigated. In this study, we propose a fast and reliable MP-based Bayesian deep-learning method. First, to train deep-learning models using MP, we introduce a batch normalization layer extended to random variables to prevent increases in the variance of activations. Second, to identify the appropriate variational distribution in MP, we investigate the treatment of moments of several variational distributions and evaluate their uncertainty quality of predictions. Experiments with regression tasks demonstrate that the MP-based method provides qualitatively and quantitatively equivalent predictive performance to MC-based methods regardless of variational distributions. In the classification tasks, we show that we can train MP-based deep models by extended batch normalization. We also show that the MP-based approach realizes 2.0-2.8 times faster inference than the MC-based approach while maintaining the predictive performance. The results of this study can help realize a fast and well-calibrated uncertainty estimation method that can be deployed in a wider range of reliability-aware applications.

A comparison of forest structural methods of semiarid mangrove species using a field-based approach

The data obtained from field-based forest inventories, mainly basal area and stem density, are relevant for the analysis of aboveground biomass and forest fragmentation. Due to its persistently flooded ground, fieldwork in mangrove forests is time-consuming and complicated. Since mangroves are sensitive to the effects of climate change, selecting a reliable field method is of utmost importance. To this end, we analyzed 4 mangrove classes: Rhizophora mangle (RM), Laguncularia racemosa (LR), Avicennia germinans (AG), and AG shrub. We georeferenced and counted all mangrove stems within four 0.04 ha (20 × 20 m square). We analyzed data from 3 circular area plots and the plotless point-centered quarter method (PCQM) based on the original square plots. Depending on the mangrove class, PCQM overestimated basal area by up to 34% and stem density by 21%. The 3 circular plot surveys underestimated basal area from –1% to –29% and stem density from –3 to –25%. Based on the results, we suggest using a circular plot of 0.04 ha (r = 11.28 m) in less dense forests (RM and AG) and a circular plot of 0.015 ha (r = 6.9 m) with forest densities greater than 3,500 stems/ha (LR and AG shrub). The advantages of using the circular plot approach over PCQM are that mangrove inventories can be quantified quickly and do not require a minimum number of sampling points.

All-Photolithography Fabrication of Ion-Gated Flexible Organic Transistor Array for Multimode Neuromorphic Computing.

Organic ion-gated transistors (OIGTs) demonstrate commendable performance for versatile neuromorphic systems. However, due to the fragility of organic materials to organic solvents, efficient and reliable all-photolithography methods for scalable manufacturing of high-density OIGT arrays with multimode neuromorphic functions are still missing, especially when all active layers are patterned in high-density. Here, a flexible high-density (9662 devices per cm2) OIGT array with high yield and minimal device-to-device variation is fabricated by a modified all-photolithography method. The unencapsulated flexible array can withstand 1000 times' bending at a radius of 1mm, and 3 months' storage test in air, without obvious performance degradation. More interesting, the OIGTs can be configured between volatile and nonvolatile modes, suitable for constructing reservoir computing systems to achieve high accuracy in classifying handwritten digits with low training costs. This work proposes a promising design of organic and flexible electronics for affordable neuromorphic systems, encompassing both array and algorithm aspects.

Explainable ensemble machine learning revealing the effect of meteorology and sources on ozone formation in megacity Hangzhou, China

Megacity Hangzhou, located in eastern China, has experienced severe O3 pollution in recent years, thereby clarifying the key drivers of the formation is essential to suppress O3 deterioration. In this study, the ensemble machine learning model (EML) coupled with Shapley additive explanations (SHAP), and positive matrix factorization were used to explore the impact of various factors (including meteorology, chemical components, sources) on O3 formation during the whole period, pollution days, and typical persistent pollution events from April to October in 2021–2022. The EML model achieved better performance than the single model, with R2 values of 0.91. SHAP analysis revealed that meteorological conditions had the greatest effects on O3 variability with the contribution of 57 %–60 % for different pollution levels, and the main drivers were relative humidity and radiation. The effects of chemical factors on O3 formation presented a positive response to volatile organic compounds (VOCs) and fine particulate matter (PM2.5), and a negative response to nitrogen oxides (NOx). Oxygenated compounds (OVOCs), alkenes, and aromatic of VOCs subgroups had higher contribution; additionally, the effects of PM2.5 and NOx were also important and increased with the O3 deterioration. The impact of seven emission sources on O3 formation in Hangzhou indicated that vehicle exhaust (35 %), biomass combustion (16 %), and biogenic emissions (12 %) were the dominant drivers. However, for the O3 pollution days, the effects of biomass combustion and biogenic emissions increased. Especially in persistent pollution events with highest O3 concentrations, the magnitude of biogenic emission effect elevated significantly by 156 % compared to the whole situations. Our finding revealed that the combination of the EML model and SHAP analysis could provide a reliable method for rapid diagnosis of the cause of O3 pollution at different event scales, supporting the formulation of control measures.

A Simplified Proteomics LC-MRM-MS Assay for Determination of apoE Genotypes in Plasma Samples.

Apolipoprotein E (apoE), a polymorphic plasma protein, plays a pivotal role in lipid transportation. The human apoE gene possesses three major alleles (ε2, ε3, and ε4), which differ by single amino acid (cysteine to arginine) substitutions. The ε4 allele represents the primary genetic risk factor for Alzheimer's disease (AD), whereas the ε2 allele protects against the disease. Knowledge of a patient's apoE genotype has high diagnostic value. A recent study has introduced an LC-MRM-MS-based proteomic approach for apoE isoform genotyping using stable isotope-labeled peptide internal standards (SIS). Here, our goal was to develop a simplified LC-MRM-MS assay for identifying apoE genotypes in plasma samples, eliminating the need for the use of SIS peptides. To determine the apoE genotypes, we monitored the chromatographic peak area ratios of isoform-specific peptides relative to a peptide that is common to all apoE isoforms. The assay results correlated well with the standard TaqMan allelic discrimination assay, and we observed a concordance between the two methods for all but three out of 172 samples. DNA sequencing of these three samples has confirmed that the results of the LC-MRM-MS method were correct. Thus, our simplified UPLC-MRM-MS assay is a feasible and reliable method for identifying apoE genotypes without using SIS internal standard peptides. The approach can be seamlessly incorporated into existing quantitative proteomics assays and kits, providing additional valuable apoE genotype information. The principle of using signal ratios of the protein isoform-specific peptides to the peptide common for all of the protein isoforms has the potential to be used for protein isoform determination in general.

Trace Determination of Phenanthrene and Fluorene in Beverages Using D-μ-SPE and Liquid Chromatography Analysis

Dispersive micro solid phase extraction (D-μ-SPE) method using Graphene Oxide (GO) for the extraction and pre-concentration of selected polycyclic aromatic hydrocarbons (PAHs) from beverage samples prior to high-performance liquid chromatography - diode array detector (HPLC - DAD) is reported. Selected PAHs Fluorene (FLU) and Phenanthrene (PHE) were used as targeted analytes. An experimental design using Response Surface Methodology (RSM) and Box–Box–Behnken design (BBD) was performed to evaluate the interactive effects of three significant parameters: mass of sorbent, sample volume and extraction time. Under the optimum conditions, the method revealed good linearity (R2 = 0.9995 – 0.9998) over a concentration range of 0.5 – 5.0 mg L-1. The limit of detection (LOD) was in the range of 0.03 – 0.24 mg L-1 with satisfactory relative recoveries (81 – 99 %) and good relative standard deviation (RSD) of ≤ 3.80 % (n = 3). The method was successfully applied to tea, coffee, and milk samples and proved to be a simple, rapid, and reliable method with good extraction efficiencies for the detection of PAHs in beverage samples.

Residual Stress Relaxation in the Laser Welded Structure after Low-Cycle Fatigue and Fatigue Life: Numerical Analysis and Neutron Diffraction Experiment

The residual stress relaxation behaviour in low-cycle fatigue brings uncertainty to accurately predict fatigue life. Therefore, establishing the residual stress relaxation model for the welded structure is critical. In this paper, the residual stress is simulated through Abaqus finite element software (6.14). The residual stress relaxation model related to the magnitude of cyclic loading and the number of cycles is proposed. Furthermore, the residual stress relaxation model is applied to predict low-cycle fatigue life. Finally, the simulation results are validated by experimental data obtained using the reliable neutron diffraction method, and a good agreement is observed.

Application of Biotechniques for In Vitro Virus and Viroid Elimination in Pome Fruit Crops.

Sustainable production of pome fruit crops is dependent upon having virus-free planting materials. The production and distribution of plants derived from virus- and viroid-negative sources is necessary not only to control pome fruit viral diseases but also for sustainable breeding activities, as well as the safe movement of plant materials across borders. With variable success rates, different in vitro-based techniques, including shoot tip culture, micrografting, thermotherapy, chemotherapy, and shoot tip cryotherapy, have been employed to eliminate viruses from pome fruits. Higher pathogen eradication efficiencies have been achieved by combining two or more of these techniques. An accurate diagnosis that confirms complete viral elimination is crucial for developing effective management strategies. In recent years, considerable efforts have resulted in new reliable and efficient virus detection methods. This comprehensive review documents the development and recent advances in biotechnological methods that produce healthy pome fruit plants. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Analysis of home-made cannabis edibles by UHPLC-HRMS after standard addition method.

With recent evolution of cannabis legalization around the world, cannabis edibles are booming, and determining their concentration in Δ9-tetrahydrocannabinol (Δ9-THC), the regulated psychoactive substance, remains a challenge for toxicology laboratories, which must prove the legal status or not of the product. Cannabinoids are a large family of structurally similar and lipophilic molecules, requiring dedicated pre-analytical methods, as well as efficient chromatographic separation to differentiate cannabinoid isomers which are distinguished by their psychoactive properties and their legal status. Here, we present two independent cases of cannabis edibles, for which we performed analysis of home-made cannabis chocolate cakes, and of the resins and herbs used for cooking. Quantitation was carried out with a new developed standard addition method, to avoid matrix effects and matrix dependent calibration. Extraction by QuEChERs method, followed by targeted and nontargeted analysis by ultra-high performance liquid chromatography hyphenated to high resolution mass spectrometry (UHPLC-HRMS) allowed the identification of several phytocannabinoids, mainly Δ9-THC, CBD and their acid precursors THCA and CBDA. Δ9-THC was identified in significant concentrations (mg/g) in both edibles, even though one was prepared with CBD herb. This work highlights the need to analyze cannabis edibles, as well as the resins and herbs used in their preparation if it is homemade, and it proposes a reliable analytical method for toxicology laboratories.

Use of Chilled Mirror /Dew-Point Method (WP4C) to Develop Soil Water Characteristic Curve for Coastal Silty Sand with a Comparison of the Existing Analytical Models

Soil Water Characteristic Curve (SWCC) is one of the methods to study the behaviour of unsaturated soil. This study presents the SWCC for Sri Lankan coastal silty sand developed using chilled mirror dew-point technique WP4C and compares the performance of a few prediction models. SWCC was also developed experimentally using pressure plate and suction-moisture sensors, to verify the results obtained using WP4C. The fitted SWCC curve was developed using Van Genuchten's (VG) model. The prediction models of Arya and Paris (1981), Aubertin et al. (2003), and Wang et al. (2017) were used to compare the performance using their own suggested values and the more suitable values for this study as the model parameters which were estimated using the least square method. This study shows that chilled mirror dew-point technique which is incorporated in WP4C instrument can be used as a fast and reliable experimental method to develop the SWCC for sandy soils compared to other experimental methods. The Wang et al. (2017) model is better suited for estimating the SWCC through analytical methods for this soil type, using either the recommended values in this study or proposed values by the authors for the modal parameters.

Fast and non- destructive multivariate test method to predict bread wheat grain major quality parameters

ABSTRACT For decades researches have been conducted in Ethiopia to improve wheat productivity and quality. This involved extensive selection of breeding lines based on agronomic and laboratory data from numerous trials. However, generating laboratory data for all these services was both time-consuming and expensive. Hence, the study aimed to develop a very rapid and cost-effective testing method for wheat genotypes and varieties using near-infrared spectroscopy. For this purpose, we collected 155 bread wheat samples from high-growing potential areas of Sinana, Kulumsa, and Holeta. The wet chemistry analysis was done in duplicate following the international standard official methods. The same samples were also scanned using TANGO’S Bruker NIRS machine in duplicate to acquire spectral data. A calibration model was then developed by fitting the reference and spectral data using the partial least square (PLS). The calibration models exhibited a high level of strength for all parameters. The performance of the calibration model for protein (R2 = 0.99; RPD = 9.63), moisture (R2 = 0.96; RPD = 5.29), ash (R2 = 0.98; RPD = 6.39), total gluten (R2 = 0.98; RPD = 8.09), dry gluten (R2 = 0.97; RPD = 5.57), gluten index (R2 = 0.96; RPD = 5.23), and falling number (R2 = 0.97 and RPD = 6.23) were deemed suitable for wheat process and quality control purposes. But the models for moisture and gluten index (R2 = 0.96) were found to be suitable for screening and simple quality control purposes only. Therefore, this study clearly demonstrated that the near-infrared spectroscopy model is a highly reliable and robust method for predicting the key quality traits of bread wheat grains, serving the needs of both breeding programs and industry raw material control.

Occluded apples orientation estimator based on deep learning model for robotic harvesting

Apple harvesting robot is in rapid development in the recent years due to the shortage of manual labor. Robotics vision system has been one of the main focus in the development to achieve successful grasping. Information such as the apple orientation is valuable for developing more effective fruit detachment strategies. Occlusions in the orchard environment and the lack of distinct features in an apple has made it challenging to identify the apple orientation. Our study presents a novel approach that aims to estimate the orientation of fruits by projecting 2D information onto a 3D space. This method specifically targets the field of robotic harvesting and leverages multiple well-established and reliable fundamental methods currently available that includes keypoint detection neural network and circle detection based on extracted line segments of occluded apple. Based on the extracted information, a unit vector that represents orientation of the apple can be computed and transformed according to the relative position between the apple and the camera. The proposed method is evaluated using a publicly available 3D point cloud data of 11 apple trees from an orchard farm that was additionally labeled manually with apple orientations. To verify the evaluation results, the proposed method is tested by integrating it into an apple harvesting robot to perform inspection. Results shows that the median angular error in the orchard setting is 17.6° whilst in the lab setting is 14.6°. The experimental results show that the performance of the proposed method is comparable to existing research when under heavy occlusion and thus it is suitable for harvesting robot operating in the orchard farm.

Sustainability Evaluation of Plant-Based Beverages and Semi-Skimmed Milk Incorporating Nutrients, Market Prices, and Environmental Costs

Developing a reliable method to compare food sustainability is gaining traction, with efforts like those by the Food and Agriculture Organization (FAO). This research aims to contribute to a comprehensive scientific comparison of food categories based on CO2 emissions linked not to weight but to their primary function: nutrient availability and uptake in the consumer’s body. The study utilizes a multi-criteria evaluation for sustainability, incorporating the Nutrient Rich Food (NRF) score, protein digestibility, and essential amino acid content. A case study compares one serving of semi-skimmed milk (SSM) with various plant-based beverages (oat, soy, rice, coconut, and almond), considering their carbon footprints in relation to nutrient content and environmental costs. The analysis integrates protein quality through essential amino acid proportion and digestibility. Findings reveal that achieving an NRF11.3 score of 50 requires more servings of unfortified plant-based beverages than semi-skimmed milk, resulting in higher carbon footprints, except for soy drink. However, when considering emerging farm management measures, semi-skimmed and soy drinks show comparable carbon footprints for a given NRF score. Fortified plant-based beverages (soy, oat, and almond) exhibit lower footprints relative to the calculated NRF scores. Yet, when converting carbon footprints to euros using the European Union Emissions Trading System and adding them to retail prices per kilogram, semi-skimmed milk emerges as the option with the lowest “societal costs” (environment and consumer costs). The research underscores that understanding a food product’s nutritional value requires more than knowledge of its composition; uptake into the body maintenance and potential synergistic effects of other components in the food matrix play crucial roles.

Enhanced conductive body heat loss during sleep increases slow-wave sleep and calms the heart

Substantial evidence suggests that the circadian decline of core body temperature (CBT) triggers the initiation of human sleep, with CBT continuing to decrease during sleep. Although the connection between habitual sleep and CBT patterns is established, the impact of external body cooling on sleep remains poorly understood. The main aim of the present study is to show whether a decline in body temperatures during sleep can be related to an increase in slow wave sleep (N3). This three-center study on 72 individuals of varying age, sex, and BMI used an identical type of a high-heat capacity mattress as a reproducible, non-disturbing way of body cooling, accompanied by measurements of CBT and proximal back skin temperatures, heart rate and sleep (polysomnography). The main findings were an increase in nocturnal sleep stage N3 (7.5 ± 21.6 min/7.5 h, mean ± SD; p = 0.0038) and a decrease in heart rate (− 2.36 ± 1.08 bpm, mean ± SD; p < 0.0001); sleep stage REM did not change (p = 0.3564). Subjects with a greater degree of body cooling exhibited a significant increase in nocturnal N3 and a decrease in REM sleep, mainly in the second part of the night. In addition, these subjects showed a phase advance in the NREM-REM sleep cycle distribution of N3 and REM. Both effects were significantly associated with increased conductive inner heat transfer, indicated by an increased CBT- proximal back skin temperature -gradient, rather than with changes in CBT itself. Our findings reveal a previously far disregarded mechanism in sleep research that has potential therapeutic implications: Conductive body cooling during sleep is a reliable method for promoting N3 and reducing heart rate.

The significance of G8 and other geriatric assessments in urologic cancer management: A comprehensive review.

In urologic oncology, which often involves older patients, it is important to consider how to manage their care appropriately. Geriatric assessment (GA) is a method that can address the specific needs of older cancer patients. The GA encompasses various assessment domains, but these domains exhibit variations across the literature. Some of the common items include functional ability, nutrition, comorbidities, cognitive ability, psychosocial disorders, polypharmacy, social and financial support, falls/imbalance, and vision/hearing. Despite the diversity of domains, there is limited consensus on reliable measurement methods. This review discusses the role of GA in managing urologic cancer in unique scenarios, such as those necessitating temporary or permanent urinary catheters or stomas due to urinary diversion. A comprehensive GA is time and human-resource-intensive in real-world clinical practice. Hence, simpler tools such as the Geriatric-8 (G8), capable of identifying high-risk patients requiring a detailed GA, are also under investigation in various contexts. Therefore, we conducted a systematic literature review on the G8. Our findings indicate that patients with low G8 scores encounter difficulties with stoma self-care after urinary diversion and have higher risks of urinary tract infections and ileus after radical cystectomy. The utilization of G8 as a screening tool for urologic cancer patients may facilitate the delivery of appropriate and personalized treatment and care.

Rat Pharmacokinetics and In Vitro Metabolite Identification of KM-819, a Parkinson's Disease Candidate, Using LC-MS/MS and LC-HRMS.

FAF1 (FAS-associated factor 1) is involved in the activation of Fas cell surface death receptors and plays a role in apoptosis and necrosis. In patients with Parkinson's disease, FAF1 is overexpressed in dopaminergic neurons in the substantia nigra. KM-819, an FAF1 inhibitor, has shown potential for preventing dopaminergic neuronal cell death, promoting the degradation of α-synuclein and preventing its accumulation. This study aimed to develop and validate a quantitative analytical method for determining KM-819 levels in rat plasma using liquid chromatography-tandem mass spectrometry. This method was then applied to pharmacokinetic (PK) studies in rats. The metabolic stability of KM-819 was assessed in rat, dog, and human hepatocytes. In vitro metabolite identification and metabolic pathways were investigated in rat, dog, and human hepatocytes. The structural analog of KM-819, namely N-[1-(4-bromobenzyl)-3,5-dimethyl-1H-pyrazol-4-yl]-2-(phenylsulfanyl) acetamide, served as the internal standard (IS). Proteins were precipitated from plasma samples using acetonitrile. Analysis was carried out using a reverse-phase C18 column with a mobile phase consisting of 0.1% formic acid in distilled water and 0.1% formic acid in acetonitrile. The analytical method developed for KM-819 exhibited linearity within the concentration range of 0.002-10 μg/mL in rat plasma. The precision and accuracy of the intra- and inter-day measurements were <15% for the lower limit of quantification (LLOQ) and all quality control samples. KM-819 demonstrated stability under various sample storage conditions (6 h at room temperature (25 °C), four weeks at -20 °C, three freeze-thaw cycles, and pretreated samples in the autosampler). The matrix effect and dilution integrity met the criteria set by the Food and Drug Administration and the European Medicines Agency. This sensitive, rapid, and reliable analytical method was successfully applied in pharmacokinetic studies in rats. Pharmacokinetic analysis revealed the dose-independent kinetics of KM-819 at 0.5-5 mg/kg, with a moderate oral bioavailability of ~20% in rats. The metabolic stability of KM-819 was also found to be moderate in rat, dog, and human hepatocytes. Metabolite identification in rat, dog, and human hepatocytes resulted in the discovery of six, six, and eight metabolites, respectively. Glucuronidation and mono-oxidation have been proposed as the major metabolic pathways. Overall, these findings contribute to a better understanding of the pharmacokinetic characteristics of KM-819, thereby aiding future clinical studies.

Rectal Sedation With Ketamine and Midazolam in the Management of Uncooperative Children During Dental Treatment: A Case Series and Method Description.

Background In pediatric dentistry, sedation aims to eliminate anxiety to facilitate the completion of dental procedures. Sedation in children is a multidimensional field that includes the child, parents/guardians, and the health care team. The rectal route is generally painless, making it suitable for children who are afraid of needles. This route has several advantages over the oral route, including reduced patient cooperation requirements, a faster and more predictable onset, and less physical trauma than the intravenous and intramuscular routes. This case series aimed to evaluate the effectiveness and success rate of rectal sedation with ketamine and midazolam in the management of uncooperative children during dental treatment. Case presentation Ten healthy children with definitely negative behavior were enrolled in this study. Each child was given7 mg/kg of ketamine in combination with midazolam 0.1 mg/kg by the rectal route. The mean onset sedation time was 9.5 minutes, and pulpotomy procedures were done. Behavioral response was monitored throughout treatment using theOhio State University Behavioral RatingScale(OSUBRS), and the depth of sedation was measured using the University of Michigan Sedation Scale (UMSS). The Houpt General Behavior Scale was used to estimate the treatment success rate based on the overall behavior rating. All 10 cases showed good anxiolysis and cooperation following rectal administration, with no side effects observed. Conclusions Rectal administration of ketamine in combination with midazolam may be considered a reliable method in the management of uncooperative children during dental treatment. No adverse effects were observed during or after the sedation procedure.

Software Effort Estimation using Machine Learning Algorithms

Effort estimation is a crucial aspect of software development, as it helps project managers plan, control, and schedule the development of software systems. This research study compares various machine learning techniques for estimating effort in software development, focusing on the most widely used and recent methods. The paper begins by highlighting the significance of effort estimation and its associated difficulties. It then presents a comprehensive overview of the different categories of effort estimation techniques, including algorithmic, model-based, and expert-based methods. The study concludes by comparing methods for a given software development project. Random Forest Regression algorithm performs well on the given dataset tested along with various Regression algorithms, including Support Vector, Linear, and Decision Tree Regression. Additionally, the research identifies areas for future investigation in software effort estimation, including the requirement for more accurate and reliable methods and the need to address the inherent complexity and uncertainty in software development projects. This paper provides a comprehensive examination of the current state-of-the-art in software effort estimation, serving as a resource for researchers in the field of software engineering.

Active Kriging-based conjugate first-order reliability method for highly efficient structural reliability analysis using resample strategy

Efficient structural reliability analysis method is crucial to solving reliability analysis of complex structural problems. High-computational cost and low-failure probability problems greatly limit the efficiency in structural reliability analysis problems, causing the safety and reliability of the structure to be questioned. In this work, a highly efficient structural reliability analysis method coupling active Kriging algorithm with conjugate first order reliability method (AK-CFORM) is proposed. Specifically, the resample strategy is considered to reduce the number of samples evaluated in each active learning process; the uniform sampling is used to better balance global and local optimal problems; the conjugate map is used to improve the robustness of analytical first order reliability method; and the approximate numerical differential formula is proposed to solve the problems of non-convergence when solving the gradient of the Kriging surrogate model. Finally, three numerical cases and four engineering cases are used to illustrate the effectiveness and robustness of the proposed method. The results show that the proposed AK-CFORM has greater advantages in the number of calling system response and surrogate model with robust and accurate performance.