Weight Values Research Articles

Analysis of Flood Inundation Vulnerability to the Deli Watershed of North Sumatra Using Remote Sensing and GIS Techniques

Deli watersheds are listed as one of the most heavily damaged watersheds in Indonesia, and this causes many problems, such as floods, flash floods, and landslides. Studying this watershed becomes essential because the Deli watershed passes through a major city and has become the main source of flood and other natural disasters in the city such as Medan, The study aims to detect the vulnerability level of the areas around the watershed and the most rapid and possible way to study it is by using remote sensing and GIS, the overlay method by considering several parameters that affect the level of vulnerability to flooding in Deli Watershed, North Sumatra. Some of the parameters considered include the topographic wetness index, slope, land elevation, river flow density, land use cover, normalized difference vegetation index, distance to river, and rainfall. Each parameter is given a weight and value according to each classifier. The overlay process is carried out using ArcGIS 10.8.1 software, results are divided into 5 levels of area vulnerabilities all around the area, the study shows that all parts of Medan City in the Deli watershed have a high level of flood vulnerability and some areas with higher ground elevation such as Tanah Karo have spots area that is vulnerable with floods such as Daulu District with 0.45 ha and Semangat Gunung District with 0.63 ha of its areas are vulnerable to floods. The Topographic wetness index, Normalized difference vegetation Index, slope, and land use are the main factors causing flooding with weighting values 16, 13, 11, and 14. The other parameters elevation, flow density, distance to the river, rainfall, and soil class each have weighting values 7, 10, 11, 8, and 10. Slope with a range of 0-8% in most areas of Medan City are classified as very prone to flood inundation. This is due to the area's flat topography and low river flow density, the existing 3.5 km old sewer in the Deli Tua area is unable to accommodate the flood inundation load in the city of Medan and it is necessary to build two new sewers which are more effective and efficient in diverting the flow of water, before entering the city of Medan which makes the area vulnerable to becoming a water catchment area when it rains, thereby increasing the risk of flooding. With this flood-prone map, efforts to manage and mitigate floods in Medan City can be carried out. Information on vulnerable locations can be used to take appropriate preventive and countermeasures, such as building adequate drainage, regulating land use, and controlling river flows.

Comprehensive assessment of the extraction of Phyllanthus emblica L. based on the G1-Entropy Method and Response Surface Methodology

In this study, the G1-Entropy method was employed together with a response surface methodology (RSM) approach to establish a multi-index-based system for evaluating and optimizing the extraction of bioactive compounds from Phyllanthus emblica L. Seven chemical classes' contents were evaluated, including TPC (total phenolic content), TC (tannin content), DEY (dry extract yield), GA (gallic acid), CO (corilagin), CA (chebulagic acid), and EA (ellagic acid). The resultant extracts were also assessed for their antioxidant activity, as measured based on their DPPH radical, ABTS+, and hydroxyl radical scavenging activity levels and their capacity to reduce iron ions. The inhibition of fluorescent AGEs generation was also assessed in BSA-Fru and MGO-BSA model assays to gauge the anti-glycation activity of these extracts. In total, a comprehensive score was derived based on 13 indices and corresponding weight values, with these scores then being used to determine the extraction conditions that yielded the best outcome. Through this strategy, the ideal extraction processing parameters were found to include an ethanol concentration of 56%, a liquid-solid ratio of 8 mL/g, and an extraction duration of 100 min. Using these optimized conditions yielded a predicted comprehensive score (Y) of 94.91, while the response value based on three independent variables from the validation experiments performed using these conditions was 92.03. These results highlight the promise of leveraging Phyllanthus emblica as a resource for use in the cosmetic, food, biochemical, and pharmaceutical industries.

Genetic parametres and performance for yield and its components for some varieties of eggplant under Mosul city environment

This study was conducted in the vegetable research field, college of agriculture and forestry, horticulture and landscape design, Mosul Univ., using RCBD design with three replicates, during the spring growing season (2018) for evaluation of the performance yield and its components in nine eggplant varieties under Mosul city environment Iraq at 36°2′ longitude and 43°7′ latitude. The data was recorded for the traits: plant height (cm), number of branches in each one of the plants, fruit weight (gm), number of fruits in each one of the plants, yield in each one of the plants (kg), length and diameter of fruit (cm), and total yield of fruits (ton /ha.). The results indicated that the nine varieties were different in most traits, the Alton kubry and karamleshy c.v gave high results in plant length(116.433, 130.00) respectively, the Mosal local c.v. and White beauty c.v were superior in the length of fruit than other genotypes under the study they gave (15.033 and 15.433 cm) respectively, whereas the number of the fruits per plant has been recorded in White long and Mosely c.v. (18.367 and 19.367 ) respectably. The genotype Alton kubry gave a high value in fruit weight (287.20gm). The phenotypic and genotypic variations (σ2p and σ2g) have been higher in the height of the plant, weight of the fruit, the number of fruits in each one of the plants, yield in each plant, and total yield of fruit for each unit area, the heritability (H2b.s.) was more than 65% for most traits under the study for nine c.v of eggplant. Also, the genetic advance was higher in most traits.

Hurdles Techniques (Combined Effects): A Novel Approach for Enhanced Broccoli Florets Stability, Quality, and Safety

This study demonstrates the changes in broccoli heads samples sensory, physicochemical, chemias blanching at 100 °C for 90 seconds, then dipping for 10 minutes in a coating solution concal, and microbial load after harvest. The samples were preserved using hurdles techniques such taining 0.15% potassium metabisulfite, 4% sodium chloride, 1% citric, 0.2% sodium benzoate and 1% chitosan. After coating samples were divided into two groups, one packaged using the aerobic method (A.P), and the other by vacuum method (V.P). Coated broccoli was stored at two conditions (ambient at 25 ± 3 °C and refrigeration at 4 ± 1 °C) for different period intervals. The control sample was blanched and packaged aerobically (uncoated). The combined treatments delayed the development loss of weight, titratable acid (TA), chlorophyll, and carotenoids, and prevented the growth of aerobic, anaerobic, psychrotrophic, coli form, mold, and yeast in the stored samples. Coated plus V.P. treatment demonstrated the highest weight, pigments, and TA values during storage. Further-more, it achieved the highest score in sensory evaluation after 50 and 240 days of both ambient and cold storage. Moreover, it inhibited the growth of tested microorganisms. So best treatment for the preservation of broccoli between 50 and 240 days of storage period was coated plus V.P. The combined technology application will lead to a safe product with optical shelf-life, improve microbial stability, and sensory quality. Thus, this technology is a technology that aims to improve the overall quality of food.

Oilfield analogy and productivity prediction based on machine learning: Field cases in PL oilfield, China

In the early time of oilfield development, insufficient production data and unclear understanding of oil production presented a challenge to reservoir engineers in devising effective development plans. To address this challenge, this study proposes a method using data mining technology to search for similar oil fields and predict well productivity. A query system of 135 analogy parameters is established based on geological and reservoir engineering research, and the weight values of these parameters are calculated using a data algorithm to establish an analogy system. The fuzzy matter-element algorithm is then used to calculate the similarity between oil fields, with fields having similarity greater than 70% identified as similar oil fields. Using similar oil fields as sample data, 8 important factors affecting well productivity are identified using the Pearson coefficient and mean decrease impurity (MDI) method. To establish productivity prediction models, linear regression (LR), random forest regression (RF), support vector regression (SVR), backpropagation (BP), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) algorithms are used. Their performance is evaluated using the coefficient of determination (R2), explained variance score (EV), mean squared error (MSE), and mean absolute error (MAE) metrics. The LightGBM model is selected to predict the productivity of 30 wells in the PL field with an average error of only 6.31%, which significantly improves the accuracy of the productivity prediction and meets the application requirements in the field. Finally, a software platform integrating data query, oil field analogy, productivity prediction, and knowledge base is established to identify patterns in massive reservoir development data and provide valuable technical references for new reservoir development.

Crescimento e produção do feijão-caupi sob estresse hídrico e cobertura de solo

ABSTRACT Cowpea is a crop of high socioeconomic importance for the semi-arid region of Brazil, although facing significant yield losses due to drought, requiring management strategies to overcome this scenario. From this perspective, this study aimed to evaluate the growth and yield of cowpea genotypes under drought in different cultivation systems. The statistical design was randomized blocks arranged in a 2 × 4 × 2 factorial scheme corresponding to two cowpea genotypes (BRS Pujante and Costela de Vaca), four levels of crop evapotranspiration (40, 60, 80, and 100%), and two cultivation systems (with and without soil mulching). The BRS Pujante showed a higher leaf area and 100-grain weight than Costela de Vaca under all levels of crop evapotranspiration. The cowpea yield parameters showed the best results at 80 and 100% crop evapotranspiration. Regardless of the genotype, soil mulching provided higher cowpea 100-grain weight values. Soil mulching combined with 80% crop evapotranspiration is an appropriate strategy for cowpea production.

Refined Dynamic Event-Triggering Cluster Consensus of Multiagent Systems With Fixed/Switching Topology.

This article is concerned with cluster consensus control of multiagent systems (MASs) with the fixed/switching topology under a dynamic event-trigger (DET) mechanism. A refined sampled-data-based DET scheme is proposed by introducing two dynamically adjusting threshold parameters to distinguish the different transmission requirements for neighboring agents intra and outer cluster. Faced with the difficulties of acquiring full state information among spatially distributed agents, output feedback is employed to construct cooperative control protocols. Both fixed and switching topologies are considered to execute the designed DET-based cooperative cluster consensus control protocols. By constructing appropriate Lyapunov-Krasovskii functionals (LKFs), some sufficient criteria in terms of matrix inequalities for the cluster consensus of MASs are derived, which can ensure that the error system with the proposed DET-based control strategy is asymptotically stable. Facing the nonconvex issue induced by output feedback, a particle swarm optimization (PSO)-based control design algorithm is novelly developed to calculate the control gains and event-triggering parameters jointly based on the derived stability criteria. The elements of the matrix variables are valued stochastically in certain ranges and the fitness function is designed as the accumulation of the weighting value of each matrix inequality. Finally, an application of multiple satellites formation flying is applied to numerically illustrate the effectiveness of the cluster consensus control strategy with the designed DET mechanism.

Anomaly detection in time-series data using evolutionary neural architecture search with non-differentiable functions

Deep neural networks have become the benchmark in diverse fields such as energy consumption forecasting, speech recognition, and anomaly detection, owing to their ability to efficiently process and analyse data. However, they face challenges in managing the complexity and variability in time series data, often leading to increased model complexity and prolonged search duration during parameter tuning. This paper proposes a novel anomaly detection approach through evolutionary neural architecture search (AD-ENAS), which is specifically designed for time series data. The proposed approach focuses on the search for the optimal and minimal neural network architecture. The AD-ENAS method consists of two main phases: architecture evolution and weight adjustment. The architecture evolution phase highlights the importance of neural network architecture by evaluating the fitness of each network agent using shared weight values. Subsequently, the convolutional matrix adaptation technique is used in the next phase for optimal weight adjustment of the neural network. The proposed AD-ENAS method operates without relying on differentiable functions, thus expanding the scope of neural network design beyond traditional backpropagation-based approaches. Various non-differentiable loss functions are explored to facilitate effective architecture search and weight adjustment. Comparative experiments are conducted with five baseline anomaly detection methods on three well-known datasets from reputable sources such as NASA SMAP, NASA MSL and Yahoo S5-A1. The results demonstrate that the AD-ENAS approach effectively evolves neural network architectures, outperforming baseline methods with F1 scores across the three datasets (MSL: 0.942, SMAP: 0.961, Yahoo S5-A1: 0.988) with non-differentiable loss functions, showcasing its efficacy in detecting anomalies in time series data.

Risk Assessment for Electricity Generation Management Process with SWARA Based Fuzzy TOPSIS Method

In the successful maintenance of electricity generation management processes in power generation plants, it is of great importance to determine the risks that may arise during the operation of the relevant processes, take measures to minimize these risks, and take the necessary actions. In this study, common risks in the electricity generation management process in HEPPs were identified and these risks were rated by experts (decision-makers) within each power plant itself. Since this rating is made by the experts of each power plant, the impact and probability values of the same risk may differ, and accordingly, different risk levels may arise for the same risk. In the study, the SWARA method was used to compare the risk levels of common risks in the electricity generation process in different power plants and calculate the final weight values of the related risks. As a result of the measures determined for each risk in the electricity generation management processes in the power plants and the actions taken for these measures, it was determined whether the relevant risks were reduced to acceptable levels by looking at the results of the internal audits. In the internal audits, the performance of HEPPs in eliminating the related risks is evaluated with fuzzy expressions separately for each risk. The risk weight values obtained by the SWARA method and the fuzzy expressions obtained as a result of the risk assessment were analyzed with the Fuzzy TOPSIS method, and the performance values of the power plants in eliminating the risks were calculated, then the performance ranking was made in the light of these values.

Fetal Anterior Abdominal Wall Thickness as an Indicator of Gestational Diabetes: A Sonographic Study

Objective: During pregnancy, sonographic measurements of subcutaneous adipose tissue may be a reliable diagnostic predictor of gestational diabetes mellitus (GDM) and fetal metabolism. A sonographic view of the fetal abdominal circumference can be used to measure the fetal anterior abdominal wall thickness (AAWT), which is straightforward and simple. Consequently, in this study, the aim was to evaluate whether the fetal AAWT value could be used as a diagnostic indicator of GDM, thus preventing the adverse neonatal and maternal outcomes thereof, including shoulder dystocia, neonatal hypoxia, and injury to the birth canal, secondary to fetal macrosomia. Materials and Methods: This prospective cross-sectional study was conducted at a hospital in Lahore, Pakistan, over a 9-month period. A total of 176 female participants in their third trimester of pregnancy were included in this study. The participants were assigned to two cohorts: a cohort of 88 participants diagnosed with GDM and a control cohort of 88 participants without GDM. An independent sample t test, analysis of variance, and chi-square test were used to compare the characteristics between the two cohorts of participants. A final analysis was performed to determine the strength of the correlations between the estimated fetal weight (EFW) and AAWT values. Results: The GDM and control cohorts had mean fetal AAWT values of 12.65 ± 1.276 and 6.389 ± 1.503, respectively. Thus, a statistically significant difference in the fetal AAWT values was observed between the GDM and control cohorts. Furthermore, a statistically significant positive correlation between the EFW and AAWT values was observed ( P = .01). Conclusion: This study highlighted that women with GDM have a considerably higher fetal AAWT values than those without GDM. In addition, the findings revealed that the AAWT value was correlated with the EFW value. Consequently, an increased fetal AAWT value can potentially be utilized as a predictive indicator for those pregnant women at risk of developing GDM, thus preventing adverse maternal and fetal outcomes.

Performance Evaluation of a Department in the Manufacturing Industry Based on Balanced Scorecard

The success of a company, especially in the manufacturing industry, is closely linked to the performance of each department. The final objective of this research is to determine KPIs, measure department performance on each KPI based on the perspective of a Balanced Scorecard-based performance assessment approach, and then determine achievements. The process of generating Key Performance Indicators (KPIs) commences with conducting unstructured interviews to get KPIs for each perspective. Subsequently, a survey was devised to conduct a systematic evaluation of Key Performance Indicators (KPIs) for each perspective, employing the Analytical Hierarchy Process methodology. The computations yielded weights for the four perspectives: financial, customer, learning, and internal business process. Each perspective was assigned a weight value of 0.25, 0.50, 0.125, and 0.125, respectively.

STGIC: A graph and image convolution-based method for spatial transcriptomic clustering.

Spatial transcriptomic (ST) clustering employs spatial and transcription information to group spots spatially coherent and transcriptionally similar together into the same spatial domain. Graph convolution network (GCN) and graph attention network (GAT), fed with spatial coordinates derived adjacency and transcription profile derived feature matrix are often used to solve the problem. Our proposed method STGIC (spatial transcriptomic clustering with graph and image convolution) is designed for techniques with regular lattices on chips. It utilizes an adaptive graph convolution (AGC) to get high quality pseudo-labels and then resorts to dilated convolution framework (DCF) for virtual image converted from gene expression information and spatial coordinates of spots. The dilation rates and kernel sizes are set appropriately and updating of weight values in the kernels is made to be subject to the spatial distance from the position of corresponding elements to kernel centers so that feature extraction of each spot is better guided by spatial distance to neighbor spots. Self-supervision realized by Kullback-Leibler (KL) divergence, spatial continuity loss and cross entropy calculated among spots with high confidence pseudo-labels make up the training objective of DCF. STGIC attains state-of-the-art (SOTA) clustering performance on the benchmark dataset of 10x Visium human dorsolateral prefrontal cortex (DLPFC). Besides, it's capable of depicting fine structures of other tissues from other species as well as guiding the identification of marker genes. Also, STGIC is expandable to Stereo-seq data with high spatial resolution.

Classification of People with Chronic Respiratory Disease into Preserved or Reduced Functional Exercise Capacity: A Retrospective Analysis of Associated Factors Considering Baseline Characteristics and Responses to Pulmonary Rehabilitation

Purpose: To classify individuals with chronic respiratory disease (CRD) into preserved or reduced functional exercise capacity (FEC) using the baseline 6-minute walk distance (6MWD), as well as to compare between these two groups other baseline characteristics, physiological and symptomatologic responses to the baseline 6-minute walk test (6MWT), and responses in health-related quality of life (HRQoL) and FEC to pulmonary rehabilitation (PR). Method: Sociodemographic and clinical data, lung function, HRQoL, and FEC (6MWT) from individuals with CRD who participated in a PR program were analyzed. Individuals were classified as with preserved or reduced FEC, if 6MWD was ≥ or < the lower limit of normality, respectively. Results: We included 117 individuals (50% male, age 61 years, 39% with chronic obstructive pulmonary disease), 76 classified as preserved FEC and 41 as reduced FEC. Individuals with reduced FEC had the lowest values for weight and lung function. No statistical difference was found when comparing the changes in HRQoL and 6MWD after PR between the preserved and reduced FEC groups. However, the mean 6MWD change in the reduced FEC group (61m) was twice the change in the preserved FEC group (30m). Conclusions: The classification into preserved or reduced FEC proved to be useful in PR as it allowed to identify individuals with CRD with lower weight and lung function at baseline, but who seemed to benefit more from the program.

Relational model of compositions of enamel coatings and sintering temperature based on back propagation neural network algorithm

Declining the sintering temperature of enamel coatings is traditionally by reducing the content of network-forming agents (such as SiO2) and increasing the content of flux (for example, alkali metal oxides), which will lower the density of the [-Si-O-] network structure, and adversely affect the corrosion resistance of enamel coatings. To ensure a low sintering temperature of enamel coatings as much as possible under a high SiO2 content, it is essential to develop novel enamel coating formulas. However, abundant compositions of enamel coatings cause enormous verification workloads. To address this issue, this paper establishes a relational model of the compositions of enamel coatings and the sintering temperature using the back propagation (BP) neural network algorithm and proposes a formula that meets the requirement of lower the sintering temperature under a high SiO2 content. When the SiO2 content is 55 %, the sintering temperature can be diminished to around 400 °C. Furthermore, the influences of common enamel components on the sintering temperature are identified in this paper using the sensitivity algorithm. The obtained general law of formulas is that the total weight value of the formula at low sintering temperatures should be around −0.3. This paper can provide valuable theoretical guidance for subsequent research and expand the application range of enamel coatings.

Postural impairments in unilateral and bilateral vestibulopathy.

Chronic imbalance is a major complaint of patients suffering from bilateral vestibulopathy (BV) and is often reported by patients with chronic unilateral vestibulopathy (UV), leading to increased risk of falling. We used the Central SensoriMotor Integration (CSMI) test, which evaluates sensory integration, time delay, and motor activation contributions to standing balance control, to determine whether CSMI measures could distinguish between healthy control (HC), UV, and BV subjects and to characterize vestibular, proprioceptive, and visual contributions expressed as sensory weights. We also hypothesized that sensory weight values would be associated with the results of vestibular assessments (vestibulo ocular reflex tests and Dizziness Handicap Inventory scores). Twenty HCs, 15 UVs and 17 BVs performed three CSMI conditions evoking sway in response to pseudorandom (1) surface tilts with eyes open or, (2) surface tilts with eyes closed, and (3) visual surround tilts. Proprioceptive weights were identified in surface tilt conditions and visual weights were identified in the visual tilt condition. BVs relied significantly more on proprioception. There was no overlap in proprioceptive weights between BV and HC subjects and minimal overlap between UV and BV subjects in the eyes-closed surface-tilt condition. Additionally, visual sensory weights were greater in BVs and were similarly able to distinguish BV from HC and UV subjects. We found no significant correlations between sensory weights and the results of vestibular assessments. Sensory weights from CSMI testing could provide a useful measure for diagnosing and for objectively evaluating the effectiveness of rehabilitation efforts and future treatments designed to restore vestibular function such as hair cell regeneration and vestibular implants.

Bridge Construction Risk Assessment Based on Variable Weight Theory and Cloud Model

In order to effectively prevent the occurrence of risky accidents during bridge construction, this study proposes a bridge construction risk assessment method based on variable weight theory and the cloud model theory. Firstly, the fishbone diagram was used to identify risk factors in constructing a bridge construction risk index system. Secondly, according to the cloud model theory, the comment cloud model of each risk index was established by using the forward cloud generator. Finally, the risk factor weights were quantified according to the intuitionistic fuzzy analytic hierarchy process (IFAHP). Combined with the variable weight theory, a zoning variable weight function was constructed and the weights were reallocated. Through the mutual aggregation of the comment cloud model and weights, the risk level of construction bridges was obtained. The method takes full account of the fuzziness and randomness existing in the evaluation process, optimizes the distribution of weight values of indicators, and uses Delphi iteration to effectively eliminate the subjective defects of individuals. A construction bridge in Changchun was used as an example for risk assessment, and the advance of the method was well verified. The results demonstrate that the method is highly feasible and effective after accuracy verification and sensitivity analysis.

Artificial neural network and multiple linear regression modeling for predicting thermal transmittance of plain-woven cotton fabric

The present research compares a machine learning model with a statistical model, with specific emphasis on artificial neural networks and multiple linear regression models. The aim of this study is to forecast the thermal transmittance of a plain-woven cotton fabric using input data such as thread density measured in ends per inch, picks per inch, and fabric thickness. The artificial neural network is built using a network with feed-forward backpropagation, and the MATLAB software’s training function trainlm is used to modify its weight and basic values based on Levenberg–Marquardt optimization techniques. The sigmoid transfer function is used to set the layer output and measure network performance in terms of the root mean squared error, mean absolute error percentage, and coefficient of determination which were determined. For the artificial neural network prediction model, the root mean squared error and mean absolute error percentage were 1.05 and 3.132%, respectively, while the coefficient of determination was 0.9307. In contrast, the multiple linear regression prediction model had root mean squared error and mean absolute error percentage values of 2.98 and 8.97%, respectively, along with a coefficient of determination of 0.4727. The results reveal that the artificial neural network model outperforms the multiple linear regression model, showing superior accuracy and robustness in capturing the intricate interactions between important fabric parameters (ends per inch, picks per inch, and thickness) and thermal transmittance values. This research emphasizes the efficiency of artificial neural network modeling as a superior tool for forecasting thermal transmittance in textile applications rather than employing the time-consuming trial-and-error process for delivering significant insights for material engineering and energy-efficient design.

Triaxial testing response of compacted iron ore tailings considering a broad spectrum of confining pressures

The filtered tailings disposal (dry stacking) up to 300 m high is an alternative to overcome the drawbacks related to the slurry tailings storage in large impoundments as it is safer and demands smaller portions of the existing landform. Even so, the understanding of the denser and dewatered material response over a broad range of confining pressures is essential to safely design tall dry stacking tailings facilities. Accordingly, the present research assesses the mechanical behavior of compacted iron ore tailings through triaxial tests. A series of compression and extension drained and undrained triaxial tests were conducted over a wide spectrum of confinements (σ́3 ranging from 75 to 8,000 kPa) to check possible particle breakage occurrence and effects. The influence of the initial density due to compaction was, as well, evaluated since the tests were performed using specimens molded at distinct dry unit weight values. The results were analyzed in the light of the critical state soil mechanics and have indicated the existence of a curvilinear critical state line in the ν: log ṕ plane. Small particle breakage has occurred and can be associated with reduction in surface roughness, breakage of asperities, and reduction in particle angularity. Moreover, a tendency for static liquefaction was observed amongst the loosest specimens sheared under the lowest confining levels.

Extrauterine Growth Restriction and Catch-up Growth Following NICU Discharge: A Tale of two Standards

Aim: Extrauterine growth restriction (EUGR) is common in premature infants, but the progression after neonatal intensive care unit (NICU) discharge is not well described. We aimed to assess EUGR after NICU discharge and to identify factors associated with catch-up growth (CUG) and neurodevelopmental outcomes at 2 years of age. Methods: Growth parameters at birth, 36 weeks postmenstrual age (PMA), and two neurodevelopmental follow-up visits for preterm graduates of our NICU were reviewed. EUGR was assessed using Fenton and Intergrowth-21 standards. Factors associated with outpatient growth and neurodevelopmental outcomes at 2 years of age were evaluated using logistic and linear regression. Results: 369 infants born at 24–32 weeks gestation comprised the cohort. EUGR prevalence was 14.9% for Intergrowth-21 versus 56.4% for Fenton ( p < .001). Although there was a significant decrease in weight z-score from birth to 36 weeks PMA, weight z-scores returned to their birth weight values at the second clinic visit (86 weeks PMA). Infants who received formula as opposed to human milk (HM) showed faster CUG. Higher scores in the Bayley-III scale at 2 years of age were associated with HM feeding. Discussion/Conclusion: Fewer infants were identified as EUGR when Intergrowth-21 versus Fenton growth standards were used, which categorizes infants with adverse clinical courses and poorer neurodevelopmental outcomes more distinctly. While growth failure during the birth hospitalization is common in preterm infants, outpatient CUG was demonstrated, with return to birth weight z-score. Our findings suggest that HM feeding may have a positive impact on cognitive, language, and motor developmental outcomes for preterm infants despite less rapid CUG.

Increase in body weight in schoolchildren from 0 to 15 years of public teaching network in Cascavel-PR in the period 2016 to 2021. Effect of the COVID-19 pandemic?

Objective: To assess the variation of body weight and Body Mass Index (BMI) of public school students in Cascavel-PR in the period 2016 to 2021 and to relate to the effects of the COVID-19 pandemic. Methods: Body weight (kg) and BMI (kg/m2) were analyzed for 20,233 schoolchildren registered by the School Health Program (PSE) in the age range 0 to 15 years between 2016 and 2021. Results: The average body weight (2016: 12.40kg; 2017:12.52kg; 2018: 13.98kg; 2019: 15.97kg 2021: 19.92kg) and BMI values (2016:17.61kg/m2; 2017:17.72kg/m2; 2018: 17.79kg/m2; 2019:18.41kg/m2 and 2021: 19.11kg/m2) were gradually increased among schoolchildren over time, with increases of 20 19 to 2021, statistically higher compared to previous years. Conclusion: Schoolchildren from the public school system showed increased body weight and BMI over time, with higher weight gain values observed in the years of the COVID-19 pandemic.