Model Of Stage Research Articles

Basic models of building pre-trial investigation as a stage of criminal proceedings in foreign countries

One of the main areas of development of criminal procedure legislation has been, and still is, the differentiation of criminal proceedings into pre­trial and trial stages. The author notes the current trends in national criminal procedure legislation aimed at optimising the procedure for investigating criminal cases and reducing the timeframe for their investigation. The desire for democratisation of the criminal procedure doctrine, strengthening of the guarantees of admissibility of evidence, adversarial process, and changes in the process model itself, along with the fundamental trends in the development of the rule of law, have led to a large-scale adoption of foreign criminal procedure institutions.
 The authors examines the main models of pre­trial investigation as a stage of criminal proceedings in the countries with the Anglo-Saxon (England, USA) and Romano-Germanic (France, Germany, Switzerland) legal systems. Given the materials studied, it is noted that when building models of pre-trial investigation of criminal proceedings, regardless of the system of law used in different States, the legislator focuses on improving two areas: criminal procedure and criminal law. The criminal law criterion is responsible for determining a more moderate sentence by reaching an agreement between the prosecution and defence. The criminal procedural criterion, in turn, defines two more areas of change: the possibility of reducing the procedural timeframe of the proceedings in a certain part or in general; reducing the procedural obligations of persons involved in criminal proceedings. The authors come to the conclusion that it is necessary to modernise the current model of the pre-trial stage of the national criminal procedure with due regard for the positive experience of the most developed Western countries.

Realizing Efficient On-Device Language-based Image Retrieval

Advances in deep learning have enabled accurate language-based search and retrieval, e.g., over user photos, in the cloud. Many users prefer to store their photos in the home due to privacy concerns. As such, a need arises for models that can perform cross-modal search on resource-limited devices. State-of-the-art cross-modal retrieval models achieve high accuracy through learning entangled representations that enable fine-grained similarity calculation between a language query and an image, but at the expense of having a prohibitively high retrieval latency. Alternatively, there is a new class of methods that exhibits good performance with low latency, but requires a lot more computational resources, and an order of magnitude more training data (i.e. large web-scraped datasets consisting of millions of image-caption pairs) making them infeasible to use in a commercial context. From a pragmatic perspective, none of the existing methods are suitable for developing commercial applications for low-latency cross-modal retrieval on low-resource devices. We propose CrispSearch, a cascaded approach that greatly reduces the retrieval latency with minimal loss in ranking accuracy for on-device language-based image retrieval. The idea behind our approach is to combine a light-weight and runtime-efficient coarse model with a fine re-ranking stage. Given a language query, the coarse model effectively filters out many of the irrelevant image candidates. After this filtering, only a handful of strong candidates will be selected and sent to a fine model for re-ranking. Extensive experimental results with two SOTA models for the fine re-ranking stage, on standard benchmark datasets show that CrispSearch results in a speedup of up to 38 times over the SOTA fine methods with negligible performance degradation. Moreover, our method does not require millions of training instances, making it a pragmatic solution to on-device search and retrieval.

Substantiation of the possibility of using bronchoalveolar lavage with perfluorocarbon fluids for the treatment of the alveolar stage of toxic pulmonary edema

Introduction. Toxic pulmonary edema is an acute syndrome characterized by the accumulation of fluid in the extravascular spaces of the lungs, impaired gas exchange, the formation of tissue hypoxia and acidosis, which can occur in acute inhalation poisoning with pulmonary toxicants at work. Existing methods of drug and respiratory therapy are ineffective in the development of alveolar pulmonary edema (end-stage acute respiratory distress syndrome; ARDS). In this regard, the search for new approaches to the treatment of this condition, which is characterized by almost 100 % mortality, is of great practical importance. One of such approaches is the use of perfluorocarbon (PFC) fluids, which, due to their unique physical and chemical properties, are able not only to ensure the evacuation of edematous fluid from the alveoli and respiratory tract, but also to restore gas exchange in the parts of the lungs filled with them. This article will present an experimental evaluation of the use of PFC fluids in a model of the alveolar stage of toxic pulmonary edema.
 The goal of the study is to determine the effect of bronchoalveolar lavage (BAL) using perfluorocarbons on the indicators of acute respiratory failure and the outcome of the alveolar stage of toxic pulmonary edema.
 Materials and methods. The study was carried out on male Wistar rats aged 4 months, weighing 180–230 g. Toxic pulmonary edema was initiated by endotracheal injection of 0.1 M HCl solution. Anesthetized rats were intubated with a cannula, then intratracheally injected with 0.1 M HCl solution at a dose of 2 ml/kg and connected to a ventilator. After that, the animals were randomized by weight into groups of 6 individuals each. Animals of the experimental group with a decrease in saturation below 80%, 2–6 procedures of bronchoalveolar lavage (BAL) with PFC-oxygenated liquid in a single dose of 2.0 ml/kg were performed. Heart rate, oxygen saturation, duration of survival, and overall survival by group were recorded.
 The resalts. In all rats, there was a decrease in oxygen saturation (SpO2) and heart rate (HR) after HCl administration, which was restored to the lower limits of normal within 5 minutes. However, after 25 to 30 minutes, the animals experienced a rapid decrease in SpO2, an increase in heart rate, the appearance of wet wheezing in the lungs, and the discharge of foamy fluid from the catheter. Against this background, there was a rapid death of animals, At the same time, the average duration of survival was 30.6±3.3 min. In turn, in the animals of the experimental group, after each BAL procedure, an increase in saturation was noted, it was possible to evacuate a total of 9.1±0.8 ml/kg of edematous fluid from the lungs. It was also noted that the average duration of survival of rats in the experimental group was 1.69 times and amounted to 51.6±3.8 minutes.
 Conclusions. The use of BAL with PFC fluids in the alveolar stage of toxic pulmonary edema makes it possible to evacuate a significant amount of edematous fluid from the lower parts of the lungs due to its displacement by a perfluorocarbon with a higher density; to short-term reduce the severity of manifestations of acute respiratory failure after instillation of oxygenated PFC liquid; to increase the duration of survival of animals by ensuring gas exchange in previously uninvolved parts of the lungs.
 Ethics. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki. The Clinical Study Protocol was reviewed at a meeting of the local Ethics Committee FSBSI IRIOH. Protocol No. 4 of May 25, 2022.

Units Coordination, Combinatorial Reasoning, and the Multiplication Principle: The Case of Ashley, an Advanced Stage 2 College Student

ABSTRACT The multiplication principle (MP) is foundational for combinatorial problem-solving. From a units-coordination perspective, applying the MP with justification entails establishing unit relationships between the number of options at each independent stage of a counting process and the total number of combinatorial outcomes. Existing research literature, however, has not captured, generally, how students establish these unit relationships. We provide a second order model of an advanced stage 2 college student, Ashley, who had no prior combinatorics instruction, as she engaged in solving combinatorics problems that we considered to involve the MP. Our findings suggest that Ashley began by interpreting combinatorics problems using her whole number iterative units coordination scheme. Through engagement with the teacher-researcher, Ashley constructed combinatorial composites using a pairing operation, units coordination, and units simplification. We also found that Ashley was able to create a three-level-of-unit structure in activity, and to use notation that she produced to re-instantiate the reasoning that produced this unit structure. Doing so provides novel insights into how advanced stage 2 students, especially those at the college level, can use notation to manage complex unit relationships.

Classification and automatic scoring of arousal intensity during sleep stages using machine learning

Arousal during sleep can result in sleep fragmentation and various physiological effects, impairing cognitive function and raising blood pressure and heart rate. However, the current definition of arousal has limitations in assessing both amplitude and duration, making it challenging to measure sleep fragmentation accurately. Moreover, there is inconsistency among inter-raters in arousal scoring, which renders it susceptible to subjective variability. Therefore, this study aims to identify a highly accurate classifier for each sleep stage by employing optimized feature selection and machine learning models. According to electroencephalography (EEG) signals during the arousal phase, the intensity level was categorized into four levels. For control, the non-arousal cases were used as level 0 and referred as sham arousal, resulting in five arousal intensity levels. Wavelet transform was applied to analyze sleep arousal to extract features from EEG. Based on these features, we classified arousal intensity levels through machine learning algorithms. Due to the different characteristics of EEG in each sleep stage, the classification model was optimized for the four sleep stages. Excluding sham arousals, a total of 13,532 arousal events were used. The lowest intensity in the entire data, level 1, was computed to be 3107, level 2 was 3384, level 3 was 3472, and the highest intensity of level 4 was 3,569. The optimized classification model for each sleep stage achieved an average sensitivity of 82.68%, specificity of 95.68%, and AUROC of 96.30%. The sensitivity of the control, arousal intensity level 0, was 83.07%, a 1.25% increase over the unoptimized model and a 14.22% increase over previous research. This study used machine learning techniques to develop classifiers for each sleep stage, improving the accuracy of arousal intensity classification. The classifiers showed high sensitivity and specificity and revealed the unique characteristics of arousal intensity during different sleep stages. These findings represent a novel approach to arousal research and have implications for developing more accurate predictive models in sleep research.

An efficient fake news classification model based on ensemble deep learning techniques

The availability and expansion of social media has made it difficult to distinguish between fake and real news. Information falsification has exponentially increased as a result of how simple it is to spread information through sharing. Social media dependability is also under jeopardy due to the extensive dissemination of false information. Therefore, it has become a research problem to automatically validate information, specifically source, content, and publisher, to identify it as true or false. Despite its limitations, machine learning (ML) has been crucial in the categorization of information. Previous studies suggested three-step methods for categorising false information on social media. In the first step of the process, the data set is subjected to a number of pre-processing processes in order to transform unstructured data sets into structured data sets. The unknowable properties of fake news and the features are extracted by the Lexicon Model in the second stage. In the third stage of this research project, a feature selection method by WOA (Whale Optimization Algorithm) for weight value to tune the classification part. Finally, a Hybrid Classification model that is hybrid with a fuzzy based Convolutional Neural Network and kernel based support vector machine is constructed in order to identify the data pertaining to bogus news. However using single classifier for fake news detection produces the insufficient accuracy. To overcome this issue in this work introduced an improved model for fake news classification. To turn unstructured data sets into structured data sets, a variety of pre-processing operations are used on the data set in the initial phase of the procedure. The unknowable properties of fake news and the features are extracted by the Lexicon Model in the second stage. In the third stage of this research project, a feature selection method by COA (Coati Optimization Algorithm) for weight value to tune the classification part. Finally, an ensemble of RNN (Recurrent Neural Networks), VGG-16 and ResNet50.A classification model was developed to recognise bogus news information. Evaluate each fake news analysis' performance in terms of accuracy, precision, recall, and F1 score. The suggested model, out of all the methodologies taken into consideration in this study, provides the highest outcomes, according to experimental findings

Development of Discrete Mathematics Module Based on Discovery Learning for Mathematical Understanding in Higher Education

This research aims to develop a discrete mathematics module based on discovery learning to improve students’ mathematical understanding in higher education. This research used the Research and Development (R&D) method with a mixed approach and ADDIE model in the product development stage. The data collection techniques used questionnaires, tests and interview guides. Meanwhile, the data analysis technique used in this research combines qualitative and quantitative descriptive. The results include (1) Reviewing the study of discrete mathematics module development; it obtained valid results after going through several assessments and revisions, and the practicality of developing this discrete mathematics module was categorized as practical; it is not surprising that the module can increase students’ mathematical understanding with a range of 37.97; (2) Judging from effectiveness; it is obtained from inferential statistics that significance is 0.00, which means there is an influence on the use of discovery learning-based discrete mathematics modules on students’ mathematical understanding. These results have implications that the module has proved effective in improving students’ mathematical understanding in higher education.

An Estimation of the Leaf Nitrogen Content of Apple Tree Canopies Based on Multispectral Unmanned Aerial Vehicle Imagery and Machine Learning Methods

Accurate nitrogen fertilizer management determines the yield and quality of fruit trees, but there is a lack of multispectral UAV-based nitrogen fertilizer monitoring technology for orchards. Therefore, in this study, a field experiment was conducted by UAV to acquire multispectral images of an apple orchard with dwarf stocks and dense planting in southern Xinjiang and to estimate the nitrogen content of canopy leaves of apple trees by using three machine learning methods. The three inversion methods were partial least squares regression (PLSR), ridge regression (RR), and random forest regression (RFR). The results showed that the RF model could significantly improve the accuracy of estimating the leaf nitrogen content of the apple tree canopy, and the validation set of the four periods of apple trees ranged from 0.670 to 0.797 for R2, 0.838 mg L−1 to 4.403 mg L−1 for RMSE, and 1.74 to 2.222 for RPD, among which the RF model of the pre-fruit expansion stage of the 2023 season had the highest accuracy. This paper shows that the apple tree leaf nitrogen content estimation model based on multispectral UAV images constructed by using the RF machine learning method can timely and accurately diagnose the growth condition of apple trees, provide technical support for precise nitrogen fertilizer management in orchards, and provide a certain scientific basis for tree crop growth.

Diverse Efficacy of Dimethyl Fumarate in Alleviating the Late Streptozotocin-Induced Cognitive Impairment and Neuropathological Features in Rat.

Our previous study showed that dimethyl fumarate (DMF) treatment performed within three weeks after intracerebroventricular (ICV) injection of streptozotocin (STZ) attenuated spatial memory impairment, hippocampal neurodegeneration, and neuroinflammation in rats. The present study is aimed at verifying the hypothesis that DMF alleviates late effects of STZ (6months after ICV injection) which reflects advanced stage of the Alzheimer's disease (AD) in human patients. Spatial memory was assessed with Morris water maze (MWM), general brain level of amyloid β (Aβ) and p-tau was measured by western blot, immunofluorescent labelling of active microglia (IBA1), Aβ and p-tau and histological assay of neurodegeneration (Fluoro-Jade C) were performed in hippocampus and cortex. Two-week oral therapy with DMF normalized spatial memory disrupted by STZ but had no influence on general brain level of Aβ and p-tau. However, immunofluorescence showed local reduction of Aβ aggregates number in parietal cortex and p-tau+ cells in CA2 hippocampal area. Microgliosis was alleviated by DMF in CA1 area and parietal cortex. DMF-treated STZ injected rats showed higher number of Aβ containing microglia than untreated group in CA2 and frontal cortex, which may be the result of increased phagocytic activity in these areas after DMF treatment. STZ-induced neurodegeneration was alleviated by DMF in dentate gyrus and frontal cortex. In conclusion DMF treatment exerts beneficial effect on spatial memory in the rat model of late stage of AD, but weakly influences neuropathological features, as only local reduction in number of Aβ aggregates, p-tau containing cells, neurodegeneration, and microgliosis was found.

Federated Learning Survival Model and Potential Radiotherapy Decision Support Impact Assessment for Non–small Cell Lung Cancer Using Real-World Data

AimsThe objective of this study was to develop a two-year overall survival model for inoperable stage I-III non–small cell lung cancer (NSCLC) patients using routine radiation oncology data over a federated (distributed) learning network and evaluate the potential of decision support for curative versus palliative radiotherapy. MethodsA federated infrastructure of data extraction, de-identification, standardisation, image analysis, and modelling was installed for seven clinics to obtain clinical and imaging features and survival information for patients treated in 2011–2019. A logistic regression model was trained for the 2011–2016 curative patient cohort and validated for the 2017–2019 cohort. Features were selected with univariate and model-based analysis and optimised using bootstrapping. System performance was assessed by the receiver operating characteristic (ROC) and corresponding area under curve (AUC), C-index, calibration metrics and Kaplan–Meier survival curves, with risk groups defined by model probability quartiles. Decision support was evaluated using a case-control analysis using propensity matching between treatment groups. Results1655 patient datasets were included. The overall model AUC was 0.68. Fifty-eight percent of patients treated with palliative radiotherapy had a low-to-moderate risk prediction according to the model, with survival times not significantly different (p = 0.87 and 0.061) from patients treated with curative radiotherapy classified as high-risk by the model. When survival was simulated by risk group and model-indicated treatment, there was an estimated 11% increase in survival rate at two years (p < 0.01). ConclusionFederated learning over multiple institution data can be used to develop and validate decision support systems for lung cancer while quantifying the potential impact of their use in practice. This paves the way for personalised medicine, where decisions can be based more closely on individual patient details from routine care.

Study on Fatigue Life of Aluminum Alloy 6061-T6 Based on Random Defect Characteristics.

A certain number of hole-like defects will occur in aluminum alloys under cyclic loading. The internal holes will reduce the strength of the material and cause stress concentration, which will aggravate the development of fatigue damage. A classification method of defect features based on X-ray CT damage data is proposed. The random hole distribution model is established through the linear congruence method and the region division method. The hole parameter is introduced as the intermediate variable of the 3D reconstruction model of internal defects. In the mesoscopic stage, the function relationship between the distribution of random holes and the fatigue life is established based on the coupling relationship between the number and proportion of pores and the fatigue life. In the macroscopic stage, the relationship between the random holes and the macroscopic crack growth life is established by taking the crack length as the damage variable. The crack propagation rate decreased with the increase in the number of holes. The prediction model of the whole life stage is established using the life function from microcrack initiation to macroscopic crack propagation. Finally, the validity of the whole stage fatigue life prediction model is demonstrated through the comparison and verification of experiments, which provides a certain engineering value for the life estimation of 6061-T6 aluminum alloy materials.

IMPROVEMENT OF THE METHODOLOGY FOR OPTIMIZING THE PARAMETERS OF COMPLEX SYSTEMS UNDER THE INFLUENCE OF THERMAL LOAD SOURCES

The article investigates some aspects of solving the problem of constructing mathematical models for optimizing the parameters of local laser action on multilayer microbiological systems. The constraints on the desired laser parameters and on the temperature field in the material subject to laser fission are set. Verification of compliance with the restrictions on not exceeding the maximum value of the temperature field requires multiple calculations of the corresponding temperature field in the microbiological material. Control over the temperature field limits ensures the viability of its parts during the biotechnological process of laser fission. To construct adequate optimization mathematical models, the author substantiates the adequacy of the computational mathematical models describing the state of a microbiological system under the action of laser radiation sources. A multi-point boundary value problem with a system of inhomogeneous differential heat conduction equations for a multilayer microbiological medium is investigated and the correctness of this problem is substantiated for minor perturbations of the right-hand side of the differential equation. The results obtained in the article guarantee the adequacy of applied optimization mathematical models for finding rational values of technical parameters of laser emitters. In order to improve the accuracy of optimization of the technical parameters of laser emitters, the article presents a mathematical modeling of the preparatory stage of embryo defrosting. The presented mathematical models and methods of their implementation are necessary to improve the quality of embryo defrosting. Improvement of the methodology for solving applied biotechnological problems will certainly lead to the complication of mathematical models, but it will increase the accuracy of calculation and optimization of technical parameters of the biotechnological process of laser embryo division.

A Mathematical Programming Approach for IoT-Enabled, Energy-Efficient Heterogeneous Wireless Sensor Network Design and Implementation.

The Internet of Things (IoT) is playing a pivotal role in transforming various industries, and Wireless Sensor Networks (WSNs) are emerging as the key drivers of this innovation. This research explores the utilization of a heterogeneous network model to optimize the deployment of sensors in agricultural settings. The primary objective is to strategically position sensor nodes for efficient energy consumption, prolonged network lifetime, and dependable data transmission. The proposed strategy incorporates an offline model for placing sensor nodes within the target region, taking into account the coverage requirements and network connectivity. We propose a two-stage centralized control model that ensures cohesive decision making, grouping sensor nodes into protective boxes. This grouping facilitates shared resource utilization, including batteries and bandwidth, while minimizing box number for cost-effectiveness. Noteworthy contributions of this research encompass addressing connectivity and coverage challenges through an offline deployment model in the first stage, and resolving real-time adaptability concerns using an online energy optimization model in the second stage. Emphasis is placed on the energy efficiency, achieved through the sensor consolidation within boxes, minimizing data transmission hops, and considering energy expenditures in sensing, transmitting, and active/sleep modes. Our simulations on an agricultural farmland highlights its practicality, particularly focusing on the sensor placement for measuring soil temperature and humidity. Hardware tests validate the proposed model, incorporating parameters from the real-world implementation to enhance calculation accuracy. This study provides not only theoretical insights but also extends its relevance to smart farming practices, illustrating the potential of WSNs in revolutionizing sustainable agriculture.

New unfreezing strategy of transfer learning in satellite imagery for mapping the diversity of slum areas: A case study in Kenitra city—Morocco

The purpose behind this paper research is to localize and detect, for the first time, three types of shantytowns that can exist in both urban and rural areas in Morocco. The reason why we have conducted this study is the absence of diversity slum mapping in the form of dynamic GIS data, which can be a part of national development strategies to tackle the risk of further proliferation of shantytowns. Therefore, we first create a new database integration from five countries whose content contains 1366 high-resolution patches of 224 × 224 pixels divided into five classes: two types of “Urban Slum”, one type of “Rural Slum”, “Formal Buildings”, and non-built-up area. Second, we suggest employing a variety of performance metrics to come up with the best transfer learning-based models for the unfreezing stage. The study reveals that by handling the unfreezing of the front layers, we can produce a reliable new model where one condition is fulfilled, namely, where high-frequency dissimilarities have arisen between the target and the source datasets.Quantitative results of these methodologies show that our dataset and our unfreezing strategy for the mobilenetv2 model are more accurate compared to other competing models. This new model can localize all possible changes in morphology and shape among the three-slum types with state-of-the-art performance that reaches an overall accuracy of 98.17%, a loss of 0.062, and a kappa score of 97.71%. Our dataset and the code will be publicly available at the github11https://github.com/mouddentarik/New_Unfreezing_Strategy. to share our results.

Convolutional neural networks combined with classification algorithms for the diagnosis of periodontitis.

We aim to develop a deep learning model based on a convolutional neural network (CNN) combined with a classification algorithm (CA) to assist dentists in quickly and accurately diagnosing the stage of periodontitis. Periapical radiographs (PERs) and clinical data were collected. The CNNs including Alexnet, VGG16, and ResNet18 were trained on PER to establish the PER-CNN models for no periodontal bone loss (PBL) and PBL. The CAs including random forest (RF), support vector machine (SVM), naive Bayes (NB), logistic regression (LR), and k-nearest neighbor (KNN) were added to the PER-CNN model for control, stage I, stage II and stage III/IV periodontitis. Heat map was produced using a gradient-weighted class activation mapping method to visualize the regions of interest of the PER-Alexnet model. Clustering analysis was performed based on the ten PER-CNN scores and the clinical characteristics. The accuracy of the PER-Alexnet and PER-VGG16 models with the higher performance was 0.872 and 0.853, respectively. The accuracy of the PER-Alexnet + RF model with the highest performance for control, stage I, stage II and stage III/IV was 0.968, 0.960, 0.835 and 0.842, respectively. Heat map showed that the regions of interest predicted by the model were periodontitis bone lesions. We found that age and smoking were significantly related to periodontitis based on the PER-Alexnet scores. The PER-Alexnet + RF model has reached high performance for whole-case periodontal diagnosis. The CNN models combined with CA can assist dentists in quickly and accurately diagnosing the stage of periodontitis.

Impairments of GABAergic transmission in hippocampus mediate increased susceptibility of epilepsy in the early stage of Alzheimer’s disease

BackgroundPatients with Alzheimer’s disease (AD) are often co-morbid with unprovoked seizures, making clinical diagnosis and management difficult. Although it has an important role in both AD and epilepsy, abnormal γ-aminobutyric acid (GABA)ergic transmission is recognized only as a compensative change for glutamatergic damage. Neuregulin 1 (NRG1)-ErbB4 signaling can promote GABA release and suppress epileptogenesis, but its effects on cognition in AD are still controversial.MethodsFour-month-old APPswe/PS1dE9 mice (APP mice) were used as animal models in the early stage of AD in this study. Acute/chronic chemical-kindling epilepsy models were established with pentylenetetrazol. Electroencephalogram and Racine scores were performed to assess seizures. Behavioral tests were used to assess cognition and emotion. Electrophysiology, western blot and immunofluorescence were performed to detect the alterations in synapses, GABAergic system components and NRG1-ErbB4 signaling. Furthermore, NRG1 was administrated intracerebroventricularly into APP mice and then its antiepileptic and cognitive effects were evaluated.ResultsAPP mice had increased susceptibility to epilepsy and resulting hippocampal synaptic damage and cognitive impairment. Electrophysiological analysis revealed decreased GABAergic transmission in the hippocampus. This abnormal GABAergic transmission involved a reduction in the number of parvalbumin interneurons (PV+ Ins) and decreased levels of GABA synthesis and transport. We also found impaired NRG1-ErbB4 signaling which mediated by PV+ Ins loss. And NRG1 administration could effectively reduce seizures and improve cognition in four-month-old APP mice.ConclusionOur results indicated that abnormal GABAergic transmission mediated hippocampal hyperexcitability, further excitation/inhibition imbalance, and promoted epileptogenesis in the early stage of AD. Appropriate NRG1 administration could down-regulate seizure susceptibility and rescue cognitive function. Our study provided a potential direction for intervening in the co-morbidity of AD and epilepsy.

Who benefit from adjuvant chemotherapy in stage I lung adenocarcinoma? A multi-dimensional model for candidate selection

BackgroundDespite promising overall survival of stage I lung adenocarcinoma (LUAD) patients, 10-25 % of them still went through recurrence after surgery. [1] While it is still disputable whether adjuvant chemotherapy is necessary for stage I patients. [2] IASLC grading system for non-mucinous LUAD shows that minor high-grade patterns are significant indicator of poor prognosis. [3] Other risk factors, such as, pleura invasion, lympho-vascular invasion, STAS, etc. are also related to poor prognosis. [4-6] There still lack evidence whether IASLC grade itself or together with other risk factors can guide the use of adjuvant therapy in stage I patients. In this article, we tried to establish a multi-variable recurrence prediction model for stage I LUAD patients that is able to identify candidates of adjuvant chemotherapy. MethodsWe retrospectively collected patients who underwent lung surgery from 2018.8.1 to 2018.12.31 at our institution and diagnosed with lung adenocarcinoma pT1-2aN0M0 (stage I). Clinical data, manifestation on CT scan, pathologic features, driver gene mutations and follow-up information were collected. Cox proportional hazards regression analyses were performed utilizing the non-adjuvant cohort to predict disease free survival (DFS) and a nomogram was constructed and applied to the total cohort. Kaplan-Meier method was used to compare DFS between groups. Statistical analysis was conducted by R version 3.6.3. FindingsA total of 913 stage I LUAD patients were included in this study. Median follow-up time is 48.1 months.4-year and 5-year DFS are 92.9 % and 89.6 % for the total cohort. 65 patient experienced recurrence or death. 4-year DFS are 97.0 %,94.6 % and 76.2 %, and 5-year DFS are 95.5 %, 90.0 % and 74.1 % in IASLC Grade1, 2 and 3, respectively(p < 0.0001). High-risk patients defined by single risk factors, such as, IASLC grade 3, pleura invasion, STAS, less LN resected could not benefit from adjuvant therapy. A LASSO-COX regression model was built and patients are divided into high-risk and low-risk groups. In the high-risk group, patients underwent adjuvant chemotherapy have longer DFS than those who did not (p = 0.024), while in the low-risk group, patients underwent adjuvant chemotherapy have inferior DFS than those who did not (p < 0.001). InterpretationIASLC grading is a significant indicator of DFS, however it could not guide adjuvant therapy in our stage I LUAD cohort. Growth patterns and T indicators together with other risk factors could identify high-risk patients that are potential candidate of adjuvant therapy, including some stage IA LUAD patients.

Two-step AI-aided Bayesian source identification of urban-scale pollution

Poor air quality produces detrimental effects worldwide; hence, it is vital to thoroughly characterize air pollution sources and effectively address and mitigate such effects. Due to the complexity of the underlying physical processes, and uncertainties in the available observations, the air pollution source identification problem is typically cast within a Bayesian inversion framework. The latter incorporates prior knowledge and observations to characterize a release event through the posterior distribution of the source parameters. In this study, we rely on two-dimensional (2D) pollutant concentration distributions as observations, and adopt the Wasserstein (W2) distance to model the likelihood probability distribution for given emission parameters. Since the posterior distribution is estimated via random sampling that involves many forward model runs, the Bayesian framework can be computationally prohibitive for realistic urban air pollution problems that are driven by computationally demanding micro-scale flow simulations. Furthermore, computing the W2 distance is resource-intensive . In this context, we develop a computationally efficient Bayesian framework by following (i) a two-stage approach that reduces the cost of the Bayesian inversion, and (ii) an artificial intelligence (AI) approximation of the W2 distance. In the two-stage approach, a low-resolution dispersion model is run in the first stage to propose representative samples of emission parameters for final selection by the original high-resolution model in the second stage. In addition, we approximate the W2 distance using a deep neural network (DNN) to achieve an appreciable reduction in the computational cost with negligible loss in the inversion performance. We design numerical experiments to test the sensitivity of the inverse solution to the characteristics of the approximative model. The results indicate that pairing the two-stage approach with the DNN approximation of the W2 distance preserves the quality of the inverse solution, while achieving at least threefold reductions in the computational cost.

Peran Pusat Pembelajaran Keluarga dalam Kontrol Parenting Era Digital di Kelurahan Bendul Merisi Kota Surabaya

The Family Learning Center plays a big role in developing parental control over the family, to prevent negative impacts that can damage the moral values of the nation's future generations. Through government-run programs, the Family Learning Center provides an understanding to citizens about the importance of monitoring their families. This research focuses on the application of Social Change theory and Rogers &amp; Kincaid Communication. Data analysis is carried out using the interactive Miles and Huberman model in stages and interactively with the predetermined data. The Family Learning Center plays a very important role in improving family welfare. By using direct communication methods at the community hall, this program has successfully achieved its set targets.

The effect of using the (POE) model (predict, observe, interpret) associated with different educational methods on teaching some basic volleyball skills to middle school students

The aim of the research is to : To prepare an educational program according to the model (POE) (predict - note - interpret) associated with different educational methods and to reveal its effect on teaching some basic skills of volleyball to middle school students The researchers took the experimental approach as a way to solve their problem, The sample was identified as (32) students representing the third intermediate grade in the (Qaraqush) intermediate school for boys for the academic year (2019-2020) distributed into two academic divisions (A and B), equivalent to (age, height, weight, and physical fitness components). And for each group (16) students, and by the method of voting, Division (B) was chosen to become the experimental group, which was studied according to the (POE) model, and Division (A) became a control group which took the usual method by the teacher in teaching it After that, the search experiment was applied and lasted for six weeks, with two educational units per week for each group. To process and analyze the data, the statistical system (SPSS) was used, and the most important conclusion of the study is: The use of the model (predict, note, interpret POE) associated with various educational methods left an evident effect in teaching some basic volleyball skills to middle school students. The phases of implementing the model experiment on the experimental group also contributed to stimulating students' creative self-capabilities in forecasting, observing and interpreting the results that favored their progress over the control group. The researchers recommended the necessity of paying attention to student-centered teaching methods by educating teachers about their models and strategies, as they have an impact on improving students' knowledge and skills. And more research is being used to model (predict, note, interpret POE) for different academic stages and multiple academic subjects.