bluuebunny/crossref_metadata_2025 huggingface dataset:
Neurodevelopment in unilateral coronal craniosynostosis: a systematic review and meta-analysis
Authors: Fotouhi Annahita R. , Chiang Sarah N. , Peterson Andrew M. , Doering Michelle M. , Skolnick Gary B. , Naidoo Sybill D. , Strahle Jennifer M. , McEvoy Sean D. , Patel Kamlesh B.
Published: October 2022

OBJECTIVE

The current literature on unilateral coronal craniosynostosis is replete with repair techniques and surgical outcomes; however, information regarding neurodevelopment remains unclear. Therefore, the aim of this systematic review and meta-analysis was to comprehensively assess the neurodevelopmental outcomes of patients with unicoronal craniosynostosis compared with their healthy peers or normative data.

METHODS

A systematic review of the Ovid MEDLINE, Embase, Web of Science, Scopus, Cochrane Library, and ClinicalTrials.gov databases from database inception to January 19, 2022, was performed. Included studies assessed neurodevelopment of patients with unicoronal craniosynostosis. Two independent reviewers selected studies and extracted data based on a priori inclusion and exclusion criteria. Results of developmental tests were compared with normative data or controls to generate Hedges’ g statistics for meta-analysis. The quality of included studies was evaluated using the National Institutes of Health Assessment Tool.

RESULTS

A total of 19 studies were included and analyzed, with an overall fair reporting quality. A meta-analysis of 325 postoperative patients demonstrated that scores of general neurodevelopment were below average but within one standard deviation of the norm (Hedges’ g = −0.68 [95% CI −0.90 to −0.45], p < 0.001). Similarly, postoperative patients exhibited lower scores in verbal, psychomotor, and mathematic outcome assessments.

CONCLUSIONS

This systematic review and meta-analysis found that patients with unicoronal craniosynostosis had poorer neurodevelopment, although scores generally remained within the normal range. These data may guide implementation of regular neurocognitive assessments and early learning support of patients with unicoronal craniosynostosis.
Visualization of multiphase pulsatile blood over single phase blood flow in a patient specific stenosed left coronary artery using image processing technique
Authors: Athani Abdulgaphur , Ghazali N.N.N. , Anjum Badruddin Irfan , Kamangar Sarfaraz , Salman Ahmed N.J. , Honnutagi Abdulrazak
Published: October 2022

BACKGROUND: Coronary arteries disease has been reported as one of the principal roots of deaths worldwide. OBJECTIVE: The aim of this study is to analyze the multiphase pulsatile blood flow in the left coronary artery tree with stenosis. METHODS: The 3D left coronary artery model was reconstructed using 2D computerized tomography (CT) scan images. The Red Blood Cell (RBC) and varying hemodynamic parameters for single and multiphase blood flow conditions were analyzed. RESULTS: Results asserted that the multiphase blood flow modeling has a maximum velocity of 1.017 m/s and1.339 m/s at the stenosed region during the systolic and diastolic phases respectively. The increase in Wall Shear Stress (WSS) observed at the stenosed region during the diastole phase as compared during the systolic phase. It was also observed that the highest Oscillatory Shear Index (OSI) regions are found in the downstream area of stenosis and across the bifurcations. The increase in RBCs velocity from 0.45 m/s to 0.6 m/s across the stenosis was also noticed. CONCLUSION: The computational multiphase blood flow analysis improves the understanding and accuracy of the complex flow conditions of blood elements (RBC and Plasma) and provides the progression of the disease development in the coronary arteries. This study helps to enhance the diagnosis of the blocked (stenosed) arteries more precisely compared to the single-phase blood flow modeling.
Analytic one-dimensional maps and two-dimensional ordinary differential equations can robustly simulate Turing machines
Authors: Graça Daniel , Zhong Ning
Published: October 2022

In this paper, we analyze the problem of finding the minimum dimension n such that an analytic map/ordinary differential equation over [Formula: see text] can simulate a Turing machine in a way that is robust to perturbations. We show that one-dimensional analytic maps are sufficient to robustly simulate Turing machines; but the minimum dimension for the analytic ordinary differential equations to robustly simulate Turing machines is two, under some reasonable assumptions. We also show that any Turing machine can be simulated by a two-dimensional [Formula: see text] ordinary differential equation on the compact sphere [Formula: see text].
The UNFCCC Regime at a Crossroads: Can You Trust Anyone Over 30?+
Authors: Doelle Meinhard
Published: October 2022

The UN Framework Convention on Climate Change (UNFCCC) was adopted in 1992. It was opened for signature at Rio de Janeiro in June of that year. The 30th anniversary of its adoption offers an opportunity to reflect on more than a quarter of a century of UN climate diplomacy, and to consider the path ahead. This contribution takes a look back at the choices made that have led the regime to its current state. It then takes a look forward and considers the prospects of the regime meeting its ultimate aim of avoiding dangerous human interference with the climate system.
The Paris Agreement on Climate Change: A Subtle Combination of Tools and Actors for Better Enforcement?+
Authors: Hautereau-Boutonnet Mathilde , Maljean-Dubois Sandrine
Published: October 2022

The Paris Climate Agreement can be seen as illustrating the evolution of how legal norms are enforced in international law. While the Agreement benefits from a carefully thought-out enforcement mechanism in the international legal order, with techniques that encourage compliance rather than sanction non-compliance, its enforcement is also supported by domestic legal orders. Indeed, the Paris Agreement benefits from both hard and soft enforcement mechanisms. Here, all techniques and all actors have a role to play. This contribution shows that in order to discern the enforcement mechanisms attached to a legal instrument, it is sometimes necessary to take a global and complex look at all legal orders, techniques and actors, since they can act in a complementary manner.
Skeleton Abstraction for Universal Temporal Properties
Authors: Wallner Sophie , Wolf Karsten
Published: October 2022

Uniform coloured Petri nets can be abstracted to their skeleton, the place/transition net that simply turns the coloured tokens into black tokens. A coloured net and its skeleton are related by a net morphism [1, 2]. For the application of the skeleton as an abstraction method in the model checking process, we need to establish a simulation relation [3] between the state spaces of the two nets. Then, universal temporal properties (properties of the ACTL* logic) are preserved. The abstraction relation induced by a net morphism is not necessarily a simulation relation, due to a subtle issue related to deadlocks [4]. We discuss several situations where the abstraction relation induced by a net morphism is as well a simulation relation, thus preserving ACTL* properties. We further propose a partition refinement algorithm for folding a place/transition net into a coloured net. This way, skeleton abstraction becomes available for models given as place/transition nets. Experiments demonstrate the capabilities of the proposed technology. Using skeleton abstraction, we are capable of solving problems that have not been solved before in the Model Checking Contest [5].
The effect of patient quality measurements and HCAHPS patient satisfaction on hospital reimbursements
Authors: Chen Hui-Chuan , Cates Tommy , Taylor Monty
Published: October 2022

BACKGROUND: The Centers of Medicare & Medicaid Services (CMS) links hospital reimbursements to quality metrics. Likewise, the Hospital Value-Based Purchasing (VBP) program offers financial incentives to acute-care hospitals based on performance improvements on several quality measures included in the national Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey. A research gap exists with regard to assessing the effectiveness of VBP incentives on improving the patient’s quality of care. OBJECTIVE: This study is to determine whether hospitals which reported better patient quality metrics and lower frequency of pressure sores received higher reimbursements. METHODS: The data were retrieved from the CMS Care Compare website utilizing matched data from 2297 US hospitals. Information on HCAHPS, the VBP Program in Patient Safety Index, and Reimbursements was obtained for this study. Partial Least Square (PLS) was utilized thru SmartPLS 3.0 to test the hypotheses. RESULTS: The results did not reveal any financial penalties when hospitals reported lower patient quality outcomes and increased numbers of pressure sores. However, lower patient quality measures were associated with lower patient satisfaction. Controversially, lower patient satisfaction scores were associated with higher reimbursement rates overall. CONCLUSIONS: The main contribution of this study reveals that the effectiveness of value-based reimbursements and the concept of continuous improvement is constrained due to the lack of unified measurement objectives across US healthcare institutions.
Autonomic Dysfunction Impairs Baroreflex Function in an Alzheimer’s Disease Animal Model
Authors: Ehlen John C. , Forman Cassadi M. , Ostrowski Daniela , Ostrowski Tim D.
Published: October 2022

Background: Alzheimer’s disease (AD) patients frequently present with orthostatic hypotension. This inability to reflexively increase blood pressure on standing is a serious health concern and increases the risk of stroke and cardiovascular diseases. Objective: Since there are no clear mechanisms for orthostatic hypotension in human AD, the present study assessed the autonomic changes that could explain this comorbidity in an AD animal model. Methods: We used the established streptozotocin-induced rat model of AD (STZ-AD), which mimics many hallmark symptoms of sporadic AD in humans. Baroreflex responses were analyzed in anesthetized STZ-AD rats using femoral catheterization for blood pressure and heart rate, and autonomic activity was assessed using specific blockers and splanchnic sympathetic nerve recordings. Expression levels of autonomic receptors at the heart were examined using the western blot technique. Results: Baroreflex function in STZ-AD showed a blunted heart rate (HR) response to low blood pressure challenges, and the maximal sympathetic nerve activity was reduced. Conversely, HR responses to high blood pressure were similar to control, indicating no change in parasympathetic nerve activity. Under resting conditions, autonomic blockade demonstrated a baseline shift to increased sympathetic tone in STZ-AD. Protein expression levels of beta-1 adrenergic receptor and muscarinic acetylcholine receptor M2 in the heart were unchanged. Conclusion: Our study provides the first data on the pathological influence of AD on baroreflex function, which primarily affected the sympathetic nervous system in STZ-AD. These results represent the first mechanisms that may correlate with the orthostatic hypotension in human AD.
It’s Groundhog Day! What Can the History of Science Say About the Crisis in Alzheimer’s Disease Research?
Authors: Jacobs Noortje , Theunissen Bert
Published: October 2022

For years now, Alzheimer’s disease (AD) research has been stuck in a Groundhog-Day scenario: an endless time loop with no breakthrough in sight. Disagreement about the validity of the field’s dominant approach, based on the Amyloid Cascade Hypothesis, has led to a seemingly unresolvable trench war between proponents and critics. Our paper evaluates the recent scientific literature on AD from a historical and philosophical perspective. We show that AD research is a classic example of the boundary work at play in a field in crisis: both parties deploy historical and philosophical references to illustrate what counts as good and bad science, as proper scientific method and appropriate scientific conduct. We also show that boundary work has proved unable to point a way out of the deadlock and argue that the science system’s tools for establishing scientific quality, such as peer review and the grant system, are unlikely to resolve the crisis. Rather, they consolidate the dominant model’s position even more. In conclusion, we suggest that some kind of reverse boundary-work is needed that reopens the discussion on the nature of AD, an issue that has never been settled scientifically. Drawing on historical and philosophical work, we make clear that the definition of AD as a biomedical disease for which a cure can be found has consequences, not only for funding opportunities, but also for patients and their lives. A reconsideration of the desirability of these consequences may lead to different choices with respect to research priorities and patient care.
Structural Alteration of Gut Microbiota During the Amelioration of Chronic Psychological Stress-Aggravated Diabetes-Associated Cognitive Decline by a Traditional Chinese Herbal Formula, ZiBu PiYin Recipe
Authors: Zhou Wen , Zhan Libin , Xu Huiying , Zhang Lijing
Published: October 2022

Background: Chronic psychological stress (PS) hinders the treatment of diabetes-associated cognitive decline (DACD). However, the impact of chronic PS on the risk of developing DACD remains unclear. There is growing evidence that gut flora interventions are promising targets for treating stress-related diseases. Objective: We examined whether chronic PS triggers or exacerbates the onset of DACD in rats and aimed to elucidate whether ZiBuPiYin recipe (ZBPYR) prevents and treats chronic PS-aggravated DACD by dynamically maintaining the components of the gut microbiota. Methods: We performed chronic PS (restraint, rotation, and congestion) on ZDF rats to establish a model. Cognitive function was evaluated by behavioral experiments, and activation of the hypothalamic-pituitary-adrenal axis was detected by ELISA. Weekly feces from rats were collected for 16 S RNA sequencing. Results: We found that chronic PS promoted cognitive abnormalities and exacerbated DACD phenotypes. Additionally, chronic PS altered intestinal flora diversity, dynamically elevating the abundance of Alistipes and Coprococcus; enriching Module 1 (Dorea, Blautia, Ruminococcus) and Module 48 (Blautia); and inhibiting Module 20 (Lactobacillus, SMB53), and Module 42 (Akkermansia). ZBPYR significantly alleviated hyperglycemia and cognitive impairment in chronic PS-aggravated DACD rats and dynamically reduced the abundance of Alistipes and Coprococcus; significantly enriched Module 3 (Ruminococcus) and Module 45 (Lactobacillus, Coprococcus, SMB53); and suppressed Module 2 (Lactobacillus), Module 16 (Turicibacter, Trichococcus, Lactobacillus, 02d06, Clostridium), Module 23 (Bifidobacterium), and Module 43 (Clostridium). Conclusion: ZBPYR might prevent and treat chronic PS-aggravated DACD by dynamically regulating Lactobacillus, Alistipes, and Coprococcus.
Age-Related Oxidative Redox and Metabolic Changes Precede Intraneuronal Amyloid-β Accumulation and Plaque Deposition in a Transgenic Alzheimer’s Disease Mouse Model
Authors: Pontrello Crystal G. , McWhirt Joshua M. , Glabe Charles G. , Brewer Gregory J.
Published: October 2022

Background: Many identified mechanisms could be upstream of the prominent amyloid-β (Aβ) plaques in Alzheimer’s disease (AD). Objective: To profile the progression of pathology in AD. Methods: We monitored metabolic signaling, redox stress, intraneuronal amyloid-β (iAβ) accumulation, and extracellular plaque deposition in the brains of 3xTg-AD mice across the lifespan. Results: Intracellular accumulation of aggregated Aβ in the CA1 pyramidal cells at 9 months preceded extracellular plaques that first presented in the CA1 at 16 months of age. In biochemical assays, brain glutathione (GSH) declined with age in both 3xTg-AD and non-transgenic controls, but the decline was accelerated in 3xTg-AD brains from 2 to 4 months. The decline in GSH correlated exponentially with the rise in iAβ. Integrated metabolic signaling as the ratio of phospho-Akt (pAkt) to total Akt (tAkt) in the PI3kinase and mTOR pathway declined at 6, 9, and 12 months, before rising at 16 and 20 months. These pAkt/tAkt ratios correlated with both iAβ and GSH levels in a U-shaped relationship. Selective vulnerability of age-related AD-genotype-specific pAkt changes was greatest in the CA1 pyramidal cell layer. To demonstrate redox causation, iAβ accumulation was lowered in cultured middle-age adult 3xTg-AD neurons by treatment of the oxidized redox state in the neurons with exogenous cysteine. Conclusion: The order of pathologic progression in the 3xTg-AD mouse was loss of GSH (oxidative redox shift) followed by a pAkt/tAkt metabolic shift in CA1, iAβ accumulation in CA1, and extracellular Aβ deposition. Upstream targets may prove strategically more effective for therapy before irreversible changes.
Novel cluster set optimization model with unique identifier tagging for twitter data analysis
Authors: Vanam Harika , JebersonRetna Raj R , Janga Vijaykumar
Published: October 2022

Blogs, internet forums, social networks, and micro-blogging sites are some of the growing number of places where users can voice their opinions. Opinions on any given product, issue, service, or idea are contained in data, making them a valuable resource in their own right. Popular social networking services like Twitter, Facebook, and Google+ allows expressing views on a variety of topics, participating in discussions, or sending messages to a global user. Twitter sentiment analysis has received a lot of attention recently.Sentiment analysis is finding how a person feels about a topic from their written response about it and it can be separated into positive and negative through its use. Doing so enables to classify the tweets made by a user in to appropriate classification category based on which some decisions can be made. The literature proposed approaches to develop the classifiers on the Twitter datasets. Operations, including tokenization, stop-word removal, and stemming will be performed. NLP converts the text to a machine-readable representation. Artificial Intelligence (AI) combines NLP data to evaluate if a situation is positive or negative. The document’s subjectivity can be identified using ML and NLP techniques to categorize them in to positive, neutral, or negative. Performing sentiment analysis in Twitter data can be tedious due to limited size, unstructured nature, misspellings, slang, and abbreviations. For this task, a Tweet Analyzing Model for Cluster Set Optimization with Unique Identifier Tagging (TAM-CSO-UIT) was built using prospects to determine positive or negative sentiment in tweets obtained from Twitter. This approach assigns a +ve/-ve value to each entry in the Tweet database based on probability assignment using n-gram model. To perform this effectively the tweet dataset is considered as a sliding window of length L. The proposed model accurately analyses and classifies the tweets.
Design and development of solar photovoltaic fed modular multilevel inverter using intelligent techniques for renewable energy applications
Authors: Thangavel Jayakumar , Chinnaraj Gnanavel , Chandrasekaran Gokul , Kumarasamy Vanchinathan
Published: October 2022

This paper presents the design and development of Modular Multilevel Inverter (MMI) to reduce Total harmonic distortion (THD) using intelligent techniques towards marine applications. Many researchers have described the additional advantage of inverter control challenges such as voltage imbalance, increasing the number of voltage levels, power quality issues, reducing the number of semiconductors switches and achieving higher efficiency. Under the intelligent techniques, the implementation is carried out with aid of Artificial Neural Networks (ANN), Fuzzy Logic Controller (FLC) and Adaptive Neuro-Fuzzy Inference System (ANFIS) to calculate the modulation index (ma) and switching angles (θ) for MMI. Based on the calculation, it is trained to form a mapping between inputs and outputs for obtaining reduced Total Harmonics Distortion (THD). The objective of the intelligent controller is to control the inverter for regulating the output voltage with lowest THD. The proposed control structure has been estimated and compared for better robustness in terms of switching angle and modulation index with least THD in the inverter. Simulations and prototype models are made to analyze the controller’s performance, for inverter output voltage and harmonics. This proposed system is designed for marine lighting load application. The FPGA performance with all intelligent methods are analyzed by in SPARTAN3E500 FPGA device.
Bipolar vague soft topological structures in term of operators and convergence of sequences
Authors: Saeed Maha Mohammed , Al-Ghour Samer , Mehmood Arif , Al-Shomrani Mohammed M. , Park Choonkil , Lee Jung Rye
Published: October 2022

This work investigates the new notion of operators, including the interior operator, exterior operator and closure operator in bipolar vague soft topological spaces. On the basis of these notions few results are addressed in bipolar vague soft topological spaces. Lastly, the intriguing concept is that of a sequence’s limit and on the basis of this concept few more results are addressed in bipolar vague soft topological spaces.
Influence of subproblem solutions on the quality of traveling thief problem solutions
Authors: Chen Junfeng , Zheng Kaijun , Li Qingwu , Ayush Altangerel
Published: October 2022

The traveling thief problem (TTP) is a typical combinatorial optimization problem that integrates the computational complexity of the traveling salesman problem (TSP) and the knapsack problem (KP). The interdependent and mutually restrictive relationship between these two sub-problems brings new challenges to the heuristic optimization algorithm for solving the TTP problem. This paper first analyzes the performance of three sub-component combined iterative algorithms: Memetic Algorithm with the Two-stage Local Search (MATLS), S5, and CS2SA algorithms, which all employ the Chained Lin-ighan (CLK) algorithm to generate the circumnavigation path. To investigate the influence of different traveling routes on the performance of TTP solving algorithms, we propose a combinatorial iterative TTP solving algorithm based on the Ant Colony Optimization (ACO) and MAX-MIN Ant System (MMAS). Finally, the experimental investigations suggest that the traveling route generation method dramatically impacts the performance of TTP solving algorithms. The sub-component combined iterative algorithms based on the MMAS algorithm to generate the circumnavigation path has the best practical effect.
Multi-Layer ResNet-DenseNet architecture in consort with the XgBoost classifier for intracranial hemorrhage (ICH) subtype detection and classification
Authors: Nizarudeen Shanu , Shunmugavel Ganesh R.
Published: October 2022

Intracerebral haemorrhage (ICH) is defined as bleeding occurs in the brain and causes vascular abnormality, tumor, venous Infarction, therapeutic anticoagulation, trauma property, and cerebral aneurysm. It is a dangerous disease and increases high mortality rate within the age of 15 to 24. It may be cured by finding what type of ICH is affected in the brain within short period with more accuracy. The previous method did not provide adequate accuracy and increase the computational time. Therefore, in this manuscript Detection and Categorization of Acute Intracranial Hemorrhage (ICH) subtypes using a Multi-Layer DenseNet-ResNet Architecture with Improved Random Forest Classifier (IRF) is proposed to detect the subtypes of ICH with high accuracy, less computational time with maximal speed. Here, the brain CT images are collected from Physionet repository publicly dataset. Then the images are pre-processed to eliminate the noises. After that, the image features are extracted by using multi layer Densely Connected Convolutional Network (DenseNet) combined with Residual Network (ResNet) architecture with multiple Convolutional layers. The sub types of ICH (Epidural Hemorrhage (EDH), Subarachnoid Hemorrhage (SAH), Intracerebral Hemorrhage (ICH), Subdural Hemorrhage (SDH), Intraventricular Hemorrhage (IVH), normal is classified by using Improved Random Forest (IRF) Classifier with high accuracy. The simulation is activated in MATLAB platform. The proposed Multilayer-DenseNet-ResNet-IRF approach attains higher accuracy 23.44%, 31.93%, 42.83%, 41.9% compared with existing approaches, like Detection with classification of intracranial haemorrhage on CT images utilizing new deep-learning algorithm (ICH-DC-CNN), Detection with classification of intracranial haemorrhage on CT images utilizing new deep-learning algorithm (ICH-DC-CNN-ResNet-50), Shallow 3D CNN for detecting acute brain hemorrhage from medical imaging sensors (ICH-DC-S-3D-CNN), Convolutional neural network: a review of models, methods and applications to object detection (ICH-DC-CNN-AlexNet) respectively.
Perfectly antimagic total graphs
Authors: Swathi P. , Kumar G. , Jeyabalan R. , Nishanthini R.
Published: October 2022

An one-one correspondence function λ from V(G) ∪ E(G) to the set {1, 2, …, |V(G) | + |E(G) |} is a total labeling of a finite undirected graph G without loops and multiple edges, where |V(G) |and |E(G) | are the cardinality of vertex and edge set of G respectively. A perfectly antimagic total labeling is a totally antimagic total labeling whose vertex and edge-weights that are also pairwise distint. Perfectly antimagic total (PAT) graph is a graph having such labeling. The topic of discovering perfectly antimagic total labeling of some families of graphs is discussed in this paper. We also came up with certain conclusions about dual of a perfectly antimagic total graphs. Finally, we provided that the necessary and sufficient condition for a dual of a regular and irregular PAT graph to be a PAT graph.
Diagnosing rheumatoid arthritis disease using fuzzy expert system and machine learning techniques
Authors: Tarakci Fatih , Ozkan Ilker Ali , Yilmaz Sema , Tezcan Dilek
Published: October 2022

Rheumatoid Arthritis (RA) is a very common autoimmune disease that causes significant morbidity and mortality, and therefore early diagnosis and treatment are important. Early diagnosis of RA and knowing the severity of the disease are very important for the treatment to be applied. The diagnosis of RA usually requires a physical examination, laboratory tests, and a review of the patient’s medical history. In this study, the diagnosis of RA was made with two different methods using a fuzzy expert system (FES) and machine learning (ML) techniques, which were designed and implemented with the help of a specialist in the field, and the results were compared. For this purpose, blood counts were taken from 286 people, including 91 men and 195 women from various age groups. In the first method, an FES structure that determines the severity of RA disease has been established from blood count using the laboratory test results of CRP, ESR, RF, and ANA. The FES result that determines RA disease severity, the Anti-CCP level that is used to distinguish RA disease, and the patient’s medical history were used to design the Decision Support System (DSS) that diagnoses RA disease. The DSS is web-based and publicly accessible. In the second method, RA disease was diagnosed using kNN, SVM, LR, DT, NB, and MLP algorithms, which are widely used in machine learning. To examine the effect of the patient’s history on RA disease diagnosis, two different models were used in machine learning techniques, one with and one without the patient’s history. The results of the fuzzy-based DSS were also compared with the diagnoses made by the specialist and the diagnoses made according to the 2010 ACR / EULAR RA classification criteria. The performed DSS has achieved a diagnostic success rate of 94.05% on 286 patients. In the study of machine learning techniques, the highest success rate was achieved with the LR model. While the success rate of the model was 91.25 % with only blood count data, the success rate was 97.90% with the addition of the patient’s history. In addition to the high success rate, the results show that the patient’s history is important in diagnosing RA disease.
Recurrent neural network based sentiment analysis of social media data during corona pandemic under national lockdown
Authors: Jayachandran Shana , Dumala Anveshini
Published: October 2022

The Corona virus pandemic has affected the normal course of life. People all over the world take the social media to express their opinions and general emotions regarding this phenomenon. In a relatively short period of time, tweets about the new Corona virus increased by an amount never before seen on the social networking site Twitter. In this research work, Sentiment Analysis of Social Media Data to Identify the Feelings of Indians during Corona Pandemic under National Lockdown using recurrent neural network is proposed. The proposed method is analyzed using four steps: that is Data collection, data preparation, Building sentiment analysis model and Visualization of the results. For Data collection, the twitter dataset are collected from social networking platform twitter by application programming interface. For Data preparation, the input data set are pre-processed for removing URL links, removing unnecessary spaces, removing punctuations and numbers. After data cleaning or preprocessing entire particular characters and non-US characters from Standard Code for Information Interchange, apart from hash tag, are extracted as refined tweet text. In addition, entire behaviors less than three alphabets are not assumed at analysis of tweets, lastly, tokenization and derivation was carried out by Porter Stemmer to perform opinion mining. To authenticate the method, categorized the tweets linked to COVID-19 national lockdown. For categorization, recurrent neural method is used. RNN classify the sentiment classification as positive, negative and neutral sentiment scores. The efficiency of the proposed RNN based Sentimental analysis classification of COVID-19 is assessed various performances by evaluation metrics, like sensitivity, precision, recall, f-measure, specificity and accuracy. The proposed method attains 24.51%, 25.35%, 31.45% and 24.53% high accuracy, 43.51%, 52.35%, 21.45% and 28.53% high sensitivity than the existing methods.
Cloud manufacturing service recommendation model based on GA-ACO and carbon emission hierarchy
Authors: Shi Zhihu
Published: October 2022

In order to improve the accuracy of cloud manufacturing service recommendation results, improve recommendation efficiency and user satisfaction, a cloud manufacturing service recommendation model based on GA-ACO and carbon emission hierarchy is proposed. According to the concept of cloud manufacturing, a cloud manufacturing platform including resource layer, service layer, operation layer and application layer is constructed, and then a cloud manufacturing service quality perception model is established; genetic algorithm is used to realize cloud manufacturing service selection, and ACO algorithm is used to optimize cloud manufacturing service portfolio; According to the selection and combination results of the constructed cloud manufacturing platform and cloud manufacturing service, taking the carbon emission field as an example, a hierarchical hierarchical model is constructed, and this model is used to further construct a cloud manufacturing service recommendation model from coarse to fine, from global to local; Identify user demand scenarios and implement cloud manufacturing service recommendations. The experimental results show that the recommendation results of the proposed method have high accuracy and efficiency, and can be recognized by most users.
Attitude tracking control design of fixed-wing UAVs having uncertain dynamics and corrupted gyro sensor outputs
Authors: Shabbir Wasif , Aijun Li , Taimoor Muhammad , Yuwei Cui
Published: October 2022

Flight performance of unmanned aerial vehicles (UAVs) strongly depends on implemented attitude tracking control. For designing better controllers, nonlinear control design techniques are often opted instead of control design based on linearized models. Uncertainty in nonlinear dynamics estimation may arise due to inaccuracies in aerodynamic derivatives and simplifications/assumptions made during the derivation of nonlinear models. This paper considers attitude tracking control of fixed-wing UAVs having uncertain dynamics and corrupted gyro sensor outputs. An integral chain differentiator (ICD) is used to provide the analytical redundancy to the gyros used to measure the angular rates. Two control design schemes are proposed, a neuro-fuzzy adaptive sliding mode control (NFASMC) and an ICD approximation-based fuzzy adaptive sliding mode control (ICD-FASMC). In NFASMC, the uncertain part of the dynamics is estimated using an adaptive radial basis function neural network. Gyro sensor output errors are estimated in real-time, using ICD based error estimation scheme and used in the control law along with the sensor’s corrupted outputs. In ICD-FASMC, the uncertain dynamics and angular rates of UAV are estimated using the ICD such that the requirement of the gyro sensor outputs for control design is bypassed. The switching gain of the designed controllers is made adaptive using fuzzy logic to mitigate the chattering effect. The stability of the proposed controllers is proved using the Lyapunov approach. The proposed schemes are implemented using a nonlinear simulation of a fixed-wing UAV. Simulation results are presented to show the effectiveness of the proposed techniques.
The ESS monolith vessel design and possibilities to introduce a UCN/VCN source
Authors: Odén Ulf
Published: October 2022

The ESS ERIC neutron source design includes a helium cooled tungsten spallation target and a liquid hydrogen moderator, enclosed in a vacuum vessel (the monolith vessel). The neutron source and moderator are shielded by stainless steel and casted iron to block neutron and gamma radiation emitted in directions other than along the neutron beam ports. After the ESS concept design was approved, questions were raised if it was possible to introduce a UCN/VCN (Ultra Cold Neutron/Very Cold Neutron) source into the ESS design. Since the shielding concept of the monolith vessel is to 100% fill the monolith void except for the proton and neutron beam paths, there are no cavities in the monolith vessel left, where one could easily place a UCN/VCN source. An additional challenge is the pipe routing for the UCN/VCN cooling media. However, some of the shielding blocks and monolith components are removable and could possibly be redesigned, enabling implementation of a UCN/VCN source. This article presents a feasibility study how to physically introduce a UCN/VCN source in the present ESS design, focusing on the geometrical possibilities. Four possible locations for the UCN/VCN source were identified, which are considered to be feasible but will require some redesign of the monolith vessel shielding.
Failure analysis of a heavy-haul train wheel
Authors: Santa J.F. , Toro-Castrillón J.J. , Perez-Giraldo M. , Jaramillo J. , Hernández G. , Toro A.
Published: October 2022

The failure analysis of a fractured wheel from a train travelling in a heavy-haul line was carried out. The microstructure of the wheel was studied by Light Optical Microscopy (LOM) and the chemical composition was analyzed by Optical Emission Spectrometry (OES). Tensile tests were performed in samples extracted from the fractured wheel to evaluate mechanical properties. The results from the material’s characterization combined with the fractographic examination and the stress analysis by finite-element modeling (FEM) allowed to conclude that the wheel failed due to fatigue originated in a heavily deformed surface layer produced by rolling contact fatigue (RCF), which was not timely removed in maintenance interventions. The main crack propagated from the surface to the rim and caused a failure known as Vertical Split Rim (VSR).
Intersectional perspectives on the employment rate in Supported Employment for people with psychiatric, neuropsychiatric, or intellectual disabilities: A scoping review
Authors: Witte Ingrid , Strandberg Thomas , Granberg Sarah , Gustafsson Johanna
Published: October 2022

BACKGROUND: Supported Employment (SE) has shown better results in the employment rate for persons with disabilities than other methods within vocational rehabilitation, but how SE affects the employment rate for subgroups in the interventions needs further attention. OBJECTIVE: To examine previous research regarding the influence of intersecting statuses on the employment rate in SE for people with psychiatric, neuropsychiatric, or intellectual disabilities according to type of diagnosis, sex, race/ethnicity, age, level of education and previous work history. METHODS: A systematic literature search was conducted in nine databases including peer-reviewed articles from 2000 to April 2021. Articles presenting the employment rate in SE interventions according to the intersecting statuses listed in the objective were included. RESULTS: The searches identified 3777 unique records, of which 53 articles were included in data extraction. In most of the included articles, intersecting statuses did not affect the employment rate for people in the SE interventions with psychiatric disabilities. Few studies have examined neuropsychiatric and intellectual disabilities. A majority of the studies subjected to full-text analysis were excluded due to a lack of reporting of the effects of intersecting statuses on the employment rate. The studies that reported on the effects of intersecting statuses on the employment rate often had small samples and lacked statistical power. CONCLUSIONS: Intersecting statuses do not appear to affect the employment rate for people receiving SE interventions, but systematic reviews with pooled samples need to be undertaken because of the low reporting rate and underpowered sample sizes in existing studies.
The association between hope and employment among individuals with multiple sclerosis: A hierarchical logistic regression model
Authors: Lee Beatrice , Rumrill Stuart , Reyes Antonio , McDaniels Bradley
Published: October 2022

BACKGROUND: Most people with multiple sclerosis (MS) are employed at the time of their diagnosis; however, due to the unpredictable nature of MS, most exit the workforce shortly thereafter. A plethora of research has examined factors that negatively affect employment outcomes for people with MS. However, little is known about how hope, a modifiable positive psychology factor, affects employment. OBJECTIVE: This study examined the role of hope and its association with employment outcomes for people with MS. METHODS: Two-hundred and fifty-five adults with MS (mean ± SD age, 45.45 years ± 10.28) completed surveys about their MS, employment, disability-related stress, and hope. A three-step hierarchical logistic regression was conducted to examine the extent to which hope explains the variance in employment, over and above demographic and disability related covariates. RESULTS: The final model explained 28% of the variance in employment, suggesting that the model was able to distinguish people with MS who were employed versus those who were unemployed. Higher levels of hope were associated with an increased probability of being employed (OR = 4.65; 95% CI [1.98, 10.92]). CONCLUSION: This study supports that hope is associated with favorable employment outcomes for people with MS. Persons with MS may benefit from working with rehabilitation professionals to enhance their hope, and this study provides a foundation for the development of hope-based interventions to improve employment outcomes among this population.
A deep learning-based recognition for dangerous objects imaged in X-ray security inspection device
Authors: Wei Qiuyue , Ma Shenlan , Tang Shaojie , Li Baolei , Shen Jiandong , Xu Yuanfei , Fan Jiulun
Published: October 2022

Several limitations in algorithms and datasets in the field of X-ray security inspection result in the low accuracy of X-ray image inspection. In the literature, there have been rare studies proposed and datasets prepared for the topic of dangerous objects segmentation. In this work, we contribute a purely manual segmentation for labeling the existing X-ray security inspection dataset namely, SIXRay, with the pixel-level semantic information of dangerous objects. We also propose a composition method for X-ray security inspection images to effectively augment the positive samples. This composition method can quickly obtain the positive sample images using affine transformation and HSV features of X-ray images. Furthermore, to improve the recognition accuracy, especially for adjacent and overlapping dangerous objects, we propose to combine the target detection algorithm (i.e., the softer-non maximum suppression, Softer-NMS) with Mask RCNN, which is named as the Softer-Mask RCNN. Compared with the original model (i.e., Mask RCNN), the Softer-Mask RCNN improves by 3.4% in accuracy (mAP), and 6.2% with adding synthetic data. The study result indicates that our proposed method in this work can effectively improve the recognition performance of dangerous objects depicting in the X-ray security inspection images.
Microbiological analysis of commonly consumed vegetables: A review on the ongoing studies
Authors: Ava Afia Ibnat , Noor Rashed
Published: October 2022

Vegetables are frequently consumed by people all over the world and hence, the microbiological quality of vegetable especially fresh produces are of significance for the sake of public health sustainability. Vegetables are spoiled by a range of microorganisms including bacteria and fungi. Among the bacterial species, _Corynebacterium_, _Pseudomonas_ spp., _Listeria monocytogenes_, _Clostridium botulinum_, _Erwinia carotovora_, and _Xanthomonas campestris_ are the common vegetable spoilage microorganisms while the fungal genera _Botrytis cinerea_, _Colletotrichum_, _Aspergillus_, _Alternaria_, _Cladosporium_, _Fusarium_, _Penicillium_, and _Rhizopus_ spp. are the prominent spoiling candidates. Understanding vegetable spoilage mechanism/ microbial pathogenesis is important. The harvesting and handling procedures, storage conditions of vegetables, processing, injury of the vegetable plant tissues, and plant resistance against the pathogens are also of significance in order to analyze the toxic metabolites in the affected sites within the vegetables as well as for the prediction of the foodborne illness outbreaks. Implementation of good agricultural practices (GAPs) and good manufacturing practices (GMPs) during vegetable cultivation, harvesting, sorting, storage, transportation, and distribution is important. Moreover, processing of vegetables prior to consumption; i.e., controlling contamination of microbes, a thorough wash with vinegar water, salt water, and other washing means are important preventive measures to ensure the prevention of microbial contamination. Furthermore, up-to-date knowledge on the vegetable spoiling microorganisms, the epidemiology of such microbial contamination, and possible sources and route of the contamination may attribute to the possible future perspective on food safety.
Irrealistic traditions in Russian prose of the first third of the 20th century
Authors:   Moscow State University of Food Production, Astashchenko Elena Vasilievna
Published: October 2022

The monograph examines the irrealistic traditions in Russian prose of the first third of the XX century. Irrealism as a semantic and style-­forming principle of artistic “belles-­lettres” (as opposed to documentary, artless) at the beginning of the XX century was mentioned in criticism, including by A. A. Blok, N. N. Rusov, N. F. Berner, and it was first designated as a term and justified theoretically in philosophical and philological studies by A. M. Evlakhov. The extreme individualization of a purely artistic, rather than a creative process as a whole, suggests the search for irrealistic features of unpopular works (drafts, manuscripts, unfinished, unpublished, misunderstood texts) or second-rate writers (A. N. Bezhetsky (Maslov), S. P. Bobrov, K. Bolshakov, A. S. Voznesensky (Brodsky), A. A. Galunov, V. V. Hoffman, M. L. Hoffman, S. I. Gusev-­Orenburgsky, A. F. Damanskaya, A. M. Evlakhov, V. Evtihiev, V. A. Zhukovskaya, A. K. Zakrzhevsky, V. A. Carrick, B. A. Koiransky, E. Kurlov, V. Lensky and N. Muravyov (Adbamovich brothers), N. I. Manaseina, O. Norwegian (Kartozhinsky), M. I. Pantyukhov, N. I. Petrovskaya, M. L. Premirov, M. A. Pozharova, A. D. Radlova, N. N. Rusov, B. A. Sadovsky, V. V. Sipovsky, Y. L. Slezkin, A. M. Fedorov, M. Khodyreva, AnChebotarevskaya, F. I. Chernova, G. I. Chulkova), because the flip side of non-demand, external distancing from public life was freedom. Despite the motley picture of the literary process of the Silver Age, this monograph attempts to systematize it (according to the criterion fashionable at that time, which deserved the attention of P. N. Sakulin, A. P. Skaftymov, etc., — irrealism), as well as, according to the Doctor of Philology, academician of the Independent Academy of Aesthetics and Free Arts B. F. Egorov, there is a “solid theoretical foundation and fresh, new material”.
Integration of geophysical information with geological cartography for mineral resources research and other applications in Colombia: examples from the Serranía de San Lucas, Antioquia Batholith and eastern Amazonia
Authors: Lara Norma Marcela , Arias Hernán Darío , Moyano Ismael Enrique , Robayo Adriana , Gómez Ernesto , Ospina Diana , Puentes Manuel , Torrado Sergio , Rojas Óscar , Prieto Gloria
Published: October 2022

La información geofísica aerotransportada es una herramienta para la interpretación de características geológicas. Las variaciones que se observan en los datos magnéticos y gamma espectrométricos aerotransportados son utilizados, principalmente, como apoyo para diferenciar unidades geológicas, delimitar estructuras y limitar cambios composicionales. En este contexto, el objetivo de este estudio es dar soporte al mapeo geológico a partir de los datos de geofísica aerotransportada de magnetometría y gamma espectrometría en dos regiones del territorio colombiano: a) serranía de San Lucas-Batolito Antioqueño, y b) oriente colombiano. El procesamiento de los datos gamma espectrométricos consiste en generar imágenes de los canales de K, U y Th, además de una composición ternaria en falso color RGB de estos tres canales, con la finalidad de delimitar dominios geológicos-geofísicos. De otro lado, la información de magnetometría se procesa para resaltar características magnéticas anómalas y proveer información acerca del marco estructural de cada una de las áreas de interés.
En la zona comprendida en la serranía de San Lucas-Batolito Antioqueño se delimitaron 27 dominios gamma espectrométricos, interpretados especialmente a partir de las imágenes de composición ternaria; en ellos se observa una buena correlación con la composición de cada una de las unidades litoestratigráficas, lo que permite identificar y delimitar zonas con altas o bajas concentraciones de radioelementos. Además, la correlación de dominios magnéticos con altas y bajas intensidades y texturas magnéticas muestra contrastes entre rocas ígneas, rocas metamórficas y rocas sedimentarias. Por su parte, en la zona oriental, por tener una geología bastante generalizada, la información geofísica, tanto de gamma espectrometría como de magnetometría, sirve de apoyo para depurar y delimitar con mayor exactitud temática las unidades geológicas presentes, así como su marco estructural.