bluuebunny/crossref_metadata_2025 huggingface dataset:
Long-term tumor control after endoscopic endonasal resection of craniopharyngiomas: comparison of gross-total resection versus subtotal resection with radiation therapy
Authors: Godil Saniya S. , Tosi Umberto , Gerges Mina , Garton Andrew L. A. , Dobri Georgiana A. , Kacker Ashutosh , Tabaee Abtin , Anand Vijay K. , Schwartz Theodore H.
Published: October 2021

OBJECTIVE

Surgical management of craniopharyngiomas (CPAs) is challenging. Controversy exists regarding the optimal goals of surgery. The purpose of this study was to compare the long-term outcomes of patients who underwent gross-total resection with the outcomes of those who underwent subtotal resection of their CPA via an endoscopic endonasal approach.

METHODS

From a prospectively maintained database of all endoscopic endonasal approaches performed at Weill Cornell Medicine, only patients with CPAs with > 3 years of follow-up after surgery were included. The primary endpoint was radiographic progression. Data were collected on baseline demographics, imaging, endocrine function, visual function, and extent of resection.

RESULTS

A total of 44 patients with a mean follow-up of 5.7 ± 2.6 years were included. Of these patients, 14 (31.8%) had prior surgery. GTR was achieved in 77.3% (34/44) of all patients and 89.5% (34/38) of patients in whom it was the goal of surgery. Preoperative tumor volume < 10 cm3 was highly predictive of GTR (p < 0.001). Radiation therapy was administered within the first 3 months after surgery in 1 (2.9%) of 34 patients with GTR and 7 (70%) of 10 patients with STR (p < 0.001). The 5-year recurrence-free/progression-free survival rate was 75.0% after GTR and 25.0% after STR (45% in subgroup with STR plus radiotherapy; p < 0.001). The time to recurrence after GTR was 30.2 months versus 13 months after STR (5.8 months in subgroup with STR plus radiotherapy; p < 0.001). Patients with GTR had a lower rate of visual deterioration and higher rate of return to work or school compared with those with STR (p = 0.02). Patients with GTR compared to STR had a lower rate of CSF leakage (0.0% vs 30%, p = 0.001) but a higher rate of diabetes insipidus (85.3% vs 50%, p = 0.02).

CONCLUSIONS

GTR, which is possible to achieve in smaller tumors, resulted in improved tumor control, better visual outcome, and better functional recovery but a higher rate of diabetes insipidus compared with STR, even when the latter was supplemented with postoperative radiation therapy. GTR should be the goal of craniopharyngioma surgery, when achievable with minimal morbidity.
Quality of life and satisfaction in surgical versus conservative treatment of nonsyndromic children with craniosynostosis
Authors: Sader Nicholas , Mehta Vivek , Hart Shannon , Bliss Lori , Moore Hanna , DaSilva Melissa , Rashid Ruksana , Riva-Cambrin Jay
Published: October 2021

OBJECTIVE

Craniosynostosis represents the second most common reason for referral to pediatric neurosurgery. However, the quality of life and neurodevelopmental impact of leaving this physical disorder uncorrected is poorly understood.

METHODS

This multicenter cross-sectional study identified previously managed nonsyndromic infants (< 24 months of age) with single-suture craniosynostosis at both pediatric neurosurgical centers in Alberta, Canada. The primary variable of interest was the allocated treatment (surgical vs conservative). The primary outcome was the Pediatric Quality of Life Inventory (PedsQL), a validated measure of quality of life examining physical, emotional, social, and school functioning. Treatment decision satisfaction and the reasons for selected management were quantified and collected directly from caregivers.

RESULTS

One hundred fourteen children met the inclusion criteria: 78% underwent surgery and 22% had conservative treatment. The most common suture affected was sagittal (54%), followed by metopic (33%), coronal (10%), and lambdoid (3%). Caregivers most commonly opted for surgery because of severe appearance (80%). Seventy-six percent and 72% of the caregivers of children with conservative management did so due to concerns of surgical risks and mild appearance, respectively. There was a statistically significant relationship between both the parents’ (p < 0.001) and the surgeon’s (p = 0.001) impression of a severe head shape and surgical management. Parental satisfaction with their child’s appearance as well as satisfaction with their treatment decision did not differ between management types. Regarding quality of life, on univariate analysis, the conservative group had a statistically higher physical summary score (p = 0.01), psychosocial summary score (p = 0.004), and mean total scale score (p = 0.003) compared to the surgical group. However, after adjusting for severity and age at consult, no significant independent associations between management type and any of the PedsQL summary scores were found.

CONCLUSIONS

Alberta families have a high number of children with craniosynostosis treated with conservative management. Conservatively managed infants were largely minimally affected patients, particularly those with metopic synostosis. The study found no independent association between management type (surgery vs conservative) and quality of life when adjusted for important patient factors.
A Novel Compression Algorithm Based on Bio-Orthogonal Transform and Encoding Methods for Computed Tomography Images
Authors: Pandian R. , LalithaKumari S , AmalaRani V. , PrabhuDassBatvari B. , RajaKumar R.
Published: October 2021

Image compression is the processing of images by using Transform operations and Encoding techniques. Nowadays, there is an essential need of these methods in the medical field. This work is focused on the performance quality assessment of medical images using Image Compression Techniques. Image compression is the process of compression of an image in such a way that it has less space than the original image. It is an organization of compression technique that reduces the size of an image file without affecting its quality to a greater extent. The bio-orthogonal transform is used for decomposing the Lung images. After decomposition, different methods of encoding are performed and finally the proposed compression methods are evaluated for finding optimum algorithm for medical image.
Design and Analysis of Hybrid Classification Approaches Across Multi-Relational Decision Tree Learning Algorithm
Authors: Michael Tressa
Published: October 2021

In today’s day of Modern era when the data handling objectives are getting bigger and bigger with respect to volume, learning and inferring knowledge from complex data becomes the utmost problem. Almost all of the real-world information are maintained under a relational fashion holding multiple relations unlike orthodox approaches containing single relational as a whole. Moreover several fields viz. biological informatics, microbiology, chemical computations needed some more dependable and expressive approach which can provide more sophisticated results with faster speed. Hence in context with multi-relational data mining in which data is directly retrieved from different records without dumping into single table, we have described a novel approach of improved Multi-Relational Decision Tree Learning Algorithm based on the implementations. In this paper provided a comparative study of the aforementioned approach in which we have taken certain results from the literature review. Experiments mainly includes results from widely used datasets viz. Mutagenesis database which demonstrates that Multi-Relational Decision Tree Learning Algorithm provides a promising alternative from previous conventional approaches such as Progol, FOIL, and Tilde.
User Recommendation in OSN
Authors: ChristiyanaArulselvi A  , Sendhilkumar S  , Mahalakshmi G S
Published: October 2021

Web based Social Network (SN) is one of the popular mediums where the information is shared among users. The combined data from different sources available on the open web needs to be evaluated for its quality and trustworthiness. A trustable community can be formed, if the social networking applications, media, forums, etc., can automatically judge the user who requests to join the community. It is necessary to know the trustability of the persons within the community to share the official and personal information. Therefore, a trust model has become an essential part of Online Social Network (OSN) to find the trustworthiness of the requester. Significance of the work detailed here is to model a trustworthy user suggestion in OSN applications.
I-Voting System Based on Block Chain
Authors: Jayashree D. , Pandithurai O. , Yogeswari S , Swetha K B , Shalini A.
Published: October 2021

The Blockchain based I-Voting uses a digital-currency analogy where in eligible voters can cast a ballot anonymously using a computing environment. BIV(Blockchain I-Voting) employs an encrypted key, smart biometrics and tamper proof real-time personal ID verification. Blockchain enable the creation of tamper-proof audit trails for voting. In this generation of Technology there is an effect way for casting vote through online(Digital Voting) to make a public electoral process faster, cheaper, and more easier. In this generation it is being a compelling one in modern society which removes a power barrier between the elected candidate and the voter.
Discriminating Parkinson and Healthy People Using Machine Learning
Authors: Lakshmi S. , Manonmani.A  , Jagadeeshwaran P  , AkashS  , JananiK  , AkilandeswariD
Published: October 2021

Parkinson’s is a disease which affects at central nervous system of human body and it is popularly known as neuro degenerative disease. We have taken a classification method were mainly focused on speech signal for detection of parkinson’s disease because speech is the only deciding factor for earlier stage of the disease identification. Here the ensemble technique is used for classification. Ensemble is method of combining multiple models like classifiers or experts to solve a particular computation .In proposed technique with help of speech signal it is easy to distinguish between diseased and non diseased person. Here we are using vowels, number and words from the speech signal in classifiers then how the signal involved in that technique are discussed.
Effective Early Stage Detection of COVID-19 Using Deep Learning
Authors: Swetha.M  , Rajendiran. M
Published: October 2021

The Corona virus Disease 2019 (COVID-19), which was formerly called as 2019 Novel Corona Virus[1] is a breath taking disease. It had its impact on millions of lives across the world. At present, as of March 2021, the rate of infection has declined throughout different parts of the world [2]. But it has been warned by scientists that this deadly disease can have its second wave over a period of time. Also, there is a possibility of this covid-19 to become a seasonal disease [3]. In such case, premature diagnosis of this virus is essential in order to save many lives. A kit called RT-PCR has been employed to detect the presence of this virus [4]. However, this method of prognosis takes time depending on the locality of the infected person [5]. This leads to the proliferation of the infection. Hence, an alternate procedure should be unearthed, which diagnose this disease within a short span of time. In this paper, a Deep Learning concept has been proposed which aids the timely detection of the corona virus infection. This, inturn reduces the spreading rate of the infection and decreases mortality rate.
IOT Based Weather Reporting System Using Arduino and Node MCU
Authors: Meera.S  , Sharmikha Sree.R  , Kalpana R.A  , Manasvinii S.R. , Haritha.V  , Valarmathi K.
Published: October 2021

Weather is an ever changing phenomenon. It can change in within seconds or milliseconds. Weather was quite difficult to monitor in the days when technology was still primitive. But now it is easy as there are various sensors and computing techniques to not only monitor weather but predict it with high accuracy as well. To simplify the detection process IOT (Internet of things) is employed in the project. As IOT is used Arduino is added to the project along with other sensor to monitor the weather. The data from the sensor is uploaded to an open source web-server. The open server can be used both to get real time data as well as download the data for the prediction process. Prediction is done using machine learning algorithms and training the detected datasets.
Data Set Preparation Using the Method of Data Augmentation for Classification of Skin Diseases
Authors: AdlinLayola J.A  , MuraliBhaskharan V.
Published: October 2021

Skin disease are mostly ignored and provided less importance at the early stages. Some ignorance among people might lead to skin cancer. In existing approach, the increased skin disease are identified at the later stage using biopsy only. The inspection is performed manually by considering many histopathological features. This paper explains the development of a model which helps to detect the various skin diseases at early stage using neural networks. For classification and prediction purpose this paper using Convolutional Neural Network. For improving the accuracy of prediction we are going to do dataset preparation. Data augmentation is using for increasing the count size of the training data. So that the accuracy of the classification will increase and will get 97% result.
Detection of Ketosis in Bovine Using Machine Learning Techniques
Authors: Pushkala V  , Nirmala Devi S.
Published: October 2021

This paper presents the different unsupervised machine learning algorithms used for Ketosis detection, based on the color characteristics taken from the Ketocheck rapid colorimetric test. The level of ketone bodies in bovine’s urine is represented by three color categories, range of dark green (right ketone level), green (normal range ketone level) and yellow/orange (higher ketone level). The color image is converted into HSV color space for better color discrimination. The proposed technique enables detection of ketosis by clustering every pixel in the image using unsupervised K-means clustering and Fuzzy C Means (FCM) clustering algorithms. The results obtained have shown that K-means algorithm is faster and it also have low computational complexity. Two android application is developed. One with K-means clustering algorithm loaded in server and the other, directly programmed in android application.
A Robotic Assisted System Using Deep Learning for Navigation and Reading IoT Based Home Automation for Visually Impaired
Authors: Jayashree S  , Jaswanthi R  , Harshitha S V
Published: October 2021

This paper presents a personal guidance system to help the visually impaired people to face the world independently; this is achieved by transferring visual world into the audio world by alerting them with a voice message. Navigation assistance is included with object detection, so object interference in the path of navigation the vision deprived person is alerted about the object in the path. The system is also implemented with an IOT based load control to help them with household objects; the load control can be operated with the help of Blynk Application which is made enable with voice assistant. In this project we have used an algorithm, real time object detection using deep learning. This model is trained with more images to recognize an object. OCR is implemented for the reading assistance module in this system.
Intelligent Waste Classification System Using Vision Transformers
Authors: Aditya Gowrish Menti , Kumar Dahlan Mukesh , Subhashini R.
Published: October 2021

The issue of waste management is a growing concern, ranging from agricultural fields to industries and villages to cities. Our project aims to contribute to this issue by solving the problem of segregation of waste by using the latest advances in computer vision. The Transformers for Image Recognition at Scale, which will give highly efficient results in less time compared to the models based on Convolutional Neural Networks which loses a lot of valuable information and ignores the relationship between part of images and as a whole. The Self-Attention in vision transformers gives them the capability to understand the connection between inputs, where operations can be processed in parallel on multiple GPUs and CPUs, which cannot be achieved in the case of Convolutional Neural Networks. This project allows us to build an effective system that can classify waste in real-time without human intervention in large scale industries and also in a regular household.
Personalized CAPTCHA Using Cognitive Cryptography
Authors: Radhika  , Somasundaram.S K  , Sivakumar.P
Published: October 2021

Cognitive Cryptography is used to improve personal verification process using the individual’s characteristics. The personal information contained can be biometrics because it is the unique information that identifies the owner. In advanced cryptographic protocol oriented for authentication of user, there is a possibility of using personal characteristics and perception abilities are required to create a new authentication procedure. This paper presents a new approach for creation of advanced multilevel user authentication protocol by using Image grid CAPTCHA codes. Here the user needs the special skills or knowledge while verifying, this is because of cognitive CAPTCHA’s. Instead of generating some random numbers or text while authentication procedure these CAPTCHA’s can be used. In multilevel authentication code the user verification can be realized in several iterations, in which the user attention can be oriented on different visual elements, region of interest or semantic content. This cognitive code will able to identify the recognition abilities of the user. Cognitive codes are having high security feature similar to traditional CAPTCHA’s because of understanding or recognizing the blurred or distorted patterns and also requires background knowledge to experience the connection with evaluated patterns. This feature guarantees the high level of security and allows to get succeeded in authentication process because the user possess specific skill that or not available for computers or answering systems. The traditional authentication protocols are to be involved with human mental capability is the vital idea of the proposed solution.
Analysis of Vedic, Wallace Tree and Array Multipliers
Authors: Srividhya G. , Kalaiyarasi D. , Pritha N. , Bharathi S.L.
Published: October 2021

Multiplications are the most important and crucial operation in any system. They are done through the process of repetitive addition. Since speed is necessary in any factor therefore multiplication must also be done in a faster way so that they are utilized properly for a faster result. This paper gives a detailed explanation on all three multipliers and compares them accordingly. In this paper, three various multiplication methods are considered and simulated. Three structural multipliers such as Vedic, Wallace tree and array multipliers are compared and their output is shown through the FGPA. For comparison, the results of multipliers are synthesized and simulated using XILINX.ISE.14.5 tool. Later the comparison is concluded by evaluating their utilization of the device.
A Novel Approach to Evaluate Reduced Inter Symbol Interference in UFMC Systems
Authors: Elavel Visuvanathan. G  , Jaya. T
Published: October 2021

The UFMC modulation scheme has been proposed as a solid competitive framework for future portable fifth generation communication. UFMC can be considered as a candidate waveform for 5G communications since it gives strength against Inter Symbol Interference (ISI) [1]. Inter-symbol interference prompted error can make the receiver neglect to reproduce the original data. Equalizers in the receivers, which are extraordinary sorts of filters, moderate the direct twisting created by the channel [2]. On the off chance that the channel’s time-fluctuating qualities are known from the earlier, at that point, the ideal setting for equalizers can be worked out. But in practical systems the channel’s time-changing attributes are not known from the earlier, so adaptive equalization method is applied in this paper based on the LMS algorithms. Adaptive equalizers are adjusted, or change the estimation of its taps as time advances [3].
Low Power, High Speed MUX Based Area Efficient Dadda Multiplier
Authors: Kalaiyarasi.D  , Pritha.N  , Srividhya.G  , Padmapriya.D
Published: October 2021

The multiplier is a fundamental building block in most digital ICs’ arithmetic units. The multiplier architecture in modern VLSI circuits must meet the main parameters of low power, high speed, and small area requirements. In this paper, a 4-bit multiplier is constructed using the Dadda algorithm with enhanced Full and Half adder blocks to achieve a smaller size, lower power consumption, and minimum propagation delay. The Dadda Algorithm-designed multiplier is used in the first phase to reduce propagation delay while adding partial products in three stages provided by AND Gates. In the second phase, each stage of the Dadda tree algorithm is built with an enhanced Full and half adders to reduce the design area, propagation delay, and power consumption while still meeting the requirements of the current scenario by using MUX logic. In an average of Conventional array Multipliers, the proposed Dadda multiplier achieved an 84.68% reduction in delay, 70.89% reduction in power, 84.68% increase in Maximum Usable Frequency (MUF), and 95.55% reduction in Energy per Samples (EPS).
A Contemporary Survey on Low Power, High Speed Comparators for Bio-Medical Applications
Authors: Rajalakshmi R. , Janaki Rani M. , Anand M.
Published: October 2021

The analysis of biomedical signals performs an important role in figuring out numerous issues in clinical science. Also, the urge to track biomedical signals in fitness and well-being control is progressively growing with the multiplied occurrence of persistent sicknesses over the last decade. By nature, the most of the real-time signals are analog. Hence, an Analog to digital converter (ADC) is required to transform the signal. In ADC architecture, the comparator is the essential block that performs a vital role and consumes greater power in ADC design. Numerous architectures for comparators relate to biomedical programs are mentioned in recent days. In this paper, the exceptional latest techniques of comparator designs are discussed with their key capabilities in conjunction with pros and cons.
Accident Prevention Black-Box and Vehicle-to-Vehicle Communication Using Li-Fi and Wi-Fi Technologies
Authors: Aishwarya I  , Carolin B  , ChalceEbenS  , Kannan P.
Published: October 2021

The focus of the paper is Vehicle to Vehicle (V2V) communication system that uses a wireless communication technology using a LED transmitter and therefore the vehicle will transmit the information continuously to the opposite vehicle ahead it using Head light. The data is stored within the Secure Digital Card in comma separated value for future reference just in case of emergency at the equivalent time the data’s are stored in the Cloud Server for Government Reference to locate the accident prone zones. The Light fidelity (Li-Fi) is that the newest technology within the field of wireless communication. As the number of users are increasing, the speed of knowledge transmission within the wireless network gets automatically decreased.
AR Guide for Food Allergic Consumers
Authors: Karthika. S  , Varsha.G  , Deepika. R
Published: October 2021

Augmented reality is a technology where real life environment is enhanced by incorporating digital or virtual elements like images, graphics, 3D objects into it. Augmented reality is a growing and trending technology which can make interactions easier when incorporated into normal applications. The aim of this project is to create an augmented reality food guide for food allergic consumers to aid them in choosing allergen free foods. A person who wants to order food online or in restaurants doesn’t know whether it would be suitable for him to eat or not. Especially if they are allergic to certain foods, they have to be preventive and cautious in choosing the type of food they eat. This AR guide will aid them in choosing the suitable food for them in an augmented environment where a consumer can view food in three dimension view along with the necessary information about the food such as the key ingredients and the presence of any allergens that will be helpful in choosing the allergen free food.
Enhanced and Efficient Carry Select Adder with Minimal Delay
Authors: Srividhya G. , Sivasakthi T. , Srivarshini R. , Varshaa P. , Vijayalakshmi S.
Published: October 2021

In today’s digital world, Arithmetic computations have been evolved as a core factor in digital signal processors, micro-controllers, and systems using arithmetic and logical operations such as adders, multipliers, image processors, and signal processors. One of the elements that play an important role in performing arithmetic calculations is an adder. Among many adders, the Carry Select Adder produces less propagation delay. However, there may be an increased delay, power consumption, and area required in the case of a normal Carry Select Adder. To overcome the mentioned drawbacks, an improved model of Carry Select Adder has been designed that uses Binary to Excess – 1 Converter. Instead of using multiple blocks of Ripple Carry Adders (RCAs), it is efficient and effective if one of the blocks is replaced with Binary to Excess – 1 Converter. As a result, we can achieve a high speed adder with minimal delay, minimal power, and reduced area.
Customer Loan Approval Prediction Using Logistic Regression
Authors: Gopinath Mahankali , Srinivas Shankar Maheep K. , Sethuraman R.
Published: October 2021

Banking Sector contains loan where it is a process of lending or borrowing a sum of money by one or more individuals, organizations, etc. from Banks. The Person who lends that money from respective financier incurs a debt, and he is responsible to pay back the money with the Interest decided by Bank within a certain period. Generally what Bank’s look into before applying for a loan is Credit History, Credit loss and Income of Applicant. So basically,loans play a major role regarding Income for Bank. Due to rapid urban development people who are applying for loans got increased rapidly. Therefore, finding the applicant to whom loan can be approved become a complexed process. In this paper, we want to predict the loan eligibility based on details of the customer. Fields that required are Matrimonial Status, Income, Education, Loan Amount, Credit History and other income sources of Applicant dependants. To predict the status, we will use Logistic Regression to spot the eligible applicants so bank will engage with them for granting loans to those people who can payback in a given time.
Drowsiness Detection using CNN
Authors: Sheela. S  , Jothika. D. E  , Swarnapriya. N  , Vishali. B. R
Published: October 2021

Driver drowsiness has become one of the leading causes of car accidents in recent years resulting in serious physical injuries, fatalities and substantial financial losses. According to statistics, are liable driver drowsiness detection system is needed to warn the driver before a collision occurs. Behavioural, physiological and vehicle based measures are used by researchers to assess driver drowsiness. A thorough examination of these measures will shed light on the current systems, their problems, and the improvements that must be made in order to create a reliable system. By doings other measures will reveal existing systems, their flaws and the changes that must be made in order to produce a credible system.
Brain Tumor Detection Using Gray Level Co-Occurrence Matrix Feature Extraction Technique
Authors: SheelaS  , Prema R. , Ramya S. , Thirumahal B.
Published: October 2021

From each and every passing year, the world has always witnessed a rise in the number of cases of brain tumor. Brain tumor classification and detection is that the most critical and strenuous task within the field of medical image processing while human aided manual detection leads to imperfect divination and diagnosing. Brain tumors have high heterogeneity in appearance and there is a same feature between tumor and non-tumor tissues and thus the extraction of tumor regions from MRI scan images becomes unyielding. A Gray Level Co-occurrence Matrix(GLCM) is applied on MRI scan images to detect tumor and non-tumor regions in brain. The main aim of medical imaging is to extract meaningful information accurately from the images. The method of detecting brain tumor from an MRI scan images are often classified into four categories: Pre-Processing, Skull Stripping, Segmentation and have Feature Extraction.
Comparison of attitudes and beliefs of physical therapists and primary care physicians regarding low back pain management: A cross-sectional study
Authors: Alhowimel Ahmed , Alodaibi Faris , Alotaibi Mazyad , Alamam Dalyah , Fritz Julie
Published: October 2021

BACKGROUND: The first-line contact for patients seeking care for low back pain (LBP) can potentially change the disease course. The beliefs and attitudes of healthcare providers (HCPs) can influence LBP management. Although referring patients with LBP to physical therapy is common, the first-line contact for patients with LBP in Saudi Arabia is the primary care physician (PCP). Physical therapy will soon be integrated into primary care; therefore, it is rational to compare physical therapists’ (PTs) beliefs and attitudes regarding LBP with those of PCPs. OBJECTIVE: We compared PCPs’ and PTs’ attitudes and beliefs regarding LBP management. METHODS: We employed a cross-sectional, voluntary response sample research design using the Pain Attitudes and Beliefs Scale (PABS). Participants were PTs and PCPs practicing in Saudi Arabia. RESULTS: In total, 153 participants completed the PABS (111 PTs and 52 PCPs). PCPs demonstrated significantly higher PABS biomedical subscale scores than did the PTs. CONCLUSIONS: HCPs in Saudi Arabia should receive additional training to adopt a biopsychosocial approach to managing LBP. In this study, the HCPs’ treatment recommendations may not correspond with contemporary clinical guidelines. Research to facilitate the implementation of optimal professional education and training to adopt a biopsychosocial approach is an urgent priority.
The evolutionary circular and human centered city: Towards an ecological and humanistic “re-generation” of the current city governance
Authors: Girard Luigi Fusco
Published: October 2021

BACKGROUND: Local governments are fundamental for achieving the ecological transition of our societies/cities. Also COVID-19 has shown that many of the barriers and bottlenecks in implementing public top-down initiatives are not technical (financial, economic, administrative, etc.) but cultural. They generate a lack of consensus with the risks of reducing the effectiveness of public interventions and investments. OBJECTIVE: The paper proposes the profile of a “circular governance” (i.e. that assumes the model of the circular economy) that is also “human centered”, capable of reducing inequalities, enhancing the processes of real participation in the construction of a desirable future for cities, through its capacity to regenerate material and no-material components/values. This perspective intersects with the cultural/educational dimension to which the new governance should devote much greater attention. CONCLUSION: A critical test is the transformation of abandoned urban spaces into attractive places for activities, investments and people. These disused spaces, which have often become repulsive waste deposits, are especially abundant in port cities. They are most often included in areas of particularly high landscape and cultural value. The paper suggests the elaboration of prototypes of “circular heritage symbiotic ecosystems” that are able to respect some general conditions (or principles). Suitable and effective assessment tools must be available to public, private and social actors to verify the proposal consistency and then to build new win-win-win partnerships. But it is also necessary to strengthen the educational perspective in the activities of the new governance, because preferences, values and needs should not be considered as already “given”, but should be constructed, thus nourishing the critical spirit of citizens so that they can creatively combine private interests and common good, thus becoming “artist of citizenship/civic engagement”.
Electrocardiographic Predictors of Cognitive Decline and Dementia: A Systematic Review
Authors: Imahori Yume , Vetrano Davide L. , Ljungman Petter , Qiu Chengxuan
Published: October 2021

Background: Markers of altered cardiac function might predict cognitive decline and dementia. Objective: This systematic review aims to review the literature that examines the associations of various electrocardiogram (ECG) markers with cognitive decline and dementia in middle-aged and elderly populations. Methods: We searched PubMed, Embase, and Web of Science through 1 July 2020 for literature and conducted a systematic literature review. We included studies examining the associations of ECG markers (e.g., left ventricular hypertrophy [LVH], spatial QRS-T angle, and QT prolongation) with cognitive function and dementia in adult populations regardless of study setting and design, but excluded studies examining atrial fibrillation and heart rate variability. Results: Fourteen community-based cross-sectional and longitudinal studies were identified. ECG markers were investigated in association with dementia in four prospective studies, and with cognitive decline in ten prospective studies. ECG-assessed LVH was associated with dementia in one study while five heterogeneous prospective studies yielded inconsistent associations with cognitive decline. Regarding ventricular repolarization markers, spatial QRS-T angle was associated with cognitive decline in one study while another study found no association between QT prolongation and cognitive decline. High resting heart rate was associated with both dementia and cognitive decline in one study but not associated with dementia in another study. P-wave abnormality was significantly associated with incident dementia and cognitive decline in one prospective study. Conclusion: Some ECG markers were associated with incident dementia and cognitive decline. However, limited number of heterogeneous studies did not allow us to make firm conclusions. Further studies are needed.
Association Between Polygenic Risk Score and the Progression from Mild Cognitive Impairment to Alzheimer’s Disease
Authors: Liu Hongliang ,   for the Alzheimer’s Disease Neuroimaging Initiative, Lutz Michael , Luo Sheng
Published: October 2021

Background: Mild cognitive impairment (MCI) is a heterogeneous condition and MCI patients are at increased risk of progression to dementia due to Alzheimer’s disease (AD). Objective: In this study, we aim to evaluate the associations between polygenic risk scores (PRSs) and 1) time to AD progression from MCI, 2) changes in longitudinal cognitive impairment, and 3) biomarkers from cerebrospinal fluid and imaging. Methods: We constructed PRS by using 40 independent non-APOE SNPs from well-replicated AD GWASs and tested its association with the progression time from MCI to AD by using 767 MCI patients from the ADNI study and 1373 patients from the NACC study. PRSs calculated with other methods were also computed. Results: We found that the PRS constructed with SNPs that reached genome-wide significance predicted the progression from MCI to AD (beta = 0.182, SE = 0.061, p = 0.003) after adjusting for the demographic and clinical variables. This association was replicated in the NACC dataset (beta = 0.094, SE = 0.037, p = 0.009). Further analyses revealed that PRS was associated with the increased ADAS-Cog11/ADAS-Cog13/ADASQ4 scores, tau/ptau levels, and cortical amyloid burdens (PiB-PET and AV45-PET), but decreased hippocampus and entorhinal cortex volumes (p < 0.05). Mediation analysis showed that the effect of PRS on the increased risk of AD may be mediated by Aβ42 (beta = 0.056, SE = 0.026, p = 0.036). Conclusion: Our findings suggest that PRS can be useful for the prediction of time to AD and other clinical changes after the diagnosis of MCI.
Characterizing the Resilience Effect of Neurodegeneration for the Mechanistic Pathway of Alzheimer’s Disease
Authors: Hu Di ,   and Alzheimer’s Disease Neuroimaging Initiative (ADNI), Liu Chuning , Xia Kai , Abramowitz Amy , Wu Guorong
Published: October 2021

Background: With the rapid development of neurobiology and neuroimaging technologies, mounting evidence shows that Alzheimer’s disease (AD) is caused by the build-up of two abnormal proteins, amyloid-β plaques (A) and neurofibrillary tangles (T). Over time, these AD-related neuropathological burdens begin to spread throughout the brain, which results in the characteristic progression of symptoms in AD. Objective: Although tremendous efforts have been made to link biological indicators to the progression of AD, limited attention has been paid to investigate the multi-factorial role of socioeconomic status (SES) in the prevalence or incidence of AD. There is high demand to explore the synergetic effect of sex and SES factors in moderating the neurodegeneration process caused by the accumulation of A and T biomarkers. Methods: We carry out a meta-data analysis on the longitudinal neuroimaging data, clinical outcomes, genotypes, and demographic data in Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). Results: Our major findings include 1) education and occupation show resilience effects at the angular gyrus, superior parietal lobule, lateral occipital-temporal sulcus, and posterior transverse collateral sulcus where we found significant slowdown of neurodegeneration due to higher education level or more advanced occupation rank; 2) A and T biomarkers manifest different spatial patterns of brain resilience; 3) BDNF (brain-derived neurotrophic factor) single nucleotide polymorphism (SNP) rs10835211 shows strong association to the identified resilience effect; 4) the identified resilience effect is associated with the clinical manifestation in memory, learning, and organization performance. Conclusion: Several brain regions manifest resilience from SES to A and T biomarkers. BDNF SNPs have a potential association with the resilience effect from SES. In addition, cognitive measures of learning and memory demonstrate the resilience effect.
Modeling the solubility of carbon dioxide in the MDEA + AEEA aqueous solution using the SAFT-HR equation of state and extended UNIQUAC model
Authors: Najafloo Azam , Sakhaeinia Hossein
Published: October 2021

In this study, a thermodynamic model has been used to determine the solubility of carbon dioxide in an aqueous solution which is the combination of methyldiethanolamine (MDEA) and aminoethylethanolamine (AEEA). The physical equilibriums have been considered between the liquid and vapor phases and chemical equilibrium in the liquid phase. The SAFT-HR equation of state has been used to specify the fugacity coefficients of the components in the vapor phase. The liquid phase is considered as an electrolyte solution besides; the extended UNIQUAC has been applied to figure out the activity coefficients. The bubble point calculation has been used in this research. This method includes two main loops. Calculations related to chemical equilibrium are performed in the interior loop and the ones associated with phase equilibrium are done in the exterior loop. The solubility of carbon dioxide has been predicted by the optimized parameters of the model in the temperature range of 308.2–368.2 K. It has been calculated that the absolute average relative deviations of the model are 16.65, 19.33, 28.91 and 19.99 in the calculation of partial pressure of carbon dioxide in various loadings at the temperatures of 308.2, 328.2, 343.2 and 368.2 K.
Optimizing storage of RDF archives using bidirectional delta chains
Authors: Taelman Ruben , Mahieu Thibault , Vanbrabant Martin , Verborgh Ruben
Published: October 2021

Linked Open Datasets on the Web that are published as RDF can evolve over time. There is a need to be able to store such evolving RDF datasets, and query across their versions. Different storage strategies are available for managing such versioned datasets, each being efficient for specific types of versioned queries. In recent work, a hybrid storage strategy has been introduced that combines these different strategies to lead to more efficient query execution for all versioned query types at the cost of increased ingestion time. While this trade-off is beneficial in the context of Web querying, it suffers from exponential ingestion times in terms of the number of versions, which becomes problematic for RDF datasets with many versions. As such, there is a need for an improved storage strategy that scales better in terms of ingestion time for many versions. We have designed, implemented, and evaluated a change to the hybrid storage strategy where we make use of a bidirectional delta chain instead of the default unidirectional delta chain. In this article, we introduce a concrete architecture for this change, together with accompanying ingestion and querying algorithms. Experimental results from our implementation show that the ingestion time is significantly reduced. As an additional benefit, this change also leads to lower total storage size and even improved query execution performance in some cases. This work shows that modifying the structure of delta chains within the hybrid storage strategy can be highly beneficial for RDF archives. In future work, other modifications to this delta chain structure deserve to be investigated, to further improve the scalability of ingestion and querying of datasets with many versions.
A new classification system for autism based on machine learning of artificial intelligence
Authors: Shahamiri Seyed Reza , Thabtah Fadi , Abdelhamid Neda
Published: October 2021

BACKGROUND: Autistic Spectrum Disorder (ASD) is a neurodevelopment condition that is normally linked with substantial healthcare costs. Typical ASD screening techniques are time consuming, so the early detection of ASD could reduce such costs and help limit the development of the condition. OBJECTIVE: We propose an automated approach to detect autistic traits that replaces the scoring function used in current ASD screening with a more intelligent and less subjective approach. METHODS: The proposed approach employs deep neural networks (DNNs) to detect hidden patterns from previously labelled cases and controls, then applies the knowledge derived to classify the individual being screened. Specificity, sensitivity, and accuracy of the proposed approach are evaluated using ten-fold cross-validation. A comparative analysis has also been conducted to compare the DNNs’ performance with other prominent machine learning algorithms. RESULTS: Results indicate that deep learning technologies can be embedded within existing ASD screening to assist the stakeholders in the early identification of ASD traits. CONCLUSION: The proposed system will facilitate access to needed support for the social, physical, and educational well-being of the patient and family by making ASD screening more intelligent and accurate.
Volume and surface coil simultaneous reception (VSSR) method for intensity inhomogeneity correction in MRI
Authors: Wu Lin , He Tian , Yu Jie , Liu Hang , Zhang Shuang , Zhang Tao
Published: October 2021

BACKGROUND: Addressing intensity inhomogeneity is critical in magnetic resonance imaging (MRI) because associated errors can adversely affect post-processing and quantitative analysis of images (i.e., segmentation, registration, etc.), as well as the accuracy of clinical diagnosis. Although several prior methods have been proposed to eliminate or correct intensity inhomogeneity, some significant disadvantages have remained, including alteration of tissue contrast, poor reliability and robustness of algorithms, and prolonged acquisition time. OBJECTIVE: In this study, we propose an intensity inhomogeneity correction method based on volume and surface coils simultaneous reception (VSSR). METHODS: The VSSR method comprises of two major steps: 1) simultaneous image acquisition from both volume and surface coils and 2) denoising of volume coil images and polynomial surface fitting of bias field. Extensive in vivo experiments were performed considering various anatomical structures, acquisition sequences, imaging resolutions, and orientations. In terms of correction performance, the proposed VSSR method was comparatively evaluated against several popular methods, including multiplicative intrinsic component optimization and improved nonparametric nonuniform intensity normalization bias correction methods. RESULTS: Experimental results show that VSSR is more robust and reliable and does not require prolonged acquisition time with the volume coil. CONCLUSION: The VSSR may be considered suitable for general implementation.
COVID-19 como Desastre a partir da perspectiva do Direito Humanitário
Authors: Jubilut Liliana Lyra , Santos Catherine de Souza , Puccinelli Silvia Maria Mantovani
Published: October 2021

A pandemia de COVID-19 é um desastre sanitário e biológico, e, por isso, se aproxima, por um lado, do Direito dos Desastres e, por outro, do Direito Humanitário, enquanto um ethos de proteção e com vistas a evitar sofrimento. A aproximação com o Direito Humanitário – na acepção mais ampla deste, de não apenas o Direito que regula os conflitos armados, mas também enquanto um ramo jurídico que apresenta normas e ações voltadas ao atendimento de indivíduos em situações de crise humanitária - deve ser considerada no desenho e na implementação de respostas e estratégias de enfrentamento à pandemia. A partir dessa abordagem, temas como assistência humanitária e assistência ao desenvolvimento passam a coexistir com questões tradicionais do Direito dos Desastres, como gestão de riscos e prevenção ganham destaque. O presente trabalho busca explorar essas possibilidades, a partir de um referencial teórico de Direitos Humanos, Direito Humanitário e Direito Internacional.
Fúziós központok az Európai Unióban
Authors: Márton Balázs
Published: October 2021

Az Európai Uniót fenyegető biztonsági kihívások összetettsége és az új keletű veszélyek elkerülhetetlenné teszik, hogy a hírszerző és biztonsági közösség által beszerzett információk gyorsan, biztonságosan és feldolgozottan kerüljenek a politikai döntéshozók asztalára. A szinergiák a fúziós központokban csúcsosodnak ki, amelyek eredetileg állami szervekként jöttek létre. Az EU akként tud leghatékonyabban hozzájárulni a tagállamok és az integráció egésze biztonságának fenntartásához, ha maga is működtet információegyesítő központokat. Az uniós intézményrendszert, az ügynökségek feladat- és hatásköreit megvizsgálva arra a következtetésre juthatunk, hogy több EU-s szerv is magán viseli a fúziós központok jellemzőit. Mindamellett az EU alkotmányos berendezkedéséből és az együttműködés jelenlegi mélységéből fakadóan e fúziós központok számos tekintetben különböznek a nemzeti rendszerektől. A közös biztonság kérdése egyre inkább befolyásolja az uniós agendákat, holott a tagállamok e területen jóval óvatosabban működnek együtt egymással. Ez a kettősség feszültséget és az integráció irányáról különbözően gondolkozó (föderalista vagy szuverenitáspárti) tagállamok között töréspontot is eredményezhet, amelyet az érdek összeurópai karakterére tekintettel kívánatos volna elkerülni.
Tihanyi Miklós: Bűn – Hit – Szabadulás. A vallás szerepe a büntetés-végrehajtásban (Budapest, Alapítvány a Rendvédelmi és Magánbiztonsági Oktatásért és Kutatásért, 2020)
Authors: Beer Miklós
Published: October 2021

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