bluuebunny/crossref_metadata_2025 huggingface dataset:
Discontinuity in the Subjective Experience of Self Among People with Mild-To-Moderate Dementia Is Associated with Poorer Psychological Health: Findings from the IDEAL Cohort
Authors: Clare Linda , Martyr Anthony , Morris Robin G. , Tippett Lynette J.
Published: August 2020

Background: The onset and progression of dementia can result in changes in the subjective experience of self, impacting on psychological health. Objective: We aimed to explore the extent to which people with mild-to-moderate dementia experience discontinuity in the subjective experience of self, and the factors associated with this experience for people with dementia and their family caregivers. Methods: We used data from the baseline assessment of the IDEAL cohort. Discontinuity in the subjective experience of self was assessed by asking participants about their agreement with the statement ‘I feel I am the same person that I have always been’. Participants were divided into those who did and did not experience discontinuity, and the two groups were compared in terms of demographic and disease-related characteristics, psychological well-being, measures of ‘living well’, and caregiver stress. Results: Responses to the continuity question were available for 1,465 participants with dementia, of whom 312 (21%) reported experiencing discontinuity. The discontinuity group experienced significantly poorer psychological well-being and had significantly lower scores on measures of ‘living well’. There was no clear association with demographic or disease-related characteristics, but some indication of increased caregiver stress. Conclusion: A significant proportion of people with mild-to-moderate dementia describe experiencing discontinuity in the subjective sense of self, and this is associated with poorer psychological health and reduced ability to ‘live well’ with the condition. Sensitively asking individuals with dementia about the subjective experience of self may offer a simple means of identifying individuals who are at increased risk of poor well-being.
Polymorphic Genetic Markers of the GABA Catabolism Pathway in Alzheimer’s Disease
Authors: Ciminelli Bianca Maria , Menduti Giovanna , Benussi Luisa , Ghidoni Roberta , Binetti Giuliano , Squitti Rosanna , Rongioletti Mauro , Nica Sabrina , Novelletto Andrea , Rossi Luisa , Malaspina Patrizia
Published: August 2020

Background: The compilation of a list of genetic modifiers in Alzheimer’s disease (AD) is an open research field. The GABAergic system is affected in several neurological disorders but its role in AD is largely understudied. Objective/Methods: As an explorative study, we considered variants in genes of GABA catabolism (ABAT, ALDH5A1, AKR7A2), and APOE in 300 Italian patients and 299 controls. We introduce a recent multivariate method to take into account the individual APOE genotype, thus controlling for the effect of the discrepant allele distributions in cases versus controls. We add a genotype-phenotype analysis based on age at onset and the Mini-Mental State Evaluation score. Results: On the background of strongly divergent APOE allele distributions in AD versus controls, two genotypic interactions that represented a subtle but significant peculiarity of the AD cohort emerged. The first is between ABAT and APOE, and the second between some ALDH5A1 genotypes and APOE. Decreased SSADH activity is predicted in AD carriers of APOE ɛ4, representing an additional suggestion for increased oxidative damage. Conclusion: We identified a difference between AD and controls, not in a shift of the allele frequencies at genes of the GABA catabolism pathway, but rather in gene interactions peculiar of the AD cohort. The emerging view is that of a multifactorial contribution to the disease, with a main risk factor (APOE), and additional contributions by the variants here considered. We consider genes of the GABA degradation pathway good candidates as modifiers of AD, contributing to energy impairment in AD brain.
Fiat Lux: The Light Became Therapy. An Overview on the Bright Light Therapy in Alzheimer’s Disease Sleep Disorders
Authors: Roccaro Ilaria , Smirni Daniela
Published: August 2020

Background: A system of photosensitive retinal ganglion cells provides ‘non-visual’ information on the circadian sequences of light to the suprachiasmatic nucleus (SCN), which, as the ‘master clock’, synchronizes the chronobiological mechanisms of all the biological clocks. Damage to SCN structure alters circadian behavioral and hormonal rhythms and interferes with a regular sleep-wake pattern. Several studies have shown that, in aging and in Alzheimer’s disease (AD), circadian rhythms change their synchronization with the environment and behavior loses sync with light. Objective: The current overview aims to examine research studies showing the effect of bright light therapy (BLT) on sleep disorders and sleep-wake patterns in AD. Methods: A literature search was conducted, taking into consideration the relevant studies over the last 20 years. Fifteen studies have been thorough: seven followed an environmental-architectural approach and eight followed a treatment devices approach. Results: Studies agree in considering BLT as a promising non-pharmacological intervention to compensate for circadian rhythm alterations and they support the need for standardized protocols that allow a comparison between multicenter studies. Conclusion: Interestingly, in an attempt to contain the spread of the COVID-19 pandemic, health authorities have forced the population to stay home. Therefore, AD people are not currently able to enjoy exposure to sunlight. It is predictable that they may experience an exacerbation of circadian disturbances and that the BLT can be an effective response to prevent such exacerbation.
Tau Acetylation in Entorhinal Cortex Induces its Chronic Hippocampal Propagation and Cognitive Deficits in Mice
Authors: Wang Xin , Liu En-Jie , Liu Qian , Li Shi-Hong , Li Ting , Zhou Qiu-Zhi , Liu Yan-Chao , Zhang Huaqiu , Wang Jian-Zhi
Published: August 2020

Background: Increased tau acetylation at K174, K274, K280, and K281 has been observed in the brains of Alzheimer’s disease (AD) patients or in transgenic mice, but the role of acetylation in tau propagation is elusive. Objective: To study the effect of tau acetylation in entorhinal cortex on tau transmission and learning and memory. Methods: Stereotactic brain injection, behavioral test, electrophysiological recording, immunohistochemistry, and immunofluorescence were used. Results: We constructed the hyperacetylation mimics of tau (AAV-Tau-4Q), the non-acetylation tau mutant (AAV-Tau-4R), and the wild-type tau (AAV-Tau-WT). By overexpressing these different tau proteins in the entorhinal cortex (EC) of 2-month-old mice, we found that overexpressing Tau-4Q in EC for 3 or 6 months (to 5 or 8 months of age) neither induces tau propagation to dentate gyrus (DG) nor glial activation in DG, nor spatial memory deficit. However, overexpressing Tau-WT and Tau-4Q in EC for 13.5 months (15.5 months of age) at 2 months promoted tau propagation respectively to granulosa and hilus of DG with glial activation, synaptic dysfunction, and memory deficit, while overexpressing Tau-4R abolished tau propagation with improved cellular pathologies and cognitive functions. Furthermore, overexpressing Tau-4Q in unilateral DG of 2-month-old mice for 8 weeks also promoted its contralateral transmission with glial activation, and mice with tau (Tau-WT, Tau-4Q, and Tau-4R) overexpression in DG showed cognitive deficits compared with the empty vector controls. Conclusion: Tau acetylation induces a time-dependent propagation from EC to DG, and only hippocampus but not EC tau accumulation induces cognitive deficits.
Oral Treatment with Iododiflunisal Delays Hippocampal Amyloid-β Formation in a Transgenic Mouse Model of Alzheimer’s Disease: A Longitudinal in vivo Molecular Imaging Study1
Authors: Rejc Luka , Gómez-Vallejo Vanessa , Rios Xabier , Cossío Unai , Baz Zuriñe , Mujica Edurne , Gião Tiago , Cotrina Ellen Y. , Jiménez-Barbero Jesús , Quintana Jordi , Arsequell Gemma , Cardoso Isabel , Llop Jordi
Published: August 2020

Background: Transthyretin (TTR) is a tetrameric, amyloid-β (Aβ)-binding protein, which reduces Aβ toxicity. The TTR/Aβ interaction can be enhanced by a series of small molecules that stabilize its tetrameric form. Hence, TTR stabilizers might act as disease-modifying drugs in Alzheimer’s disease. Objective: We monitored the therapeutic efficacy of two TTR stabilizers, iododiflunisal (IDIF), which acts as small-molecule chaperone of the TTR/Aβ interaction, and tolcapone, which does not behave as a small-molecule chaperone, in an animal model of Alzheimer’s disease using positron emission tomography (PET). Methods: Female mice (AβPPswe/PS1A246E/TTR+/–) were divided into 3 groups (n = 7 per group): IDIF-treated, tolcapone-treated, and non-treated. The oral treatment (100 mg/Kg/day) was started at 5 months of age. Treatment efficacy assessment was based on changes in longitudinal deposition of Aβ in the hippocampus (HIP) and the cortex (CTX) and determined using PET-[18F]florbetaben. Immunohistochemical analysis was performed at age = 14 months. Results: Standard uptake values relative to the cerebellum (SUVr) of [18F]florbetaben in CTX and HIP of non-treated animals progressively increased from age = 5 to 11 months and stabilized afterwards. In contrast, [18F]florbetaben uptake in HIP of IDIF-treated animals remained constant between ages = 5 and 11 months and significantly increased at 14 months. In the tolcapone-treated group, SUVr progressively increased with time, but at lower rate than in the non-treated group. No significant treatment effect was observed in CTX. Results from immunohistochemistry matched the in vivo data at age = 14 months. Conclusion: Our work provides encouraging preliminary results on the ability of small-molecule chaperones to ameliorate Aβ deposition in certain brain regions.
Animal Models of Alzheimer’s Disease Should Be Controlled for Roseolovirus
Authors: Denner Joachim , Tanzi Rudolph , Jacobson Steve
Published: August 2020

Animal models to study Alzheimer’s disease (AD) pathogenesis are under development. Since herpesviruses have been postulated to be capable of triggering the pathogenic process, AD animal models (mouse, pig, and non-human primates) should be controlled for the presence of these viruses. Only virus-free models allow studying the genetic factors and the effect of adding viruses. Roseoloviruses such as human herpesvirus 6 and the related viruses in the animals are the main topic of this commentary.
Presenilin 1 Regulates Membrane Homeostatic Pathways that are Dysregulated in Alzheimer’s Disease
Authors: Deaton Carol A. , Johnson Gail V.W.
Published: August 2020

Mutations in the PSEN1 gene, encoding presenilin 1 (PS1), are the most common cause of familial Alzheimer’s disease (fAD). Since the first mutations in the PSEN1 gene were discovered more than 25 years ago, many postulated functions of PS1 have been investigated. The majority of earlier studies focused on its role as the catalytic component of the γ-secretase complex, which in concert with β site amyloid precursor protein cleaving enzyme 1 (BACE1), mediates the formation of Aβ from amyloid-β protein precursor (AβPP). Though mutant PS1 was originally considered to cause AD by promoting Aβ pathology through its protease function, it is now becoming clear that PS1 is a multifunctional protein involved in regulating membrane dynamics and protein trafficking. Therefore, through loss of these abilities, mutant PS1 has the potential to impair numerous cellular functions such as calcium flux, organization of proteins in different compartments, and protein turnover via vacuolar metabolism. Impaired calcium signaling, vacuolar dysfunction, mitochondrial dysfunction, and increased ER stress, among other related membrane-dependent disturbances, have been considered critical to the development and progression of AD. Given that PS1 plays a key regulatory role in all these processes, this review will describe the role of PS1 in different cellular compartments and provide an integrated view of how PS1 dysregulation (due to mutations or other causes) could result in impairment of various cellular processes and result in a “multi-hit”, integrated pathological outcome that could contribute to the etiology of AD.
Collapsin Response Mediator Proteins: Novel Targets for Alzheimer’s Disease
Authors: Quach Tam T. , Moutal Aubin , Khanna Rajesh , Deems Nicholas P. , Duchemin Anne-Marie , Barrientos Ruth M.
Published: August 2020

Numerous experimental and postmortem studies have increasingly reported dystrophic axons and dendrites, and alterations of dendritic spine morphology and density in the hippocampus as prominent changes in the early stages of Alzheimer’s disease (AD). Furthermore, these alterations tend to correlate well with the progressive cognitive decline observed in AD. For these reasons, and because these neurite structures have a capacity to re-grow, re-establish lost connections, and are critical for learning and memory, there is compelling evidence to suggest that therapeutic interventions aimed at preventing their degradation or promoting their regrowth may hold tremendous promise in preventing the progression of AD. In this regard, collapsin response mediator proteins (CRMPs), a family of phosphoproteins playing a major role in axon guidance and dendritic growth, are especially interesting. The roles these proteins play in neurons and immune cells are reviewed here.
QSPR prediction of polymers’ solubility parameters by radial basis functional link net
Authors: Koç Dilek İmren , Koç Mehmet Levent
Published: August 2020

This research aims to introduce a novel radial basis functional link net (RBFLN)-based QSPR (quantitative structure-property relationship) model to predict the solubility parameters of the polymers with the structure – (C1H-2-C2R3R4) – and provides its comparison with the multi-layer feed forward network (MLFFN)-based QSPR model, as well as previous genetic programming (GP) and multiple linear regression (MLR)-based QSPR models in the literature. During the implementation of the RBFLN and MLFFN-based QSPR models, the networks which are associated with the minimum weighted average AIC (Akaike’s information criterion) and BIC (Bayesian information criterion) scores are trained by using a hybrid scheme combining the cuckoo search and Levenberg-Marquardt algorithm. Our results show that the RBFLN-based QSPR model outperforms the other ones in terms of the external validation metrics. The study also reveals that it may have a promising potential to study the relationship between various measurement/experimental data or processing elements in a hybrid way of artificial intelligence modelling.
BP neural network trade volume prediction and enterprises HRM optimization model based on ES-LM training
Authors: Zhang Zhixing
Published: August 2020

In this paper, the author analyze the neural network trade volume prediction and enterprises HRM optimization model based on ES-LM training. In order to improve the quality of human resource performance management, international trade enterprises need to introduce the concept of fine management, so as to create a good working environment for employees and effectively improve the effect of performance management. Moreover, this study uses a city’s import and export trade quota forecast to verify the prediction accuracy of the improved algorithm network. In addition, this study decomposes data into training data and test data based on normalization and principal component extraction. Finally, this study conducted a comparative analysis of the algorithm performance analysis of this study. The research results show that the proposed algorithm has certain effects, and it meets the forecasting requirements in terms of convergence speed and prediction accuracy and can provide theoretical reference for subsequent related research.
Risk assessment of logistics finance enterprises based on BP neural network and fuzzy mathematical model
Authors: Aiqun Wang , Zicong He , Yilin Wang
Published: August 2020

Neural network is used to deal with the nonlinear relationship, usually there is a strong nonlinear relationship between input and output. Through the self-learning of neural network, the weight of data samples is determined after training, and the optimal solution is obtained according to the process steps. In this paper, thea authors analyze the risk assessment of logistics finance enterprises based on BP neural network and fuzzy mathematical model. For logistics companies, it is necessary to determine the ability of logistics companies to engage in logistics finance business, and then to make detailed and accurate grasp of relevant information. The difference between the actual output and the expected output of the training sample is small, so the fitting is completed well, and the parameters of the neural network are further adjusted. The results show that the model has a good ability of learning nonlinear function relations. To sum up, in order to reduce logistics financial risks, we must fully understand the factors that affect logistics financial risks, determine the proportion of risk factors, and then use the fuzzy evaluation method to analyze the financial business risks.
Extending the linguistic decision suite FLINTSTONES to deal with comparative linguistic expressions with symbolic translation information
Authors: Labella Álvaro , Rodríguez Rosa M. , Martínez Luis
Published: August 2020

Uncertainty is so common in real decision situations that it has given rise to a new decision making approach so-called linguistic decision making, in which such uncertainty is modeled by using linguistic information. Many contributions have been proposed in order to solve LDM problems by following a Computing with Words (CW) approach to obtain linguistic outputs from linguistic premises by emulating the human beings’ reasoning process. Nowadays, there are several LDM models that, together with the complexity of LDM problems, make almost impossible to find a proper solution for these problems without a support tool. FLINTSTONES is a fuzzy decision support system that facilitates the decision process in LDM problems. This software aimed to solve LDM problems by means of the 2-tuple linguistic model, whose main advantages are high interpretability and precision of the results. However, it has other drawbacks such as the modeling of linguistic information by using solely single linguistic terms or the impossibility to model the experts’ hesitancy. Recently, the Extended Comparative Linguistic Expressions with Symbolic Translation (ELICIT) linguistic representation model was introduced in order to overcome existing drawbacks in terms of interpretability and accuracy in CW processes. This model allows to model experts’ hesitancy and, at the same time, carry out precise linguistic computations and provide interpretable results, overcoming the limitations of previous LDM models. Therefore, this contribution presents an updated version of FLINTSTONES able to manage ELICIT information in LDM problems and which integrates the ELICIT CW approach.
An XGBoost-lasso ensemble modeling approach to football player value assessment
Authors: Yigit Ahmet Talha , Samak Baris , Kaya Tolga
Published: August 2020

Sports analytics is a field that is growing in popularity and application throughout the world. One of the open problems in this field is the valuation of football players. The aim of this study is to establish a football player value assessment model using machine learning techniques to support the transfer decisions of football clubs. The proposed model is mainly based on the intrinsic features of the individual players which are provided in Football Manager simulation game. To do this, based on the individual statistics of 5316 players who are active in 11 different major leagues from Europe and South America, different value assessment models are conducted using advanced supervised learning techniques which include ridge and lasso regressions, random forests and extreme gradient boosting. All the models have been built in R programming language. The performances of the models are compared based on their mean squared errors and their fit to the real world examples. An ensemble model with inflation is proposed as the output.
Some statistical and CI models to predict chaotic high-frequency financial data
Authors: Marcek Dusan
Published: August 2020

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.
Combining predictive base models using deep ensemble learning
Authors: Oner Mahir , Ustundag Alp
Published: August 2020

Since information science and communication technologies had improved significantly, data volumes had expanded. As a result of that situation, advanced pre-processing and analysis of collected data became a crucial topic for extracting meaningful patterns hidden in the data. Therefore, traditional machine learning algorithms generally fail to gather satisfactory results when analyzing complex data. The main reason of this situation is the difficulty of capturing multiple characteristics of the high dimensional data. Within this scope, ensemble learning enables the integration of diversified single models to produce weak predictive results. The final combination is generally achieved by various voting schemes. On the other hand, if a large amount of single models are utilized, voting mechanism cannot be able to combine these results. At this point, Deep Learning (DL) provides the combination of the ensemble results in a considerable time. Apart from previous studies, we determine various predictive models in order to forecast the outcome of two different case studies. Consequently, data cleaning and feature selection are conducted in advance and three predictive models are defined to be combined. DL based integration is applied substituted for voting mechanism. The weak predictive results are fused based on Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) using different parameters and datasets and best predictors are extracted. After that, different experimental combinations are evaluated for gathering better prediction results. For comparison, grouped individual results (clusters) with proper parameters are compared with DL based ensemble results.
Efficient top representative for multi-authorship encrypted cloud data to assist cognitive search
Authors: Shanthi P. , Umamakeswari A.
Published: August 2020

Cloud computing is gaining ground in the digital and business world. It delivers storage service for user access using Internet as a medium. Besides the numerous benefits of cloud services, migrating to public cloud storage leads to security and privacy concerns. Encryption method protects data privacy and confidentiality. However, encrypted data stored in cloud storage reduces the flexibility in processing data. Therefore, the development of new technologies to search top representatives from encrypted public storage is the current requirement. This paper presents a similarity-based keyword search for multi-author encrypted documents. The proposed Authorship Attribute-Based Ranked Keyword Search (AARKS) encrypts documents using user attributes, and returns ranked results to authorized users. The scheme assigns weight to index vectors by finding the dominant keywords of the specific authority document collection. Search using the proposed indexing prunes away branches and processes only fewer nodes. Re-weighting documents using the relevant feedback also improves user experience. The proposed scheme ensures the privacy and confidentiality of data supporting the cognitive search for encrypted cloud data. Experiments are performed using the Enron dataset and simulated using a set of queries. The precision obtained for the proposed ranked retrieval is 0.7262. Furthermore, information leakage to a cloud server is prevented, thereby proving its suitability for public storage.
Violent activity recognition by E-textile sensors based on machine learning methods
Authors: Randhawa Princy , Shanthagiri Vijay , Kumar Ajay
Published: August 2020

In the new era of technology with the development of wearable sensors, it is possible to collect data and analyze the same for recognition of different human activities. Activity recognition is used to monitor humans’ activity in various applications like assistance for an elderly and disabled person, Health care, physical activity monitoring, and also to identify a physical attack on a person etc. This paper presents the techniques of classifying the data from normal activity and violent attack on a victim. To solve this problem, the paper emphasis on classifying different activities using machine learning (supervised) techniques. Various experiments have been conducted using wearable inertial fabric sensors for different activities. These wearable e-textile sensors were woven onto the jacket worn by a healthy subject. The main steps which outline the process of activity recognition: location of sensors, pre-processing of the statistical data and activity. Three supervised algorithmic techniques were used namely Decision tree, k-NN classifier and Support Vector Machine (SVM). Based on the experimental work, the results obtained show that the SVM algorithm offers an overall good performance matched in terms of accuracy i.e. 97.6% and computation time of 0.85 seconds for k-NN and Decision Tree for all activities.
Intelligent color image restoration system using cognitive fuzzy concept
Authors: Jeon Gwanggil
Published: August 2020

Deinterlacing is the procedure of reworking interlaced video signal into a non-interlaced format. The interlaced video signal is generally used in both of analog TV and HDTV with 1080i. The deinterlacing method was designed to solve a legacy issue, therefore the interlaced signal which was requested by analog TV must be trans-formed to be displayed on HDTV. This paper introduces fuzzy concept-based deinterlacing method and shows how it can be executed on various test set. The proposed method includes weight calculation stage and deinterlacing implementation stage. In weight calculation stage, I compute correct weight values on each edge direction, which are calculated using fuzzy concepts. In deinterlacing implementation stage, the computed weight values are used to obtain final deinterlacing pixels by multiplying them with candidate existent pixels. The presented method provides outstanding subjective and objective results in various data set.
Fuzzy implemented harmonic analysis in a three phase induction motor
Authors: Babu Bandla Pavan , Indragandhi V
Published: August 2020

In an Electrical system, Power Quality (PQ) is becoming significant to all types of consumers. With the increase of power demand from end users, maintaining the quality of power within the limitations is a major problem. In this paper, harmonic analysis in a grid connected three phase induction motor is tested according to PQ international standards which are found in the International Electro technical Commission (IEC). These mentioned standards are maintained in the transmission line and fed to the induction motor through a regenerative grid simulator. With the results obtained, execution of this fuzzy control system can be investigated through the digital simulation, which is based on MATLAB-SIMULINK package. It provides human operands to constitute a knowledge base which is used for diagnosing power quality and capable of predicting abnormal operation in Industries.
Fuzzy rule based SLA generation algorithms for web based multi party negotiation systems
Authors: Alamelu M. , Pradeep Kumar T.S. , Vijayakumar V.
Published: August 2020

Service Level Agreement (SLA) is an agreement between the service provider and consumer to provide the verifiable quality of services. Using the valuable metrics in SLA, a service consumer could easily evaluate the service provider. Though there are different types of SLA models are available between the consumer and provider, the proposed approach describes the Fuzzy rule base SLA agreement generation among multiple service providers. A negotiation system is designed in this work to collect the different sets of provider services. With their desired quality metrics, a common Fuzzy based SLA report is generated and compared against the existing consumer requirements. From the analysis of the common agreement report, consumers can easily evaluate the best service with the desired Impact service, cost and Quality. The main advantage of this approach is that it reduces the time consumption of a consumer. Moreover, the best service provider can be selected among multiple providers with the desired QoS parameters. At the same time, the bilateral negotiation is enhanced with the approach of multilateral negotiation to improve the searching time of consumers.
Modified fuzzy-based greedy routing protocol for VANETs
Authors: Stephan Thompson , Rajappa Ananthnarayan , Sendhil Kumar K.S. , Gupta Shivang , Shankar Achyut , Vijayakumar V.
Published: August 2020

Vehicular Ad Hoc Networks (VANETs) is the most growing research area in wireless communication and has been gaining significant attention over recent years due to its role in designing intelligent transportation systems. Wireless multi-hop forwarding in VANETs is challenging since the data has to be relayed as soon as possible through the intermediate vehicles from the source to destination. This paper proposes a modified fuzzy-based greedy routing protocol (MFGR) which is an enhanced version of fuzzy logic-based greedy routing protocol (FLGR). Our proposed protocol applies fuzzy logic for the selection of the next greedy forwarder to forward the data reliably towards the destination. Five parameters, namely distance, direction, speed, position, and trust have been used to evaluate the node’s stability using fuzzy logic. The simulation results demonstrate that the proposed MFGR scheme can achieve the best performance in terms of the highest packet delivery ratio (PDR) and minimizes the average number of hops among all protocols.
Suppliers selection based on intuitionistic fuzzy dimensional analysis
Authors: Pérez-Domínguez Luis , David Luviano-Cruz , Picón Luis Alberto Rodríguez , López Roberto Romero , Yohana Sánchez-Mojica Karla
Published: August 2020

Supplier selection is a crucial activity in the supply chain management, and fundamentally it is a multi-criteria decision-making problem. However, the interaction of the criteria, the uncertain regarding to subjectivity and the imprecise information generally present in the provider selection process is still an open issue. The main purpose of this paper is to arise a new method to support the provider selection process. In this sense, this paper introduces the intuitionistic fuzzy version of dimensional analysis under a group decision making and multi-criteria environment applied to suppliers selection. Likewise, a set of decision makers reveal their preferences about the criteria and options in evaluation by mean of a linguistic grade that are mapped via intuitionistic numbers. Consequently, the importance of each decision maker and criteria is considered in the method. Hence, the methodology assumes that exist a choice better than the rest which is called ideal alternative. Each choice in evaluation, is compared with that ideal to induce an index of similarity. Additionally, the alternative with highest index of similarity is proposed as solution to the supplier selection problem. The method offer a systematic mode to deal with the interrelationship between the criteria attached into the decision making sphere. Finally, a numerical example is addressed to supplier selection to facilitate the understanding of the proposed method.
Business and management research in Latin America: A country-level bibliometric analysis
Authors: Coronado Freddy C. , Merigó José M. , Cancino Christian A.
Published: August 2020

Bibliometrics is a scientific discipline that studies quantitatively the bibliographic material of a particular topic. This study analyzes management research published by Latin American countries between 1990 and 2019. The work uses the Web of Science database and provides several country-level bibliometric indicators including the total number of publications and citations, and the h-index. The results indicate that Brazil, Chile and Mexico have constantly led the region’s scientific publications. The temporal evolution shows a significant increase on the number of publications during the last years that seems to continue in the future. The results also show that operations research and finance are the most significant topics in the region.
Fuzzy metatopics predicting prices of Airbnb accommodations
Authors: Sánchez-Franco Manuel J. , Troyano José A. , Alonso-Dos-Santos Manuel
Published: August 2020

The purpose of this study is to guide pricing policies of Airbnb accommodation rentals to reduce inefficient pricing strategies through a novel application of topic modelling and a fuzzy clustering. In particular, the method proposes the application of Structural Topic Modelling, which explains a set of observations from latent topics. The associations between topics by Fuzzy C-Means Clustering are analysed to obtain new, more compact representations of topics (i.e., metatopics). This research identifies 15-metatopics related to Airbnb accommodations based on location and connectivity, enjoyment of domestic and everyday services, and the possibility of more authentic local experiences, among others. The influence of key metatopics on the price of Airbnb accommodations is determined by applying Extreme Gradient Boosting (an efficient and scalable implementation of gradient boosting framework) and Shapley Additive Explanations values. To sum up, our research provides an explicit contribution of user-generated content to promote the development of mutually beneficial relationships between guests and hosts, and detects future lines of research and practical and conceptual implications of the findings.
An efficient method for determining the optimal convolutional neural network structure based on Taguchi method
Authors: Lee Pin-Chan , Lo Tzu-Ping , Sun Haoqing , Wen I-Jyh
Published: August 2020

Structure of convolutional neural network (CNN) applied for image recognition requires large numbers of tuning for designated datasets in practice. It is a time-consuming process to finally come up with a feasible structure for specific requirement. This paper proposes a method based on Taguchi method which can efficiently determine the optimal structure of hyperparameters combination. Five hyperparameters with four levels are defined as control factors and two indicators are chosen to measure the performance of CNN structure. L16 (45) orthogonal array is used to arrange the experiment. S/N ratio and main effect plot are used to identify the optimal structure (hyperparameter combination) of CNN. The classic case of MNIST is employed to verify the practicability of the proposed method. Results show that the proposed method can identify the optimal CNN structure efficiently and also rank the significance priority of hyperparameters.
A recommender system based on a new similarity metric and upgraded crow search algorithm
Authors: Sinha Bam Bahadur , Dhanalakshmi R.
Published: August 2020

In the current era of big data, the recommender system aspires to provide users with a tailored set of personalized items from a pool of a large population. The most popular collaborative filtering system performs this information filtering process by computing similarity among users or items. This paper proposes a similarity metric that comprises of weights and values. Values are calculated by considering the matching set of users for which similarity is to be computed. The optimal values of weights are decided using an upgraded form of the Crow Search Algorithm (CSA). The exploration and exploitation stability of CSA is improvised by making use of Levy flight diffusion, adaptive operator adjustment, and event factor. The performance of the implemented metaheuristic approach is validated on Jester, MovieLens 100K, and MovieLens 1M dataset. Comparative analysis of proposed model against several other traditional metaheuristic based personalization systems reveal that our model is less delicate to the dimension of datasets and it also presents exceptional refinement in terms of prediction complexity and accuracy.
A novel second-order cone programming support vector machine model for binary data classification
Authors: Dong Guishan , Mu Xuewen
Published: August 2020

The support vector machine is a classification approach in machine learning. The second-order cone optimization formulation for the soft-margin support vector machine can ensure that the misclassification rate of data points do not exceed a given value. In this paper, a novel second-order cone programming formulation is proposed for the soft-margin support vector machine. The novel formulation uses the l2-norm and two margin variables associated with each class to maximize the margin. Two regularization parameters α and β are introduced to control the trade-off between the maximization of margin variables. Numerical results illustrate that the proposed second-order cone programming formulation for the soft-margin support vector machine has a better prediction performance and robustness than other second-order cone programming support vector machine models used in this article for comparision.
A novel genetic algorithm-based improvement model for online communities and trust networks
Authors: Bekmezci Ilker , Ermis Murat , Cimen Egemen Berki
Published: August 2020

Social network analysis offers an understanding of our modern world, and it affords the ability to represent, analyze and even simulate complex structures. While an unweighted model can be used for online communities, trust or friendship networks should be analyzed with weighted models. To analyze social networks, it is essential to produce realistic social models. However, there are serious differences between social network models and real-life data in terms of their fundamental statistical parameters. In this paper, a genetic algorithm (GA)-based social network improvement method is proposed to produce social networks more similar to real-life data sets. First, it creates a social model based on existing studies in the literature, and then it improves the model with the proposed GA-based approach based on the similarity of the average degree, the k-nearest neighbor, the clustering coefficient, degree distribution and link overlap. This study can be used to model the structural and statistical properties of large-scale societies more realistically. The performance results show that our approach can reduce the dissimilarity between the created social networks and the real-life data sets in terms of their primary statistical properties. It has been shown that the proposed GA-based approach can be used effectively not only in unweighted networks but also in weighted networks.
Hash-enhanced elliptic curve bit-string generator for medical image encryption
Authors: Reyad Omar , Hamed Kadry , Karar Mohamed Esmail
Published: August 2020

Bit-string generator (BSG) is based on the hardness of known number theoretical problems, such as the discrete logarithm problem with the elliptic curve (ECDLP). Such type of generators will have good randomness and unpredictability properties as it is challenged to find a solution regarding this mathematical dilemma. Hash functions in turn play a remarkable role in many cryptographic tasks to accomplish different security levels. Hash-enhanced elliptic curve bit-string generator (HEECBSG) mechanism is proposed in this study based on the ECDLP and secure hash function. The cryptographic hash function is used to achieve integrity and security of the obtained bit-strings for highly sensitive plain data. The main contribution of the proposed HEECBSG is transforming the x-coordinate of the elliptic curve points using a hash function H to generate bit-strings of any desirable length. The obtained pseudo-random bits are tested by the NIST test suite to analyze and verify its statistical and randomness properties. The resulted bit-string is utilized here for encrypting various medical images of the vital organs, i.e. the brain, bone, fetuses, and lungs. Then, extensive evaluation metrics have been applied to analyze the successful performance of the cipherimage, including key-space analysis, histogram analysis, correlation analysis, entropy analysis and sensitivity analysis. The results demonstrated that our proposed HEECBSG mechanism is feasible for achieving security and privacy purposes of the medical image transmission over unsecure communication networks.
Taking a Strohl Through History: Putting Strohl Back in Guillain-Barré-Strohl Syndrome
Authors: Bondi Steven , Carroll Elizabeth , Bhatt Jaydeep
Published: August 2020

Guillain-Barré Syndrome is a popular eponym that comes from a 1916 paper by Drs. Guillain, Barré, and Strohl. These physicians described two soldiers in the French Sixth Army during World War I who developed acute progressive motor weakness. Although Drs. Guillain and Barré have continued to be included in the syndrome’s eponym, Dr. Strohl has been forgotten despite having strongly contributed to the original paper. The reasons previously mentioned for Dr. Strohl’s absence appear trivial in contemporary practice and thus, his name deserves to be reintroduced to Guillain-Barré-Strohl Syndrome.
The Role of Architecture and Design in the Management of Parkinson’s Disease: A Systematic Review
Authors: Ramos Joana Beisl , Duarte Gonçalo S. , Bouça-Machado Raquel , Fabbri Margherita , Mestre Tiago A. , Costa João , Ramos Tânia Beisl , Ferreira Joaquim J.
Published: August 2020

Background: Parkinson’s disease (PD) is a neurological condition characterized by the development of daily disabling symptoms. Although the architecture and design of a PD patient’s environment can hinder or facilitate full participation in daily activities, their putative role in the management of these patients has received little attention to date. Objective: We conducted a systematic review to evaluate the evidence of architectural and design features in the management of people with PD. Methods: An electronic database search of observational and experimental studies was conducted in MEDLINE and Embase from inception to May 2020, with two independent reviewers identifying the studies. Falls, fear of falling, postural instability, gait impairment/disability, and functional mobility were our outcomes of interest. Results: Thirty-six studies were included, among which nineteen were observational and seventeen were experimental studies (overall participants = 2,965). Pavement characteristics, notably unstable surfaces and level differences, were found to be a major cause of falling. Ground-based obstacles and confined/narrowed spaces were found to disturb gait, increase postural instability, and decrease functional mobility. Housing type did not appear to increase risk of falling, nor to significantly explain concerns about falling. Conclusion: Findings suggest a need to adjust architectural features of the surrounding space to ensure appropriate care and provide a safe environment to PD patients. More evidence about the impact of such modifications on PD outcomes is needed.
An Open-Label, 8-Week Study of Safety and Efficacy of Pimavanserin Treatment in Adults with Parkinson’s Disease and Depression
Authors: DeKarske Daryl , Alva Gustavo , Aldred Jason L. , Coate Bruce , Cantillon Marc , Jacobi Lori , Nunez Rene , Norton James C. , Abler Victor
Published: August 2020

Background: Many patients with Parkinson’s disease (PD) experience depression. Objective: Evaluate pimavanserin treatment for depression in patients with PD. Methods: Pimavanserin was administered as monotherapy or adjunctive therapy to a selective serotonin reuptake inhibitor or serotonin/noradrenaline reuptake inhibitor in this 8-week, single-arm, open-label phase 2 study (NCT03482882). The primary endpoint was change from baseline to week 8 in Hamilton Depression Scale–17-item version (HAMD-17) score. Safety, including collection of adverse events and the Mini-Mental State Examination (MMSE) and Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale Part III (MDS-UPDRS III) scores, was assessed in patients who received ≥1 pimavanserin dose. Results: Efficacy was evaluated in 45 patients (21 monotherapy, 24 adjunctive therapy). Mean (SE) baseline HAMD-17 was 19.2 (3.1). Change from baseline to week 8 (least squares [LS] mean [SE]) in the HAMD-17 was –10.8 (0.63) (95% CI, –12.0 to –9.5; p < 0.0001) with significant improvement seen at week 2 (p < 0.0001) and for both monotherapy (week 8, –11.2 [0.99]) and adjunctive therapy (week 8,–10.2 [0.78]). Most patients (60.0%) had ≥50% improvement at week 8, and 44.4% of patients reached remission (HAMD-17 score ≤7). Twenty-one of 47 patients experienced 42 treatment-emergent adverse events; the most common by system organ class were gastrointestinal (n = 7; 14.9%) and psychiatric (n = 7; 14.9%). No negative effects were observed on MMSE or MDS-UPDRS Part III. Conclusion: In this 8-week, single-arm, open-label study, pimavanserin as monotherapy or adjunctive therapy was well tolerated and associated with early and sustained improvement of depressive symptoms in patients with PD.
RETRACTED: Protective role of protocatechuic acid in sevoflurane-induced neuron apoptosis, inflammation and oxidative stress in mice
Authors: Gao Yuhua , Ma Liping , Han Tao , Wang Meng , Zhang Dongmei , Wang Yana
Published: August 2020

This article has been retracted, and the online PDF has been watermarked “RETRACTED”. A retraction notice is available at DOI: 10.3233/RNN-239001 .
Micro data linking: Addressing new emerging topics without increasing the respondent burden
Authors: Luppes Martin , Nielsen Peter Bøegh
Published: August 2020

Micro data linking (MDL) has become an important cornerstone in the production of new statistical insights. MDL is now widely acknowledged as a strategic activity to avoid increasing the respondent burden when meeting new user demands on e.g. globalisation. MDL is a strong method in combining micro data on individual entities such as enterprises, people or in a combination of both (Linked Employer-Employee Data). MDL is one of the most powerful methods to answer urgent questions on emerging policy or research topics such as the interconnectedness of the economies and its consequences for jobs, income and growth. Not only for national purposes, where economic behavior and dynamics by enterprises can be expressed in terms of job dynamics, income and welfare for its citizens, but also from an international perspective where consistent and coherent indicators play an important role. Firstly, the article addresses the central role of the Business Register for any MDL approach related to business statistics; secondly the current official statistics based on MDL such as Trade by Enterprise Characteristics (TEC) are described. Thirdly, the most common use of MDL in terms of producing experimental statistics are described, including linked employer-employee data (LEED).