bluuebunny/crossref_metadata_2025 huggingface dataset:
Is Saline Injection a True Sham/Placebo Treatment in Randomized Controlled Trials? A Systematic Review
Authors: Mekhail Nagy , Mekhail Nagy , Saweris Youssef , Acevedo-Moreno Lou-Anne
Published: July 2021

Objective: To explore whether saline is a real sham/placebo agent, or it has potential therapeutic effects when used as control treatment in randomized controlled trials for the management of discogenic low back pain.
Methods: A comprehensive literature search was conducted investigating the effects of saline as a placebo in the treatment of chronic pain when administered into the intervertebral disc. Following stepwise filtering, selected articles were assessed for their levels of evidence, followed by a discussion of their contribution to the understanding of the role of saline in chronic pain management.
Results: Out of 95 articles that described the administration of intradiscal saline solution used as a placebo for chronic pain management, 8 articles met all of the inclusion criteria. Their levels of evidence ranged from 1a to 4 (Oxford Centre CEBM). Intradiscal administration of saline solution was found to have measurable therapeutic benefits. In some studies, the pain relief was similar to that provided by local anaesthetics and steroids.
Conclusion: Although the exact mechanism of the analgesic effects of saline is not clear, yet the use of intradiscal saline appears to have some analgesic benefits like local anaesthetics and steroids when used individually. Researchers should practice caution when designing RCTs using intradiscal saline injection as a sham/placebo treatment for the control arm or maybe, when possible, avoid the use of intradiscal saline injection as a sham treatment.
FLOTATION MULTISTAGE AND GENERALIZED MODELS OF THE PROCESS HARVESTERS OF KSENOFONTOV TYPE AND FOR SPECIAL PURPOSE
Authors: Ksenofontov Boris
Published: July 2021

A multistage and generalised flotation model, suggested more than 30 years ago by the author, is considered in a wide aspect for the first time in world literature for reader’s attention in monography. The possibilities of its usage are shown in different directions of water flotation purification, sediment thickening and enrichment of minerals. We have shed a light widely on matters concerning new flotation equipment as flotation harvesters of KBS type and for special purposes, which are developed on the basis of flotation process multistage and generalized models. Perspectives and intensification ways of water purification flotation processes are pointed out.
It is suggested for a wide range of readers, including researches, Higher education teachers, PhD students, Masters and Bachelors, Graduate students.
Synopsis on Non-invasive Ventilation in Neonatology
Authors: Mehta Akshay
Published: July 2021

Non-invasive ventilation (NIV) is a mode of respiratory support commonly used on the neonatal unit. Since the advent of NIV, it has evolved from being used as a mode of respiratory support to wean infants from mechanical ventilation (MV) to a primary mode of respiratory support. NIV improve the functional residual capacity in the newborn (at term or preterm) avoiding invasive actions such as tracheal intubation. Newer methods of NIV support such as nasal bilevel positive airway pressure (BiPAP) and humidified high flow nasal cannula oxygen therapy (HHFNC) have emerged in attempts to reduce intubation rates and subsequent MV in preterm infants. With this synopsis, we aim to discuss various available NIV modes of ventilation in Neonatology, including indications, physiological principle, practical aspects and effects on important short and long-term morbidities associated with the use of NIV.
Extensive Angiokeratomas of the Trunk-An Unsual Presentation of Fabry Disease
Authors: Panigrahi Inusha
Published: July 2021

A 23 year old man presented with erythematous non-pruritic rash over his trunk since childhood. Family history was non-contributory, and he did not have any major or chronic medical illness in the past. There was no history suggestive of paresthesias, exertional dyspnea or focal neurological deficit.
News consumption and COVID-19: Social perception
Authors:   Ramon Llull University, Masip Pere , Ruiz Caballero Carlos , Suau Jaume
Published: July 2021

On 14 March 2020, the Spanish Government declared a state of alert for the first time since the reinstatement of democracy, confining millions of people to their homes in effort to combat the COVID-19 pandemic. Amid emergency situations such as this, people need to be informed (Seeger et al., 2003). In light of this demand for information, the media responded by heightening the attention afforded to the pandemic and its news coverage, a phenomenon which has occurred repeatedly in similar situations (Ducharme, 2020; Pieri, 2018). Based on a survey of over 2,000 Catalan citizens during the period of lockdown, the main aim of this article is to analyse how the pandemic changed their information habits, gauge their perception of the media’s coverage and determine whether this coverage produced information overload. The results show an upsurge in media consumption as well as information overload among virtually half the population. This generates a paradox: despite the increased consumption of information, the media did not help to improve people’s understanding of the pandemic, but instead resulted in information fatigue, thus hindering comprehension.
Elecciones en Estados Unidos: representaciones sociales del voto latino en favor de Trump a través del análisis lexicométrico de la prensa digital en Facebook
Authors:   Dirección General de Cultura y Educación, Adolfo Liloff Gustavo , Ortega Rubí María Estela ,   Universidad Autónoma de Nuevo León
Published: July 2021

La presente investigación tuvo como objetivo identificar las representaciones sociales del voto latino en favor de Trump a partir del análisis del post publicado luego de las elecciones por el medio digital PlayGround, como así también revelar las explicaciones que se les atribuyen. Se plantea una metodología exploratoria con un diseño explicativo secuencial cuantitativo–cualitativo. La muestra estuvo compuesta por 1 396 comentarios directos —419 mujeres, 890 varones y 87 indeterminados—, tomados desde la página de Facebook del medio digital en cuestión, bajo el posteo “Pregunta para latinoamericanos”. Se evidencian organizaciones representacionales diferentes según el género de los participantes. Para los varones el voto latino hacia Trump surge como respuesta a la percepción de un otro latino, indocumentado o ilegal, como un extranjero enemigo, aquella competencia que hay que eliminar, mientras que para las mujeres surge a partir de la figura latina que se ve como superior por haber resuelto su estatus legal, a la vez que se expone con una visión clasista xenófoba de un nacionalismo exacerbado. En ambos casos, el miedo a repetir historias del comunismo o socialismo de sus países de origen se transforma en un fuerte proveedor de identidad del votante latino de Trump.
Pobreza y espacios abandonados: análisis enfocado en tres ciudades fronterizas del norte de México
Authors: Curzio Claudio
Published: July 2021

Los espacios abandonados pueden comprenderse como un fenómeno urbano relacionado con aquellas edificaciones obsoletas que han ido perdiendo gradualmente su condición de habitabilidad. De esta manera, el objetivo del presente artículo es analizar cuál es la naturaleza de su distribución geo–espacial, haciendo énfasis en exponer y examinar la correlación que existe entre este fenómeno y la desigualdad socio–económica de la población. Para ello, se presenta un análisis geo–espacial enfocado en tres ciudades fronterizas del norte de México —Mexicali, Nuevo Laredo y San Luis Río Colorado—. Se implementó una metodología descriptivo–comparativa basada en efectuar levantamientos digitales y físicos de los espacios abandonados, con lo cual se conformaron bases de datos para cada una de las ciudades. De este modo, usando técnicas de estadística y análisis espacial fue posible obtener los modelos y resultados. Como conclusión, se presenta una discusión comparativa entre las tres ciudades, mostrando las diferencias y coincidencias detectadas.
Pengaruh Brand Trust dan Celebrity Endorse terhadap Keputusan Pembelian Konsumen
Authors:   Universitas Pendidikan Indonesia, Isfahami Mubdi Mulya , Hurriyati Ratih ,   Universitas Pendidikan Indonesia, Dirgantari Puspo Dewi ,   Universitas Pendidikan Indonesia
Published: July 2021

Penelitian ini bertujuan untuk mengetahui pengaruh brand trust dan celebrity endorse terhadap keputusan pembelian dari konsumen. Saat ini banyak bisnis yang mencoba menerapkan strategi viral marketing, tetapi masih banyak yang tidak mendukung tujuan penerapan strategi itu sendiri. Strategi viral marketing yaitu: trust brand dan celebrity endorsment. bisnis yang membuat strategi viral marketing sebagai cara untuk memasarkan dan memperkenalkan produknya adalah Zhofira. Teori yang digunakan dalam penelitian ini adalah Viral marketing, keputusan pembelian, kepercayaan merek, dan celebrity endorsement sebagai peneliti untuk melakukan penelitian, untuk membuktikan pengaruh (X1) kepercayaan merek, (X2) celebrity endorsement terhadap (Y) keputusan pembelian. Metode penelitian yang digunakan adalah metode kuantitatif. Penelitian ini menggunakan sampel dari populasi sebanyak 384 responden, teknik pengambilan sampel yang digunakan adalah probability sampling yaitu teknik pengambilan sampel yang memberikan kesempatan sama bagi setiap unsur populasi untuk dipilih menjadi sampel. Penulis melakukan analisis regresi berganda dan menguji asumsi klasik sebelum melakukan analisis regresi berganda. Pada hasil analisis regresi berganda menunjukkan hasil “R Square” 0,515 atau 51,5%, yang dapat diartikan sebagai variabel (X1) dan (X2) mempunyai pengaruh terhadap (Y) sebesar 51,5%. Sedangkan sisanya 48,5% ada faktor lain. Hal ini dapat dinyatakan tidak relevan lagi karena nilai faktor lebih besar dari (X1) dan (X2).
The Use of Mobile Applications as Communication Aids for People with Dementia: Opportunities and Limitations
Authors: Ambegaonkar Anjay , Ritchie Craig , de la Fuente Garcia Sofia
Published: July 2021

Background:

Communication difficulties are one of the primary symptoms associated with dementia, and mobile applications have shown promise as tools for facilitating communication in patients with dementia (PwD). The literature regarding mobile health (mHealth) applications, especially communications-based mHealth applications, is limited.

Objective:

This review aims to compile the existing literature on communications-based mobile applications regarding dementia and assess their opportunities and limitations. A PICO framework was applied with a Population consisting of PwD, Interventions consisting of communication technology, focusing primarily on mobile applications, Comparisons between patient well-being with and without technological intervention, and Outcomes that vary but can include usability of technology, quality of communication, and user acceptance.

Methods:

Searches of PubMed, IEEE XPLORE, and ACM Digital Library databases were conducted to establish a comprehensive understanding of the current literature on dementia care as related to 1) mobile applications, 2) communication technology, and 3) communications-based mobile applications. Applying certain inclusion and exclusion criteria, yielded a set of articles ( n = 11).

Results:

The literature suggests that mobile applications as tools for facilitating communication in PwD are promising. Mobile applications are not only feasible socially, logistically, and financially, but also produce meaningful communication improvements in PwD and their caregivers. However, the number of satisfactory communications-based mobile applications in the mHealth marketplace and their usability is still insufficient.

Conclusion:

Despite favorable outcomes, more research involving PwD using these applications are imperative to shed further light on their communication needs and on the role of mHealth.
Morphological Study of Fabricated PVDF Based Hydrophobic Membrane for Different Additives and Coagulation Bath Temperature
Authors: Yadav Meenakshi , Upadhyaya Sushant , Singh Kailash , Vashishtha Manish
Published: July 2021

The demand of membrane distillation (MD) has increased since last few decades for numerous applications. The membrane used in MD is hydrophobic; therefore, the focus has been emphasised on the development of a suitable membrane with desired microstructure. In this study, the flat sheet hydrophobic membrane of suitable properties has been casted with various additives such as water, ethane-di-ol, and propan-2-ol in dope solution using a non-solvent induced phase separation (NIPS) technique. The effect of water content in dope solution has been studied on casted membrane porosity and contact angle. The maximum contact angle and porosity were found to be 96° and 53.23% at 4 weight percent of water content in dope solution of PVDF polymer and di.methyl.acetamide as solvent. It was found that SEM micrograph when ethane-di-ol and propan-2-ol are used as an additive shows more finger-like pores and nodules, respectively, in the microstructure of the casted membrane. Furthermore, synergistic effects using water with other additives were also identified using SEM micrograph of casted membrane and it was observed that water with ethane-di-ol and propan-2-ol form contact angle of 98° and 105°, respectively, for 2 weight percent each additive in dope. In this study, the membrane was also cast by dissolving PVDF powder in di.methyl.acetamide solvent with lithium chloride and the effect of the temperature difference between coagulation bath and film temperature was investigated using an SEM micrograph. Overall, it was found that water content and temperature difference aid in developing hydrophobic porous membrane of desired properties for MD applications.
The Effect of Environmentally Safe Nanosynthesis with Copper Particles by using Citrus aurantium Fruit Extract Against Harmful Mosquitoes
Authors: Al-Hamdani Hamdia , Ahmed Sundus , Hameed Rasha , Getan Azhar
Published: July 2021

The global agricultural sector suffers from the damage due to insect pests on plants all over the world, forcing farmers to indiscriminately use mineral fertilisers and chemical pesticides that are toxic and affecting the environment, plants and animals, as well as causing serious health problems to farmers and consumers. Therefore, scientists and researchers were forced to investigate and discover new methods until they explored the field of nanoscience, which shows great potential for applications in diverse fields, including agriculture, chemicals and plant protection by controlling harmful insect pests. Therefore, a biological method was used to synthesise the laboratory-prepared copper nanoparticles that were tested on both eggs and larvae phases in mosquitoes as nanocides using acidic Citrus Nargin broth (10%). A total of 1 mM aqueous CuSO4 of the plant extract was reduced and synthesised into stable copper nanoparticles with an average size of less than 450 nm. Then, the composition, size and percentage of the synthesised nanoparticles were determined using ultraviolet spectroscopy, X-ray diffraction, FTIR, SEM and AFM techniques. The results of this study showed high mortality and a significant difference ( P ≤ 0.01) for mosquito eggs and larvae when exposed to the nanocide, than those found in the extract of bitter leaves and the regular copper sulfate insecticide used in agriculture against insects. The results also showed a high relative efficiency of 100% with a highly significant difference ( P ≤ 0.01) after exposure of insects and their different stages to the nanocide after 72 hours. We conclude from this study the possibility of synthesising nanoparticles using mineral compounds with natural, safe and environmentally friendly bio-plant extracts as insecticides for various agricultural pests.
Influence of Vermicompost, Seaweed Extract and Nitrogen Fertilisers on Maize (Zea mays L.) Soil Rhizosphere Microbes
Authors: Al-Taweel Luma S. , Al-Budairy Zahraa J.
Published: July 2021

This study shows the influence of adding vermicompost fertiliser, seaweed extracts and nitrogen fertiliser on the number of total bacteria in the soil in the stages of flowering and full maturity. An experiment was carried out in one of the farms located in Nouriya district - Al-Diwaniyah governorate - Iraq, during the autumn season 2019-2020.

The experiment was applied in the order of global experiments, according to the design of full randomised sectors, with three replications. The first factor included adding vermicompost fertiliser with three levels (0, 2 and 4 tons h-1) which were coded as A0, A1 and A2, respectively. The second factor included adding seaweed extract at two levels (0 and 40 kg h-1), which were symbolised by B0 and B1, respectively. The third factor included adding nitrogen fertiliser in three levels (0, 120 and 240 kg N h-1) symbolised as C0, C1 and C2, respectively.

The levels of vermicompost differed significantly in most of the studied descriptions, as level A2 scored the highest results for the number of bacteria in the two stages of flowering and full maturity, i.e., 88.59 × 107 and 111.54 × 107 CFU gm-1 of dry soil. The treatment of adding seaweed extracts B1 significantly affected the number of bacteria in the two stages of flowering and full maturity (80.04 × 107 and 103.29 × 107 CFU gm-1 dry soil).

The treatment of adding C1 nitrogen fertiliser was significantly superior to the number of bacteria in the flowering stage (81.40 × 107 CFU gm-1 dry soil).
The Effect of Fertilisation using Trichoderma harzianum and Cow Manure on Releasing CO2 in Two Soils with Different Textures in North of Iraq
Authors: Harbawi Mohammed Ayad , AlBadrani Waheeda Ali , Ghazal Rand Abdulhadi
Published: July 2021

laboratory experiment was conducted using the completely randomised design (CRD) to estimate CO2 released from the soil in one of the labs at the College of Agriculture and Forestry using two levels of fungus inoculant (without inoculation and inoculation with Trichoderma harzianum) with 10 cm3, and two levels of cow amendment (without manuring and 2% manure). This experiment was conducted in two types of soils, the first was taken from one of the fields at the College of Agriculture and Forestry and the second soil was taken from the Zaweetah area, which is planted with pine trees targeting the release of CO2 that is considered as an indicator of the biological decomposition of organic fertilisers. The experimental units were incubated for 10, 20, 40 and 60 days at a temperature of 28°C ±2. The results showed that inoculating the soil with T. harzianum fungus, in the presence of the organic amendment (cow manure), gave the highest value of CO2 release at the incubation period of 40 days for both soils as the CO2 release values were 52.9 and 57.7 mg.100 g–1 soil. Moreover, a decrease in the quantity of CO2 released was observed at the incubation period of 60 days in both soils.
Activity recognition from smartphone data using weighted learning methods
Authors: Abidine M’hamed Bilal , Fergani Belkacem
Published: July 2021

Mobile phone based activity recognition uses data obtained from embedded sensors to infer user’s physical activities. The traditional approach for activity recognition employs machine learning algorithms to learn from collected labeled data and induce a model. To enhance the accuracy and hence to improve the overall efficiency of the system, the good classifiers can be combined together. Fusion can be done at the feature level and also at the decision level. In this work, we propose a new hybrid classification model Weighted SVM-KNN to perform automatic recognition of activities that combines a Weighted Support Vector Machines (WSVM) to learn a model with a Weighted K-Nearest Neighbors (WKNN), to classify and identify the ongoing activity. The sensory inputs to the classifier are reduced with the Linear Discriminant Analysis (LDA). We demonstrate how to train the hybrid approach in this setting, introduce an adaptive regularization parameter for WSVM approach, and illustrate how our method outperforms the state-of-the-art on a large benchmark datasets.
New hybrid AHP-QFD-PROMETHEE decision-making support method in the hesitant fuzzy environment: an application in packaging design selection
Authors: Porto de Lima Byanca , da Silva Aneirson Francisco , Marins Fernando Augusto Silva
Published: July 2021

This paper presents a new hybrid decision-making support method (New Hesitant Fuzzy AHP-QFD-PROMETHEE II Method), which jointly uses the Analytic Hierarchy Process (AHP), the Quality Function Deployment (QFD) and the Preference Ranking Method for Enrichment Evaluation (PROMETHEE II), as well as the Hesitant Fuzzy Linguistic Term Sets (HFLTS) to capture hesitation and aggregate divergent opinions from different experts. A real application of the new method to a packaging design selection problem for an automotive company is described, finding that AHP assisted in determining the importance of QFD’s customer requirements (CRs) and PROMETHEE II was used to select the best packaging design. With this same problem, for the purpose of validating the proposed method, a comparative analysis was made with the use of the Hesitant Fuzzy AHP-QFD-TOPSIS method and also with the traditional AHP-QFD-PROMETHEE method, which makes it impossible to capture the hesitation of decision makers. The result showed similarity in the rankings of design alternatives found in the three methods application. The proposed method proved advantageous for solving problems that can generally be solved with the QFD House of Quality but have serious difficulties when decision makers have divergent opinions and hesitate in evaluating criteria and alternatives.
Dimensionality reduction of tensor data based on local linear embedding and mode product
Authors: Gao Ting , Ma Zhengming , Gao Wenxu , Liu Shuyu
Published: July 2021

There are three contributions in this paper. (1) A tensor version of LLE (short for Local Linear Embedding algorithm) is deduced and presented. LLE is the most famous manifold learning algorithm. Since its proposal, various improvements to LLE have kept emerging without interruption. However, all these achievements are only suitable for vector data, not tensor data. The proposed tensor LLE can also be used a bridge for various improvements to LLE to transfer from vector data to tensor data. (2) A framework of tensor dimensionality reduction based on tensor mode product is proposed, in which the mode matrices can be determined according to specific criteria. (3) A novel dimensionality reduction algorithm for tensor data based on LLE and mode product (LLEMP-TDR) is proposed, in which LLE is used as a criterion to determine the mode matrices. Benefiting from local LLE and global mode product, the proposed LLEMP-TDR can preserve both local and global features of high-dimensional tenser data during dimensionality reduction. The experimental results on data clustering and classification tasks demonstrate that our method performs better than 5 other related algorithms published recently in top academic journals.
The first named entity recognizer in Maithili: Resource creation and system development
Authors: Priyadarshi Ankur , Saha Sujan Kumar
Published: July 2021

In this paper, we present our effort on the development of a Maithili Named Entity Recognition (NER) system. Maithili is one of the official languages of India, with around 50 million native speakers. Although various NER systems have been developed in several Indian languages, we did not find any openly available NER resource or system in Maithili. For the development, we manually annotated a Maithili NER corpus containing around 200K words. We prepared a baseline classifier using Conditional Random Fields (CRF). Then we ran many experiments using various recurrent neural networks (RNN). We collected larger raw corpus to obtain better word embedding and character embedding. In our experiments, we found, neural models are better than CRF; a CRF layer is effective for the prediction of the final output in the RNN models; character embedding is effective in Maithili language. We also investigated the effectiveness of gazetteer lists in neural models. We prepared a few gazetteer lists from various web resources and used those in the neural models. The incorporation of the gazetteer layer caused performance improvement. The final system achieved an f-measure of 91.6% with 94.9% precision and 88.53% recall.
A new cross-efficiency ranking method in group decision making environment
Authors: Wu Meiqin , Hou Xiaoqing , Fan Jianping
Published: July 2021

As an extended model of data envelopment analysis (DEA), cross-efficiency is utilized in various fields. It applies the relative evaluation values of homogeneous decision-making units (DMUs) in ranking completely. Due to the non-uniqueness of DEA optimal weights, and the cross-efficiency methods focus on the aggregation of cross-efficiency matrix values. There is a low correction between the final efficiency evaluation value and the value assigned to the criteria weight or the value of the criteria. This paper uses the modified aggressive cross-efficiency model to calculate the weights of the decision-making units (DMUs). Then according to the consistency of peer-evaluation criteria, the entropy weight method is used to aggregate the index weights to generate a set of common weights, and a new Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method is used to calculate the utility score of DMUs that can rank them completely. Finally, a numerical case of supplier selection is offered to illustrate the feasibility and effectiveness of the proposed method.
Outburst prediction and influencing factors analysis based on Boruta-Apriori and BO-SVM algorithms
Authors: Zixian Zhang , Xuning Liu , Zhixiang Li , Hongqiang Hu
Published: July 2021

The influencing factors of coal and gas outburst are complex, and now the accuracy and efficiency of outburst prediction are not high. In order to obtain the effective features from influencing factors and realize the accurate and fast dynamic prediction of coal and gas outburst, this article proposes an outburst prediction model based on the coupling of feature selection and intelligent optimization classifier. Firstly, in view of the redundancy and irrelevance of the influencing factors of coal and gas outburst, we use Boruta feature selection method to obtain the optimal feature subset from influencing factors of coal and gas outburst. Secondly, based on Apriori association rules mining method, the internal association relationship between coal and gas outburst influencing factors is mined, and the strong association rules existing in the influencing factors and samples that affect the classification of coal and gas outburst are extracted. Finally, svm is used to classify coal and gas outburst based on the above obtained optimal feature subset and sample data, and Bayesian optimization algorithm is used to optimize the kernel parameters of svm, and the coal and gas outburst pattern recognition prediction model is established, which is compared with the existing coal and gas outburst prediction model in literatures. Compared with the method of feature selection and association rules mining alone, the proposed model achieves the highest prediction accuracy of 93% when the feature dimension is 3, which is higher than that of Apriori association rules and Boruta feature selection, and the classification accuracy is significantly improved. However, the feature dimension decreased significantly, the results show that the proposed model is better than other prediction models, which further verifies the accuracy and applicability of the coupling prediction model.
Application of catastrophe progression method on hybrid multiple attribute decision-making problems based on regret theory
Authors: Zhang Nian , Pan Qian , Wei Guiwu
Published: July 2021

In order to effectively solve the decision-making problems with the diversity of evaluation information, the dynamics of research objects, the limitations of subjective authorization, and the irrational behavior of decision-makers, this paper extends catastrophe progression method to solve hybrid multiple attribute decision-making problems based on regret theory. Firstly, some basic theories are introduced. Secondly, the original catastrophe progression method is extended by using the regret theory, which is employed to solve the multiple attribute decision-making problems with hybrid evaluation information. Finally, a real-life case study of selecting a fresh cold chain logistics service provider is used to verify the practicality and effectiveness of the proposed method, and a comparative analysis with the TOPSIS method and the sensitivity of the regret avoidance coefficient is analyzed in this article.
Complex hesitant fuzzy sets and its applications in multiple attributes decision-making problems
Authors: Talafha Mohammad , Alkouri Abd Ulazeez , Alqaraleh Sahar , Zureigat Hamzeh , Aljarrah Anas
Published: July 2021

Decision-makers (DMs) usually face many obstacles to give the right decision, multiplicity of them highlights a problem to represent a set of potential values to assign a collective membership degree of an object to a set for several DM’s opinions. However, a hesitant fuzzy set (HFS) deals with such problems. The complexity appears in DM’s opinion which can be changed for the same object but with different times/phases. Each of them has a set of potential values in different times/phases of an object. In this paper, the periodicity of hesitant fuzzy information is studied and applied by extending the range of HFS from [0, 1] to the unit disk in the complex plane to provide more ability for illustrating the full meaning of information to overcome the obstacles in decision making in the mathematical model. Moreover, the advantage of complex hesitant fuzzy set (CHFS) is that the amplitude and phase terms of CHFSs can represent hesitant fuzzy information, some basic operations on CHFS are also presented and we study its properties, in addition, several aggregation operators under CHFS are introduced, also, the relation between CHFS and complex intuitionistic fuzzy sets (CIFS) are presented. Finally, an efficient algorithm with a consistent process and an application in multiple attributes decision-making (MADM) problems are presented to show the effectiveness of the presented approach by using CHFS aggregation operators.
Segmentation of wood cell in cross-section using deep convolutional neural networks
Authors: Ergun Halime
Published: July 2021

Fiber and vessel structures located in the cross-section are anatomical features that play an important role in identifying tree species. In order to determine the microscopic anatomical structure of these cell types, each cell must be accurately segmented. In this study, a segmentation method is proposed for wood cell images based on deep convolutional neural networks. The network, which was developed by combining two-stage CNN structures, was trained using the Adam optimization algorithm. For evaluation, the method was compared with SegNet and U-Net architectures, trained with the same dataset. The losses in these models trained were compared using IoU (Intersection over Union), accuracy, and BF-score measurements on the test data. The automatic identification of the cells in the wood images obtained using a microscope will provide a fast, inexpensive, and reliable tool for those working in this field.
An improved low-complexity DenseUnet for high-accuracy iris segmentation network
Authors: Zhou Weibin , Chen Tao , Huang Huafang , Sheng Chang , Wang Yangfeng , Wang Yang , Zhang Daqiang
Published: July 2021

Iris segmentation is one of the most important steps in iris recognition. The current iris segmentation network is based on convolutional neural network (CNN). Among these methods, there are still problems with the segmentation networks such as high complexity, insufficient accuracy, etc. To solve these problems, an improved low complexity DenseUnet is proposed to this paper based on U-net for acquiring a high-accuracy iris segmentation network. In this network, the improvements are as follows: (1) Design a dense block module that contains five convolutional layers and all convolutions are dilated convolutions aimed at enhancing feature extraction; (2) Except for the last convolutional layer, all convolutional layers output feature maps are set to the number 64, and this operation is to reduce the amounts of parameters without affecting the segmentation accuracy; (3) The solution proposed to this paper has low complexity and provides the possibility for the deployment of portable mobile devices. DenseUnet is used on the dataset of IITD, CASIA V4.0 and UBIRIS V2.0 during the experimental stage. The results of the experiments have shown that the iris segmentation network proposed in this paper has a better performance than existing algorithms.
Roles of Non-Coding RNAs as Novel Diagnostic Biomarkers in Parkinson’s Disease
Authors: Manna Ida , Quattrone Andrea , De Benedittis Selene , Iaccino Enrico , Quattrone Aldo
Published: July 2021

Parkinson’s disease (PD) is the second most common neurodegenerative disorder, affecting 5%of the elderly population. Currently, the diagnosis of PD is mainly based on clinical features and no definitive diagnostic biomarkers have been identified. The discovery of biomarkers at the earliest stages of PD is of extreme interest. This review focuses on the current findings in the field of circulating non-coding RNAs in PD. We briefly describe the more established circulating biomarkers in PD and provide a more thorough review of non-coding RNAs, in particular microRNAs, long non-coding RNAs and circular RNAs, differentially expressed in PD, highlighting their potential for being considered as biomarkers for diagnosis. Together, these studies hold promise for the use of peripheral biomarkers for the diagnosis of PD.
Green synthesis of methadone in eutectic solvent
Authors: Golzadeh Robab , Mahkam Mehrdad , Rezaii Ebrahim , Nazmi Miardan Leila
Published: July 2021

Eutectic solvents (DES), have attracted much attention in the last decade. With the advantages of nonflammability, thermal and chemical stability, high solubility and partial vapor pressure, non-toxicity and reasonable prices, these solvents are suggested as useful solvents. On the other hand, the eutectic solvents developed by Abbott are the new generation of ionic liquids. The mixture of eutectics is from an ammonium salt and a hydrogen bonding compound such as urea, acid, amine, and non-toxic amines. Choline chloride and urea, are quite environmentally friendly and are known practically as green solvents. The purpose of the present research is to present the synthesis of diphenyl acetonitrile with 1-dimethylamino-2-chloropropane by a eutectic’s solvent. In addition, methadone is synthesized from the reaction of 2,2-Diphenyl-4-dimethylaminovaleronitrile with ethyl magnesium bromide in the presence of solvent eutectic, which is in optimal and environmentally compatible conditions and by principles of green chemistry.
Testing of photocatalytic potential of silver nanoparticles produced through nonthermal plasma reduction reaction and stabilized with saccharides
Authors: Altaf N.U.H. , Naz M.Y. , Shukrullah S. , Bhatti H.N.
Published: July 2021

In this study, silver nanoparticles (AgNPs) were produced through an atmospheric pressure plasma reduction reaction and tested for photodegradation of methyl blue (MB) under sunlight exposure. The argon plasma born reactive species were used to reduce silver ions to AgNPs in the solution. Glucose, fructose and sucrose were also added in the solution to stabilize the growth process. The glucose stabilized reaction produced the smallest nanoparticles of 12 nm, while sucrose stabilized reaction produced relatively larger nanoparticles (14 nm). The nanoparticles exhibited rough morphology and narrow diameter distribution regardless of stabilizer type. The narrow diameter distribution and small band gap helped activating majority of nanoparticles at a single wavelength of light spectrum. The band gap energy of AgNPs varied from 2.22 eV to 2.41 eV, depending on the saccharide type. The photoluminescence spectroscopy of AgNPs produced emission peaks at 413 nm, 415 nm, and 418 nm. The photocatalytic potential of AgNP samples was checked by degrading MB dye under sunlight. The degradation reaction reached a saturation level of 98% after 60 min of light exposure.
Simultaneously stimulating both brain hemispheres by rTMS in patients with unilateral brain lesions decreases interhemispheric asymmetry
Authors: Zhong Yuhua , Fan Jianzhong , Wang Huijuan , He Renhong
Published: July 2021

Background:

Interhemispheric asymmetry caused by brain lesions is an adverse factor in the recovery of patients with neurological deficits. Repetitive transcranial magnetic stimulation (rTMS) has been shown to modulate cortical oscillation and proposed as an approach to rebalance the symmetry, which has not been documented well.

Objective:

In this study, we investigated the influence of repetitive transcranial magnetic stimulation (rTMS) on EEG power in patients with unilateral brain lesions by simultaneously stimulating both brain hemispheres and to elucidate asymmetrical changes in rTMS-induced neurophysiological activity.

Methods:

Fourteen patients with unilateral brain lesions were treated with one active and one sham session of 10 Hz rTMS over the vertex (Cz position). Resting-state EEGs were recorded before and immediately after rTMS. The brain symmetry index (BSI), calculated from a fast Fourier transform, was employed to quantify the power asymmetry in both hemispheres and paired channels over the entire range and five frequency bands (delta, theta, alpha, beta and gamma bands).

Results:

Comparison between active and sham sessions demonstrated rTMS-induced EEG after-effects. rTMS in the active session significantly reduced the BSI in patients with unilateral brain lesions over the entire frequency range ( t = 2.767, P = 0.016). Among the five frequency bands, rTMS only induced a noticeable decrease in the BSI in the delta band ( t = 2.254, P = 0.042). Furthermore, analysis of different brain regions showed that significant changes in the BSI of the alpha band were only demonstrated in the posterior parietal lobe. In addition, EEG topographic mapping showed a decreased power of delta oscillations in the ipsilesional hemisphere, whereas distinct cortical oscillations were observed in the alpha band around the parietal-occipital lobe in the contralesional hemisphere.

Conclusions:

When both brain hemispheres were simultaneously activated, rTMS decreased interhemispheric asymmetry primarily via reducing the delta band in the lesioned hemisphere.
Sequential linked data: The state of affairs
Authors: Daga Enrico , Meroño-Peñuela Albert , Motta Enrico
Published: July 2021

Sequences are among the most important data structures in computer science. In the Semantic Web, however, little attention has been given to Sequential Linked Data. In previous work, we have discussed the data models that Knowledge Graphs commonly use for representing sequences and showed how these models have an impact on query performance and that this impact is invariant to triplestore implementations. However, the specific list operations that the management of Sequential Linked Data requires beyond the simple retrieval of an entire list or a range of its elements – e.g. to add or remove elements from a list –, and their impact in the various list data models, remain unclear. Covering this knowledge gap would be a significant step towards the realization of a Semantic Web list Application Programming Interface (API) that standardizes list manipulation and generalizes beyond specific data models. In order to address these challenges towards the realization of such an API, we build on our previous work in understanding the effects of various sequential data models for Knowledge Graphs, extending our benchmark and proposing a set of read-write Semantic Web list operations in SPARQL, with insert, update and delete support. To do so, we identify five classic list-based computer science sequential data structures (linked list, double linked list, stack, queue, and array), from which we derive nine atomic read-write operations for Semantic Web lists. We propose a SPARQL implementation of these operations with five typical RDF data models and compare their performance by executing them against six increasing dataset sizes and four different triplestores. In light of our results, we discuss the feasibility of our devised API and reflect on the state of affairs of Sequential Linked Data.
Estrés y actitudes resilientes en el personal de la Policía Nacional del Perú frente a la pandemia causada por el covid-19
Authors: Marquina-Lujan Román Jesús , Benavides-Román Alexander Masías
Published: July 2021

El objetivo del presente estudio busca establecer la relación que existe entre la capacidad resiliente y el estrés que presentan los efectivos policiales que se desempeñan en la ciudad de Lima en tiempos de COVID-19. Para el presente estudio se utilizó la escala de resiliencia de 14 ítems (RS-14) la cual presenta una confiabilidad mediante alfa de Cronbach de .787 y para medir el estrés se elaboró un cuestionario en relación a la sintomatología que desarrolla una persona estresada en su dimensión física, psicología y conductual, encontrando una fiabilidad de .912 La muestra estuvo conformada por 408 efectivos policiales en actividad, de los cuales son 219 varones y 189 mujeres. Los resultados nos indican que existe relación entre la actitud resiliente y el estrés en los agentes policiales de Lima Metropolitana frente a la pandemia causada por la presencia del COVID-19. Se concluye que la capacidad resiliente ubicada en un nivel medio permite adaptarse a las exigencias del contexto y tener un desempeño adecuado, a pesar de los niveles alto de estrés encontrados.
Policy Forum: Future Workforce Models—Enabling the Shift
Authors: Jason Guy , Joshi Shivani
Published: July 2021

Embracing remote work, either fully or partially, allows employers to offer the flexibility that many employees are seeking and also to benefit from various cost savings. As organizations embark on this journey, there are many tax and payroll considerations that should be reviewed up front so that risks relating to non-compliance can be mitigated. What are these corporate tax and payroll considerations? Can a formal policy help to mitigate compliance risks? What are other considerations besides tax and payroll? Should government policy support this workforce shift? This article summarizes the various considerations in developing an organizational remote work policy and concludes that, as is the case for any other business strategy, remote work arrangements have their benefits provided that the associated complexity and costs can be effectively managed.
Towards Effective Implementation of the English for Academic Purposes (EAP) Curriculum in Tertiary Institutions in Nigeria
Authors: Ihejirika Richard C. , Ogu Anthony E. , Agwuocha Ugomma A. , Okere Mary C.
Published: July 2021

One defect that is easily noticed among some students of university, polytechnic or college of education in Nigeria in contemporary time is their inability to communicate effectively in both spoken and written English even though they are taught English for Academic Purposes (EAP) either in form of “Use of English” or “General English” depending on the institution before they graduate. Linguistic scholars have considered the defect as one of the major factors responsible for poor academic performance among some students of tertiary institutions in Nigeria. In recognition of this fact, this paper primarily reviewed the state of affairs with regard to the implementation of the EAP curriculum in tertiary institutions and pointed out some of the anomalies. The paper, however, highlighted some measures considered to be adequate and appropriate for the effective implementation of the EAP curriculum in tertiary institutions in Nigeria.
Selection Satisfaction and Attributes of Higher Education Institutions: Mediation of Information Satisfaction
Authors: Buamonabot Irfandi , Arilaha Muhammad Asril , Fahri Johan
Published: July 2021

This study aimed to examine the satisfaction of information availability in mediating the relationship between attributes of and satisfaction in choosing higher education institutions for Universitas Khairun and IAIN Ternate. A total of 252 students from both institutions were involved in this survey, with an effective rate of return of 70%. A hierarchical regression analysis was run by using IBM SPSS version 24. The results showed that for Universitas Khairun, information satisfaction fully mediates the relationship between attributes and satisfaction in choosing a university. In contrast, for IAIN Ternate, information satisfaction is only an independent variable. The implications and suggestions for further research are also discussed in this study.
ORGANIZATION OF EXPERIMENTAL RESEARCH DETERMINATION OF TRANSPORT NOISE LEVELS AT THE FIRST STAGE OF MOTORING
Authors: Reshetchenko A.
Published: July 2021

Monitoring studies are considered as a system of environmental monitoring of anthropogenic factors, based on the legislation of Ukraine, monitoring of physical impacts, including noise levels, are necessary and important in terms of monitoring the air of populated cities. The article is devoted to the substantiation of the need to organize monitoring observations of the levels of noise pollution caused by the operation of vehicles in the residential areas of populated cities. Scientific and methodological approaches to the organization of experimental research to determine noise levels as the first stage of the monitoring system have been developed. The state normative-legal base is analyzed, it is revealed that the organization of experimental researches of noise characteristics of the transport streams going along apartment houses, isn't fixed at the legislative level. The system of monitoring noise pollution levels should consist of the following main stages: 1) data collection (measurement of noise levels in the field); 2) processing of the received data (calculation of equivalent and maximum noise levels on the basis of the received field observations); 3) compilation of databases; 4) detection of discomfort zones (comparison of the obtained equivalent and maximum noise levels with the allowable ones). Unified schemes of location of the control point for instrumental measurements of traffic noise levels, depending on the category of roads adjacent to residential buildings, are proposed. This approach takes into account all possible layout options of the urban area, which allows to identify noise-absorbing properties of green areas of roadside space and provide environmentally friendly noise protection solutions to reduce man-made pollution and quantify the noise load in residential areas affected by the road. The results of approbation of experimental measurements according to the offered schemes which give the chance of revealing of uncomfortable zones in territories of inhabited building for acceptance of the further ecologically safe noise protection practical decisions are resulted.
CURRENT ISSUES OF TECHNOLOGICAL AND ORGANIZATION-ECONOMIC TOOLS OF NATURE MANAGEMENT, ENVIRONMENTAL ASSESSMENT AND ENTREPRENEURSHIP
Authors: Teliura N.
Published: July 2021

Significant technological advances are being made across a range of fields, including technological and organization-economic tools, particularly in terms of nature management, environmental assessment and entrepreneurship to name but a few. The Green entrepreneurship as alternative vision for growth and development. These breakthroughs are expected to be highly disruptive and bring about major transformative shifts in how societies function. These advances promise significant social and economic benefits, increased efficiency, and enhanced productivity across a host of sectors. These innovations including technological and organization-economic tools are centered on the gathering, processing, and analyzing of enormous reams of data emerging from the information sciences with implications for countless areas of research and development.