bluuebunny/crossref_metadata_2025 huggingface dataset:
Deep Hierarchical Attention Active Learning for Mental Disorder Unlabeled Data in AIoMT
Authors: Ahmed Usman , Lin Jerry Chun-Wei , Srivastava Gautam
Published: March 2022

In the Artificial Intelligence of Medical Things (AIoMT), Internet-Delivered Psychological Treatment (IDPT) effectively improves the quality of mental health treatments. With the advent of COVID-19, psychological tasks have become overloaded and complicated for medical professionals due to the overlap of sentimental values. The development of an AIoMT tool requires labeling of data to achieve clinical-level performance. Text data requires an appropriate set of linguistic features for vector latent representation and segmentation. Emotional biases could lead to incorrect segmentation of patient-authorized texts, and labeling emotional data is time-consuming. In this article, we propose an assistant tool for psychologists to assist them in mental health treatment and note-taking. We first extend the word and emotion lexicon and then apply a hierarchical attention method to support data labeling. The learned latent representation uses word position prediction and sentence-level attention to create a semantic framework. The augmented vector representation helps in highlighting words and classifying nine different symptoms from the text written by the patient. Our experimental results show that the emotion lexicon helps to increase the accuracy by 5% without affecting the overall results, and that the hierarchical attention method achieves an F1 score of 0.89.
Improved Machine Learning Method for Intracranial Tumor Detection with Accelerated Particle Swarm Optimization
Authors: Pradeep K. R. , Gangadharan Syam Machinathu Parambil , Hatamleh Wesam Atef , Tarazi Hussam , Shukla Piyush Kumar , Tiwari Basant
Published: March 2022

The field of image processing is distinguished by the variety of functions it offers and the wide range of applications it has in biomedical imaging. It becomes a difficult and time-consuming process for radiologists to do the manual identification and categorization of the tumour. It is a complex and time-consuming procedure conducted by radiologists or clinical professionals to remove the contaminated tumour region from magnetic resonance (MR) pictures. It is the goal of this study to improve the performance and reduce the complexity of the image segmentation process by investigating FCM predicted image segmentation procedures in order to reduce the intricacy of the process. Furthermore, relevant characteristics are collected from each segmented tissue and aligned as input to the classifiers for autonomous identification and relegation of encephalon cancers in order to increase the accuracy and quality rate of the neural network classifier. An evaluation, validation, and presentation of the experimental performance of the suggested approach have been completed. A unique APSO (accelerated particle swarm optimization) based artificial neural network model (ANNM) for the relegation of benign and malignant tumours is presented in this study effort, which allows for the automated identification and categorization of brain tumours. Using APSO training to improve the suggested ANNM model parameters would give a unique method to alleviate the stressful work of radiologists performing manual identification of encephalon cancers from MR images. The use of an APSO-based ANNM (artificial neural network model) model for automated brain tumour classification has been presented in order to demonstrate the resilience of the classification model. It has been suggested to utilise the improved enhanced fuzzy c means (IEnFCM) method for image segmentation, while the GLCM (gray level co-occurrence matrix) feature extraction approach has been employed for feature extraction from magnetic resonance imaging (MR pictures).
Optimization Method for Energy Saving of Rural Architectures in Hot Summer and Cold Winter Areas Based on Artificial Neural Network
Authors: Yang Yong , Liu Xiancheng , Tian Congxiang
Published: March 2022

With the phased spatial planning of the rural revitalization strategy, the proportion of architecture energy consumption in the overall social energy consumption is also increasing year by year. Considering the hot summer and cold winter areas, the proportion of architecture energy consumption in the total energy consumption is very large. The ecological environment and natural resources have been greatly threatened, and the issue of energy conservation and environmental protection is imminent. Energy consumption prediction and analysis is an important branch of building energy conservation in the field of building technology and science. Aiming at the energy consumption characteristics of rural architectures in areas with hot summer and cold winter, this paper proposes a method for constructing a neural network model. When building a neural network, the dataset is called and the function is applied randomly to training samples. The data are used for simulation tests to analyze the fit between the predicted results and the calculated results. Flexible forecasting of specific target building energy consumption is achieved, which can provide optimization strategies for updating and adjusting architecture energy efficiency design. The experimental analysis benchmark parameters and the output value in the dataset are compared with the target simulation value. The relative error is less than 4%, and the average relative error value (mean) and the root mean square error (RMSE) value are both controlled within 2%. It is proved that the method in this paper can directly reflect the evaluation of energy consumption by the neural network and realize the high-speed conversion of the generalized model to the concrete goal, which has a certain value and research significance.
English Translation Model Based on Intelligent Recognition and Deep Learning
Authors: Yu JinLin , Ma Xiuli
Published: March 2022

Aiming at the problems of low accuracy of English phrase part of speech recognition, poor English translation effect, and long translation time in the traditional English translation model, an English translation model based on intelligent recognition and deep learning is designed. An English phrase corpus was built, the phrase antecedent and postscript likelihood of the improved GLR algorithm by using the quaternion cluster were calculated, and the part of speech of the English phrase corpus was identified. According to the recognition results, the feature extraction algorithm is introduced to extract the best contextual features. On this basis, a neural machine translation model is constructed by integrating the traditional neural network in deep learning and combining the attention mechanism. It is used as a neural machine translation model for English translation. The simulation results show that the English translation model based on intelligent recognition and deep learning has high phrase recognition accuracy, good translation effect, and short translation time, which improves the quality of English translation.
HCK is a Potential Prognostic Biomarker that Correlates with Immune Cell Infiltration in Acute Myeloid Leukemia
Authors: Cheng Fang , Li Qiang , Wang Jinglin , Wang Liu , Li Weiming , Zeng Fang
Published: March 2022

Background. The tumor microenvironment (TME) plays a significant role in the progression and prognosis of acute myeloid leukemia (AML). This study is aimed at exploring TME-associated biomarkers and identify their potential mechanism in the microenvironment of AML. Method. In this study, the stromal, immune, and ESTIMATE scores of AML patients were evaluated with the ESTIMATE and CIBERSORT algorithms; then, the AML samples were divided into high- and low-score groups. We evaluated the association between clinicopathological characteristics, survival rate, and the stromal/immune/ESTIMATE scores. Furthermore, we identified TME-associated differentially expressed genes (DEGs) then carried out pathway enrichment analysis, protein-protein interaction (PPI) network, Cox regression analysis, and Kaplan-Meier survival analysis to select the most crucial genes. In addition, we further explored the potential mechanism of HCK in the AML microenvironment. Results. We identified 624 TME-associated DEGs and found that HCK was the most promising biomarker associated with AML. The results of the gene set enrichment analysis (GSEA) indicated that HCK was mainly involved in immune and inflammation-related signaling pathways. In addition, CIBERSORT analysis showed that HCK was closely related to tumor immune infiltration, with HCK expression associated with various infiltrating immune cells, including B cells, T cells, tumor-associated macrophages (TAM), NK cells, plasma cells, eosinophils, and neutrophils. Furthermore, HCK expression was closely related with ELN risk stratification in patients with AML. Conclusion. HCK could regulate immune cell infiltration in the microenvironment of AML and may act as a potential biomarker for the treatment and prognosis of AML patients.
Brain-Derived Neurotrophic Factor and Nerve Growth Factor Therapeutics for Brain Injury: The Current Translational Challenges in Preclinical and Clinical Research
Authors: Sims Serena-Kaye , Wilken-Resman Brynna , Smith Crystal J. , Mitchell Ashley , McGonegal Lilly , Sims-Robinson Catrina
Published: March 2022

Ischemic stroke and traumatic brain injury (TBI) are among the leading causes of death and disability worldwide with impairments ranging from mild to severe. Many therapies are aimed at improving functional and cognitive recovery by targeting neural repair but have encountered issues involving efficacy and drug delivery. As a result, therapeutic options for patients are sparse. Neurotrophic factors are one of the key mediators of neural plasticity and functional recovery. Neurotrophic factors such as brain-derived neurotrophic factor (BDNF) and nerve growth factor (NGF) serve as potential therapeutic options to increase neural repair and recovery as they promote neuroprotection and regeneration. BDNF and NGF have demonstrated the ability to improve functional recovery in preclinical and to a lesser extent clinical studies. Direct and indirect methods to increase levels of neurotrophic factors in animal models have been successful in improving postinjury outcome measures. However, the translation of these studies into clinical trials has been limited. Preclinical experiments have largely failed to result in significant impacts in clinical research. This review will focus on the administration of these neurotrophic factors in preclinical and clinical stroke and TBI and the challenges in translating these therapies from the bench to the clinic.
Effects of Strain Node on the Actuation Performance of Multilayer Cantilevered Piezoactuator with Segmented Electrodes
Authors: Bai Quan , Zheng Xuejun
Published: March 2022

A model of a segmented electrode multilayer cantilever piezoelectric actuator was established to predict its actuation performance, and then, theoretical and numerical analyses of the strain nodes were performed based on normalized deflection and strain distributions. The segmented electrodes instead of the continuous electrodes are applied in a multilayer cantilever piezoelectric actuator which can avoid the modal displacement offsets at the high vibration modes, thereby enhancing the tip deflection. The theoretical analysis and simulation results show that the tip deflection of the segmented electrode at the second mode was almost 100% larger than that of the continuous electrode. At the second mode, the maximum error between the theoretical calculation value of the tip deflection and the simulation result is 6.8%. It is because the segmented electrode is optimally designed at the strain node, which avoids the modal displacement offsets of a multilayer cantilever piezoelectric actuator at the high vibration modes; meanwhile, the theoretical results are closer to the FEM simulation results. It reveals that the tip deflection of a multilayer cantilever piezoelectric actuator can be precisely estimated by the proposed model. This research can provide some useful guidance improving the actuation performance and optimizing the design of a multilayer cantilever piezoelectric actuator.
ANP32 Family as Diagnostic, Prognostic, and Therapeutic Biomarker Related to Immune Infiltrates in Hepatocellular Carcinoma
Authors: Liu Xuxu , He Yuanhang , Wang Pengfei , Hu Jie , Hao Chenjun , Wang Qiang , Yang Yang , Sun Yuanyuan , Ma Biao , Sun Hezheng , Xue Dongbo , Meng Xianzhi
Published: March 2022

Hepatocellular carcinoma (HCC) is one of the most common tumors worldwide, with high incidence and mortality rate. There is an urgent need to identify effective diagnostic and prognostic biomarkers for HCC. Members of the acidic leucine-rich nucleophosphoprotein 32 (ANP32) family, which mainly includes ANP32A, ANP32B, and ANP32E, are abnormally expressed and have prognostic value in certain cancers. However, the diagnostic, prognostic, and therapeutic value of ANP32 family members in HCC has not yet been fully studied. In this study, we identified the diagnostic and prognostic value of ANP32 family members in HCC. Transcriptome data from public databases, such as the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, suggested that ANP32A, ANP32B, and ANP32E were upregulated in HCC tissues, and high expression of ANP32 family members was associated with advanced pathologic stage and histologic grade. Our immunohistochemistry and western blot results further verified the differential expression of ANP32 family members. ANP32A, ANP32B, and ANP32E had an outstanding diagnostic potential. Survival analysis of HCC patients in TCGA databases demonstrated that ANP32A, ANP32B, and ANP32E were associated with poor overall survival (OS) and disease-specific survival (DSS). Univariate and multivariate Cox analyses suggested the capability of ANP32B and ANP32E to independently predict the OS and DSS of HCC patients. Gene set enrichment analysis (GSEA) showed that ANP32 family members were associated with immune response, epidermal cell differentiation, and stem cell proliferation. Expression of ANP32 family members was associated with immune cell infiltration and immune status in the tumor microenvironment of HCC, and patients with high ANP32 family expression had poor sensitivity to immunotherapy. Finally, we identified potential chemotherapy drugs for HCC patients with high ANP32 family expression by CellMiner database. This study suggested the diagnostic, prognostic, and therapeutic roles of the ANP32 family in HCC patients, providing potential therapeutic targets for HCC.
Optimizing the Method for Differentiation of Macrophages from Human Induced Pluripotent Stem Cells
Authors: Li Shanshan , Song Lili , Zhang Yingwen , Zhan Zhiyan , Yang Yi , Yu Lisha , Zhu Hua , Huang Weihua , Wang Wanqiao , Feng Haizhong , Li Yanxin
Published: March 2022

Macrophage is a very promising cell type for cancer immunotherapy, yet it is difficult to obtain enough functional macrophages for clinical cell therapy. Herein, we descibe a reliable method to produce functional macrophages through the differentiation of human induced pluripotent stem cells (hiPSCs). By optimizing the size control of embryoid bodies (EBs), we accelerated the differentiation process of macrophages and increased the production of macrophages without attenuating macrophage functions. Our final yield of macrophages was close to 50-fold of starting iPSCs. The macrophages showed phagocytic capacity in vitro and a xenograft tumor model. M0 macrophages could be further polarized into M1 and M2 subtypes, and M1 cells exhibited typical proinflammatory characteristics. Moreover, we found that hematopoietic differentiation originated from the outside of EB and matured inward gradually. Taken together, our protocol provides an effective method for the generation of macrophages comparable to blood-derived macrophages, which provides potential value for cell therapy and gene editing studies.
A Radial Basis Function-Based Optimization Algorithm with Regular Simplex Set Geometry in Ellipsoidal Trust-Regions
Authors: Lefebvre Tom , De Belie Frederik , Crevecoeur Guillaume
Published: March 2022

In this paper, we investigate two ideas in the context of the interpolation-based optimization paradigm tailored to derivative-free black-box optimization problems. The proposed architecture maintains a radial basis function interpolation model of the actual objective that is managed according to a trust-region globalization scheme. We focus on two distinctive ideas. Firstly, we explore an original sampling strategy to adapt the interpolation set to the new trust region. A better-than-linear interpolation model is guaranteed by maintaining a well-poised supporting subset that pursues a near regular simplex geometry of

n
+
1

points plus the trust-region center. This strategy improves the geometric distribution of the interpolation points whilst also optimally exploiting the existing interpolation set. On account of the associated minimal interpolation set size, the better-than-linear interpolation model will exhibit curvature, which is a necessary condition for the second idea. Therefore, we explore the generalization of the classic spherical to an ellipsoidal trust-region geometry by matching the contour ellipses with the inverse of the local problem hessian. This strategy is enabled by the certainty of a curved interpolation model and is introduced to accounts for the local output anisotropy of the objective function when generating new interpolation points. Instead of adapting the sampling strategy to an ellipsoid, we carry out the sampling in an affine transformed space. The combination of both methods is validated on a set of multivariate benchmark problems and compared with ORBIT.
Information Fusion Method for Building Grassroots Teaching Organization Based on Intelligent Optimization
Authors: Zhao Ying
Published: March 2022

At present, the existing teaching construction information fusion methods are based on the classification results of teaching resources, but ignoring the global intelligent optimization of the fusion results, which leads to the deviation of the grassroots teaching organization construction information fusion results, and the fusion index value is low. Therefore, the information fusion method of the grassroots teaching organization construction based on intelligent optimization is proposed. The basic framework of information fusion is designed, and the information fusion is divided into the data layer, characteristic layer, and decision layer. The information characteristics of the construction of grassroots teaching organizations were extracted. The particle swarm optimization algorithm is used to search the maximum value of the work entropy of teaching grassroots unit, and the threshold value of teacher construction and grassroots unit work is used to realize the information fusion of teaching grassroots unit construction. Experimental results show that the proposed method has higher error rate, stability, efficiency index, and higher information fusion value and accuracy than the traditional method.
A Study on Prediction Skills and Reading Efficiency in College English Based on Optimized BP Networks
Authors: Cui Dan
Published: March 2022

In this paper, an in-depth study and analysis of college English prediction skills and reading efficiency are conducted using an optimized BP network algorithm, which is designed separately, and the accuracy of scoring results is verified and the experimental results are analyzed using two datasets; finally, under the guidance of writing feedback theory and data visualization design criteria, a reasonable visualizable writing feedback is created. The learning and training of the BP neural network model revealed that there was significant information loss in the similar nearest-neighbor user dataset used for training, and there were instances where some items were rated by the target user but not by similar nearest-neighbor users. As a result, such data is useless in training, and the neural network loses a significant amount of useful information when learning. To address this issue, this paper proposes using the singular value decomposition technique for filling, which alleviates the sparsity of the filled matrix data and improves the accuracy of recommendations even more. Both scoring models constructed using 1D CNN and LSTM networks belong to the second class of models, and this type of “end-to-end” scoring model does not require feature engineering. Finally, using 650 spoken recordings and their corresponding manual scoring data, the model is trained and tested. The experimental results show that, with a smaller training dataset, the BP network model achieves a better overall scoring performance.
Global Oral Anticoagulation Use Varies by Region in Patients With Recent Diagnosis of Atrial Fibrillation: The GLORIA‐AF Phase III Registry
Authors: Bayer Valentina , Kotalczyk Agnieszka , Kea Bory , Teutsch Christine , Larsen Peter , Button Dana , Huisman Menno V. , Lip Gregory Y. H. , Olshansky Brian
Published: March 2022

Background

Effective stroke prevention with oral anticoagulants (OAC) is recommended for some patients with atrial fibrillation (AF). We aimed to describe OAC use by geographical region and type of site in patients with recent‐onset AF enrolled in a large global registry.

Methods and Results

Eligible participants were recruited into GLORIA‐AF (Global Registry on Long‐Term Oral Antithrombotic Treatment in Patients With Atrial Fibrillation), a prospective observational cohort study from 2014 to 2016 in 4 international regions: North America, Europe, Asia, and Latin America. Cumulative incidence functions were generated for direct OACs (DOAC), vitamin K antagonists, and antiplatelet drugs considering competing risks, stratified by region and type of site. Time‐to‐treatment initiation after AF diagnosis was analyzed with Fine‐Gray subdistribution hazard models. A total of 21 237 patients eligible for analysis were identified. By 30 days after AF diagnosis, 40%, 16%, and 8.6% of patients had DOAC, vitamin K antagonists, and antiplatelet drugs initiated, respectively. Earlier initiation of DOACs was observed in Europe, with Asia and Latin America having lower hazard rates of DOAC time‐to‐treatment initiation than Europe (hazard ratio [HR], 0.66; 95% CI, 0.62–0.70 and HR, 0.79; 95% CI, 0.73–0.85, respectively). DOAC initiation was highest in community hospitals, vitamin K antagonists in outpatient health care centers/anticoagulation clinics, and antiplatelet drugs in primary care clinics.

Conclusions

Important geographic variability exists with the use of OACs for patients with AF. Differences in the time‐to‐treatment initiation of OAC by type of site suggests suboptimal implementation of guideline recommendations and could result in less benefit and more harm. Optimizing OAC use for patients with AF may improve outcomes and reduce health care costs.

Registration

URL:
http://www.clinicaltrials.gov
; Unique identifiers: NCT01468701, NCT01671007.
Epitranscriptome in Ischemic Cardiovascular Disease: Potential Target for Therapies
Authors: Quiles-Jiménez Ana , Dahl Tuva B. , Bjørås Magnar , Alseth Ingrun , Halvorsen Bente , Gregersen Ida
Published: March 2022

The global risk of cardiovascular disease, including ischemic disease such as stroke, remains high, and cardiovascular disease is the cause of one-third of all deaths worldwide. The main subjacent cause, atherosclerosis, is not fully understood. To improve early diagnosis and therapeutic strategies, it is crucial to unveil the key molecular mechanisms that lead to atherosclerosis development. The field of epitranscriptomics is blossoming and quickly advancing in fields like cancer research, nevertheless, poorly understood in the context of cardiovascular disease. Epitranscriptomic modifications are shown to regulate the metabolism and function of RNA molecules, which are important for cell functions such as cell proliferation, a key aspect in atherogenesis. As such, epitranscriptomic regulatory mechanisms can serve as novel checkpoints in gene expression during disease development. In this review, we describe examples of the latest research investigating epitranscriptomic modifications, in particular A-to-I editing and the covalent modification
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6
-methyladenosine and their regulatory proteins, in the context of cardiovascular disease. We additionally discuss the potential of these mechanisms as therapeutic targets and novel treatment options.
Meloidogyne-SP4 effector gene silencing reduces reproduction of root-knot nematodes in rice (Oryza sativa)
Authors: Nguyê˜n Phong V. , Biện Thanh LT. , Tôn Linh B. , Lê Ðôn Ð. , Wright M. Kathryn , Mantelin Sophie , Petitot Anne-Sophie , Fernandez Diana , Bellafiore Stéphane
Published: March 2022

The root-knot nematodes (RKN) Meloidogyne graminicola and M. incognita are responsible for rice yield losses worldwide, particularly in Asia and Africa. Previous studies demonstrated that nematode-secreted proteins are crucial for root invasion and establishment in the host. We present some characteristics of a pioneer effector, M. incognita-secreted protein 4 (Mi-SP4), which is conserved in RKN and required for infection in compatible rice-RKN interactions. In situ hybridisation assays revealed Mi-SP4 expression in the dorsal pharyngeal gland of M. incognita second-stage juveniles (J2). Meloidogyne-SP4 transcripts strongly accumulated in pre-parasitic J2 and decreased in later parasitic stages of M. incognita and M. graminicola. Transient expression of the nematode effector gene in Nicotiana benthamiana leaves and onion cells indicated that GFP-tagged Mi-SP4 was present in the cytoplasm and accumulated in the nucleus of the plant cells. In vitro RNA interference (RNAi) gene silencing, obtained by soaking J2 with small-interfering (si)RNA si4-1, decreased Mi -SP4 expression in J2 by 35% and significantly reduced M. incognita reproduction in rice by at least 30%. Similarly, host-mediated gene silencing of the nematode SP4 effector candidate gene in transgenic rice plants significantly reduced M. graminicola reproduction by 26% to 47%. The data obtained demonstrate that Mi -SP4 is a pioneer virulence effector, which plays an essential role in both M. incognita and M. graminicola pathogenicity on rice.
Christianity and Shona Religion and Ecology
Authors: Beyers Jaco , Muza Kudakwashe
Published: March 2022

The biggest single threat to ecology and biodiversity is human-induced climate change. This study assesses ethics and attitudes in an African context for coping with climate change. This assessment takes place within an ethical-practical framework functioning within the mutuality model of interreligious dialogue. Christianity and Shona religion are brought into dialogue with one another to create a conversation on how to deal with ecology. As the main source of livelihood in Zimbabwe and the whole of Africa, agriculture has been affected by climatic changes. Disease outbreaks, floods, and droughts are on the rise since the world is experiencing severe temperature rise. In addressing the ecological crisis religious considerations must be taken into account. The pivotal role of religion in issues of climate change and environmental conservation hinges on religious ethics and religion’s ability to inspire its adherents to have environmentally friendly attitudes. Christianity and Shona religion can collaborate on addressing ecological problems since they have shared sets of ethics.
Prayer Camps, Mental Health, and Human Rights Concerns in Ghana
Authors: Benyah Francis
Published: March 2022

This article discusses the role that Ghana’s prayer camps provide in mental health care and the human rights concerns that are expressed. The article argues for the recognition of both state and nonstate actors in dealing with the problem of mental illness and its related human rights concerns. The article maintains that the mere existence of mental health legislation to protect the rights of mental health patients is not enough if it fails to recognise the religious dimensions – the beliefs, faith, or transcendental orientation – of the people who are the target object of such legislation. The article recommends to policy makers, academics, clinicians, and international organisations whose work focuses on mental health, ways in which religious views on mental illness can be harmonised to support modern projects such as human rights aimed at transforming the lives of people.
Media, Gender, and Religion in the Twenty-First Century
Authors: Gbadegesin Enoch Olujide , Wale-Olaitan Kemi
Published: March 2022

This paper asserts that since the Nigerian media is patriarchal, they inexorably present images that give the impression that men have a superior knowledge of God than women. This idea is incidentally reechoing in contemporary Nigerian Christian practices, especially in the age of the new media. The paper examines gender, religion, and media from historical perspectives; analyses the theoretical framework at the root of the construction of the female gender and their representation in religion; and lastly, presents selected examples from day-to-day empirical evidence from Christian organizations in the Nigerian media space with the aim of deconstructing the public image of Christian women in the Nigerian public square. It thus aims to reconstruct the public image of women in religion via the media, showing the importance of equal gender representation. The narrative concerning reconstruction is deeply contextual, analytical, rigorous, and interdisciplinary.
Sulle tracce del Galeno di Teodoro Gaza
Authors: Orlandi Luigi
Published: March 2022

This paper aims to shed light on the history of a book mentioned in the will of Theodoros Gazes, which has not been identified so far. It is a manuscript of Galen’s Methodus medendi. A combined palaeographic, philological, codicological and prosopographic analysis of some extant witnesses to the text leads to a tentative identification of the book belonged to Gazes, providing insights into the transmission of Galen’s work in the frame of the Italian Humanism.
Brokers and Translators
Authors: Lloydd Marnie
Published: March 2022

Through ethnographic observations from international humanitarian work and interviews with humanitarian professionals, Joe Cropp’s The Humanitarian Fix: Navigating Civilian Protection in Contemporary Wars engagingly describes the daily practice of humanitarian negotiation and persuasion. The book illustrates how the international humanitarian has morphed from the more classical neutral intermediary to emerge as a broker and translator with a toolbox of more official and less official reframings of international law and policy to grapple with local problems and persuade different actors. In this review essay, I highlight the hidden insight for international lawyers in Cropp’s book regarding questions of universality and pluralism in international humanitarian law and the double-bind practitioners face: while these practices of translation look like an openness to pluralism in humanitarian law, within the current international framework, any translation by necessity re-emphasises how these reframings are not the law, and ultimately work as techniques to reinforce the authority and claimed universality of the central system.
Uṣūl al-fiqh et philosophie : état des lieux
Authors: Bou Akl Ziad
Published: March 2022

Ce texte présente un état des lieux de la recherche moderne sur les uṣūl al-fiqh, en s’attachant notamment à en montrer les liens avec la philosophie, considérée dans un premier temps au sens strict et historique de tradition hellénistique autonome pratiquée par les falāsifa de l’époque médiévale, et dans un second temps, en un sens plus large qui englobe toute réflexion à portée universelle que renferment les traités de uṣūl al-fiqh.
The Legacy of Willem Beurs ‒ Bridging the Gap between Art and Material Perception
Authors: Di Cicco Francesca
Published: March 2022

Dutch Golden Age painters could convincingly depict all sorts of materials. How did they do it and how do we perceive them as such, are questions that only recently have started to be addressed by art historians and vision scientists, respectively. This paper aims to discuss how a booklet of pictorial recipes written by the Dutch painter Willem Beurs in 1692 constitutes an index of key image features for material depiction and perception. Beurs’ recipes connect different materials according to their shared visual features, and offer the profiles, i.e., the optimal combinations, of these features to render a wide range of materials. By combining representation and perception, the knowledge of painters about the depiction of materials can help to understand the mechanisms of the visual system for material perception, and these in turn can explain the pictorial features that make the pictorial representation of materials so convincing.
Daltonia hookeriana, a new name for Daltonia heterophylla (Wilson ex Mitt.) B.C.Ho & L.Pokorny (Daltoniaceae, Bryophyta)
Authors: DESHMUKH UMAKANT BHOOPATI , MAJUMDAR SHUVADEEP , HO BOON-CHUAN
Published: March 2022

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A new species of the idiocerine leafhopper genus Chinaocerus Zhang & Li (Hemiptera: Cicadellidae) from northwest China, with a checklist and key to species
Authors: LIU GUIHUA , YANG HEFENG , LI GUOQING , CHEN ZHENNING , ZHANG BIN
Published: March 2022

A new leafhopper species, Chinaocerus dentatus Zhang sp. nov., is described and illustrated from Qinghai and Gansu Provinces, China. Specimens were collected from willow (Salix sp.). A checklist and key to species of the genus are provided.
Behavior Based Safety Approach to Improve Workplace Safety in Multistory Building Construction
Authors: Dinagaran D. , Balasubramanian K. R. , Sivapirakasam S. P.
Published: March 2022

Behavior Based Safety (BBS) approach is the “usage of science for changing the behavior of an individual”. “Behavior Based Safety” focuses on action of workmen on day-to-day safety behavior and its improvement. This study presents a case study within the multistory
building construction in India. This research work plans the outline of the BBS method into 7 steps and encapsulates a number of behavioral safety methods to prevent the workplace accident. The objective of this paper is to create a safe work environment in construction industries by decreasing
frequency severity of injuries and increase the safe behavior. The outcome of this research work revealed that a method related to observation, a real-time measure of safety behavior with appreciative and constructive feedback for the significant improvement of the safety performance in multistory
building construction. Observers educated to provide appreciative feedback when the workers performed the task safely and to offer constructive feedback to unsafe task performers. Baseline data of current safety performance was calculated before the BBS approach implementation and compared
with the safety performance weekly data to identify the improvements.
Recent Trends in Friction Stir Welding
Authors: Gupta Arun , Vipin
Published: March 2022

Welding technology has brought a milestone change in metal joining processes and its applications and almost eradicated some other metal joining processes (riveting etc.). Friction stir welding is a solid state welding technique comparatively recent than others. This study intends to
elucidate the current scenario in the field of friction stir welding with special focus on dissimilar metal friction stir welding, underwater friction stir welding and hybrid friction stir welding. Further, future challenges in the friction stir welding have been described.
Performance and Emission Analysis of Diesel Engine Fueled with Tyre Pyrolysis Oil and Diesel Blends
Authors: Patel Saumil C. , Brahmbhatt Pragnesh K.
Published: March 2022

In this research work, performance and emissions of Tyre Pyrolysis Oil (TPO) in a diesel engine are experimentally examined as per Indian Standard: 10000. The main objective of this research work is to find the best proportion of blend based on performance and emission analysis. For
the study, Tyre Pyrolysis Oil is added to diesel fuel, numbered D100 (pure diesel), D95T5 (by volume of 5% TPO and 95% diesel), D90T10 (by volume of 10% TPO and 90% diesel), D85T15 (by volume of 15% TPO and 85% diesel), D80T20 (by volume of 20% TPO and 80% diesel) and D75T25 (by volume of
25% TPO and 75% diesel). Fuels are tested in four strokes, single cylinder, direct injection, water cooled diesel engine. The effect of various blends on Mechanical Efficiency, Specific Fuel Consumption (SFC) and Thermal Efficiency are investigated by the performance test. The influences of
blends on CO, O2, NOx, CO2 and HC are investigated by an emission test using the 5-gas analyzer. The investigation shows that D85T15 blend has been proved best based on performance test compared to other blends. The investigation of Emission analysis
shows that D85T15 blend has better combustion characteristics compared to others.
Indication of Mesopic Effectiveness in Light Emitting Diode (LED)-Based Lighting System
Authors: Kasera Shikha , Kumar Harish
Published: March 2022

Mesopic vision plays a substantial role in energy saving, and comes into picture when we move from photopic (bright light condition, luminance > 3cd/m2) to scotopic Vision (dark light condition, luminance < 0.001 cd/m2). Mesopic vision lies in between photopic
vision and scotopic vision, describing the night vision in urban areas. Here, the spectral irradiance value of white light LED and color coordinates values was theoretically calculated using McCamy formula as per CIE 1931 followed by S/P ratio (ratio of scotopic Luminaire lumens to photopic
Luminaire lumens) for the different blue light component. Comparative study of the consequence of increasing blue light component has been done and resulted that S/P ratio increases with increasing the peak of blue light. In this way, LED produces more scotopic lumens than photopic
lumens, and the light energy contributing in scotopic vision goes waste. Focusing on the electrical energy saving aspects of LED, a correction factor needed to be applied for getting more spectral irradiance power with minimized electrical power.
Prospects and Challenges of Computer Assisted Virtual Education System During COVID-19 in Developing Countries from Students’ Perspectives
Authors: Akter Sharmin , Nayeem Imtiaz , Humayun M. A.
Published: March 2022

This research paper presents an analysis of prospects and challenges of computer assisted virtual education systems in developing countries during COVID-19 pandemic. A questionnaire-based survey has been conducted through an online platform to assess the prospects and challenges of
adopting computer assisted virtual education systems during the current pandemic in the 21st century. The questionnaire is composed of ten questions related to the computer assisted virtual education systems in Bangladesh. This analysis has been performed based on the data obtained from students’
perspectives. The outcome of the proposed research work is based on the response of 194 students of a Private University in Dhaka, Bangladesh. Almost equal number of students from urban and rural areas of Bangladesh have taken part in this survey willingly. From the analysis of the outcomes
obtained from the survey it is revealed that there are a lot of prospects of online education along with some challenges in a developing country like Bangladesh. The outcome of the research based on prospects and challenges of online education systems can be a center of attraction for the
researchers all over the world to develop a cost effective, user friendly, reliable computer assisted virtual education system for the developing countries, which may be also helpful for the underdeveloped countries in the very near future.
Fuzzy Based Cluster Greedy Optimization and Convolutional Neural Networks Based Scheme for Internet of Medical Things Based Healthcare Resource Allocation in Cognitive Wireless Powered Communication Network
Authors: Bhuvaneswari M. , SasiPriya S.
Published: March 2022

A cognitive wireless powered communication network (CWPCN) for spectrum distribution in IoMT based healthcare systems is employed with a principal network, which in turn deals with security issues from various attacks like Denial of Service (DoS), Man-In-the-Middle, or phishing attacks.
In this, a new protocol is proposed for wireless powered SU (secondary users) so as to cooperate with PU (primary user) of the healthcare network. At the time of wireless power transfer (WPT) in a IoMT based healthcare network, the first harvest energy of SUs was carried from power signals
broadcasted by the cognitive hybrid access point. Then the harvested energy is employed while gaining transmission opportunities simultaneously all through the phase of Wireless Information Transfer (WIT) of healthcare system. Furthermore, Fuzzy based cluster greedy algorithm is introduced
for reducing the interruption of PU secrecy prospect and to offer the best optimal values in the healthcare data. In this approach, the injection impact and reactive jamming attacks on wireless transmission are analyzed. These can be recognized through a Convolutional Neural Network (CNN)
to detect different attack types and classify them. Finally, the results were compared with the existing method.
Health Consumer Social Economic Factors and Health Conditions as Predictor for Health Literacy in Radiology Domain
Authors: Alarifi Mohammad , Patrick Timothy , Jabour Abdulrahman , Wu Min , Luo Jake
Published: March 2022

Patient literacy of radiology is imperative for patient engagement in care and management of their own health. Little is known about the factors that could predict patient literacy of radiology reports, testing, or treatment. This study aims to identify the most important factors of
health consumer social economic and health conditions as a predictor of health literacy in the radiology domain. The study recruited 616 participants using Amazon.com’s Mechanical Turk (MTURK) and presented
these participants with our questionnaire. We measured the level of participants’ radiology awareness, social factors, and health status. Descriptive statics including Chi-Square and linear regression models were used to test if the factors could predict radiology literacy. The area
under the receiver–operator curve was calculated to determine the prediction accuracy of the regression models. linear regression indicated that 15 of the 19 social-economic factors and health conditions were significantly associated with radiology literacy (P < .05). On the
other hand, only 12 of the 19 factors were significant by using Pearson Chi-Square (P < .05). Stepwise linear regression analysis demonstrated the r squared linear of 9 out of 12 common factors. These factors are the level of education, smoking, radiology experience, insurance status,
white race, employment status, disability status, gender, and income at 0.209. These nine factors had a good ability to predict radiology literacy (area under the receiver operator curve of 0.677 [95%CI 0.549; 0.804, P = 0.013]). Social economic factors and health conditions can be
used to successfully predict radiology literacy. We were able to successfully identify the predictive factors that have a high association with the radiology literacy by comparing social factors and health status versus radiology awareness.
Application of Class Based Association Rule Pruning to Generate Optimal Association Rules in Healthcare
Authors: Sasikala D. , Premalatha K.
Published: March 2022

The association rule mining approach produces uninteresting association rules. When the set of association rules become large, it becomes less interesting to the user. In order to pick interesting association rules among peak volumes of found association rules, it is critical to aid
the decision-maker with an efficient post-processing phase. Theymotivate the need for association analysis performance. Practically it is an overhead to analyze the large set of association rules. In this work, association rule pruning technique called Class Based Association Rule Pruning
(CBARP). This pruning techniques is proposed to prune the weak association rules of the healthcare system. The results are compared with Semantic Tree Based Association Rule Mining (STAR) technique and it demonstrate that the CBARP method outperforms other methods for the given support values.
Application of Harmony Search Algorithm to Optimize SPARQL Protocol and Resource Description Framework Query Language Queries in Healthcare Data
Authors: Gomathi R. , Sharmila D.
Published: March 2022

The rapid developing international of internet, Semantic Web has become a platform for intelligent agents mainly in the healthcare sector. Inside the beyond few years there is a widening in the Semantic web data field in the healthcare industry. With a growth in the quantity of Semantic
web data field in health industry, there exist some challenges to be resolved. One such challenge is to provide an efficient querying mechanism that can handle large number of Semantic web data. Consider many query languages; especially SPARQL (SPARQL Protocol and RDF Query Language) is the
most popular query language. Each of these query languages has their own design strategy and it was identified in research that it is difficult to handle and query large quantity of RDF data efficiently using these languages. In the proposed process, Harmony search identify met heuristic algorithm
to optimize the SPARQL queries in the healthcare data in the applicable manner. The application of Harmony search algorithm is evaluated with large Resource Description Framework (RDF) datasets and SPARQL queries. To assess performance, the algorithm’s implementation is compared to existing
nature-inspired algorithms. The performance analysis shows that the proposed application performs well for large RDF datasets.
Strikingly similar: Comparing visual political communication of populist and non-populist parties across 28 countries
Authors: Farkas Xénia , Jackson Daniel , Baranowski Paweł , Bene Márton , Russmann Uta , Veneti Anastasia
Published: March 2022

Along with the recent boom in support of populist movements in Europe, social media seems to be the ideal place for their interaction with the public. While Facebook has been thoroughly explored for populist campaigning, there is still scarce research on visual aspects of their communication. Analysing the 2019 European Parliament campaign, this study seeks to determine the distinct characteristics of a populist visual communication style and its differences in relation to the non-populist parties. Applying quantitative content analysis to the images ( N = 997) posted on Facebook by political parties from 28 countries enabled us to show that there is a predominance of similarities in both communication styles. Although populists demonstrated a higher propensity to depict their leader and use national symbols, these were exceptions to the overwhelming evidence of uniformity in campaigning methods. Hence, we argue that despite evidence of textual visibility, populist communication does not explicitly manifest through images.