bluuebunny/crossref_metadata_2025 huggingface dataset:
Estrategia didáctica de aprendizaje basada en inteligencias múltiples predominantes y procesos autorregulatorios en estudiantes rurales de primaria
Authors: Tobón Villada Julio Enrique , Marín Buitrago Orlando , Tapia Llanos Rubén Camilo , Martín García Mónica Alexandra
Published: September 2021

Esta investigación tuvo como propósito analizar las inteligencias múltiples y las formas de autogestión de los estudiantes del grado 5° de Básica Primaria en la Escuela Rural Nicolás Esguerra de Ibagué – Colombia y diseñar una estrategia didáctica basada en la caracterización de inteligencias múltiples predominantes que optimice el rendimiento académico de los alumnos a través de sus prácticas de autorregulación en el aprendizaje. Este estudio se desarrolló bajo el enfoque metodológico cualitativo, con un diseño fenomenológico de alcance descriptivo, que contó con una unidad informante 13 estudiantes, 7 niños y 6 niñas con edades entre los 9 y 10 años, además de la participación de los padres de familia de los estudiantes y cuatro maestros quienes dieron respuesta a formularios web diseñados con preguntas abiertas y cerradas, así como a una entrevista semiestructurada a uno de los profesores, ambos instrumentos validados por expertos. Los resultados indican que las inteligencias múltiples predominantes en el grupo de análisis son la Ecológica y la Interpersonal y que sus procesos de autorregulación requieren ser fortalecidos en pro de su rendimiento académico. Lo anterior, se constituyó como el principal insumo para que los autores diseñaran una estrategia didáctica innovadora que transversaliza el ámbito curricular de la institución.
Calculando el riesgo de insolvencia, de los métodos tradicionales a las redes neuronales artificiales. Una revisión de literatura
Authors: Ludeña Dávila Maryann Katherine , Tonon Ordóñez Luis Bernardo
Published: September 2021

En la administración financiera de toda organización el cálculo del riesgo de insolvencia se ha convertido en un parámetro importante, buscando anticiparse a la eventualidad de llegar a tener un problema económico y generar insolvencia. El objetivo de este trabajo es determinar si en el cálculo del riesgo de insolvencia, el uso de redes neuronales artificiales genera mejores resultados que las metodologías tradicionales, buscando las principales características dentro de las aplicaciones realizadas por distintos autores a través del tiempo. De esta manera se observan las principales variables que pueden evidenciar que la aplicación de la metodología de redes neuronales facilita el cálculo del riesgo de insolvencia.  A través de la revisión bibliográfica, en el período 1992-2021, con el uso del método analítico-sintético se puede evidenciar que el modelo expuesto es considerado como eficiente según sus resultados, con ajustes que, en la mayoría de los casos expuestos, superan el 80% de eficacia. Los resultados encontrados permitieron concluir que la estructura básica de una red neuronal viene dada por tres capas: una de entrada, una de salida y una oculta. Sin embargo, el número de nodos es el que varía en cada una de las aplicaciones realizadas por los distintos autores, dado que los mismos representan a las variables, en este caso indicadores financieros más relevantes según la aplicación planteada. Finalmente se logró evidenciar cuáles son los indicadores financieros más usados en las distintas aplicaciones de redes neuronales. Todo indica que las redes neuronales generan resultados más efectivos que los métodos tradicionales.
La responsabilidad social empresarial como estrategia de diferenciación de las empresas exportadoras
Authors: Rodriguez Eugenio Kelvin Ronaldo , Gallo Apolo Jennifer Edith , González Illescas Mayiya Lisbeth , Carmenate Fuentes Luis Pastor
Published: September 2021

Las empresas exportadoras se enfrentan a transformaciones constantes de los mercados y necesitan adoptar estrategias distintas a las tradicionales para diferenciarse de sus competidores. Ofrecer productos con precios accesibles y con altos estándares de calidad ya no son un diferencial, sino más bien, requisitos básicos. Actualmente, la responsabilidad social empresarial (RSE) se presenta como un diferencial para las empresas exportadoras que contribuyen a generar ventajas competitivas. En este contexto, el objetivo de esta investigación fue: caracterizar las prácticas o atributos de la RSE aplicadas en empresas exportadoras como estrategia de diferenciación. De forma complementaria se planteó la interrogante: ¿de qué manera esas estrategias de diferenciación basadas en la RSE contribuyen en la obtención de ventajas competitivas en las empresas exportadoras? Se empleó una investigación documental de enfoque cualitativo y alcance descriptivo con la aplicación de los métodos teóricos: inductivo-deductivo y analítico-sintético. Como resultado se tuvo que las empresas exportadoras realizan prácticas de RSE ligadas principalmente a las dimensiones social, económica y ambiental y a los estándares de las certificaciones ambientales o de responsabilidad social. Asimismo, se evidenciaron casos de empresas que destacan los atributos de sus productos mediante certificaciones orgánicas. Estas estrategias les han permitido la internacionalización e incursión en mercados exigentes, la creación de una imagen y reputación positiva y sobre todo la diferenciación ante sus competidores. Se concluye que las empresas exportadoras implementan estrategias de diferenciación mediante la aplicación de protocolos de RSE tanto a nivel producto como a nivel empresa, siendo esta última la de mayor uso.
Potato Surface Defect Detection Based on Deep Transfer Learning
Authors: Wang Chenglong , Xiao Zhifeng
Published: September 2021

Food defect detection is crucial for the automation of food production and processing. Potato surface defect detection remains challenging due to the irregular shape of potato individuals and various types of defects. This paper employs deep convolutional neural network (DCNN) models for potato surface defect detection. In particular, we applied transfer learning by fine-tuning a base model through three DCNN models—SSD Inception V2, RFCN ResNet101, and Faster RCNN ResNet101—on a self-developed dataset, and achieved an accuracy of 92.5%, 95.6%, and 98.7%, respectively. RFCN ResNet101 presented the best overall performance in detection speed and accuracy. It was selected as the final model for out-of-sample testing, further demonstrating the model’s ability to generalize.
Biomass and Protein Yields of Field Peas and Oats Intercrop Affected by Sowing Norms and Nitrogen Fertilizer at Two Different Stages of Growth
Authors: Krga Ivan , Simić Aleksandar , Dželetović Željko , Babić Snežana , Katanski Snežana , Nikolić Svetlana Roljević , Damnjanović Jelena
Published: September 2021

In limited growing conditions, intercropped field peas and oats can represent a significant source of forage rich in protein. If applied correctly, factors such as nitrogen fertilizer, the mowing phase, and sowing norms can significantly increase the productivity of these mixtures. Field trials were conducted to examine their productivity under different nitrogen levels (0, 40, 80 kg ha−1), different sowing norms/mixtures (field peas: oats—100:15%; 100:30%), and two stages of growth (full flowering, full pod formation). Nitrogen fertilizer and different sowing norms had a significant effect on the biomass, hay, and crude protein yields. On average, the highest hay yields were achieved with 80 kg ha−1 N (4.96 t ha−1), followed by 40 kg ha−1 N (4.27 t ha−1). The highest protein yields were achieved with 40 kg ha−1 N (CP—704.1 kg ha−1), followed by 80 kg ha−1 N (CP—637.6 kg ha−1). Sowing norm 100:30% achieved higher hay yields: 100:30%—4.82 t ha−1; 100:15%—4.44 t ha−1, while 100:15% achieved higher crude protein yields: 100:15%—730.4 kg ha−1; 100:30%—692.7 kg ha−1 on average. The costs were not significantly increased with the nitrogen fertilizer, but the net profits were increased by as much as 163%, depending on the nitrogen level and the mixture. Nitrogen fertilizer also achieves higher economic efficiency for the mixture 100:15% compared to the 100:30% mixture. Mixtures of field peas and oats outperform single-grown crops and provide cost-effective feed for a short time. Using optimal seed ratios and nitrogen fertilizer can significantly increase the productivity and profitability of the feed with minimal impact on the overall production costs.
Climate-Resilient Grazing in the Pastures of Queensland: An Integrated Remotely Piloted Aircraft System and Satellite-Based Deep-Learning Method for Estimating Pasture Yield
Authors: Barnetson Jason , Phinn Stuart , Scarth Peter
Published: September 2021

The aim of this research is to expand recent developments in the mapping of pasture yield with remotely piloted aircraft systems to that of satellite-borne imagery. To date, spatially explicit and accurate information of the pasture resource base is needed for improved climate-adapted livestock rangeland grazing. This study developed deep learning predictive models of pasture yield, as total standing dry matter in tonnes per hectare (TSDM (tha−1)), from field measurements and both remotely piloted aircraft systems and satellite imagery. Repeated remotely piloted aircraft system structure measurements derived from structure from motion photogrammetry provided measures of pasture biomass from many overlapping high-resolution images. These measurements were taken throughout a growing season and were modelled with persistent photosynthetic pasture responses from various Planet Dove high spatial resolution satellite image-derived vegetation indices. Pasture height modelling as an input to the modelling of yield was assessed against terrestrial laser scanning and reported correlation coefficients (R2) from 0.3 to 0.8 for both a coastal grassland and inland woodland pasture. Accuracy of the predictive modelling from both the remotely piloted aircraft system and the Planet Dove satellite image estimates of pasture yield ranged from 0.8 to 1.8 TSDM (tha−1). These results indicated that the practical application of repeated remotely piloted aircraft system derived measures of pasture yield can, with some limitations, be scaled-up to satellite-borne imagery to provide more temporally and spatially explicit measures of the pasture resource base.
PGPR-Mediated Plant Growth Attributes and Metal Extraction Ability of Sesbania sesban L. in Industrially Contaminated Soils
Authors: Zainab Nida , Amna  , Khan Amir Abdullah , Azeem Muhammad Atif , Ali Baber , Wang Tongtong , Shi Fuchen , Alghanem Suliman Mohammed , Hussain Munis Muhammad Farooq , Hashem Mohamed , Alamri Saad , Abdel Latef Arafat Abdel Hamed , Ali Omar M. , Soliman Mona H. , Chaudhary Hassan Javed
Published: September 2021

The release of harmful wastes via different industrial activities is the main cause of heavy metal toxicity. The present study was conducted to assess the effects of heavy metal stress on the plant growth traits, antioxidant enzyme activities, chlorophyll content and proline content of Sesbania sesban with/without the inoculation of heavy-metal-tolerant Bacillus gibsonii and B. xiamenensis. Both PGP strains showed prominent ACC-deaminase, indole acetic acid, exopolysaccharides production and tolerance at different heavy metal concentrations (50–1000 mg/L). Further, in a pot experiment, S. sesban seeds were grown in contaminated and noncontaminated soils. After harvesting, plants were used for the further analysis of growth parameters. The experiment comprised of six different treatments. The effects of heavy metal stress and bacterial inoculation on the plant root length; shoot length; fresh and dry weight; photosynthetic pigments; proline content; antioxidant activity; and absorption of metals were observed at the end of the experiment. The results revealed that industrially contaminated soils distinctly reduced the growth of plants. However, both PGPR strains enhanced the root length up to 105% and 80%. The shoot length was increased by 133% and 75%, and the fresh weight was increased by 121% and 129%. The proline content and antioxidant enzymes posed dual effects on the plants growing in industrially contaminated soil, allowing them to cope with the metal stress, which enhanced the plant growth. The proline content was increased up to 190% and 179% by the inoculation of bacterial strains. Antioxidant enzymes, such as SOD, increased to about 216% and 245%, while POD increased up to 48% and 49%, respectively. The results clearly show that the utilized PGPR strains might be strong candidates to assist S. sesban growth under heavy metal stress conditions. We highly suggest these PGPR strains for further implementation in field experiments.
Description of an Ultrasound-Guided Transverse Approach to the Transversus Thoracis Plane Block and Evaluation of Injectate Spread in Canine Cadavers
Authors: Alaman Manuel , González-Marrón Adrián , Lorente Cristina , Bonastre Cristina , Laborda Alicia
Published: September 2021

Transversus thoracis plane (TTP) block has demonstrated to produce analgesia in humans undergoing median sternotomy. The objectives of the study were to describe an ultrasound-guided transverse approach to the transversus thoracis plane (t-TTP) and to evaluate the spread of two injectable volumes in canine cadavers. Two cadavers were used to describe relevant gross anatomy of the ventral thoracic area and sonoanatomy between the fifth and sixth costal cartilages. Then, eight cadavers were used to describe the ultrasound-guided injection into the TTP and were dissected to evaluate the injectate spread and the intercostal nerves staining with two different dye-lidocaine volumes: low volume (LV) 0.5 mL kg−1 and high volume (HV) 1 mL kg−1. To compare the spread between both volumes the Fisher’s exact test and Wilcoxon signed-rank test were used. The solution spread along the TTP after all injections, staining a median number (range) of 3 (2–4) and 4 (3–5) nerves with LV and HV, respectively (p = 0.014). The injection of HV versus LV increases the number of stained nerves. Ultrasound-guided t-TTP is a feasible technique that provides staining of several intercostal nerves with a single injection site, so it could be useful to provide analgesia to the ventral chest wall.
Improving Stereotaxic Neurosurgery Techniques and Procedures Greatly Reduces the Number of Rats Used per Experimental Group—A Practice Report
Authors: Ferry Barbara , Gervasoni Damien
Published: September 2021

Techniques of stereotaxic surgery are commonly used in research laboratories by a range of students, technicians, and researchers. To meet the evolving requirements imposed by international legislation, and to promote the implementation of 3R rules (replacement, reduction, and refinement) by reducing experimental error, animal morbidity, and mortality, it is essential that standard operating procedures and proper conduct following such complex surgeries be precisely described and respected. The present report shows how refinements of our own neurosurgical techniques over decades, have significantly reduced the number of animals (rats) used in experiments and improved the animals’ well-being during the post-surgical recovery period. The current pre-, per-, and post-surgical procedures used in our laboratory are detailed. We describe the practical aspects of stereotaxic neurosurgery that have been refined in our laboratory since 1992 and that cover various areas including appropriate anesthesia and pain management during and after surgery, methods to determine the stereotaxic coordinates, and the best approach to the target brain structure. The application of these optimal surgical methods that combine reliable and reproducible results with an acute awareness of ethics and animal welfare leads to a significant reduction in the number of animals included in experimental research in accordance with ethical and regulatory rules as required by the European Directive on laboratory animal welfare.
The Formation and Invariance of Canine Nose Pattern of Beagle Dogs from Early Puppy to Young Adult Periods
Authors: Choi Hyeong In , Lee Yoonsuk , Shin Hyunjin , Lee Sungjin , Choi Stephanie Sujin , Han Chang Yong , Kwon Song-Hwa
Published: September 2021

The formation and invariance of the canine nose pattern is studied. Nose images of ten beagle dogs were collected for ten months from month two to month eleven. The nose patterns in these images are examined visually and by a biometric algorithm. It is found that the canine nose pattern is fully formed at the end of the second month since birth and remains invariant until the end of the eleventh month. This study also strongly indicates that the canine nose pattern can be used as a unique biometric marker for each individual dog.
Novel Growth Age Characterization for Fresh Herbal Ginseng Based on Quantitative Counting of the Calcium Oxalate Crystals through FEG-ESEM
Authors: Fan Siqing , Huang Yong , Gao Puxin , Cheng Chunsong
Published: September 2021

To reveal the accumulation of the calcium oxalate crystals (COH Crystals) during the growth and development of the taproot of Panax ginseng, and develop a novel and rapid characterization method to evaluate the growth age of commercial ginseng, multiple methods in micro characterization techniques of SAXS, Micro-CT, FEG-ESEM and Micro-Raman were used to identify the COH Crystals and establish a quantitative counting method for growth age identification. In this study, a cross-analysis with multiple methods proved for the first time with a Raman and Energy spectrum that the high-density particles widely distributed in the parenchyma cells of the xylem and cortex are COH Crystals; we also first realized quantitative counting of the COH Crystals on the cross-section of fresh ginseng samples. Moreover, catering to the testing requirements of the modern trading of fresh ginseng products, we also specifically established an interesting and useful mathematical equation (Y = 2.3797X − 1.2404) for growth age identification. The technology and strategy in this study effectively compensated for the shortcomings of chemical testing and other methods in technical limitations; hence, the application of more ginseng varieties to perform the technical optimization is expected.
Accented Speech Recognition Based on End-to-End Domain Adversarial Training of Neural Networks
Authors: Na Hyeong-Ju , Park Jeong-Sik
Published: September 2021

The performance of automatic speech recognition (ASR) may be degraded when accented speech is recognized because the speech has some linguistic differences from standard speech. Conventional accented speech recognition studies have utilized the accent embedding method, in which the accent embedding features are directly fed into the ASR network. Although the method improves the performance of accented speech recognition, it has some restrictions, such as increasing the computational costs. This study proposes an efficient method of training the ASR model for accented speech in a domain adversarial way based on the Domain Adversarial Neural Network (DANN). The DANN plays a role as a domain adaptation in which the training data and test data have different distributions. Thus, our approach is expected to construct a reliable ASR model for accented speech by reducing the distribution differences between accented speech and standard speech. DANN has three sub-networks: the feature extractor, the domain classifier, and the label predictor. To adjust the DANN for accented speech recognition, we constructed these three sub-networks independently, considering the characteristics of accented speech. In particular, we used an end-to-end framework based on Connectionist Temporal Classification (CTC) to develop the label predictor, a very important module that directly affects ASR results. To verify the efficiency of the proposed approach, we conducted several experiments of accented speech recognition for four English accents including Australian, Canadian, British (England), and Indian accents. The experimental results showed that the proposed DANN-based model outperformed the baseline model for all accents, indicating that the end-to-end domain adversarial training effectively reduced the distribution differences between accented speech and standard speech.
GIS-Based Simulated Annealing Algorithm for the Optimum Location of Fire Stations in the Madrid Region, Spain: Monitoring the Collapse Index
Authors: Martín-Fernández Susana , Martínez-Falero Eugenio , Peribáñez José Ramón , Ezquerra Alejandra
Published: September 2021

Recent increases in incidents make it unlikely for emergency systems to be able to meet incident requirements. In this paper, we formulate a new territorial measurement approach for the reliability of fire departments, the collapse index, to help decision makers determine their response capability. This new index expresses the maximum simultaneous workload in a pixel over one year, measured over time. Based on this index, we propose a new fire station (FS) optimum location model by applying the simulated annealing method in conjunction with a geographic information system. The formulation of the cost function as the minimum standard deviation of the FS workload, combined with the constraint that the maximum collapse index in any pixel must be less than a certain threshold, are two contributions of this work. Five optimisation processes are developed to locate between up to five FS and create collapse index maps in the Madrid Region. The maximum collapse index in a pixel with a new FS decreases from its initial value of 10,485 min to 2500 min when five new FS are built. The conclusion is that the proposed optimisation model meets the need for reliability in the emergency services and that the collapse index is a good measure to prevent overlapping in the system.
A Novel Ring Spring Vibration Isolator for Metro Superstructure
Authors: Ling Yuhong , Wu Shan , Gu Jingxin , Lai Hongtao
Published: September 2021

Due to the serious impact of metro vibration on people’s lives, it is important to design vibration isolators. In this study, the dynamic characteristics of a thick-walled ring spring are studied first. Through theoretical derivation, a new formula suitable for thick-walled ring springs is proposed. Finite element numerical analysis was performed to study the load–displacement curve and stress of the ring spring and verified the correctness of the formula. According to the studied mechanic characteristics, a novel ring spring isolator is proposed for vibration isolation of the metro superstructure. With the help of a ring spring, the proposed isolator has good energy absorption and self-reset function. The dynamic simulations were conducted in a multi-story building with the ring spring isolator as the isolator to study the vibration performance. It is common knowledge that the vertical natural frequency of the superstructure that is isolated by compression springs is given by the mass of the superstructure and the spring stiffness. In order to obtain vibration attenuation and control the vertical deformation, the spring stiffness needs to be 500–1000 kN/mm. Hence, it is clear that the vibration isolator does reduce the vertical eigenfrequency. By comparing the isolated structure with the non-isolated structure, it is proved that the new isolator can effectively improve a building’s serviceability.
6G Enabled Tactile Internet and Cognitive Internet of Healthcare Everything: Towards a Theoretical Framework
Authors: Padhi Prafulla , Charrua-Santos Fernando
Published: September 2021

Digital era deficiencies traditionally exist in healthcare applications because of the unbalanced distribution of medical resources, especially in rural areas globally. Cognitive data intelligence, which constitute the integration of cognitive computing, massive data analytics, and tiny artificial intelligence, especially tiny machine learning, can be used to palpate a patient’s health status, physiologically and psychologically transforming the current healthcare system. To remotely detect patients’ emotional state of diagnosing diseases, the integration of 6G enabled Tactile Internet, cognitive data intelligence, and Internet of Healthcare Everything is proposed to form the 6GCIoHE system that aims at achieving global ubiquitous accessibility, extremely low latency, high reliability, and elevated performance in cognitive healthcare in real time to ensure patients receive prompt treatment, especially for the haptic actions. Judiciously, a model-driven methodology is proffered to facilitate the 6GCIoHE system design and development that adopts different refinement levels to incorporate the cognitive healthcare requirements through the interactions of semantic management, process management, cognitive intelligence capabilities, and knowledge sources. Based on the 6GCIoHE system architecture, applications, and challenges, the aim of this study was accomplished by developing a novel theoretical framework to captivate further research within the cognitive healthcare field.
Accurate Sequence-Based Prediction of Deleterious nsSNPs with Multiple Sequence Profiles and Putative Binding Residues
Authors: Song Ruiyang , Cao Baixin , Peng Zhenling , Oldfield Christopher J. , Kurgan Lukasz , Wong Ka-Chun , Yang Jianyi
Published: September 2021

Non-synonymous single nucleotide polymorphisms (nsSNPs) may result in pathogenic changes that are associated with human diseases. Accurate prediction of these deleterious nsSNPs is in high demand. The existing predictors of deleterious nsSNPs secure modest levels of predictive performance, leaving room for improvements. We propose a new sequence-based predictor, DMBS, which addresses the need to improve the predictive quality. The design of DMBS relies on the observation that the deleterious mutations are likely to occur at the highly conserved and functionally important positions in the protein sequence. Correspondingly, we introduce two innovative components. First, we improve the estimates of the conservation computed from the multiple sequence profiles based on two complementary databases and two complementary alignment algorithms. Second, we utilize putative annotations of functional/binding residues produced by two state-of-the-art sequence-based methods. These inputs are processed by a random forests model that provides favorable predictive performance when empirically compared against five other machine-learning algorithms. Empirical results on four benchmark datasets reveal that DMBS achieves AUC > 0.94, outperforming current methods, including protein structure-based approaches. In particular, DMBS secures AUC = 0.97 for the SNPdbe and ExoVar datasets, compared to AUC = 0.70 and 0.88, respectively, that were obtained by the best available methods. Further tests on the independent HumVar dataset shows that our method significantly outperforms the state-of-the-art method SNPdryad. We conclude that DMBS provides accurate predictions that can effectively guide wet-lab experiments in a high-throughput manner.
Sensitivity of Acute Myelocytic Leukemia Cells to the Dienone Compound VLX1570 Is Associated with Inhibition of the Ubiquitin-Proteasome System
Authors: Selvaraju Karthik , Lotfi Kourosh , Gubat Johannes , Miquel Maria , Nilsson Amanda , Hill Julia , Jensen Lasse D. , Linder Stig , D’Arcy Pádraig
Published: September 2021

Dienone compounds with a 1,5-diaryl-3-oxo-1,4-pentadienyl pharmacophore have been widely reported to show tumor cell selectivity. These compounds target the ubiquitin-proteasome system (UPS), known to be essential for the viability of tumor cells. The induction of oxidative stress, depletion of glutathione, and induction of high-molecular-weight (HMW) complexes have also been reported. We here examined the response of acute myeloid leukemia (AML) cells to the dienone compound VLX1570. AML cells have relatively high protein turnover rates and have also been reported to be sensitive to depletion of reduced glutathione. We found AML cells of diverse cytogenetic backgrounds to be sensitive to VLX1570, with drug exposure resulting in an accumulation of ubiquitin complexes, induction of ER stress, and the loss of cell viability in a dose-dependent manner. Caspase activation was observed but was not required for the loss of cell viability. Glutathione depletion was also observed but did not correlate to VLX1570 sensitivity. Formation of HMW complexes occurred at higher concentrations of VLX1570 than those required for the loss of cell viability and was not enhanced by glutathione depletion. To study the effect of VLX1570 we developed a zebrafish PDX model of AML and confirmed antigrowth activity in vivo. Our results show that VLX1570 induces UPS inhibition in AML cells and encourage further work in developing compounds useful for cancer therapeutics.
Exploring the Relation between Emotional Intelligence, Subjective Wellness, and Psychological Distress: A Case Study of University Students in Taiwan
Authors: Edara Inna Reddy
Published: September 2021

Given the importance of emotions in human life and the necessity of managing one’s emotions, this research project conducted an 18 week course on emotional management for a group of undergraduate students, investigated the differences in emotional intelligence (EI) levels before and after the course, and assessed EI’s effect on selected subjective wellness and psychological distress variables. The study indicated many significant results. Most importantly, the comparison of the pre-course and post-course EI scores indicated that the EI skills and competencies could be learned and enhanced through formal education. Additionally, there were also significant regression coefficients of pre-course and post-course EI scores on both subjective wellness and psychological distress variables. The significant results endorse that EI knowledge, skills, and competencies could indeed be enhanced through formal education. In particular, the understanding of EI could help the educationists and helping professionals in assessing people’s level of EI, designing relevant courses, and raising the impact of EI on both overall wellness and psychological distress.
Contextual Communicative Competence in Multinational Infrastructure Projects
Authors: Järvenpää Anna-Therése , Pavlik Anthony , Gustavsson Tina Karrbom
Published: September 2021

Communication is dynamic, social, challenging, and a key quality factor for construction projects. This is especially the case in multinational and inter-organizational infrastructure projects where factors like culture and language differ among the involved actors. As infrastructure projects usually extend over longer periods of time, collaborative relationships need to be established in which the actors can develop, for example, mutual understanding, learning, and efficient working routines. By building on empirical data from contemporary infrastructure projects, we explore how international contractors and a large public client communicate in multinational infrastructure projects (i.e., what the challenges are and what competences are needed). The analysis is based on the linguistic framework of communicative competence, and we contribute to the development of collaborative models in construction project management by suggesting the concept of contextual communicative competence.
Bottom-Up Synthesis of Multi-Grained Ziegler–Natta Catalyst Based on MgO/MgCl2/TiCl4 Core–Shell Catalyst
Authors: Ikeda Tomohiro , Wada Toru , Bando Yusuke , Chammingkwan Patchanee , Taniike Toshiaki
Published: September 2021

Morphology control plays a major role in the design of solid catalysts. Since the heterogeneous Ziegler–Natta catalyst (ZNC) is based on the in situ synthesis of MgCl2 support in a top-down manner, the individual control of the exterior and the interior structure of the catalyst macro-particles is challenging. In this study, we successfully prepared a ZNC with a multi-grain interior structure by the spray-drying of MgO nanoparticles, inspired by the fact that the MgO/MgCl2/TiCl4 core–shell catalyst can maintain the morphology of the raw MgO nanoparticles. This catalyst is the first example of the bottom-up preparation of MgCl2-supported ZNC. Here, we report its basic preparation method, characterization results, and performance in the homo-polymerization of ethylene and propylene, and copolymerization with 1-hexene.
Evolution of CD8+ T Cell Receptor (TCR) Engineered Therapies for the Treatment of Cancer
Authors: Sun Yimo , Li Fenge , Sonnemann Heather , Jackson Kyle R. , Talukder Amjad H. , Katailiha Arjun S. , Lizee Gregory
Published: September 2021

Engineered T cell receptor T (TCR-T) cell therapy has facilitated the generation of increasingly reliable tumor antigen-specific adaptable cellular products for the treatment of human cancer. TCR-T cell therapies were initially focused on targeting shared tumor-associated peptide targets, including melanoma differentiation and cancer-testis antigens. With recent technological developments, it has become feasible to target neoantigens derived from tumor somatic mutations, which represents a highly personalized therapy, since most neoantigens are patient-specific and are rarely shared between patients. TCR-T therapies have been tested for clinical efficacy in treating solid tumors in many preclinical studies and clinical trials all over the world. However, the efficacy of TCR-T therapy for the treatment of solid tumors has been limited by a number of factors, including low TCR avidity, off-target toxicities, and target antigen loss leading to tumor escape. In this review, we discuss the process of deriving tumor antigen-specific TCRs, including the identification of appropriate tumor antigen targets, expansion of antigen-specific T cells, and TCR cloning and validation, including techniques and tools for TCR-T cell vector construction and expression. We highlight the achievements of recent clinical trials of engineered TCR-T cell therapies and discuss the current challenges and potential solutions for improving their safety and efficacy, insights that may help guide future TCR-T studies in cancer.
Control of Neuroinflammation through Radiation-Induced Microglial Changes
Authors: Boyd Alexandra , Byrne Sarah , Middleton Ryan J. , Banati Richard B. , Liu Guo-Jun
Published: September 2021

Microglia, the innate immune cells of the central nervous system, play a pivotal role in the modulation of neuroinflammation. Neuroinflammation has been implicated in many diseases of the CNS, including Alzheimer’s disease and Parkinson’s disease. It is well documented that microglial activation, initiated by a variety of stressors, can trigger a potentially destructive neuroinflammatory response via the release of pro-inflammatory molecules, and reactive oxygen and nitrogen species. However, the potential anti-inflammatory and neuroprotective effects that microglia are also thought to exhibit have been under-investigated. The application of ionising radiation at different doses and dose schedules may reveal novel methods for the control of microglial response to stressors, potentially highlighting avenues for treatment of neuroinflammation associated CNS disorders, such as Alzheimer’s disease and Parkinson’s disease. There remains a need to characterise the response of microglia to radiation, particularly low dose ionising radiation.
Development of a Pressure-Sensitive Coating Using Platinum Octaethylporphyrin
Authors: Cristea George Catalin , Ilina Sorina , Pelin George , Stefan Adriana , Pelin Cristina Elisabeta
Published: September 2021

In recent years, researchers have developed a new method of measuring the pressure on the surface using sensitive paints. This is an optical technique for determining surface pressure distributions by measuring changes in the intensity, emitted by certain excited molecules. The main advantage of the method over traditional techniques is the high resolution of the information. The only limitation of the resolution of a global map generated by the PSP (pressure-sensitive paint) technique is given by the capabilities of the image capture device. This paper describes the development of a technology for obtaining pressure-sensitive paint, in laboratory conditions, as an advanced measurement technique. The method has an application in many fields such as automotive, aerospace, or even medical.
Evaluating Impact of Race in Facial Recognition across Machine Learning and Deep Learning Algorithms
Authors: Coe James , Atay Mustafa
Published: September 2021

The research aims to evaluate the impact of race in facial recognition across two types of algorithms. We give a general insight into facial recognition and discuss four problems related to facial recognition. We review our system design, development, and architectures and give an in-depth evaluation plan for each type of algorithm, dataset, and a look into the software and its architecture. We thoroughly explain the results and findings of our experimentation and provide analysis for the machine learning algorithms and deep learning algorithms. Concluding the investigation, we compare the results of two kinds of algorithms and compare their accuracy, metrics, miss rates, and performances to observe which algorithms mitigate racial bias the most. We evaluate racial bias across five machine learning algorithms and three deep learning algorithms using racially imbalanced and balanced datasets. We evaluate and compare the accuracy and miss rates between all tested algorithms and report that SVC is the superior machine learning algorithm and VGG16 is the best deep learning algorithm based on our experimental study. Our findings conclude the algorithm that mitigates the bias the most is VGG16, and all our deep learning algorithms outperformed their machine learning counterparts.
Three-Dimensional Central Moment Lattice Boltzmann Method on a Cuboid Lattice for Anisotropic and Inhomogeneous Flows
Authors: Yahia Eman , Schupbach William , Premnath Kannan N.
Published: September 2021

Lattice Boltzmann (LB) methods are usually developed on cubic lattices that discretize the configuration space using uniform grids. For efficient computations of anisotropic and inhomogeneous flows, it would be beneficial to develop LB algorithms involving the collision-and-stream steps based on orthorhombic cuboid lattices. We present a new 3D central moment LB scheme based on a cuboid D3Q27 lattice. This scheme involves two free parameters representing the ratios of the characteristic particle speeds along the two directions with respect to those in the remaining direction, and these parameters are referred to as the grid aspect ratios. Unlike the existing LB schemes for cuboid lattices, which are based on orthogonalized raw moments, we construct the collision step based on the relaxation of central moments and avoid the orthogonalization of moment basis, which leads to a more robust formulation. Moreover, prior cuboid LB algorithms prescribe the mappings between the distribution functions and raw moments before and after collision by using a moment basis designed to separate the trace of the second order moments (related to bulk viscosity) from its other components (related to shear viscosity), which lead to cumbersome relations for the transformations. By contrast, in our approach, the bulk and shear viscosity effects associated with the viscous stress tensor are naturally segregated only within the collision step and not for such mappings, while the grid aspect ratios are introduced via simpler pre- and post-collision diagonal scaling matrices in the above mappings. These lead to a compact approach, which can be interpreted based on special matrices. It also results in a modular 3D LB scheme on the cuboid lattice, which allows the existing cubic lattice implementations to be readily extended to those based on the more general cuboid lattices. To maintain the isotropy of the viscous stress tensor of the 3D Navier–Stokes equations using the cuboid lattice, corrections for eliminating the truncation errors resulting from the grid anisotropy as well as those from the aliasing effects are derived using a Chapman–Enskog analysis. Such local corrections, which involve the diagonal components of the velocity gradient tensor and are parameterized by two grid aspect ratios, augment the second order moment equilibria in the collision step. We present a numerical study validating the accuracy of our approach for various benchmark problems at different grid aspect ratios. In addition, we show that our 3D cuboid central moment LB method is numerically more robust than its corresponding raw moment formulation. Finally, we demonstrate the effectiveness of the 3D cuboid central moment LB scheme for the simulations of anisotropic and inhomogeneous flows and show significant savings in memory storage and computational cost when used in lieu of that based on the cubic lattice.
Environmental and Economic Assessment of Portable Systems: Production of Wood-Briquettes and Torrefied-Briquettes to Generate Heat and Electricity
Authors: Sahoo Kamalakanta , Alanya-Rosenbaum Sevda , Bergman Richard , Abbas Dalia , Bilek E. M. (Ted)
Published: September 2021

This study assessed the environmental impacts and economic feasibility of generating heat using wood-briquettes (WBs), and heat and electricity using torrefied-wood-briquettes (TWBs). WBs and TWBs were manufactured from forest residues using portable systems and delivered to either residential consumers or power plants in the United States. An integrated cradle-to-grave life-cycle assessment (LCA) and techno-economic analysis (TEA) approach was used to quantify environmental impacts and minimum-selling prices (MSPs) of heat and electricity, respectively. Results illustrated that 82% and 59% of the cradle-to-grave global warming (GW) impact of producing heat resulted from the feedstock preparation in WBs and torrefaction in TWBs, respectively. About 46–54% of total cost in the production of heat were from labor and capital costs only. The GW impact of electricity production with TWBs was dominated by the torrefaction process (48% contribution). Capital cost (50%) was a major contributor to the total cost of electricity production using TWBs. The GW impacts of producing heat were 7–37 gCO₂eq/MJ for WBs, and 14–51 gCO₂eq/MJ for TWBs, whereas producing electricity using TWBs was 146–443 gCO₂eq/kWhe. MSPs of generating heat from WBs and TWBs were €1.09–€1.73 and €1.60–€2.26/MJ, respectively, whereas the MSP of electricity from TWBs was €20–€25/kWhe. Considering carbon and pile-burn credits, MSPs of heat and electricity were reduced by 60–90% compared to the base-case.
A Southwestern United States Pilot Investigation of Triatomine–Mite Prevalence
Authors: Dye-Braumuller Kyndall C. , Waltz Hanna , Lynn Mary K. , Klotz Stephen A. , Schmidt Justin O. , Romero Alvaro , Rodriguez Aquino Marvin Stanley , Palacios Valladares Jose Ricardo , Cornejo Rivas Pamela Michelle , Nolan Melissa S.
Published: September 2021

Background: Chagas disease is a leading cause of cardiac failure in Latin America. Due to poor safety profiles and efficacy of currently available therapeutics, prevention is a priority for the millions living at risk for acquiring this clinically important vector-borne disease. Triatomine vectors of the Chagas disease parasite, Trypanosoma cruzi, are found in the southwestern United States, but risk for autochthonous transmission is thought to be low. The role of ectoparasitic mites is under-explored regarding the ecology of triatomines and Chagas disease transmission. Methods: Triatomine collections were performed using three common entomologic techniques in 2020–2021 from four different locations in southern Arizona and New Mexico. Triatomines were analyzed visually under a 112.5× microscope for the presence of externally attached mites. Following mite removal, triatomines were tested for T. cruzi infection by PCR. Results: Approximately 13% of the collected triatomines had mites securely attached to their head, thorax, abdomen, and legs. More than one mite attached was a common finding among ectoparasitized triatomines. Mite presence, however, did not statistically influence triatomine T. cruzi status. Conclusions: Our findings add to a growing body of literature demonstrating the sustainability of mite-infested triatomine populations throughout the Western Hemisphere. Future investigations are warranted to better understand the biologic impact of triatomine mites and their potential to serve as a potential biological control tool.
The European Stag Beetle (Lucanus cervus) Monitoring Network: International Citizen Science Cooperation Reveals Regional Differences in Phenology and Temperature Response
Authors: Thomaes Arno , Barbalat Sylvie , Bardiani Marco , Bower Laura , Campanaro Alessandro , Fanega Sleziak Natalia , Gonçalo Soutinho João , Govaert Sanne , Harvey Deborah , Hawes Colin , Kadej Marcin , Méndez Marcos , Meriguet Bruno , Rink Markus , Rossi De Gasperis Sarah , Ruyts Sanne , Jelaska Lucija Šerić , Smit John , Smolis Adrian , Snegin Eduard , Tagliani Arianna , Vrezec Al
Published: September 2021

To address the decline in biodiversity, international cooperation in monitoring of threatened species is needed. Citizen science can play a crucial role in achieving this challenging goal, but most citizen science projects have been established at national or regional scales. Here we report on the establishment and initial findings of the European Stag Beetle Monitoring Network (ESBMN), an international network of stag beetle (Lucanus cervus) monitoring schemes using the same protocol. The network, started in 2016, currently includes 14 countries (see results) but with a strong variation in output regarding the number of transects (148 successful transects in total) and transect walks (1735). We found differences across European regions in the number of stag beetles recorded, related to phenology and temperature, but not for time of transect start. Furthermore, the initial experiences of the ESBMN regarding international cooperation, citizen science approach, and drop-out of volunteers is discussed. An international standardised protocol that allows some local variation is essential for international collaboration and data management, and analysis is best performed at the international level, whereas recruiting, training, and maintaining volunteers is best organised locally. In conclusion, we appeal for more joint international citizen science-based monitoring initiatives assisting international red-listing and conservation actions.