bluuebunny/crossref_metadata_2025 huggingface dataset:
Integration of HIV Prevention With Sexual and Reproductive Health Services: Evidence for Contraceptive Options and HIV Outcomes Study Experience of Integrating Oral Pre-exposure HIV Prophylaxis in Family Planning Services in Lusaka, Zambia
Authors: Kasaro Margaret Phiri , Sindano Ntazana , Chinyama Manze , Mudenda Mayaba , Chilaisha Florence , Price Joan T. , Chileshe Modesta
Published: July 2021

The WHO guideline on the integration of family planning (FP) and pre-exposure HIV prophylaxis (PrEP) to enhance the health of women and adolescent girls is reflected in the Zambia Consolidated Guidelines for Treatment and Prevention of HIV Infection, 2020. There is however a dearth of data on the integration of PrEP and FP in Zambia. We describe the integration of oral PrEP in FP services using the Evidence for Contraceptive Options and HIV Outcomes (ECHO) study experience at Kamwala District Health Center in Lusaka, Zambia. The provision of oral PrEP at Kamwala started in October 2017, lasting for ~11 months, and utilized the model where initial processes to offer PrEP were on-site followed by off-site referral to laboratory and PrEP provider services. The characteristics of 658 women who enrolled in ECHO at Kamwala are representative of women accessing FP services in Lusaka. About 644 of the enrollees were offered oral PrEP. The proportion of women accepting PrEP was low at 1.08% and the proportion of study visits at which PrEP was requested was also low at 0.57%. Those who accepted PrEP were above 20 years old, married, with at least primary education, sexual behavior, and risk comparable to decliners. The ECHO study experience indicates that the setup and integration of oral PrEP and FP services are feasible in the setting. However, uptake of PrEP was very low. Possible contributory factors were as follows: (1) timing of introduction of PrEP midway in the study, (2) PrEP being a new intervention, (3) challenges of autonomy of young women to include a daily pill into their lives and anticipated challenges to adherence because of fear of adverse events, (4) possible underdetermined risk due to use of an unvalidated risk assessment tool and assessment by health care provider vs. self-assessment, and (5) extra layer of challenges to negotiate due to needing for off-site referrals. Following these findings, we conclude that further research through demonstration projects of integration of oral PrEP and FP may provide solutions to low uptake. This information is critical for scaling up of integration HIV prevention services and sexual and reproductive health (SRH) services.
Machine Learning Algorithm Using Electronic Chart-Derived Data to Predict Delirium After Elderly Hip Fracture Surgeries: A Retrospective Case-Control Study
Authors: Zhao Hong , You Jiaming , Peng Yuexing , Feng Yi
Published: July 2021

Background: Elderly patients undergoing hip fracture repair surgery are at increased risk of delirium due to aging, comorbidities, and frailty. But current methods for identifying the high risk of delirium among hospitalized patients have moderate accuracy and require extra questionnaires. Artificial intelligence makes it possible to establish machine learning models that predict incident delirium risk based on electronic health data.

Methods: We conducted a retrospective case-control study on elderly patients (≥65 years of age) who received orthopedic repair with hip fracture under spinal or general anesthesia between June 1, 2018, and May 31, 2019. Anesthesia records and medical charts were reviewed to collect demographic, surgical, anesthetic features, and frailty index to explore potential risk factors for postoperative delirium. Delirium was assessed by trained nurses using the Confusion Assessment Method (CAM) every 12 h during the hospital stay. Four machine learning risk models were constructed to predict the incidence of postoperative delirium: random forest, eXtreme Gradient Boosting (XGBoosting), support vector machine (SVM), and multilayer perception (MLP). K-fold cross-validation was deployed to accomplish internal validation and performance evaluation.

Results: About 245 patients were included and postoperative delirium affected 12.2% (30/245) of the patients. Multiple logistic regression revealed that dementia/history of stroke [OR 3.063, 95% CI (1.231, 7.624)], blood transfusion [OR 2.631, 95% CI (1.055, 6.559)], and preparation time [OR 1.476, 95% CI (1.170, 1.862)] were associated with postoperative delirium, achieving an area under receiver operating curve (AUC) of 0.779, 95% CI (0.703, 0.856).

The accuracy of machine learning models for predicting the occurrence of postoperative delirium ranged from 83.67 to 87.75%. Machine learning methods detected 16 risk factors contributing to the development of delirium. Preparation time, frailty index uses of vasopressors during the surgery, dementia/history of stroke, duration of surgery, and anesthesia were the six most important risk factors of delirium.

Conclusion: Electronic chart-derived machine learning models could generate hospital-specific delirium prediction models and calculate the contribution of risk factors to the occurrence of delirium. Further research is needed to evaluate the significance and applicability of electronic chart-derived machine learning models for the detection risk of delirium in elderly patients undergoing hip fracture repair surgeries.
Size of Business Unit as a Factor Influencing Adoption of Digital Marketing: Empirical Analysis of SMEs Operating in the Central European Market
Authors: Pollák František , Markovič Peter
Published: July 2021

The issue of using marketing communication tools in the internet environment is quite extensive. Innovation of the usual procedures is usually influenced by market development. The presented study examines selected factors that may affect the acceptance of digital marketing tools in business practice in small and medium-sized enterprises (SMEs). The aim of the study is to answer the basic question of whether company size influences the willingness to adopt online marketing tools within the usual communication mix. Based on a thorough empirical analysis performed on a sample of companies operating in the Central European market, it can be stated that company size does not play a role in adopting online marketing tools. Most organizations, regardless of their size, still have reservations about investing in digital marketing. On the other hand, previous experience is a key determinant to perceive the benefits of using the internet for business purposes. Organizations actively using online marketing tools evaluate their contribution to their business very positively. It is a well-known fact that product testing significantly increases the degree of its acceptance. This knowledge is one of the key starting points in traditional marketing. Obviously, this assumption needs to be taken into account in both physical and digital environments. At the same time, it should be noted that the application of the basic online marketing tools in business practice is a prerequisite for all subsequent online activities.
Tuning of NASA Standard Breakup Model for Fragmentation Events Modelling
Authors: Cimmino Nicola , Isoletta Giorgio , Opromolla Roberto , Fasano Giancarmine , Basile Aniello , Romano Antonio , Peroni Moreno , Panico Alessandro , Cecchini Andrea
Published: July 2021

The continuous growth of space debris motivates the development and the improvement of tools that support the monitoring of a more and more congested space environment. Satellite breakup models play a key role to predict and analyze orbital debris evolution, and the NASA Standard Breakup Model represents a widely used reference, with current activities relevant to its evolution and improvements especially towards fragmentation of small mass spacecraft. From an operational perspective, an important point for fragmentation modelling concerns the tuning of the breakup model to achieve consistency with orbital data of observed fragments. In this framework, this paper proposes an iterative approach to estimate the model inputs, and in particular, the parents’ masses involved in a collision event. The iterative logic exploits the knowledge of Two Line Elements (TLE) of the fragments at some time after the event to adjust the input parameters of the breakup model with the objective of obtaining the same number of real fragments within a certain tolerance. Atmospheric re-entry is accounted for. As a result, the breakup model outputs a set of fragments whose statistical distribution, in terms of number and size, is consistent with the catalogued ones. The iterative approach is demonstrated for two different scenarios (i.e., catastrophic collision and non-catastrophic collision) using numerical simulations. Then, it is also applied to a real collision event.
Prediction of Key Crop Growth Parameters in a Commercial Greenhouse Using CFD Simulation and Experimental Verification in a Pilot Study
Authors: Chalill Subin Mattara , Chowdhury Snehaunshu , Karthikeyan Ramanujam
Published: July 2021

Controlled crop growth parameters, such as average air velocity, air temperature, and relative humidity (RH), inside the greenhouse are necessary prerequisites for commercial greenhouse operation. Frequent overshoots of such parameters are noticed in the Middle East. Traditional heating ventilation and air-conditioning (HVAC) systems in such greenhouses use axial fans and evaporative cooling pads to control the temperature. Such systems fail to respond to the extreme heat load variations during the day. In this study, we present the design and implementation of a single span, commercial greenhouse using box type evaporative coolers (BTEC) as the backbone of the HVAC system. The HVAC system is run by a fully-automated real time feedback-based climate management system (CMS). A full-scale, steady state computational fluid dynamics (CFD) simulation of the greenhouse is carried out assuming peak summer outdoor conditions. A pilot study is conducted to experimentally monitor the environmental parameters in the greenhouse over a 20-h period. The recorded data confirm that the crop growth parameters lie within their required ranges, indicating a successful design and implementation phase of the commercial greenhouse on a pilot scale.
Enhancing the Quality of Two Species of Baby Leaves Sprayed with Moringa Leaf Extract as Biostimulant
Authors: Toscano Stefania , Ferrante Antonio , Branca Ferdinando , Romano Daniela
Published: July 2021

Natural biostimulants obtained by plants are intensively used nowadays to improve crop yield and quality. The current study aimed to evaluate the effects of leaf extract of moringa (Moringa oleifera Lam.) (MLE) in modifying baby leaf characteristics of two genotypes of Brassica. The trial was started in October 2020 in a greenhouse; a cultivar of kale ‘Cavolo Laciniato Nero di Toscana’ (CL) and a Sicilian landrace of sprouting broccoli ‘Broccoli Nero’ (BN) were used. The plants, after 15, 30 and 40 days from sowing, were treated with MLE, while the control plants (C) with distilled water. Treatment with MLE modified morphological and nutritional value, but with different behavior in the two genotypes. In fact, in BN the treatment reduced the antioxidant activity (2.2-diphenyl-1-picrylhydrazyl (DPPH)) by 54%, while in CL the treatment increased this parameter by 40%. For the phenolic concentration and the sugar content the values recorded were significantly increased by MLE compared to control plants in CL, where in BN a significant reduction was registered. The CL plants treated with MLE showed a significant reduction (−70%) in nitrate content compared to the control plants; a negative effect was, instead, observed in BN, where the plants treated with moringa showed an increase of 60%. Results of this study showed how the foliar application of MLE was effective in improving various nutraceutical parameters, in particular in kale, because it appears to be a species-specific response.
Growth Performance, Biochemical Blood Indices, and Large Intestine Physiology of Rats Fed Diets with Alfalfa Protein-Xanthophyll Concentrate
Authors: Barszcz Marcin , Tuśnio Anna , Bachanek-Matusiewicz Ilona , Gawin Kamil , Skomiał Jacek , Taciak Marcin
Published: July 2021

The effect of dietary levels of alfalfa protein-xanthophyll concentrate (PXC) was determined in growing rats. Three groups of eight four-week-old male Wistar rats, with an average initial body weight of 61 g, were fed for 28 days either natural-ingredient diets without PXC or supplemented with 1.5% or 3% PXC. Growth performance, blood biochemistry, caecal fermentation, morphology of the large intestine, and mucin gene expression were evaluated. PXC did not affect growth performance but tended to decrease relative liver weight. Among biochemical blood parameters, only bilirubin decreased and uric acid increased in response to 1.5% and 3% PXC, respectively. Caecal fermentation was not affected, with the exception of isovaleric acid concentration, which tended to be higher in rats fed the diet containing 3% PXC. Colonic crypts tended to be deeper in rats fed the 3% PXC diet and the thickness of the colonic mucus layer was reduced by both PXC levels. In conclusion, PXC did not affect growth performance or caecal fermentation but decreased thickness of the protective mucus layer in the colon.
Immunization against Gonadotropin-Releasing Hormone in Female Beef Calves to Avoid Pregnancy at Time of Slaughter
Authors: Schütz Julia , Rudolph Jürn , Steiner Adrian , Rothenanger Esther , Hüsler Jürg , Hirsbrunner Gaby
Published: July 2021

Precocious puberty in beef heifers can result in unwanted pregnancies due to accidental breeding by farm bulls. Inbreeding, premature calving followed by dystocia and a high stillbirth rate or slaughtering of pregnant heifers are the consequences of this behaviour. The aim of the study was to postpone puberty by using Improvac®, an anti-GnRH vaccine. Therefore, n = 25 calves were twice vaccinated, once at the age of 5 and then at 6.5 months. n = 24 calves served as unvaccinated case controls. The onset of puberty was assigned if progesterone analysis in the blood exceeded 1 ng/mL. Progesterone values were excluded if the corresponding serum cortisol levels were ≥60 nmol/L. Our target was met, as in the vaccinated group none of the calves exceeded a progesterone value >1 ng/mL until the scheduled age of slaughter at 11 months and only 12.5% of the animals exceeded a progesterone value of 1 ng/mL over the whole measuring period (>400 days) compared with 56.5% of the calves in the control group. In conclusion, the favourable results from our study using the vaccine Improvac® represent an animal-friendly, non-invasive and reliable way to avoid early pregnancy in heifers as well as the slaughter of pregnant cattle.
Exploring the Multimodal Role of Yucca schidigera Extract in Protection against Chronic Ammonia Exposure Targeting: Growth, Metabolic, Stress and Inflammatory Responses in Nile Tilapia (Oreochromis niloticus L.)
Authors: Elbialy Zizy I. , Salah Abdallah S. , Elsheshtawy Ahmed , Rizk Merna , Abualreesh Muyassar H. , Abdel-Daim Mohamed M. , Salem Shimaa M. R. , Askary Ahmad El , Assar Doaa H.
Published: July 2021

Ammonia is a critical hazardous nitrogen metabolic product in aquaculture. Despite trials for its control, ammonia intoxication remains one of the most critical issues to overcome. In this study, we explored the modulatory effect and potential mechanism by which Yucca schidigera extract (YSE) can ameliorate ammonia intoxication-induced adverse effects on tilapia health and metabolism. A total number of 120 Nile tilapia were evenly assigned into four groups with three replicates each. The first group served as normal control group; the second group was exposed to ammonia alone from the beginning of the experiment and for four weeks. The third group was supplied with YSE in water at a dose of 8 mg/L and exposed to ammonia. The fourth group was supplied with YSE only in water at a dose of 8 mg/L. YSE supplementation succeeded in improving water quality by reducing pH and ammonia levels. Moreover, YSE supplementation markedly alleviated chronic ammonia-induced adverse impacts on fish growth by increasing the final body weight (FBW), specific growth rate (SGR), feed intake and protein efficiency ratio (PER) while reducing the feed conversion ratio (FCR) via improvements in food intake, elevation of hepatic insulin-like growth factor (ILGF-1) and suppression of myostatin (MSTN) expression levels with the restoration of lipid reserves and the activation of lipogenic potential in adipose tissue as demonstrated by changes in the circulating metabolite levels. In addition, the levels of hepato-renal injury biomarkers were restored, hepatic lipid peroxidation was inhibited and the levels of hepatic antioxidant biomarkers were enhanced. Therefore, the current study suggests that YSE supplementation exerted an ameliorative role against chronic ammonia-induced oxidative stress and toxic effects due to its free radical-scavenging potential, potent antioxidant activities and anti-inflammatory effects.
A Comparison of the Behavior, Physiology, and Offspring Resilience of Gestating Sows When Raised in a Group Housing System and Individual Stalls
Authors: Liu Xin , Song Pengkang , Yan Hua , Zhang Longchao , Wang Ligang , Zhao Fuping , Gao Hongmei , Hou Xinhua , Shi Lijun , Li Bugao , Wang Lixian
Published: July 2021

Being in a confined environment causes chronic stress in gestating sows, which is detrimental for sow health, welfare and, consequently, offspring physiology. This study assessed the health and welfare of gestating sows housed in a group housing system compared to individual gestation stalls. After pregnancy was confirmed, experimental sows were divided randomly into two groups: the group housing system (GS), with the electronic sow feeding (ESF) system; or individual stall (IS). The behavior of sows housed in the GS or IS was then compared; throughout pregnancy, GS sows displayed more exploratory behavior, less vacuum chewing, and less sitting behavior (p < 0.05). IS sows showed higher stress hormone levels than GS sows. In particular, at 41 days of gestation, the concentration of the adrenocorticotropic hormone (ACTH) and adrenaline (A) in IS sows was significantly higher than that of GS sows, and the A level of IS sows remained significantly higher at 71 days of gestation (p < 0.01). The lipopolysaccharide (LPS) test was carried out in the weaned piglets of the studied sows. Compared with the offspring of gestating sows housed in GS (PG) or IS (PS), PG experienced a shorter period of high temperature and showed a quicker return to the normal state (p < 0.05). Additionally, their lower levels of stress hormone (p < 0.01) suggest that PG did not suffer from as much stress as PS. These findings suggested that gestating sows housed in GS were more able to carry out their natural behaviors and, therefore, had lower levels of stress and improved welfare. In addition, PG also showed better disease resistance and resilience. These results will provide a research basis for the welfare and breeding of gestating sows.
The Goat as a Risk Factor for Parasitic Infections in Ovine Flocks
Authors: García-Dios David , Panadero Rosario , Díaz Pablo , Viña Miguel , Remesar Susana , Prieto Alberto , López-Lorenzo Gonzalo , Martínez-Calabuig Néstor , Díez-Baños Pablo , Morrondo Patrocinio , López Ceferino M.
Published: July 2021

Small ruminants in northwestern Spain are frequently managed in mixed flocks. Sheep–goat joint management has not been considered a risk factor for parasite infection, so the main objective of this study was to establish if mixed management with goats supposes a risk factor for parasitic infections in ovine flocks. Two thousand and ninety-three sheep were sampled from 74 commercial meat ovine flocks for diagnostic of the main parasites. Goat contact was a risk factor for sheep to be infected by protostrongylids, Dictyocaulus filaria, gastrointestinal nematodes and Eimeria spp. In contrast, Moniezia, Fasciola hepatica, Dicrocoelium dendriticum and Paramphistomidae prevalences were not influenced. Sheep–goat mixed management can be considered as a risk factor, since goats would act as a source of pasture contamination for interspecific parasites (protostrongylids, Dictyocaulus filaria and gastrointestinal nematodes). In relation to host-specific parasites, such as Eimeria spp., goats cannot be considered as a source for sheep, but competition for food and spaces between both small ungulates can suppose a reduced grazing area to sheep, provoking high environmental contamination and stress that facilitate their infection. Future epidemiological studies for parasitic infections in small ruminants should consider sheep–goat mixed management as a possible risk factor to be included in multivariate analyses.
Efficient Management of Power Losses from Renewable Sources Using Removable E.V. Batteries
Authors: Bocean Claudiu George , Vărzaru Anca Antoaneta , Al-Floarei Andreea Teodora , Dumitriu Simona , Diaconescu Dragoş Laurenţiu , Barbu Mihai Constantin Răzvan
Published: July 2021

Electric vehicles (E.V.) are one of the feasible solutions to address the challenges of sustainable development that require particular attention, such as climate change, depletion of fossil fuel reserves, and greenhouse gas emissions. In addition to the environmental benefits of electric vehicles, they can also be used as a storage system to alleviate the challenges posed by the variability of renewable electricity sources and to provide the network with ancillary benefits, such as voltage regulation and frequency regulation. Furthermore, using removable batteries by electric vehicles to store renewable energy is an innovative and effective solution to combat the increase in GHG emissions. In this article, using the autoregressive integrated moving average forecast model, we estimate the necessary storage capacity to contribute to the adjustment of the energy system increasingly powered by renewable energy sources. Also, we estimate the number of electric vehicles needed to take over the excess energy produced by renewable sources when the conventional grid cannot take over this surplus. The forecasts have the year 2050 as a time horizon. The results show that removable E.V. batteries can be an efficient solution for managing and storing energy lost in the temporal incongruity of demand with supply in the energy market.
What Disease Does This Patient Have? A Large-Scale Open Domain Question Answering Dataset from Medical Exams
Authors: Jin Di , Pan Eileen , Oufattole Nassim , Weng Wei-Hung , Fang Hanyi , Szolovits Peter
Published: July 2021

Open domain question answering (OpenQA) tasks have been recently attracting more and more attention from the natural language processing (NLP) community. In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA, collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. We implement both rule-based and popular neural methods by sequentially combining a document retriever and a machine comprehension model. Through experiments, we find that even the current best method can only achieve 36.7%, 42.0%, and 70.1% of test accuracy on the English, traditional Chinese, and simplified Chinese questions, respectively. We expect MedQA to present great challenges to existing OpenQA systems and hope that it can serve as a platform to promote much stronger OpenQA models from the NLP community in the future.
Classification of Apple Disease Based on Non-Linear Deep Features
Authors: Ayaz Hamail , Rodríguez-Esparza Erick , Ahmad Muhammad , Oliva Diego , Pérez-Cisneros Marco , Sarkar Ram
Published: July 2021

Diseases in apple orchards (rot, scab, and blotch) worldwide cause a substantial loss in the agricultural industry. Traditional hand picking methods are subjective to human efforts. Conventional machine learning methods for apple disease classification depend on hand-crafted features that are not robust and are complex. Advanced artificial methods such as Convolutional Neural Networks (CNN’s) have become a promising way for achieving higher accuracy although they need a high volume of samples. This work investigates different Deep CNN (DCNN) applications to apple disease classification using deep generative images to obtain higher accuracy. In order to achieve this, our work progressively modifies a baseline model by using an end-to-end trained DCNN model that has fewer parameters, better recognition accuracy than existing models (i.e., ResNet, SqeezeNet, and MiniVGGNet). We have performed a comparative study with state-of-the-art CNN as well as conventional methods proposed in the literature, and comparative results confirm the superiority of our proposed model.
Nozzle Thermal Estimation for Fused Filament Fabricating 3D Printer Using Temporal Convolutional Neural Networks
Authors: Agron Danielle Jaye S. , Lee Jae-Min , Kim Dong-Seong
Published: July 2021

A preventive maintenance embedded for the fused deposition modeling (FDM) printing technique is proposed. A monitoring and control integrated system is developed to reduce the risk of having thermal degradation on the fabricated products and prevent printing failure; nozzle clogging. As for the monitoring program, the proposed temporal neural network with a two-stage sliding window strategy (TCN-TS-SW) is utilized to accurately provide the predicted thermal values of the nozzle tip. These estimated thermal values are utilized to be the stimulus of the control system that performs countermeasures to prevent the anomaly that is bound to happen. The performance of the proposed TCN-TS-SW is presented in three case studies. The first scenario is when the proposed system outperforms the other existing machine learning algorithms namely multi-look back LSTM, GRU, LSTM, and the generic TCN architecture in terms of obtaining the highest training accuracy and lowest training loss. TCN-TS-SW also outperformed the mentioned algorithms in terms of prediction accuracy measured by the performance metrics like RMSE, MAE, and R2 scores. In the second case, the effect of varying the window length and the changing length of the forecasting horizon. This experiment reveals the optimized parameters for the network to produce an accurate nozzle thermal estimation.
Mechanical Properties of Cement-Treated Soil Mixed with Cellulose Nanofibre
Authors: Takahashi Hidenori , Omori Shinya , Asada Hideyuki , Fukawa Hirofumi , Gotoh Yusuke , Morikawa Yoshiyuki
Published: July 2021

Cellulose nanofibre (CNF), a material composed of ultrafine fibres of wood cellulose fibrillated to nano-order level, is expected to be widely used because of its excellent properties. However, in the field of geotechnical engineering, almost no progress has been made in the development of techniques for using CNFs. The authors have focused on the use of CNF as an additive in cement treatment for soft ground, where cement is added to solidify the ground, because CNF can reduce the problems associated with cement-treated soil. This paper presents the results of a study on the method of mixing CNF, the strength and its variation obtained by adding CNF, and the change in permeability. CNF had the effect of mixing the cement evenly and reducing the variation in the strength of the treated soil. The CNF mixture increased the strength at the initial age but reduced the strength development in the long term. The addition of CNF also increased the flexural strength, although it hardly changed the permeability.
“Gymmy”: Designing and Testing a Robot for Physical and Cognitive Training of Older Adults
Authors: Krakovski Maya , Kumar Shikhar , Givati Shai , Bardea Moshe , Zafrani Oded , Nimrod Galit , Bar-Haim Simona , Edan Yael
Published: July 2021

Physical and cognitive training can maintain and improve older adults’ independence and quality of life. Given the demographic growth of the older adult population and the shortage of caregivers, there is a need for personal trainers for physical and cognitive activities. This study suggests that social robots can satisfy this demand and presents the development of “Gymmy”, a robotic system for the physical and cognitive training of older adults. The system design includes a humanoid mechanical-looking robot to demonstrate exercises, an RGB-Depth (RGB-D) camera to measure performance and a touch screen and speakers to provide instructions and feedback. Experiments with 26 older adults (65–84 years of age) were performed in home environments to examine the effect of users’ characteristics (age, gender, education and attitude toward robots), the addition of cognitive training and the success rate of the acceptability of a robot trainer. The results showed that age, attitude and education influenced the acceptance of the robotic system. The findings highlight the importance of customizing the system to the needs of different users and the role of meaningful feedback. The system was proven to be robust and reliable, demonstrating clear potential to be used as a personal trainer and as a means of motivating older adults.
Remote Virtual Simulation for Incident Commanders—Cognitive Aspects
Authors: Wijkmark Cecilia Hammar , Metallinou Maria Monika , Heldal Ilona
Published: July 2021

Due to the COVID-19 restrictions, on-site Incident Commander (IC) practical training and examinations in Sweden were canceled as of March 2020. The graduation of one IC class was, however, conducted through Remote Virtual Simulation (RVS), the first such examination to our current knowledge. This paper presents the necessary enablers for setting up RVS and its influence on cognitive aspects of assessing practical competences. Data were gathered through observations, questionnaires, and interviews from students and instructors, using action-case research methodology. The results show the potential of RVS for supporting higher cognitive processes, such as recognition, comprehension, problem solving, decision making, and allowed students to demonstrate whether they had achieved the required learning objectives. Other reported benefits were the value of not gathering people (imposed by the pandemic), experiencing new, challenging incident scenarios, increased motivation for applying RVS based training both for students and instructors, and reduced traveling (corresponding to 15,400 km for a class). While further research is needed for defining how to integrate RVS in practical training and assessment for IC education and for increased generalizability, this research pinpoints current benefits and limitations, in relation to the cognitive aspects and in comparison, to previous examination formats.
Production of Microfibrillated Cellulose from Fast-Growing Poplar and Olive Tree Pruning by Physical Pretreatment
Authors: Ibarra David , Martín-Sampedro Raquel , Wicklein Bernd , Fillat Úrsula , Eugenio María E.
Published: July 2021

Motivated by the negative impact of fossil fuel consumption on the environment, the need arises to produce materials and energy from renewable sources. Cellulose, the main biopolymer on Earth, plays a key role in this context, serving as a platform for the development of biofuels, chemicals and novel materials. Among the latter, micro- and nanocellulose have been receiving increasing attention in the last few years. Their many attractive properties, i.e., thermal stability, high mechanical resistance, barrier properties, lightweight, optical transparency and ease of chemical modification, allow their use in a wide range of applications, such as paper or polymer reinforcement, packaging, construction, membranes, bioplastics, bioengineering, optics and electronics. In view of the increasing demand for traditional wood pulp (e.g., obtained from eucalypt, birch, pine, spruce) for micro/nanocellulose production, dedicated crops and agricultural residues can be interesting as raw materials for this purpose. This work aims at achieving microfibrillated cellulose production from fast-growing poplar and olive tree pruning using physical pretreatment (PFI refining) before the microfibrillation stage. Both raw materials yielded microfibrillated cellulose with similar properties to that obtained from a commercial industrial eucalypt pulp, producing films with high mechanical properties and low wettability. According to these properties, different applications for cellulose microfibers suspensions and films are discussed.
Visualized Malware Multi-Classification Framework Using Fine-Tuned CNN-Based Transfer Learning Models
Authors: El-Shafai Walid , Almomani Iman , AlKhayer Aala
Published: July 2021

There is a massive growth in malicious software (Malware) development, which causes substantial security threats to individuals and organizations. Cybersecurity researchers makes continuous efforts to defend against these malware risks. This research aims to exploit the significant advantages of Transfer Learning (TL) and Fine-Tuning (FT) methods to introduce efficient malware detection in the context of imbalanced families without the need to apply complex features extraction or data augmentation processes. Therefore, this paper proposes a visualized malware multi-classification framework to avoid false positives and imbalanced datasets’ challenges through using the fine-tuned convolutional neural network (CNN)-based TL models. The proposed framework comprises eight different FT CNN models including VGG16, AlexNet, DarkNet-53, DenseNet-201, Inception-V3, Places365-GoogleNet, ResNet-50, and MobileNet-V2. First, the binary files of different malware families were transformed into 2D images and then forwarded to the FT CNN models to detect and classify the malware families. The detection and classification performance was examined on a benchmark Malimg imbalanced dataset using different, comprehensive evaluation metrics. The evaluation results prove the FT CNN models’ significance in detecting malware types with high accuracy that reached 99.97% which also outperforms the performance of related machine learning (ML) and deep learning (DL)-based malware multi-classification approaches tested on the same malware dataset.
An Efficient Polynomial Chaos Expansion Method for Uncertainty Quantification in Dynamic Systems
Authors: Son Jeongeun , Du Yuncheng
Published: July 2021

Uncertainty is a common feature in first-principles models that are widely used in various engineering problems. Uncertainty quantification (UQ) has become an essential procedure to improve the accuracy and reliability of model predictions. Polynomial chaos expansion (PCE) has been used as an efficient approach for UQ by approximating uncertainty with orthogonal polynomial basis functions of standard distributions (e.g., normal) chosen from the Askey scheme. However, uncertainty in practice may not be represented well by standard distributions. In this case, the convergence rate and accuracy of the PCE-based UQ cannot be guaranteed. Further, when models involve non-polynomial forms, the PCE-based UQ can be computationally impractical in the presence of many parametric uncertainties. To address these issues, the Gram–Schmidt (GS) orthogonalization and generalized dimension reduction method (gDRM) are integrated with the PCE in this work to deal with many parametric uncertainties that follow arbitrary distributions. The performance of the proposed method is demonstrated with three benchmark cases including two chemical engineering problems in terms of UQ accuracy and computational efficiency by comparison with available algorithms (e.g., non-intrusive PCE).
Non-Linear Thermoelectric Devices with Surface-Disordered Nanowires
Authors: Markoš Peter , Muttalib Khandker
Published: July 2021

We reviewed some recent ideas to improve the efficiency and power output of thermoelectric nano-devices. We focused on two essentially independent aspects: (i) increasing the charge current by taking advantage of an interplay between the material and the thermodynamic parameters, which is only available in the non-linear regime; and (ii) decreasing the heat current by using nanowires with surface disorder, which helps excite localized phonons at random positions that can strongly scatter the propagating phonons carrying the thermal current.
Water Particles Monitoring in the Atacama Desert: SPC Approach Based on Proportional Data
Authors: Fonseca Anderson , Ferreira Paulo Henrique , Nascimento Diego Carvalho do , Fiaccone Rosemeire , Ulloa-Correa Christopher , García-Piña Ayón , Louzada Francisco
Published: July 2021

Statistical monitoring tools are well established in the literature, creating organizational cultures such as Six Sigma or Total Quality Management. Nevertheless, most of this literature is based on the normality assumption, e.g., based on the law of large numbers, and brings limitations towards truncated processes as open questions in this field. This work was motivated by the register of elements related to the water particles monitoring (relative humidity), an important source of moisture for the Copiapó watershed, and the Atacama region of Chile (the Atacama Desert), and presenting high asymmetry for rates and proportions data. This paper proposes a new control chart for interval data about rates and proportions (symbolic interval data) when they are not results of a Bernoulli process. The unit-Lindley distribution has many interesting properties, such as having only one parameter, from which we develop the unit-Lindley chart for both classical and symbolic data. The performance of the proposed control chart is analyzed using the average run length (ARL), median run length (MRL), and standard deviation of the run length (SDRL) metrics calculated through an extensive Monte Carlo simulation study. Results from the real data applications reveal the tool’s potential to be adopted to estimate the control limits in a Statistical Process Control (SPC) framework.
Exploration of Feature Representations for Predicting Learning and Retention Outcomes in a VR Training Scenario
Authors: Moore Alec G. , McMahan Ryan P. , Ruozzi Nicholas
Published: July 2021

Training and education of real-world tasks in Virtual Reality (VR) has seen growing use in industry. The motion-tracking data that is intrinsic to immersive VR applications is rich and can be used to improve learning beyond standard training interfaces. In this paper, we present machine learning (ML) classifiers that predict outcomes from a VR training application. Our approach makes use of the data from the tracked head-mounted display (HMD) and handheld controllers during VR training to predict whether a user will exhibit high or low knowledge acquisition, knowledge retention, and performance retention. We evaluated six different sets of input features and found varying degrees of accuracy depending on the predicted outcome. By visualizing the tracking data, we determined that users with higher acquisition and retention outcomes made movements with more certainty and with greater velocities than users with lower outcomes. Our results demonstrate that it is feasible to develop VR training applications that dynamically adapt to a user by using commonly available tracking data to predict learning and retention outcomes.
Big Data Research in Fighting COVID-19: Contributions and Techniques
Authors: Riswantini Dianadewi , Nugraheni Ekasari , Arisal Andria , Khotimah Purnomo Husnul , Munandar Devi , Suwarningsih Wiwin
Published: July 2021

The COVID-19 pandemic has induced many problems in various sectors of human life. After more than one year of the pandemic, many studies have been conducted to discover various technological innovations and applications to combat the virus that has claimed many lives. The use of Big Data technology to mitigate the threats of the pandemic has been accelerated. Therefore, this survey aims to explore Big Data technology research in fighting the pandemic. Furthermore, the relevance of Big Data technology was analyzed while technological contributions to five main areas were highlighted. These include healthcare, social life, government policy, business and management, and the environment. The analytical techniques of machine learning, deep learning, statistics, and mathematics were discussed to solve issues regarding the pandemic. The data sources used in previous studies were also presented and they consist of government officials, institutional service, IoT generated, online media, and open data. Therefore, this study presents the role of Big Data technologies in enhancing the research relative to COVID-19 and provides insights into the current state of knowledge within the domain and references for further development or starting new studies are provided.
COVID-19 Biomarkers and Advanced Sensing Technologies for Point-of-Care (POC) Diagnosis
Authors: Etienne Ernst Emmanuel , Nunna Bharath Babu , Talukder Niladri , Wang Yudong , Lee Eon Soo
Published: July 2021

COVID-19, also known as SARS-CoV-2 is a novel, respiratory virus currently plaguing humanity. Genetically, at its core, it is a single-strand positive-sense RNA virus. It is a beta-type Coronavirus and is distinct in its structure and binding mechanism compared to other types of coronaviruses. Testing for the virus remains a challenge due to the small market available for at-home detection. Currently, there are three main types of tests for biomarker detection: viral, antigen and antibody. Reverse Transcription-Polymerase Chain Reaction (RT-PCR) remains the gold standard for viral testing. However, the lack of quantitative detection and turnaround time for results are drawbacks. This manuscript focuses on recent advances in COVID-19 detection that have lower limits of detection and faster response times than RT-PCR testing. The advancements in sensing platforms have amplified the detection levels and provided real-time results for SARS-CoV-2 spike protein detection with limits as low as 1 fg/mL in the Graphene Field Effect Transistor (FET) sensor. Additionally, using multiple biomarkers, detection levels can achieve a specificity and sensitivity level comparable to that of PCR testing. Proper biomarker selection coupled with nano sensing detection platforms are key in the widespread use of Point of Care (POC) diagnosis in COVID-19 detection.
Third Molar Agenesis Is Associated with Facial Size
Authors: Gkantidis Nikolaos , Tacchi Manuel , Oeschger Elias S. , Halazonetis Demetrios , Kanavakis Georgios
Published: July 2021

Individuals with congenitally missing permanent teeth, other than third molars, present smaller craniofacial configurations compared to normal controls. However, it is not known if agenesis of third molars is part of the same mechanism. Therefore, this study assessed individuals with and without isolated third molar agenesis and tested the relation of this condition to the size of their facial configurations, using geometric morphometric methods. We show that the absence of one or more third molars is associated with a smaller maxilla, smaller mandible and a smaller overall facial configuration. The effect was larger as the number of missing third molars increased. For example, the size of the mandibular centroids in five 16-year-old females with no, one, two, three or four missing third molars showed a size reduction of approximately 2.5 mm per missing third molar. In addition, in cases with third molar agenesis in one jaw only, the effect was also evident on the opposite jaw. Our findings suggest that isolated third molar agenesis is part of a developmental mechanism resulting also in craniofacial size reduction. This might be the effect of an evolutionary process observed in humans, leading to fewer and smaller teeth, as well as smaller facial structures.
Lymphadenopathy after the Anti-COVID-19 Vaccine: Multiparametric Ultrasound Findings
Authors: Cocco Giulio , Delli Pizzi Andrea , Fabiani Stefano , Cocco Nino , Boccatonda Andrea , Frisone Alessio , Scarano Antonio , Schiavone Cosima
Published: July 2021

Background: Post-anti-COVID-19 vaccine lymphadenopathy has recently been described in the literature. In this study, we investigated the multiparametric US findings of patients with post-vaccine lymphadenopathy and compared these findings among different anti-COVID-19 vaccines. Methods: We retrospectively evaluated 24 patients who underwent US between January and May 2021 due to post-anti-COVID-19 lymphadenopathy. The presence, size, location, number, morphology, cortex-hilum, superb microvascular imaging (SMI) and elastosonography of lymph nodes were assessed. Descriptive statistics were calculated and differences among anti-COVID-19 vaccines were analyzed using the Kruskal–Wallis test. A p-value ≤ 0.05 was considered statistically significant. Results: Sixty-six nodes were assessed. They were axillary (mean 1.6 cm ± 0.16) in 11 patients (45.8%) and supraclavicular (mean 0.9 cm ± 0.19) in 13 patients (54.2%). In 20 patients (83.3%), the number of nodes was ≤3. Prevalent US features included oval morphology (18, 75%), asymmetric cortex with hilum evidence (9, 37.5%), central and peripheral vascular signals (12, 50%) at SMI and elastosonography patterns similar to the surrounding tissue (15, 71.4%). No significant differences among the three anti-COVID-19 vaccines were observed (p > 0.05). Conclusions: Anti-COVID-19 vaccines may present lymphadenopathy with “worrisome” US features regarding size, shape, morphology, cortex-hilum, SMI and elastosonography. An awareness of the patient’s history and US findings may help in the early recognition of this clinical scenario and in the appropriate selection of patients for a short-term US follow-up.
MCPIP1 Enhances TNF-α-Mediated Apoptosis through Downregulation of the NF-κB/cFLIP Axis
Authors: Suk Fat-Moon , Chang Chi-Ching , Sun Pei-Chi , Ke Wei-Ting , Chung Chia-Chen , Lee Kun-Lin , Chan Tze-Sian , Liang Yu-Chih
Published: July 2021

Monocyte chemoattractant protein-1-induced protein 1 (MCPIP1) is rapidly produced under proinflammatory stimuli, thereby feeding back to downregulate excessive inflammation. In this study, we used the stable, inducible expressions of wild-type (WT) MCPIP1 and an MCPIP1-D141N mutant in T-REx-293 cells by means of a tetracycline on (Tet-on) system. We found that WT MCPIP1 but not MCPIP1-D141N mutant expression dramatically increased apoptosis, caspase-3, -7, -8, and -9 activation, and c-Jun N-terminal kinase (JNK) phosphorylation in TNF-α-treated cells. The pan-caspase inhibitor, z-VAD-fmk, and the caspase-1 inhibitor, z-YVAD-fmk, but not the JNK inhibitor, SP600125, significantly reversed apoptosis and caspase activation in TNF-α/MCPIP1-treated cells. Surprisingly, MCPIP1 itself was also cleaved, and the cleavage was suppressed by treatment with the pan-caspase inhibitor and caspase-1 inhibitor. Moreover, MCPIP1 was found to contain a caspase-1/-4 consensus recognition sequence located in residues 234~238. As expected, the WT MCPIP1 but not the MCPIP1-D141N mutant suppressed NF-κB activation, as evidenced by inhibition of IκB kinase (IKK) phosphorylation and IκB degradation using Western blotting, IKK activity using in vitro kinase activity, and NF-κB translocation to nuclei using an immunofluorescence assay. Interestingly, MCPIP1 also significantly inhibited importin α3 and importin α4 expressions, which are major nuclear transporter receptors for NF-κB. Inhibition of NF-κB activation further downregulated expression of the caspase-8 inhibitor, cFLIP. In summary, the results suggest that MCPIP1 could enhance the TNF-α-induced apoptotic pathway through decreasing NF-κB activation and cFLIP expression.