bluuebunny/crossref_metadata_2025_split huggingface dataset:
A pressure driven electric energy generator exploiting a micro- to nano-scale glass porous filter with ion flow originating from water
Authors: Tanaka Yo , Amaya Satoshi , Funano Shun-ichi , Sugawa Hisashi , Nagafuchi Wataru , Ito Yuri , Aishan Yusufu , Liu Xun , Kamamichi Norihiro , Yalikun Yaxiaer 
Published: October 2022

AbstractWe demonstrated a pressure driven energy harvesting device using water and that features a glass filter with porous channels. We employed powder sintering to fabricate the glass filter (2 cm diameter, 3 mm thickness) by packing a powder of borosilicate glass particles into a carbon mold and then thermally fusing this at 700°C under pressure. In constant flow rate experiment, the optimum average pore radius of the filter for power generation was 12 μm. Using this filter, power of 3.8 mW (27 V, 0.14 mA, 0.021% energy efficiency) was generated at a water flow speed of 50 mm/s. In constant pressure experiment, a power generator was equipped with a foot press unit with a 60 kg weight (830 kPa) and 50 mL of water. The optimum average pore radius for power generation in this experiment was 12 μm and power of 4.8 mW (18 V, 0.26 mA, 0.017% energy efficiency) was generated with 1.7 s duration. This was enough power for direct LED lighting and the capacitors could store enough energy to rotate a fan and operate a wireless communicator. Our pressure driven device is suitable for energy harvesting from slow movements like certain human physiological functions, e.g. walking.
Environmentally friendly, highly efficient, and large stokes shift-emitting ZnSe:Mn2+/ZnS core/shell quantum dots for luminescent solar concentrators
Authors: Byambasuren Nyamsuren , Hong A-Ra , Lee Woo-Young , Byun Ji Young , Kang Gumin , Ko Hyungduk , Jang Ho Seong 
Published: October 2022

AbstractIn this study, heavy-metal-free orange light-emitting ZnSe:Mn2+/ZnS doped-core/shell (d-C/S) quantum dots (QDs) were synthesized using a nucleation doping strategy. To synthesize high quality d-C/S QDs with high photoluminescence (PL) quantum yield (QY), the Mn2+ concentration was optimized. The resulting ZnSe:Mn2+(5%)/ZnS d-C/S QDs showed a high PL QY of 83.3%. The optical properties of the synthesized QDs were characterized by absorption and PL spectroscopy. Their structural and compositional properties were studied by X-ray diffraction, transmission electron microscopy, and energy dispersive X-ray spectroscopy. After doping Mn2+ into a ZnSe core, the ZnSe:Mn2+/ZnS d-C/S QDs showed a large Stokes shift of 170 nm. The ZnSe:Mn2+/ZnS d-C/S QDs were embedded in a poly(lauryl methacrylate) (PLMA) polymer matrix and the ZnSe:Mn2+/ZnS-based polymer film was fabricated. The fabricated ZnSe:Mn2+/ZnS-PLMA film was highly transparent in the visible spectral region (transmittance > 83.8% for λ ≥ 450 nm) and it exhibited bright orange light under air mass (AM) 1.5G illumination using a solar simulator. The optical path-dependent PL measurement of the ZnSe:Mn2+/ZnS-PLMA film showed no PL band shift and minimal PL decrease under variation of excitation position. These results indicate that the highly efficient and large Stokes shift-emitting ZnSe:Mn2+/ZnS QDs are promising for application to luminescent solar concentrators.
Chill coma recovery of Ceratitis capitata adults across the Northern Hemisphere
Authors: Moraiti Cleopatra A. , Verykouki Eleni , Papadopoulos Nikos T. 
Published: October 2022

AbstractThe Mediterranean fruit fly,Ceratitis capitata(Diptera: Tephritidae), is an invasive pest, that is currently expanding its geographic distribution from the Mediterranean coasts to more temperate areas of Europe. Given that low temperature is a primary determinant of insect species’ range boundaries especially in the Northern Hemisphere with pronounced seasonality, we used chill coma recovery time for assessing latitudinal clines in basal chill tolerance ofC. capitataadults. We selected six populations obtained from areas with broad climatic variability based on the main bioclimatic variables of temperature and precipitation, spanning a latitudinal range of about 19° from Middle East to Central Europe. Adults were exposed to 0 °C for 4 h, and time to regain the typical standing position of a fly at 25 °C were recorded. The post-stress survival after a period of 8 days was also recorded. Results revealed that adults from Israel and Austria were less chill tolerant than those from Greece, resulting in curvilinear trends with latitude. Analysis of macroclimatic conditions revealed combined effects of latitude (as a proxy of photoperiod) and macroclimatic conditions on chill coma recovery time. Nonetheless, there was not a deleterious effect on post-recovery survival, except for flies obtained from the northern most point (Vienna, Austria). Overall, it seems that evolutionary patterns of basal chill coma recovery time ofC. capitataadults are driven mainly by local climatic variability.
Determinable and interpretable network representation for link prediction
Authors: Deng Yue 
Published: October 2022

AbstractAs an intuitive description of complex physical, social, or brain systems, complex networks have fascinated scientists for decades. Recently, to abstract a network’s topological and dynamical attributes, network representation has been a prevalent technique, which can map a network or substructures (like nodes) into a low-dimensional vector space. Since its mainstream methods are mostly based on machine learning, a black box of an input-output data fitting mechanism, the learned vector’s dimension is indeterminable and the elements are not interpreted. Although massive efforts to cope with this issue have included, say, automated machine learning by computer scientists and learning theory by mathematicians, the root causes still remain unresolved. Consequently, enterprises need to spend enormous computing resources to work out a set of model hyperparameters that can bring good performance, and business personnel still finds difficulties in explaining the learned vector’s practical meaning. Given that, from a physical perspective, this article proposes two determinable and interpretable node representation methods. To evaluate their effectiveness and generalization, this article proposes Adaptive and Interpretable ProbS (AIProbS), a network-based model that can utilize node representations for link prediction. Experimental results showed that the AIProbS can reach state-of-the-art precision beyond baseline models on some small data whose distribution of training and test sets is usually not unified enough for machine learning methods to perform well. Besides, it can make a good trade-off with machine learning methods on precision, determinacy (or robustness), and interpretability. In practice, this work contributes to industrial companies without enough computing resources but who pursue good results based on small data during their early stage of development and who require high interpretability to better understand and carry out their business.
Prediction of malignant lymph nodes in NSCLC by machine-learning classifiers using EBUS-TBNA and PET/CT
Authors: Guberina Maja , Herrmann Ken , Pöttgen Christoph , Guberina Nika , Hautzel Hubertus , Gauler Thomas , Ploenes Till , Umutlu Lale , Wetter Axel , Theegarten Dirk , Aigner Clemens , Eberhardt Wilfried E. E. , Metzenmacher Martin , Wiesweg Marcel , Schuler Martin , Karpf-Wissel Rüdiger , Santiago Garcia Alina , Darwiche Kaid , Stuschke Martin 
Published: October 2022

AbstractAccurate determination of lymph-node (LN) metastases is a prerequisite for high precision radiotherapy. The primary aim is to characterise the performance of PET/CT-based machine-learning classifiers to predict LN-involvement by endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) in stage-III NSCLC. Prediction models for LN-positivity based on [18F]FDG-PET/CT features were built using logistic regression and machine-learning models random forest (RF) and multilayer perceptron neural network (MLP) for stage-III NSCLC before radiochemotherapy. A total of 675 LN-stations were sampled in 180 patients. The logistic and RF models identified SUVmax, the short-axis LN-diameter and the echelon of the considered LN among the most important parameters for EBUS-positivity. Adjusting the sensitivity of machine-learning classifiers to that of the expert-rater of 94.5%, MLP (P = 0.0061) and RF models (P = 0.038) showed lower misclassification rates (MCR) than the standard-report, weighting false positives and false negatives equally. Increasing the sensitivity of classifiers from 94.5 to 99.3% resulted in increase of MCR from 13.3/14.5 to 29.8/34.2% for MLP/RF, respectively. PET/CT-based machine-learning classifiers can achieve a high sensitivity (94.5%) to detect EBUS-positive LNs at a low misclassification rate. As the specificity decreases rapidly above that level, a combined test of a PET/CT-based MLP/RF classifier and EBUS-TBNA is recommended for radiation target volume definition.
Oral supplementation with selected Lactobacillus acidophilus triggers IL-17-dependent innate defense response, activation of innate lymphoid cells type 3 and improves colitis
Authors: Hrdý Jiří , Couturier-Maillard Aurélie , Boutillier Denise , Lapadatescu Carmen , Blanc Philippe , Procházka Jan , Pot Bruno , Ryffel Bernhard , Grangette Corinne , Chamaillard Mathias 
Published: October 2022

AbstractLive biotherapeutic products constitute an emerging therapeutic approach to prevent or treat inflammatory bowel diseases. Lactobacillus acidophilus is a constituent of the human microbiota with probiotic potential, that is illustrated by improvement of intestinal inflammation and antimicrobial activity against several pathogens. In this study, we evaluated the immunomodulatory properties of the L. acidophilus strain BIO5768 at steady state and upon acute inflammation. Supplementation of naïve mice with BIO5768 heightened the transcript level of some IL-17 target genes encoding for protein with microbicidal activity independently of NOD2 signaling. Of these, the BIO5768-induced expression of Angiogenin-4 was blunted in monocolonized mice that are deficient for the receptor of IL-17 (but not for NOD2). Interestingly, priming of bone marrow derived dendritic cells by BIO5768 enhanced their ability to support the secretion of IL-17 by CD4+ T cells. Equally of importance, the production of IL-22 by type 3 innate lymphoid cells is concomitantly heightened in response to BIO5768. When administered alone or in combination with Bifidobacterium animalis spp. lactis BIO5764 and Limosilactobacillus reuteri, BIO5768 was able to alleviate at least partially intestinal inflammation induced by Citrobacter rodentium infection. Furthermore, BIO5768 was also able to improve colitis induced by 2,4,6-trinitrobenzene sulfonic acid (TNBS). In conclusion, we identify a new potential probiotic strain for the management of inflammatory bowel diseases, and provide some insights into its IL-17-dependent and independent mode of action.
Effect of pre-operative carbohydrate loading on aspiration risk evaluated with ultrasonography in type 2 diabetes patients: a prospective observational pilot study
Authors: Lee Seohee , Sohn Jin Young , Lee Ho-jin , Yoon Susie , Bahk Jae-Hyon , Kim Bo Rim 
Published: October 2022

AbstractOwing to concerns about delayed gastric emptying or hyperglycemia, evidence is lacking regarding whether pre-operative carbohydrate loading can be routinely administered to patients with type 2 diabetes. The objective of this study was to determine the aspiration risk and gastric volume after pre-operative carbohydrate loading in patients with type 2 diabetes. A prospective, single-center, observational cohort study. The study was conducted at a tertiary teaching hospital in Seoul, Korea, from May 2020 to May 2021. Patients (n = 49) with type 2 diabetes underwent elective noncardiac surgery. All patients were administered carbohydrate loading two hours before surgery. Once in the operating room, they underwent gastric ultrasonography to determine gastric volume. The anesthesiologists monitored the patients' glucose concentrations during and after surgery. The primary outcome was the predicted risk of aspiration. The secondary outcomes were gastric volume, antral grade, satisfaction score, and perioperative glucose profile. Forty-nine patients were analyzed. All patients had a low risk of aspiration after carbohydrate loading, as follows: 33 (67.3%) patients classified as antral grade 0 and 16 (32.7%) patients classified as antral grade 1. The median time from carbohydrate drink ingestion to ultrasound examination was 120 min (IQR 115–139). After carbohydrate loading, the median gastric volume in the right-lateral position after carbohydrate loading was 2.64 ml (IQR 0.00–32.05). The mean glucose concentrations (SD) were 134 (24) mg/dl, 159 (37) mg/dl, 150 (32) mg/dl, and 165 (36) mg/dl at baseline, after induction, 30 min after surgery, and in the post anesthesia care unit, respectively. The median satisfaction score of the patients was 5 (IQR 4–5). Pre-operative carbohydrate loading may be feasible for patients with type 2 diabetes and without complications.Trial registration: ClinicalTrials.gov (NCT04456166). Registered on 2 July 2020.
Mental distress and associated factors among college students in Kemisie district, Ethiopia
Authors: Siraji Aziza , Molla Asressie , Ayele Wolde Melese , Kebede Natnael 
Published: October 2022

AbstractMental health problems such as distress affect society in a non-differential fashion. In recent decades, mental distress is becoming a common health problem among students. In this regard, there is limited information about the problem available in Ethiopia. Therefore, this study aimed to determine the prevalence and associated factors of mental distress among college students in Kemisie district, Ethiopia. An institution-based cross-sectional study was conducted among 408 students from February 11 to 14 2020. A stratified sampling technique was used to select the study participants. Data were collected using a structured self-administered questionnaire. Self-reporting questionnaire (referred to as the SRQ-20) is a standardized questionnaire having 20-item questions and was used as a tool for mental distress. Appropriate descriptive statistics was done. A binary logistic regression model was used to identify factors associated with mental distress. An adjusted odds ratio with a 95% confidence interval was computed to determine the level of significance. Apvalue < 0.05 was used to declare the statistical significance of the variables. Prevalence of mental distress among students was 17.6% (95% CI 13.8–21.4%). Not having close friends (AOR = 3.61; 95% CI 1.61–8.14), attend religious programs (AOR = 0.23; 95% CI 0.14–0.53), conflict with friend (AOR = 3.07; 95% CI 1.44–6.33), not having pocket money (AOR = 2.72; 95% CI 1.27–25.80), ever use of Chat (AOR = 5.06; 95% CI 2.12–11.80), current use of Chat (AOR = 3.12; 95% CI 1.04–9.82), decreased grade than anticipated (AOR = 3.20; 95% CI 1.436–7.16), and low and moderate social support (AOR = 3.34; 95% CI 1.41–7.92) and (AOR = 1.47; 95% CI 1.08–5.68), respectively were statistically significantly associated factors of mental distress. The overall prevalence of mental distress among students in Kemisie district, Ethiopia was high. In Ethiopia, along with the current economic crisis and the absence of social support, the problem could be increased. Therefore, the mental health needs of the college students require attention with special emphasis on not having close friends, never attending religious programs, conflict with friends, absence of pocket money, students who use Khat, and those who have low social support.
A study on the impact of role stress on engineer intention to leave in Indian construction firms
Authors: Hazeen Fathima M. , Umarani C. 
Published: October 2022

AbstractConstruction jobs are stressful, and high employee turnover is one of the significant issues affecting the growth and development of construction firms worldwide. This study examines the relationship between role stress and intentions to leave, as well as the role of emotional exhaustion and job satisfaction as mediators in the role stress and intention to leave of engineers working in the construction sector. The sample of this study consists of three hundred and sixty engineers working in private construction firms across southern parts of India. The relationship between the study variables is examined using structural equation modeling. The result of the study reveals a significant positive relationship between role conflict and intention to leave, whereas the direct relationship between role ambiguity, role overload, and intention to leave is not significant. In terms of mediation, emotional exhaustion plays a significant mediator between all the components of role stress and intention to leave. At the same time, the role of job satisfaction is significant only between role ambiguity and intention to leave. Role ambiguity negatively influences job satisfaction, subsequently increasing engineers’ turnover intention. Emotional exhaustion caused due to role stress is identified as one of the significant predictors of engineers’ intention to leave. Thus, construction organizations should focus on redesigning construction jobs to reduce role stress and emotional exhaustion to retain engineers working with them.
Motif and conserved module analysis in DNA (promoters, enhancers) and RNA (lncRNA, mRNA) using AlModules
Authors: Aydinli Muharrem , Liang Chunguang , Dandekar Thomas 
Published: October 2022

AbstractNucleic acid motifs consist of conserved and variable nucleotide regions. For functional action, several motifs are combined to modules. The tool AIModules allows identification of such motifs including combinations of them and conservation in several nucleic acid stretches. AIModules recognizes conserved motifs and combinations of motifs (modules) allowing a number of interesting biological applications such as analysis of promoter and transcription factor binding sites (TFBS), identification of conserved modules shared between several gene families, e.g. promoter regions, but also analysis of shared and conserved other DNA motifs such as enhancers and silencers, in mRNA (motifs or regulatory elements e.g. for polyadenylation) and lncRNAs. The tool AIModules presented here is an integrated solution for motif analysis, offered as a Web service as well as downloadable software. Several nucleotide sequences are queried for TFBSs using predefined matrices from the JASPAR DB or by using one’s own matrices for diverse types of DNA or RNA motif discovery. Furthermore, AIModules can find TFBSs common to two or more sequences. Demanding high or low conservation, AIModules outperforms other solutions in speed and finds more modules (specific combinations of TFBS) than alternative available software. The application also searches RNA motifs such as polyadenylation site or RNA–protein binding motifs as well as DNA motifs such as enhancers as well as user-specified motif combinations (https://bioinfo-wuerz.de/aimodules/; alternative entry pages:https://aimodules.heinzelab.deorhttps://www.biozentrum.uni-wuerzburg.de/bioinfo/computing/aimodules). The application is free and open source whether used online, on-site, or locally.
Micro-expression recognition model based on TV-L1 optical flow method and improved ShuffleNet
Authors: Liu Yanju , Li Yange , Yi Xinhan , Hu Zuojin , Zhang Huiyu , Liu Yanzhong 
Published: October 2022

AbstractMicro-expression is a kind of facial action that reflects the real emotional state of a person, and has high objectivity in emotion detection. Therefore, micro-expression recognition has become one of the research hotspots in the field of computer vision in recent years. Research with neural networks with convolutional structure is still one of the main methods of recognition. This method has the advantage of high operational efficiency and low computational complexity, but the disadvantage is its localization of feature extraction. In recent years, there are more and more plug-and-play self-attentive modules being used in convolutional neural networks to improve the ability of the model to extract global features of the samples. In this paper, we propose the ShuffleNet model combined with a miniature self-attentive module, which has only 1.53 million training parameters. First, the start frame and vertex frame of each sample will be taken out, and its TV-L1 optical flow features will be extracted. After that, the optical flow features are fed into the model for pre-training. Finally, the weights obtained from the pre-training are used as initialization weights for the model to train the complete micro-expression samples and classify them by the SVM classifier. To evaluate the effectiveness of the method, it was trained and tested on a composite dataset consisting of CASMEII, SMIC, and SAMM, and the model achieved competitive results compared to state-of-the-art methods through cross-validation of leave-one-out subjects.
PM2.5 forecasting for an urban area based on deep learning and decomposition method
Authors: Zaini Nur’atiah , Ean Lee Woen , Ahmed Ali Najah , Abdul Malek Marlinda , Chow Ming Fai 
Published: October 2022

AbstractRapid growth in industrialization and urbanization have resulted in high concentration of air pollutants in the environment and thus causing severe air pollution. Excessive emission of particulate matter to ambient air has negatively impacted the health and well-being of human society. Therefore, accurate forecasting of air pollutant concentration is crucial to mitigate the associated health risk. This study aims to predict the hourly PM2.5 concentration for an urban area in Malaysia using a hybrid deep learning model. Ensemble empirical mode decomposition (EEMD) was employed to decompose the original sequence data of particulate matter into several subseries. Long short-term memory (LSTM) was used to individually forecast the decomposed subseries considering the influence of air pollutant parameters for 1-h ahead forecasting. Then, the outputs of each forecast were aggregated to obtain the final forecasting of PM2.5 concentration. This study utilized two air quality datasets from two monitoring stations to validate the performance of proposed hybrid EEMD-LSTM model based on various data distributions. The spatial and temporal correlation for the proposed dataset were analysed to determine the significant input parameters for the forecasting model. The LSTM architecture consists of two LSTM layers and the data decomposition method is added in the data pre-processing stage to improve the forecasting accuracy. Finally, a comparison analysis was conducted to compare the performance of the proposed model with other deep learning models. The results illustrated that EEMD-LSTM yielded the highest accuracy results among other deep learning models, and the hybrid forecasting model was proved to have superior performance as compared to individual models.