bluuebunny/crossref_metadata_2025 huggingface dataset:
The Effect of Titanium Oxyfluoride Morphology on Photocatalytic Activity of Fluorine-Doped Titanium(IV) Oxide
Authors: Kowalkińska Marta , Karczewski Jakub , Zielińska-Jurek Anna
Published: February 2023

Titanium oxyfluoride (TiOF2) is a metastable product that can be obtained in a fluorine-rich environment. This material can also be a valuable precursor in the synthesis of titanium(IV) oxide (TiO2). However, the effect of TiOF2 morphology on the physicochemical properties of TiO2 has not been studied so far. In this work, single-phase TiOF2 was prepared by a solvothermal method. The as-synthesized samples exhibited a variety of morphologies, including different shapes and crystallite sizes. These materials were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM) combined with energy-dispersive X-ray spectroscopy (EDS), surface area measurements, thermal gravimetric analysis (TGA) and UV–vis diffuse reflectance spectroscopy (DR/UV–vis). Furthermore, TiOF2 samples were used as precursors in the synthesis of fluorine-doped titanium(IV) oxide and applied in photocatalytic phenol degradation under UV-vis light. The experiments showed that the crystallite size of the precursor, as well as the number of fluoride ions used in the synthesis, were the predominant factors that affected the photocatalytic activity of the final photocatalyst.
Predicting the Level of Background Current Noise in Graphene Biosensor through a Non-Covalent Functionalization Process
Authors: Zhu Chao-yi , Lin Zi-hong , Zhang Da-yong , Shi Jing-yuan , Peng Song-ang , Jin Zhi
Published: February 2023

The rapid worldwide spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created a series of problems. Detection platforms based on graphene field-effect transistors (GFETs) have been proposed to achieve a rapid diagnosis of SARS-CoV-2 antigen or antibody. For GFET-based biosensors, the graphene surface usually needs to be functionalized to immobilize the bioreceptor and the non-covalent approach is preferred for functionalization because it is believed not to significantly alter the electronic properties of graphene. However, in this work, the non-covalent functionalization introduced by 1-pyrenebutyric acid N-hydroxysuccinimide ester (PBASE) was determined to lead to different changes in electrical properties in graphene samples with different defect densities. The fabricated graphene biosensor can successfully detect SARS-CoV-2 antigen with a concentration as low as 0.91 pg/mL. Further, by careful comparison, we determined that, for GFET fabricated on graphene with a higher defect density, the current variation caused by PBASE modification is greater and the background current noise in the subsequent antigen detection is also larger. Based on this relationship, we can predict the background current noise of the biosensors by evaluating the current change induced by the modification and screen the devices at an early stage of graphene biosensor fabrication for process optimization.
Colorimetric Plasmonic Hydrogen Gas Sensor Based on One-Dimensional Nano-Gratings
Authors: Zarei Majid , Hamidi Seyedeh M. , Chee K. -W. -A.
Published: February 2023

Plasmonic hydrogen gas sensors have become widely used in recent years due to their low cost, reliability, safety, and measurement accuracy. In this paper, we designed, optimized, and fabricated a palladium (Pd)-coated nano-grating-based plasmonic hydrogen gas sensor; and investigated using the finite-difference time-domain method and experimental spectral reflectance measurements, the calibrated effects of hydrogen gas exposure on the mechano-optical properties of the Pd sensing layer. The nanostructures were fabricated using DC sputter deposition onto a one-dimensional nano-grating optimized with a thin-film gold buffer to extend the optical response dynamic range and performance stability; the color change sensitivity of the Pd surface layer was demonstrated for hydrogen gas concentrations as low as 0.5 vol.%, up to 4 vol.%, based on the resonance wavelength shift within the visible band corresponding to the reversible phase transformation. Visual color change detection of even the smallest hydrogen concentrations indicated the high sensitivity of the gas sensor. Our technique has potential for application to high-accuracy portable plasmonic sensors compatible with biochemical sensing with smartphones.
Micro-Nanoarchitectonics of Ga2O3/GaN Core-Shell Rod Arrays for High-Performance Broadband Ultraviolet Photodetection
Authors: Tang Ruifan , Li Guanqi , Hu Xun , Gao Na , Li Jinchai , Huang Kai , Kang Junyong , Zhang Rong
Published: February 2023

This study presents broadband ultraviolet photodetectors (BUV PDs) based on Ga2O3/GaN core-shell micro-nanorod arrays with excellent performance. Micro-Nanoarchitectonics of Ga2O3/GaN core-shell rod arrays were fabricated with high-temperature oxidization of GaN micro-nanorod arrays. The PD based on the microrod arrays exhibited an ultrahigh responsivity of 2300 A/W for 280 nm at 7 V, the peak responsivity was approximately 400 times larger than those of the PD based on the planar Ga2O3/GaN film. The responsivity was over 1500 A/W for the 270–360 nm band at 7 V. The external quantum efficiency was up to 1.02 × 106% for 280 nm. Moreover, the responsivity was further increased to 2.65 × 104 A/W for 365 nm and over 1.5 × 104 A/W for 270–360 nm using the nanorod arrays. The physical mechanism may have been attributed to the large surface area of the micro-nanorods coupled with the Ga2O3/GaN heterostructure, which excited more photogenerated holes to be blocked at the Ga2O3 surface and Ga2O3/GaN interface, resulting in a larger internal gain. The overall high performance coupled with large-scale production makes it a promising candidate for practical BUV PD.
Vitamin D and Sun Exposure: A Community Survey in Australia
Authors: Tran Vu , Janda Monika , Lucas Robyn M. , McLeod Donald S. A. , Thompson Bridie S. , Waterhouse Mary , Whiteman David C. , Neale Rachel E.
Published: February 2023

Sun exposure carries both harms and benefits. Exposing the skin to the sun is the main modifiable cause of skin cancers, which exert a considerable health and economic burden in Australia. The most well-established benefit of exposure to ultraviolet (UV) radiation is vitamin D production. Australia has the highest incidence of skin cancer in the world but, despite the high ambient UV radiation, approximately one quarter of the population is estimated to be vitamin D deficient. Balancing the risks and benefits is challenging and requires effective communication. We sought to provide a snapshot of public knowledge and attitudes regarding sun exposure and vitamin D and to examine the associations between these factors and sun protective behaviors. In 2020 we administered an online survey; 4824 participants with self-reported fair or medium skin color were included in this analysis. Only 25% and 34% of participants were able to identify the amount of time outdoors needed to maintain adequate vitamin D status in summer and winter, respectively and 25% were concerned that sunscreen use inhibits vitamin D synthesis. This lack of knowledge was associated with suboptimal sun protection practices. Public education is warranted to prevent over-exposure, while supporting natural vitamin D production.
Mate Choice in Molluscs of the Genus Littorina (Gastropoda: Littorinidae) from White Sea
Authors: Kozminsky Eugene V. , Serbina Elena A. , Smurov Alexey O.
Published: February 2023

We studied the reproductive behaviour associated with mate choice by size in three species of White Sea molluscs of the genus Littorina—L. saxatilis, L. obtusata and L. fabalis. Three behavioural patterns were revealed after a comparative analysis of the behaviour of the male snails. The males of L. obtusata, regardless of their size, could mate with all females but preferred to mate with larger ones. The males of L. fabalis chose females of their own size or larger. The males of L. saxatilis mated with females of approximately their own size. In the first case (L. obtusata), there is a free exchange of genes between all the size groups in the population. In the second case (L. fabalis), the gene transfer from small males to females of any size is free but the gene transfer from large males to small females is limited. In the third case (L. saxatilis), the gene flow is limited between groups of individuals of dramatically different size. Thus, in the case of L. saxatilis and L. fabalis, to varying degrees restriction of random mating associated with size assortativity in choosing a mating partner was expressed, which under certain circumstances (divergence of individuals in different econiches, the presence of ecotypes differing in size, lack of stronger natural selection in the other direction) can favour sympatric speciation. The males of all the three periwinkles’ species preferred larger females but sexual selection in favour of larger females was found only in L. obtusata. In the other two species, there were no statistically significant size differences between copulating and non-copulating females. This appears to be due to the occurrence of size-assortative mating in these species, which limits the ability of males to choose larger female partners.
The Exceptional Presence of Megaloceros giganteus in North-Eastern Iberia and Its Palaeoecological Implications: The Case of Teixoneres Cave (Moià, Barcelona, Spain)
Authors: Uzunidis Antigone , Rivals Florent , Rufà Anna , Blasco Ruth , Rosell Jordi
Published: February 2023

In this article we announce the discovery of the first remains of Megaloceros giganteus found in Catalonia (north-eastern Iberia) from the Late Pleistocene: a fragment of maxillary. Dated between 35,000 and 37,000 cal BP, it is also among the youngest occurrence of this taxon in the Iberian Peninsula, while its last known occurrence is dated to the Neolithic period. Through a comparison with the giant deer of the northern Pyrenees, we analyzed the herbivore guilds in which this taxon was associated to understand the context in which it was able to enter the Iberian Peninsula. By comparing its diet with those of specimens from Northern Europe, we detail the ecological adaptations of this taxon in this new environment. We suggest that Megaloceros accompanied the migrations of cold-adapted species by taking advantage of the opening of corridors on both sides of the Pyrenees during the coldest periods of the Late Pleistocene. The diet of the Iberian individuals, which is oriented towards abrasive plants, suggests an adaptation to a different ecological niche than that found in Northern European individuals. The northern Iberian Peninsula may have been an extreme in the geographical expansion of M. giganteus. More specimens will be needed in the future to establish the variability of the southern Megaloceros populations.
Diversity, Distribution, and Habitat Association of Anuran Species from Keffa, Southwest Ethiopia
Authors: Kassie Abeje , Simegn Afework Bekele , Bogale Bezawork Afework , Goutte Sandra , Boissinot Stephane
Published: February 2023

The aim of the current study was to provide a description of the species composition and habitat association of anuran species found in the Keffa area of southwest Ethiopia, among the least studied natural areas in Ethiopia. Data were gathered in both the rainy and dry seasons. Opportunistic observations, audio, and visual encounters were used to perform the survey. To capture different frog species, drift fence and pitfall trap techniques were used. Three habitat types—wetlands, agricultural land, and riverine forests—were explored for the frog species. A total of 3672-person field hours were spent and 5678 individuals of 26 different anuran species from eight different families were recorded. Of these, six species were new records for the Keffa area. Species determination was assessed using morphology and validated with genetic barcoding. The mitochondrial 16S rRNA and COI gene sequence analysis have been used in molecular systematic analyses of the frog species. The Leptopelis ragazzii that was discovered to be present in the earlier study was actually Leptopelis vannutellii, as evidenced by DNA sequencing. The Hyperolius individuals were initially categorized as H. nasutus, but after conducting DNA barcoding, they were found to be H. microps. At least three potential new candidate species (Leptopelis sp. 1, Leptopelis sp. 2, and Phrynobatrachus sp. 1) were discovered. Hyperoliidae was the most abundant family from the recorded anurans, followed by Phrynobatrachidae, while Conrauidae was the least abundant. Most species were found in the wetland habitat, followed by riverine forest, while agricultural land had the least. Wetlands had the highest values for the Shannon diversity index (2.131), followed by agricultural land (1.58) and riverine forests (1.459). The environmental variables revealed that temperature had a considerable negative impact, while precipitation had a non-significantly positive relationship with anuran abundance. The Keffa area is rich in frog species, accounting for 35% of the country’s batrachofauna. However, the habitats have been fragmented and ruined by agricultural expansion, water drainage from the wetlands, and the conversion of horticulture to field crops. We recommend that non-governmental organizations, districts, regional officials, and local people in and around Keffa actively engage in wetland and forest conservation and protection.
Diversity and Endemism of Southern African Gekkonids Linked with the Escarpment Has Implications for Conservation Priorities
Authors: Nicolau Gary K. , Edwards Shelley
Published: February 2023

South Africa is recognised for its high reptile diversity and endemism, specifically among lizards. Phylogenetic diversity, endemism, and richness can have clear implications or raise important questions in a range of fields, and most urgently in conservation. Among squamate reptiles, these indices are very commonly associated with high temperatures and topographic heterogeneity. Indeed, mountainous biogeography has been a critical driver in the radiation of the family Gekkonidae within the subregion. Here, we assess the species richness, diversity, and endemism of Gekkonidae species inhabiting South Africa, Lesotho, and Eswatini, accounting for phylogenetic relationships. We also employ the CANAPE method to identify regions that have neo- and/or paleoendemics. Southern African gekkonids appear to be most diverse and show high levels of endemism in three regions of Southern Africa: the northwestern Richtersveld, the escarpment running west to southeast, and the northeastern escarpment in the Limpopo province. Implications for conservation priorities are discussed.
Federated Learning for Data Analytics in Education
Authors: Fachola Christian , Tornaría Agustín , Bermolen Paola , Capdehourat Germán , Etcheverry Lorena , Fariello María Inés
Published: February 2023

Federated learning techniques aim to train and build machine learning models based on distributed datasets across multiple devices while avoiding data leakage. The main idea is to perform training on remote devices or isolated data centers without transferring data to centralized repositories, thus mitigating privacy risks. Data analytics in education, in particular learning analytics, is a promising scenario to apply this approach to address the legal and ethical issues related to processing sensitive data. Indeed, given the nature of the data to be studied (personal data, educational outcomes, and data concerning minors), it is essential to ensure that the conduct of these studies and the publication of the results provide the necessary guarantees to protect the privacy of the individuals involved and the protection of their data. In addition, the application of quantitative techniques based on the exploitation of data on the use of educational platforms, student performance, use of devices, etc., can account for educational problems such as the determination of user profiles, personalized learning trajectories, or early dropout indicators and alerts, among others. This paper presents the application of federated learning techniques to a well-known learning analytics problem: student dropout prediction. The experiments allow us to conclude that the proposed solutions achieve comparable results from the performance point of view with the centralized versions, avoiding the concentration of all the data in a single place for training the models.
Prevalence and Significance of Autoantibody Seropositivity in Children with Wilson’s Disease
Authors: Jańczyk Wojciech , Bierła Joanna B. , Trojanowska Ilona , Wierzbicka-Rucińska Aldona , Cukrowska Bożena , Socha Piotr
Published: February 2023

Autoantibodies occur in healthy subjects as well as in children with Wilson’s disease (WD), but their prevalence and significance are unknown. Thus, we aimed to assess the prevalence of autoantibodies and autoimmune markers, and their relationship to liver injury in WD children. The study included 74 WD and 75 healthy children as a control group. Patients with WD underwent transient elastography (TE) examinations, as well as determination of liver function tests, copper metabolism markers, and serum immunoglobulins (Ig). In the sera of the WD patients and controls, anti-nuclear (ANA), anti-smooth muscle, anti-mitochondrial, anti-parietal cell, anti-liver/kidney microsomal, anti-neutrophil cytoplasmic autoantibodies, and specific celiac antibodies were determined. Among the autoantibodies, only the prevalence of ANA in children with WD was higher than in the controls. There was no significant relationship between the presence of autoantibodies and liver steatosis or stiffness after TE. However, advanced liver stiffness (E > 8.2 kPa) was related to IgA, IgG, and gamma globulin production. The type of treatment did not influence the prevalence of autoantibodies. Our results suggest that autoimmune disturbances in WD might not be directly related to liver damage as expressed by steatosis and/or liver stiffness after TE.
Lung Dual-Energy CT Perfusion Blood Volume as a Marker of Severity in Chronic Thromboembolic Pulmonary Hypertension
Authors: Si-Mohamed Salim A. , Zumbihl Léa , Turquier Ségolène , Boccalini Sara , Mornex Jean-Francois , Douek Philippe , Cottin Vincent , Boussel Loic
Published: February 2023

In chronic thromboembolic pulmonary hypertension (CTEPH), assessment of severity requires right heart catheterization (RHC) through cardiac index (CI). Previous studies have shown that dual-energy CT allows a quantitative assessment of the lung perfusion blood volume (PBV). Therefore, the objective was to evaluate the quantitative PBV as a marker of severity in CTEPH. In the present study, thirty-three patients with CTEPH (22 women, 68.2 ± 14.8 years) were included from May 2017 to September 2021. Mean quantitative PBV was 7.6% ± 3.1 and correlated with CI (r = 0.519, p = 0.002). Mean qualitative PBV was 41.1 ± 13.4 and did not correlate with CI. Quantitative PBV AUC values were 0.795 (95% CI: 0.637–0.953, p = 0.013) for a CI ≥ 2 L/min/m2 and 0.752 (95% CI: 0.575–0.929, p = 0.020) for a CI ≥ 2.5 L/min/m2. In conclusion, quantitative lung PBV outperformed qualitative PBV for its correlation with the cardiac index and may be used as a non-invasive marker of severity in CTPEH patients.
Deep-EEG: An Optimized and Robust Framework and Method for EEG-Based Diagnosis of Epileptic Seizure
Authors: Mir Waseem Ahmad , Anjum Mohd , Izharuddin Izharuddin , Shahab Sana
Published: February 2023

Detecting brain disorders using deep learning methods has received much hype during the last few years. Increased depth leads to more computational efficiency, accuracy, and optimization and less loss. Epilepsy is one of the most common chronic neurological disorders characterized by repeated seizures. We have developed a deep learning model using Deep convolutional Autoencoder—Bidirectional Long Short Memory for Epileptic Seizure Detection (DCAE-ESD-Bi-LSTM) for automatic detection of seizures using EEG data. The significant feature of our model is that it has contributed to the accurate and optimized diagnosis of epilepsy in ideal and real-life situations. The results on the benchmark (CHB-MIT) dataset and the dataset collected by the authors show the relevance of the proposed approach over the baseline deep learning techniques by achieving an accuracy of 99.8%, classification accuracy of 99.7%, sensitivity of 99.8%, specificity and precision of 99.9% and F1 score of 99.6%. Our approach can contribute to the accurate and optimized detection of seizures while scaling the design rules and increasing performance without changing the network’s depth.
A Regression-Based Approach to Diabetic Retinopathy Diagnosis Using Efficientnet
Authors: Vijayan Midhula , S Venkatakrishnan
Published: February 2023

The aim of this study is to develop a computer-assisted solution for the efficient and effective detection of diabetic retinopathy (DR), a complication of diabetes that can damage the retina and cause vision loss if not treated in a timely manner. Manually diagnosing DR through color fundus images requires a skilled clinician to spot lesions, but this can be challenging, especially in areas with a shortage of trained experts. As a result, there is a push to create computer-aided diagnosis systems for DR to help reduce the time it takes to diagnose the condition. The detection of diabetic retinopathy through automation is challenging, but convolutional neural networks (CNNs) play a vital role in achieving success. CNNs have been proven to be more effective in image classification than methods based on handcrafted features. This study proposes a CNN-based approach for the automated detection of DR using Efficientnet-B0 as the backbone network. The authors of this study take a unique approach by viewing the detection of diabetic retinopathy as a regression problem rather than a traditional multi-class classification problem. This is because the severity of DR is often rated on a continuous scale, such as the international clinical diabetic retinopathy (ICDR) scale. This continuous representation provides a more nuanced understanding of the condition, making regression a more suitable approach for DR detection compared to multi-class classification. This approach has several benefits. Firstly, it allows for more fine-grained predictions as the model can assign a value that falls between the traditional discrete labels. Secondly, it allows for better generalization. The model was tested on the APTOS and DDR datasets. The proposed model demonstrated improved efficiency and accuracy in detecting DR compared to traditional methods. This method has the potential to enhance the efficiency and accuracy of DR diagnosis, making it a valuable tool for healthcare professionals. The model has the potential to aid in the rapid and accurate diagnosis of DR, leading to the improved early detection, and management, of the disease.
Risk of Secondary Cancer after Adjuvant Tamoxifen Treatment for Ductal Carcinoma In Situ: A Nationwide Cohort Study in South Korea
Authors: Kim Dooreh , Oh Jooyoung , Seok Jeong-Ho , Lee Hye Sun , Jeon Soyoung , Yoon Chang Ik
Published: February 2023

Endocrine therapy is the mainstay treatment for hormone receptor-positive ductal carcinoma in situ. The aim of this study was to examine the long-term secondary malignancy risk of tamoxifen therapy. The data of patients diagnosed with breast cancer between January 2007 and December 2015 were retrieved from the database of the Health Insurance Review and Assessment Service of South Korea. The International Classification of Diseases, 10th revision, was used to track all-site cancers. Age at the time of surgery, chronic disease status, and type of surgery were considered covariates in the propensity score matching analysis. The median follow-up duration was 89 months. Forty-one patients in the tamoxifen group and nine in the control group developed endometrial cancer. The Cox regression hazard ratio model showed that tamoxifen therapy was the only significant predictor of the development of endometrial cancer (hazard ratio, 2.791; 95% confidence interval, 1.355–5.747; p = 0.0054). No other type of cancer was associated with long-term tamoxifen use. In consonance with the established knowledge, the real-world data in this study demonstrated that tamoxifen therapy is related to an increased incidence of endometrial cancer.
Trace Minerals and Anxiety: A Review of Zinc, Copper, Iron, and Selenium
Authors: Totten Melissa S. , Davenport Tyler S. , Edwards Laken F. , Howell Jenna M.
Published: February 2023

Anxiety disorder is characterized by excessive fear or avoidance of perceived threats that can be persistent and debilitating. Diet is a modifiable risk factor that may contribute to the pathogenesis or treatment of anxiety, depending on diet quality. Although the biological mechanisms by which food, specific nutrients, and nutraceuticals impact mental health are not completely understood, implicated pathways include inflammation, oxidative stress, brain plasticity effects, mitochondrial dysfunction, and neurotransmitter metabolism. Essential dietary trace minerals such as zinc, copper, iron, and selenium are critical components for numerous biological proteins and may have a role in these proposed mechanisms related to brain health and anxiety. This narrative review examines the influence of essential trace elements zinc, copper, iron, and selenium on anxiety symptoms based on the latest peer-reviewed scientific evidence. Current research indicates that deficiencies in zinc, iron, and selenium are associated with anxiety, while copper overload may impact anxiety to a greater degree than copper deficiency. Further investigation is needed to understand the specific neurobiological mechanisms involved. Overall, the collective findings demonstrate the importance of optimizing trace mineral homeostasis for the mitigation of anxiety disorders and preservation of mental health.
Wheat Lodging Segmentation Based on Lstm_PSPNet Deep Learning Network
Authors: Yu Jun , Cheng Tao , Cai Ning , Zhou Xin-Gen , Diao Zhihua , Wang Tianyi , Du Shizhou , Liang Dong , Zhang Dongyan
Published: February 2023

Lodging is one of the major issues that seriously affects wheat quality and yield. To obtain timely and accurate wheat lodging information and identify the potential factors leading to lodged wheat in wheat breeding programs, we proposed a lodging-detecting model coupled with unmanned aerial vehicle (UAV) image features of wheat at multiple plant growth stages. The UAV was used to collect canopy images and ground lodging area information at five wheat growth stages. The PSPNet model was improved by combining the convolutional LSTM (ConvLSTM) timing model, inserting the convolutional attention module (CBAM) and the Tversky loss function. The effect of the improved PSPNet network model in monitoring wheat lodging under different image sizes and different growth stages was investigated. The experimental results show that (1) the improved Lstm_PSPNet model was more effective in lodging prediction, and the precision reached 0.952; (2) choosing an appropriate image size could improve the segmentation accuracy, with the optimal image size in this study being 468 × 468; and (3) the model of Lstm_PSPNet improved its segmentation accuracy sequentially from early flowering to late maturity, and the three evaluation metrics increased sequentially from 0.932 to 0.952 for precision, from 0.912 to 0.940 for recall, and from 0.922 to 0.950 for F1-Score, with good extraction at mid and late reproductive stages. Therefore, the lodging information extraction model proposed in this study can make full use of temporal sequence features to improve image segmentation accuracy and effectively extract lodging areas at different growth stages. The model can provide more comprehensive reference and technical support for monitoring the lodging of wheat crops at different growth stages.
Cryptocurrencies Are Becoming Part of the World Global Financial Market
Authors: Wątorek Marcin , Kwapień Jarosław , Drożdż Stanisław
Published: February 2023

In this study the cross-correlations between the cryptocurrency market represented by the two most liquid and highest-capitalized cryptocurrencies: bitcoin and ethereum, on the one side, and the instruments representing the traditional financial markets: stock indices, Forex, commodities, on the other side, are measured in the period: January 2020–October 2022. Our purpose is to address the question whether the cryptocurrency market still preserves its autonomy with respect to the traditional financial markets or it has already aligned with them in expense of its independence. We are motivated by the fact that some previous related studies gave mixed results. By calculating the q-dependent detrended cross-correlation coefficient based on the high frequency 10 s data in the rolling window, the dependence on various time scales, different fluctuation magnitudes, and different market periods are examined. There is a strong indication that the dynamics of the bitcoin and ethereum price changes since the March 2020 COVID-19 panic is no longer independent. Instead, it is related to the dynamics of the traditional financial markets, which is especially evident now in 2022, when the bitcoin and ethereum coupling to the US tech stocks is observed during the market bear phase. It is also worth emphasizing that the cryptocurrencies have begun to react to the economic data such as the Consumer Price Index readings in a similar way as traditional instruments. Such a spontaneous coupling of the so far independent degrees of freedom can be interpreted as a kind of phase transition that resembles the collective phenomena typical for the complex systems. Our results indicate that the cryptocurrencies cannot be considered as a safe haven for the financial investments.
Improving Multiple Pedestrian Tracking in Crowded Scenes with Hierarchical Association
Authors: Xiao Changcheng , Luo Zhigang
Published: February 2023

Recently, advances in detection and re-identification techniques have significantly boosted tracking-by-detection-based multi-pedestrian tracking (MPT) methods and made MPT a great success in most easy scenes. Several very recent works point out that the two-step scheme of first detection and then tracking is problematic and propose using the bounding box regression head of an object detector to realize data association. In this tracking-by-regression paradigm, the regressor directly predicts each pedestrian’s location in the current frame according to its previous position. However, when the scene is crowded and pedestrians are close to each other, the small and partially occluded targets are easily missed. In this paper, we follow this pattern and design a hierarchical association strategy to obtain better performance in crowded scenes. To be specific, at the first association, the regressor is used to estimate the positions of obvious pedestrians. At the second association, we employ a history-aware mask to filter out the already occupied regions implicitly and look carefully at the remaining regions to find out the ignored pedestrians during the first association. We integrate the hierarchical association in a learning framework and directly infer the occluded and small pedestrians in an end-to-end way. We conduct extensive pedestrian tracking experiments on three public pedestrian tracking benchmarks from less crowded to crowded scenes, demonstrating the proposed strategy’s effectiveness in crowded scenes.
Is Distributed Leadership Universal? A Cross-Cultural, Comparative Approach across 40 Countries: An Alignment Optimisation Approach
Authors: Eryilmaz Nurullah , Sandoval-Hernandez Andres
Published: February 2023

Distributed leadership (DL) is defined as the degree of contact and involvement of various people in making choices or carrying out responsibilities, and is an increasingly used concept among researchers, policymakers, and educationalists worldwide. However, few studies have investigated the cross-cultural comparability of the distributed leadership scale for school principals, and few have ranked countries according to their levels of distributed leadership. This study employs an innovative alignment optimisation approach to compare the latent means of distributed leadership, as perceived by school principals, across 40 countries, using data from the OECD Teaching and Learning International Survey (TALIS, 2018). We found that South Korea, Colombia, Shanghai (China), and Lithuania had the highest levels of distributed leadership in school decisions, from the perspective of school principals. In contrast, the Netherlands, Belgium, Argentina, and Japan had the lowest levels. Our findings may serve as guidance for education stakeholders over which nations they could learn from in order to enhance school principal distributed leadership.
Associations between Sleep, Depression, and Cognitive Performance in Adolescence
Authors: Quevedo-Blasco Raúl , Díaz-Román Amparo , Quevedo-Blasco Víctor J.
Published: February 2023

The relevance of cognitive performance during adolescence requires further studies that analyze potential associated factors. This study aimed to analyze inductive reasoning, reading comprehension, and mathematical thinking (problem-solving and number and calculation) in relation to sleep and depression in 244 students aged 12–17 years (47.6% boys and 52.4% girls). Daytime sleepiness, sleep quality, dysthymia, and euthymia (state and trait) were assessed by self-reported questionnaires. Moreover, correlations between these variables and cognitive performance, and differences depending on sociodemographic variables (sex, age, or academic year) were analyzed using non-parametric tests. Robust regression models were also conducted to evaluate the predictive role of significant variables on cognitive performance. The results showed significant bidirectional relationships between sleep- and depression-related variables, and between the latter ones and cognitive performance. Depression-trait was more related to cognitive performance than depression-state, and euthymia more than dysthymia, but neither daytime sleepiness nor sleep quality significantly correlated with it. As for sociodemographic variables, girls reported worse sleep and more depressive symptoms than boys did, and younger students reported better sleep but performed worse than the older ones. Although these findings should be further explored in forthcoming studies adding other promising variables, they highlight the importance of promoting euthymia to improve cognitive performance in adolescents.
Meteorological Variables Forecasting System Using Machine Learning and Open-Source Software
Authors: Segovia Jenny Aracely , Toaquiza Jonathan Fernando , Llanos Jacqueline Rosario , Rivas David Raimundo
Published: February 2023

The techniques for forecasting meteorological variables are highly studied since prior knowledge of them allows for the efficient management of renewable energies, and also for other applications of science such as agriculture, health, engineering, energy, etc. In this research, the design, implementation, and comparison of forecasting models for meteorological variables have been performed using different Machine Learning techniques as part of Python open-source software. The techniques implemented include multiple linear regression, polynomial regression, random forest, decision tree, XGBoost, and multilayer perceptron neural network (MLP). To identify the best technique, the mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination (R2) are used as evaluation metrics. The most efficient techniques depend on the variable to be forecasting, however, it is noted that for most of them, random forest and XGBoost techniques present better performance. For temperature, the best performing technique was Random Forest with an R2 of 0.8631, MAE of 0.4728 °C, MAPE of 2.73%, and RMSE of 0.6621 °C; for relative humidity, was Random Forest with an R2 of 0.8583, MAE of 2.1380RH, MAPE of 2.50% and RMSE of 2.9003 RH; for solar radiation, was Random Forest with an R2 of 0.7333, MAE of 65.8105 W/m2, and RMSE of 105.9141 W/m2; and for wind speed, was Random Forest with an R2 of 0.3660, MAE of 0.1097 m/s, and RMSE of 0.2136 m/s.
A Perfect Security Key Management Method for Hierarchical Wireless Sensor Networks in Medical Environments
Authors: Muhajjar Raad A. , Flayh Nahla A. , Al-Zubaidie Mishall
Published: February 2023

Wireless sensor networks (WSNs) have developed during the past twenty years as a result of the accessibility of inexpensive, short-range, and simple-to-deploy sensors. A WSN technology sends the real-time sense information of a specific monitoring environment to a backend for processing and analysis. Security and management concerns have become hot topics with WSN systems due to the popularity of wireless communication channels. A large number of sensors are dispersed in an unmonitored medical environment, making them not safe from different risks, even though the information conveyed is vital, such as health data. Due to the sensor′s still limited resources, protecting information in WSN is a significant difficulty. This paper presents a hierarchical key management method for safeguarding heterogeneousWSNs on hybrid energy-efficient distributed (HEED) routing. In the proposed method, the Bloom scheme is used for key management and a pseudo-random number generator (PRNG) to generate keys in an efficient method to keep sensor resources. In addition, using cipher block chaining-Rivest cipher 5 (CBC-RC5) in this method achieved cryptography goals such as confidentiality. A comparison is made between the proposed and existing methods such as dynamic secret key management (DSKM) and smart security implementation (SSI) under the same circumstance to determine the performance of the new method. The data transmission in WSN consumes about 71 percent of a sensor′s energy, while encryption computation consumes only 2 percent. As a result, our method reduces the frequency with which data transmissions are made during the key management process. The simulation findings demonstrated that, in comparison to earlier techniques, the proposed method is significantly more secure, flexible, scalable, and energy-efficient. Our proposed method is also able to prevent classifications of node capture attacks.
A Novel Chinese Overlapping Entity Relation Extraction Model Using Word-Label Based on Cascade Binary Tagging
Authors: Tuo Meimei , Yang Wenzhong , Wei Fuyuan , Dai Qicai
Published: February 2023

In recent years, overlapping entity relation extraction has received a great deal of attention and has made good progress in English. However, the research on overlapping entity relation extraction in Chinese still faces two key problems: one is the lack of datasets with overlapping entity instances, and the other is the lack of a neural network model that can effectively solve overlapping entity relation extraction. To address the above problems, this paper produces an interpersonal relationship dataset, NewsPer, for news texts and proposes a Chinese overlapping entity relation extraction model, DepCasRel. First, the model uses “Word-label” to incorporate the character features of Chinese text into the dependency analysis graph, and then uses the same binary labeling method to label the head and tail entities embedded in the text. Finally, the text’s triples are extracted. DepCasRel solves the problem that traditional methods make it difficult to extract triples with overlapping entities. Experiments on our manually annotated dataset NewsPer show that DepCasRel can effectively encode the semantic and structural information of text and improve the performance of an overlapping entity relation extraction model.
The Advents of Ubiquitous Computing in the Development of Smart Cities—A Review on the Internet of Things (IoT)
Authors: Ali Jawad , Zafar Mohammad Haseeb , Hewage Chaminda , Hassan Syed Raheel , Asif Rameez
Published: February 2023

By leveraging ubiquitous computing and the Internet of Things (IoT), smart cities gain potential to provide a wider range of services. Different homogeneous and heterogeneous networking schemes and applications have been proposed in the literature to date. In these networking schemes, human and computer are connected for social, economic, physiological, and technological growth. However, there is a dearth of recent literature that incorporates recently proposed and operating techniques and technologies capable of enhancing the productivity of human and machine in IoT technologies. The role of this research is to investigate the protocols, followed by the advance frameworks for IoT, the characteristics and services that are being governed using IoT for establishing information-rich smart cities. To this end, likewise, physical layer, media access control, networking and applications protocols, and encapsulation standards of IoT for smart cities applications are critically reviewed. Certain open issues are discussed based on the literature collected that would improve the autonomous behavior, process control, device handling, and the QoS in smart environments.
Studies on the Control of Dermanyssus gallinae via High-Voltage Impulse
Authors: Ueno Takahisa , Mizobe Yuma , Ninomiya Junko , Inoue Takahiro , Furukawa Takashi , Hatta Takeshi
Published: February 2023

Dermanyssus gallinae, a parasitic mite that subsists on the avian blood of chickens, poses a considerable threat to the poultry industry. D. gallinae infestation can result in a plethora of detrimental effects for the host birds, including decreased egg production and anemia. Pyrethroid pesticides have been the primary means of combating this issue and have demonstrated high levels of efficacy. However, in recent years, D. gallinae has exhibited resistance to these chemicals, resulting in a marked decrease in their mortality; thus, an integrated control strategy in addition to the chemical use should be required for the sustainable control of this mite. This study confirms that D. gallinae can be effectively controlled through the utilization of high-voltage impulse discharges and that various electrical parameters possess optimal values that are required for mite control. The alterations in the body surface of the mite caused by high-voltage impulses were akin to those caused by heat, but no alteration in the elemental composition of the body surface was observed, suggesting a change in organization caused by currents flowing inside the exoskeleton. Comparatively, the mite control efficacy of high-voltage impulse was found to be substantially superior to that of ultraviolet light or ozone, with up to 95% more mites being killed in as little as 30 seconds.
Research on sEMG Feature Generation and Classification Performance Based on EBGAN
Authors: Zhang Xia , Ma Mingyu
Published: February 2023

Surface electromyography signal (sEMG) recognition technology requires a large number of samples to ensure the accuracy of the training results. However, sEMG signals generally have the problems of a small amount of data, complicated acquisition process and large environmental influence, which hinders the improvement of the accuracy of sEMG classification. In order to improve the accuracy of sEMG classification, an sEMG feature generation method based on an energy generative adversarial network (EBGAN) is proposed in this paper for the first time. The energy concept is introduced into the discriminant network instead of the traditional binary judgment, and the distribution of the real EMG dataset is learned and captured by multiple fully connected layers, with similar sEMG data being generated. The experimental results show that, compared with other types of GAN networks, this method achieves a small maximum mean discrepancy in comparison with that of the original data. The experimental results using different typical classification models show that the data augmentation method proposed can effectively improve the classification accuracy of typical classification models, and the accuracy increase range is 1~5%.
A Fully Integrated Solid-State Charge Detector with through Fused Silica Glass via Process
Authors: Wu Xiaomeng , Wen Liangjian , Cao Liqiang , Cao Guofu , Li Gaosong , Fu Yasheng , Yu Zhongyao , Fang Zhidan , Wang Qidong
Published: February 2023

A charge detector is a vital component in neutrino and dark matter detection. The integration of a charge collector in the form of flat pads and readout modules has been proposed as an optimization method as it can reduce noise and installation complexity. As a substrate, fused silica glass has attracted considerable attention due to its low radioactive background properties. In this research, based on the application requirements of a high charge collection rate and low noise, the structure of the charge detector was designed using calculation and simulation methods. The entire manufacturing process is described. In addition, a novel through glass via (TGV) structure composed of a conformal metal layer and a photosensitive material that is easy to fabricate and has high morphological compatibility with via filling is proposed. The curing property of the new material was characterized. A fully integrated solid-state charge detector with 32 groups of TGVs was realized. Additionally, the electrical properties of key structures were tested and analyzed.
Generating the Generator: A User-Driven and Template-Based Approach towards Analog Layout Automation
Authors: Prautsch Benjamin , Eichler Uwe , Hatnik Uwe
Published: February 2023

Various analog design automation attempts have addressed the shortcomings of the still largely manual and, thus, inefficient and risky analog design approach. These methods can roughly be divided into synthesis and procedural generation. An important key aspect has, however, rarely been considered: usability. While synthesis requires sophisticated constraints, procedural generators require expert programmers. Both prevent users from adopting the respective method. Thus, we propose a new approach to automatically create procedural generators in a user-driven way. First, analog generators, which also create symbols and layouts, are utilized during schematic entry to encapsulate common analog building blocks. Second, automatic code creation builds a hierarchical generator for all views with the schematic as input. Third, the approach links the building block generators with the layout through an object-oriented template library that is accessible through generator parameters, allowing the user to control the arrangement. No programming is required to reach this state. We believe that our approach will significantly ease the transition of analog designers to procedural generation. At the same time, the templates allow for a “bridge” to open frameworks and synthesis approaches so that the methodologies can be both better spread and combined. This way, comprehensive frameworks of both synthesis-based and procedural-based analog automation methods can be built in a user-driven way, and designers are enabled to gain early layout insight and ease IP reusability.