bluuebunny/crossref_metadata_2025 huggingface dataset:
Activities, Housing Situation and Other Factors Influencing Psychological Strain Experienced During the First COVID-19 Lockdown in Switzerland
Authors: Hansmann Ralph , Fritz Livia , Pagani Anna , Clément Garance , Binder Claudia R.
Published: September 2021

Background: The Coronavirus disease 2019 (COVID-19) crisis and the corresponding first nationwide lockdown from mid-March to 10 May 2020 engendered considerable psychological strain among people in Switzerland. This study analyzes determinants of changes in subjective levels of psychological strain experienced during the lockdown.

Methods: An online survey conducted as part of a larger mixed methods study examined the material and emotional aspects of individual reactions to the lockdown from a socio-ecological perspective. Participants (N = 5932) were asked about their personal and employment status, housing features, changes in various activities (e.g., physical activity, watching TV, social media use) and aspects of mental distress and well-being.

Results: A substantial share of participants reported to feel depressed (33%) and anxious (43%) more often during the COVID-19 lockdown than before, whereas significantly (p < 0.001) less persons reported a decrease of these negative feelings (depressed 17%; anxious 14%). Women, single people, students and people who lost their jobs or were temporally unemployed due to the lockdown experienced a particularly strong increase of subjective psychological strain. Important residential factors reducing subjective psychological strain were the general comfort of the housing situation and having a private garden or multiple types of outdoor space. Considering leisure activities, the strongest positive psychological effect resulted from increased physical activities, followed by reading and cooking. However, 45% of the participants reported a decreased frequency of physical activity during the lockdown compared to before, whereas significantly less persons (26%) reported a corresponding increase (p < 0.001).

Conclusion: Consistent with other studies, the results indicate a substantial reduction of subjective psychological well-being of the population during the first COVID-19 lockdown in Switzerland. The psychological burdens which the participants experienced differ depending on personal characteristics and situational factors. Negative psychological and economic consequences and gender inequalities should accordingly be carefully considered and actively prevented when designing COVID-19 measures. Supportive economic and social, cognitive and behavioral psychological interventions need to be designed and implemented to maintain the well-being of residents during lockdown.
Sentiment Classification of News Text Data Using Intelligent Model
Authors: Zhang Shitao
Published: September 2021

Text sentiment classification is a fundamental sub-area in natural language processing. The sentiment classification algorithm is highly domain-dependent. For example, the phrase “traffic jam” expresses negative sentiment in the sentence “I was stuck in a traffic jam on the elevated for 2 h.” But in the domain of transportation, the phrase “traffic jam” in the sentence “Bread and water are essential terms in traffic jams” is without any sentiment. The most common method is to use the domain-specific data samples to classify the text in this domain. However, text sentiment analysis based on machine learning relies on sufficient labeled training data. Aiming at the problem of sentiment classification of news text data with insufficient label news data and the domain adaptation of text sentiment classifiers, an intelligent model, i.e., transfer learning discriminative dictionary learning algorithm (TLDDL) is proposed for cross-domain text sentiment classification. Based on the framework of dictionary learning, the samples from the different domains are projected into a subspace, and a domain-invariant dictionary is built to connect two different domains. To improve the discriminative performance of the proposed algorithm, the discrimination information preserved term and principal component analysis (PCA) term are combined into the objective function. The experiments are performed on three public text datasets. The experimental results show that the proposed algorithm improves the sentiment classification performance of texts in the target domain.
Health Information Systems in the COVID-19 Pandemic: A Short Survey of Experiences and Lessons Learned From the European Region
Authors: Negro-Calduch Elsa , Azzopardi-Muscat Natasha , Nitzan Dorit , Pebody Richard , Jorgensen Pernille , Novillo-Ortiz David
Published: September 2021

Introduction:The COVID-19 crisis provides an opportunity to reflect on what worked during the pandemic, what could have been done differently, and what innovations should become part of an enhanced health information system in the future.

Methods:An online qualitative survey was designed and administered online in November 2020 to all the 37 Member States that are part of the WHO European Health Information Initiative and the WHO Central Asian Republics Information Network.

Results:Nineteen countries responded to the survey (Austria, Belgium, Croatia, Czech Republic, Finland, Greece, Iceland, Ireland, Israel, Italy, Kazakhstan, Latvia, Lithuania, Romania, Russian Federation, Sweden, Turkey, United Kingdom, and Uzbekistan). The COVID-19 pandemic required health information systems (HIS) to rapidly adapt to identify, collect, store, manage, and transmit accurate and timely COVID-19 related data. HIS stakeholders have been put to the test, and valuable experience has been gained. Despite critical gaps such as under-resourced public health services, obsolete health information technologies, and lack of interoperability, most countries believed that their information systems had worked reasonably well in addressing the needs arising during the COVID-19 pandemic.

Conclusion:Strong enabling environments and advanced and digitized health information systems are vital to controlling epidemics. Sustainable finance and government support are required for the continued implementation and enhancement of HIS. It is important to promote digital solutions beyond the COVID-19 pandemic. Now is the time to discuss potential solutions to obtain timely, accurate, and reliable health information and steer policy-making while protecting privacy rights and meeting the highest ethical standards.
Design Implications for Explanations: A Case Study on Supporting Reflective Assessment of Potentially Misleading Videos
Authors: Inel Oana , Duricic Tomislav , Kaur Harmanpreet , Lex Elisabeth , Tintarev Nava
Published: September 2021

Online videos have become a prevalent means for people to acquire information. Videos, however, are often polarized, misleading, or contain topics on which people have different, contradictory views. In this work, we introducenatural language explanationsto stimulate more deliberate reasoning about videos and raise users’ awareness of potentially deceiving or biased information. With these explanations, we aim to support users in actively deciding and reflecting on theusefulnessof the videos. We generate the explanations through an end-to-end pipeline that extractsreflection triggersso users receive additional information to the video based on its source, covered topics, communicated emotions, and sentiment. In a between-subjects user study, we examine the effect of showing the explanations for videos on three controversial topics. Besides, we assess the users’ alignment with the video’s message and how strong their belief is about the topic. Our results indicate that respondents’ alignment with the video’s message is critical to evaluate the video’s usefulness. Overall, the explanations were found to be useful and of high quality. While the explanations do not influence the perceived usefulness of the videos compared to only seeing the video, people with anextreme negative alignmentwith a video’s message perceived it as less useful (with or without explanations) and felt more confident in their assessment. We relate our findings to cognitive dissonance since users seem to be less receptive to explanations when the video’s message strongly challenges their beliefs. Given these findings, we provide a set of design implications for explanations grounded in theories on reducing cognitive dissonance in light of raising awareness about online deception.
Rate of Force Development Is Related to Maximal Force and Sit-to-Stand Performance in Men With Stages 3b and 4 Chronic Kidney Disease
Authors: Gollie Jared M. , Harris-Love Michael O. , Patel Samir S. , Shara Nawar M. , Blackman Marc R.
Published: September 2021

Introduction: The primary aims of the present study were to assess the relationships of early (0–50 ms) and late (100–200 ms) knee extensor rate of force development (RFD) with maximal voluntary force (MVF) and sit-to-stand (STS) performance in participants with chronic kidney disease (CKD) not requiring dialysis.

Methods: Thirteen men with CKD (eGFR = 35.17 ±.5 ml/min per 1.73 m2, age = 70.56 ±.4 years) and 12 non-CKD men (REF) (eGFR = 80.31 ± 4.8 ml/min per 1.73 m2, age = 70.22 ±.9 years) performed maximal voluntary isometric contractions to determine MVF and RFD of the knee extensors. RFD was measured at time intervals 0–50 ms (RFD0−50) and 100–200 ms (RFD100−200). STS was measured as the time to complete five repetitions. Measures of rectus femoris grayscale (RF GSL) and muscle thickness (RF MT) were obtained via ultrasonography in the CKD group only. Standardized mean differences (SMD) were used to examine differences between groups. Bivariate relationships were assessed by Pearson's product moment correlation.

Results: Knee extensor MVF adjusted for body weight (CKD=17.14 ±.1 N·kg0.67, REF=21.55 ±.3 N·kg0.67, SMD = 0.79) and STS time (CKD = 15.93 ±.4 s, REF = 12.23 ±.7 s, SMD = 1.03) were lower in the CKD group than the REF group. Absolute RFD100−200 was significantly directly related to adjusted MVF in CKD (r = 0.56, p = 0.049) and REF (r = 0.70, p = 0.012), respectively. STS time was significantly inversely related to absolute (r = −0.75, p = 0.008) and relative RFD0−50 (r = −0.65, p = 0.030) in CKD but not REF (r = 0.08, p = 0.797; r = 0.004, p = 0.991). Significant inverse relationships between RF GSL adjusted for adipose tissue thickness and absolute RFD100−200 (r =−0.59, p = 0.042) in CKD were observed.

Conclusion: The results of the current study highlight the declines in strength and physical function that occur in older men with CKD stages 3b and 4 not requiring dialysis. Moreover, early RFD was associated with STS time in CKD while late RFD was associated MVF in both CKD and REF.

Clinical Trial Registration: ClinicalTrials.gov, identifier: NCT03160326 and NCT02277236.
Use of mHealth Solutions for Improving Access to Adolescents' Sexual and Reproductive Health Services in Resource-Limited Settings: Lessons From Zimbabwe
Authors: Dhakwa Dominica , Mudzengerere Fungai H. , Mpofu Mulamuli , Tachiwenyika Emmanuel , Mudokwani Florence , Ncube Blessing , Pfupajena Mutsa , Nyagura Tendai , Ncube Getrude , Tafuma Taurayi A.
Published: September 2021

Background:Gaps still exist in reducing new HIV infections among adolescent girls and young women (AGYW) aged 10–24 years. High Internet coverage and mobile phone penetration rates present opportunities for the use of mobile health (mHealth) to support access to health services. We present results of an FHI 360 and Zimbabwe Health Interventions-implemented mHealth intervention for reproductive health (RH) and HIV testing service (HTS) referral among AGYW aged 10–19 years between October 2019 and September 2020.

Methods:Adolescent girls and young women referred for RH and HTS under the Determined, Resilient, Empowered, AIDS-free, Mentored, and Safe (DREAMS) program had automatic reminders sent to their phones to facilitate access to services through short message service (SMS) and also using a paper-based system. These data were captured in a web-based District Health Information System (DHIS) database, which captured the referral completion status of the AGYW. Data for AGYW referred for RH and HTS for the period October 2018 to September 2019 for the paper-based system and October 2018 to September 2020 for the mHealth were extracted from District Health Information System version 2 (DHIS2) database and analyzed using SPSS to generate descriptive statistics. The Chi-square test was used to assess differences in referral completion rates by age-group; marital status, district, and type of service, as well as differences between mHealth and paper-based referral completion rates within each of the groups for the variables above.

Results:A total of 8,800 AGYW referred for RH and HTS, where 4,355 and 4,445 were referred through the mHealth and paper-based systems, respectively. About 95.2% (4,148/4,355) and 87.8% (3,903/4,445) referred through mHealth and the paper-based system, respectively completed referrals. The median time for referral completion was 1 day (Range = 0–9 days) for mHealth and 11 days (Range = 0–28 days) for the paper-based system. AGYW referred through mHealth were 17.995 times

more likely to complete the referral system than those referred through the paper-based system (OR =17.995;p<0.001).

Conclusion:Compared to the paper-based referral system the mHealth solution resulted in a higher, service referral completion rates and shorter turnaround time. We recommend expansion of the mHealth solution to all DREAMS supported districts to increase uptake of RH and HTS among AGYW aged 10–19 years.
Field (In)dependence Flexibility Following a Virtual Immersion Is Associated With Cybersickness and Sense of Presence
Authors: Maneuvrier A. , Decker L. M. , Renaud P. , Ceyte G. , Ceyte H.
Published: September 2021

Field dependence–independence (FDI) is a psychological construct determining an individual’s approach of the perception–cognition coupling. In virtual reality (VR) context, several studies suggest that an individual’s perceptive style is susceptible to shift toward a more FI mode through down-weighting of conflicting visual cues. The present study proposes to investigate the potential flexible nature of FDI following a virtual immersion and to assess if this flexibility might be associated with the subjective experience of VR. 86 participants explored a real-world–like virtual environment for approximately 10 min. FDI levels were measured before and after the VR exposure using the rod-and-frame test. Their subjective experience of VR was measured a posteriori (cybersickness and sense of presence) and used in order to build two experimental groups via a cluster analysis. The results showed that only participants with a poor subjective experience of VR (i.e., a low level of sense of presence associated with a high level of cybersickness) significantly shifted to a more FI mode, which is discussed as a sensory re-weighting mechanism. Pragmatical applications are discussed, and future studies are outlined, based on the conclusion that FDI might be more flexible than we thought, which could shed light on the psychophysiology of VR.
Agro-Ecological Management of Coffee Pests in Brazil
Authors: Venzon Madelaine
Published: September 2021

Coffee plants host several herbivorous species, but only few are considered pests. Brazil is the largest coffee producer of the world, and the two key coffee pests of the crop in the country are the coffee leaf minerLeucoptera coffeellaand the coffee berry borerHypothenemus hampei. However, in some regions or on specific conditions, species of mites and scales can also cause damage to coffee plants. Conventional management of coffee pests relies on chemical pesticides, and it is the most commonly used strategy in Brazil, but environmental problems, pest resistance, and toxicity-related issues have led coffee growers to search for alternatives for pest control. Agro-ecological strategies suitable to coffee cultivation can be adopted by farmers, based on plant diversification, in order to provide resources for natural enemies, such as nectar, pollen, shelter, microclimate conditions, and oviposition sites, thereby promoting conservation biological control. Here I revise these strategies and report the results from research in Brazil. I include results on agroforestry, use of cover crops, and non-crop plant management. These are complemented by curative measures based on the use of organic farming-approved pesticides that can be employed when the agro-ecological practices are not yet consolidated. I also present the cultural control method used by several coffee producers in Brazil to decrease coffee berry borer damage.
Fourth Branchial Anomalies: Diagnosis, Treatment, and Long-Term Outcome
Authors: Boonen Annelien , Hens Greet , Meulemans Jeroen , Hermans Robert , Delaere Pierre , Vander Poorten Vincent
Published: September 2021

Introduction: Fourth branchial anomalies, the rarest among anomalies of the branchial apparatus, often present diagnostic and therapeutic challenges. We evaluated the clinical presentation and radiographic features, the treatment and the long-term outcome of patients in this setting.

Patients and Methods: Of 12 patients treated in the University Hospitals Leuven from 2004 until 2020, 12 variables were collected: date of birth, gender, age of onset of the symptoms, age at final diagnosis, presentation, laterality, previous procedures, diagnostic tools, treatment (open neck surgery, endoscopic laser excision, or combination), complications, recurrence, and period of follow-up. Descriptive statistics were calculated and results were compared to the existing literature.

Results: The most common clinical manifestations were recurrent neck infections with and without abcedation. Definitive diagnosis using direct laryngoscopy, visualizing the internal sinus opening, was possible in all patients. A CT study revealed the typical features of fourth branchial anomalies in seven patients out of nine, an ultrasound study in five out of nine patients. All patients underwent open neck surgery. If this was insufficient, secondary endoscopic laser resection of the ostium at the apex of the piriform sinus was performed (n = 4). In eight patients a thyroid lobectomy was needed for safe complete resection. Postoperative complications were minimal and at long-term, none of the patients showed further recurrence. Average time of follow-up was 8.6 years.

Conclusions: Direct laryngoscopy and CT are the most accurate diagnostic tools. Our recommended treatment schedule consists of complete excision of the sinus tract by open neck surgery as the primary treatment because this ensures the best results. In case of recurrence afterwards, endoscopic laser resection of the pharyngeal ostium solved the problem.
Forms of Copper in the Aspect of Anthropogenic Changes in the Profiles of Horticultural Soils in the Cities of South-Eastern Poland
Authors: Makuch-Pietraś Iwona , Wójcikowska-Kapusta Anna
Published: September 2021

The aim of the research was to show the changes taking place in soils as a result of the influence of anthropogenic factors in the form of intensive horticulture and pollution within the city. It was made on the basis of the distribution of individual fractions as well as available and total Cu forms in the profiles of garden soils located at houses and in the family allotment gardens in six cities in south-eastern Poland. The research took into account the granulometric composition according to which the cities were divided into two groups. The pollution assessment was based on the concentration indicator, Igeo, and indicators taking into account the forms available for plants: Cav/Ct and BF. There was no copper contamination with regard to legislation act and Igeo. The role of organic matter as an important component in copper in the humus horizons of soils in long-term horticultural cultivation has been considered. The content of all forms of copper was higher in humus horizon which also indicated the anthropogenic changes. Among the two groups of soils, a greater impact of the treatments used in horticulture on those made of sands was found in relation to the silty ones.
Diurnal Variation and Distribution of Short-Duration Heavy Rainfall in Beijing–Tianjin–Hebei Region in Summer Based on High-Density Automatic Weather Station Data
Authors: Cheng Conglan , Li Qingchun , Dou Youjun , Wang Yaoting
Published: September 2021

The distribution and diurnal variation of short-duration heavy rainfall (SDHR) and the influence of a complex underlying surface were studied by using fine-scale hourly precipitation data in the Beijing–Tianjin–Hebei (BTH) region during the summers of 2014–2020. Areas prone to SDHR are located mainly in the southern foothills of the Yanshan Mountains, the foothills area, and the trumpet-shaped topographic entrance area north of Beijing, areas inland of the west coast of Bohai Bay, and the northern Beijing urban area. Owing to the influence of topography and the geographical location, the distribution and diurnal variation SDHR is significantly different in the western and northern mountainous areas, the foothills, and the plains. Compared to the underlying urban surface, the topography and the land–sea interface have considerable effects on the distribution of SDHR. A key finding is that the foothills of northern of Beijing, eastern slope and piedmont area of the Taihang Mountains, and the land–sea interface of Bohai Bay play important roles in the formation and propagation of SDHR.
Characterization of Wave Fronts of Ultradistributions Using Directional Short-Time Fourier Transform
Authors: Atanasova Sanja , Maksimović Snježana , Pilipović Stevan
Published: September 2021

In this paper we give a characterization of Sobolev k-directional wave front of order p∈[1,∞) of tempered ultradistributions via the directional short-time Fourier transform.
Advances in Convolution Neural Networks Based Crowd Counting and Density Estimation
Authors: Gouiaa Rafik , Akhloufi Moulay A. , Shahbazi Mozhdeh
Published: September 2021

Automatically estimating the number of people in unconstrained scenes is a crucial yet challenging task in different real-world applications, including video surveillance, public safety, urban planning, and traffic monitoring. In addition, methods developed to estimate the number of people can be adapted and applied to related tasks in various fields, such as plant counting, vehicle counting, and cell microscopy. Many challenges and problems face crowd counting, including cluttered scenes, extreme occlusions, scale variation, and changes in camera perspective. Therefore, in the past few years, tremendous research efforts have been devoted to crowd counting, and numerous excellent techniques have been proposed. The significant progress in crowd counting methods in recent years is mostly attributed to advances in deep convolution neural networks (CNNs) as well as to public crowd counting datasets. In this work, we review the papers that have been published in the last decade and provide a comprehensive survey of the recent CNNs based crowd counting techniques. We briefly review detection-based, regression-based, and traditional density estimation based approaches. Then, we delve into detail regarding the deep learning based density estimation approaches and recently published datasets. In addition, we discuss the potential applications of crowd counting and in particular its applications using unmanned aerial vehicle (UAV) images.
Organismal Design and Biomimetics: A Problem of Scale
Authors: Perricone Valentina , Santulli Carlo , Rendina Francesco , Langella Carla
Published: September 2021

Organisms and their features represent a complex system of solutions that can efficiently inspire the development of original and cutting-edge design applications: the related discipline is known as biomimetics. From the smallest to the largest, every species has developed and adapted different working principles based on their relative dimensional realm. In nature, size changes determine remarkable effects in organismal structures, functions, and evolutionary innovations. Similarly, size and scaling rules need to be considered in the biomimetic transfer of solutions to different dimensions, from nature to artefacts. The observation of principles that occur at very small scales, such as for nano- and microstructures, can often be seen and transferred to a macroscopic scale. However, this transfer is not always possible; numerous biological structures lose their functionality when applied to different scale dimensions. Hence, the evaluation of the effects and changes in scaling biological working principles to the final design dimension is crucial for the success of any biomimetic transfer process. This review intends to provide biologists and designers with an overview regarding scale-related principles in organismal design and their application to technical projects regarding mechanics, optics, electricity, and acoustics.
Analysis of Local and Global Aromaticity in Si3C5 and Si4C8 Clusters. Aromatic Species Containing Planar Tetracoordinate Carbon
Authors: Torres-Vega Juan J. , Alcoba Diego R. , Oña Ofelia B. , Vásquez-Espinal Alejandro , Báez-Grez Rodrigo , Lain Luis , Torre Alicia , García Víctor , Tiznado William
Published: September 2021

The minimum energy structures of the Si3C5 and Si4C8 clusters are planar and contain planar tetracoordinate carbons (ptCs). These species have been classified, qualitatively, as global (π) and local (σ) aromatics according to the adaptive natural density partitioning (AdNDP) method, which is an orbital localization method. This work evaluates these species’ aromaticity, focusing on confirming and quantifying their global and local aromatic character. For this purpose, we use an orbital localization method based on the partitioning of the molecular space according to the topology of the electronic localization function (LOC-ELF). In addition, the magnetically induced current density is analyzed. The LOC-ELF-based analysis coincides with the AdNDP study (double aromaticity, global, and local). Moreover, the current density analysis detects global and local ring currents. The strength of the global and local current circuit is significant, involving 4n + 2 π- and σ-electrons, respectively. The latter implicates the Si-ptC-Si fragment, which would be related to the 3c-2e σ-bond detected by the orbital localization methods in this fragment.
Pilot Sleep Behavior across Time during Ultra-Long-Range Flights
Authors: Devine Jaime K. , Choynowski Jake , Garcia Caio R. , Simoes Audrey S. , Guelere Marina R. , Godoy Bruno de , Silva Diego S. , Pacheco Philipe , Hursh Steven R.
Published: September 2021

Fatigue risk to the pilot has been a deterrent for conducting direct flights longer than 12 h under normal conditions, but such flights were a necessity during the COVID-19 pandemic. Twenty (N = 20) pilots flying across five humanitarian missions between Brazil and China wore a sleep-tracking device (the Zulu watch), which has been validated for the estimation of sleep timing (sleep onset and offset), duration, efficiency, and sleep score (wake, interrupted, light, or deep Sleep) throughout the mission period. Pilots also reported sleep timing, duration, and subjective quality of their in-flight rest periods using a sleep diary. To our knowledge, this is the first report of commercial pilot sleep behavior during ultra-long-range operations under COVID-19 pandemic conditions. Moreover, these analyses provide an estimate of sleep score during in-flight sleep, which has not been reported previously in the literature.
Effect of Glutaraldehyde on Corrosion of X80 Pipeline Steel
Authors: Tian Feng , Pan Lin
Published: September 2021

Glutaraldehyde (GA) is widely employed as a biocide to control microbiologically influenced corrosion in oil fields and industrial water treatment. It might be corrosive to metal. In this study, the effect of glutaraldehyde on the corrosion behavior of X80 pipeline steel was investigated using electrochemical measurement, weight-loss tests and scanning electron microscope (SEM). The weight-loss and electrochemical data show that GA accelerates the corrosion of samples under aerobic conditions, but just slightly influences the corrosion of steel under anaerobic conditions. The results showed that the glutaraldehyde has a minor effect on the corrosion of steel under anaerobic conditions.
Global DNA 5hmC and CK195hmC+ Contents: A Promising Biomarker for Predicting Prognosis in Small Hepatocellular Carcinoma
Authors: Jiang Jinhua , Yan Tinghua , Guo Fang
Published: September 2021

Background: 5-Hydroxymethylcytosine (5hmC) with dynamic existence possesses multiple regulatory functions. Whereas, 5hmC’s impact on small hepatocellular carcinoma (SHCC) remains unclear. The present work focused on characterizing 5hmC content within SHCC and assessing the possibility of using global genomic 5hmC level as the predicative factor of clinical outcome. Methods: This study applied ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) in measuring 5mC, 5fC and 5hmC contents. In addition, immunohistochemistry (IHC) was adopted to measure CK19 and 5hmC contents. Results: Research showed 5mC, 5hmC, and 5fC contents from global genomics of SHCC reduced extensively compared with healthy samples (p < 0.001). Moreover, SHCC was associated with lymph node metastasis (LNM). Greater 5mC and 5hmC levels were observed in non-metastasis group compared with the metastasis group (p < 0.001). Correlation analysis between the HBV DNA level and 5mC, 5fC and 5hmC levels exhibited that HBV DNA was associated with 5mC, 5hmC, and 5fC content reduction, which was verified in the cytological experiments. Moreover, 5hmC content had a negative correlation with the expression level of CK19 in SHCC. The decrease in 5hmC and CK19 containing 5hmC positive cell (called CK195hmC+) should be ascribed to the bad prognosis among SHCC patients. Conclusions: The contents of 5hmC and CK195hmC+ of genomic DNA might be adopted for predicting SHCC survival as an important biomarker.
A Monocular Vision Obstacle Avoidance Method Applied to Indoor Tracking Robot
Authors: Wang Shubo , Wang Ling , He Xiongkui , Cao Yi
Published: September 2021

The overall safety of a building can be effectively evaluated through regular inspection of the indoor walls by unmanned ground vehicles (UGVs). However, when the UGV performs line patrol inspections according to the specified path, it is easy to be affected by obstacles. This paper presents an obstacle avoidance strategy for unmanned ground vehicles in indoor environments. The proposed method is based on monocular vision. Through the obtained environmental information in front of the unmanned vehicle, the obstacle orientation is determined, and the moving direction and speed of the mobile robot are determined based on the neural network output and confidence. This paper also innovatively adopts the method of collecting indoor environment images based on camera array and realizes the automatic classification of data sets by arranging cameras with different directions and focal lengths. In the training of a transfer neural network, aiming at the problem that it is difficult to set the learning rate factor of the new layer, the improved bat algorithm is used to find the optimal learning rate factor on a small sample data set. The simulation results show that the accuracy can reach 94.84%. Single-frame evaluation and continuous obstacle avoidance evaluation are used to verify the effectiveness of the obstacle avoidance algorithm. The experimental results show that an unmanned wheeled robot with a bionic transfer-convolution neural network as the control command output can realize autonomous obstacle avoidance in complex indoor scenes.
An Insider Data Leakage Detection Using One-Hot Encoding, Synthetic Minority Oversampling and Machine Learning Techniques
Authors: Al-Shehari Taher , Alsowail Rakan A.
Published: September 2021

Insider threats are malicious acts that can be carried out by an authorized employee within an organization. Insider threats represent a major cybersecurity challenge for private and public organizations, as an insider attack can cause extensive damage to organization assets much more than external attacks. Most existing approaches in the field of insider threat focused on detecting general insider attack scenarios. However, insider attacks can be carried out in different ways, and the most dangerous one is a data leakage attack that can be executed by a malicious insider before his/her leaving an organization. This paper proposes a machine learning-based model for detecting such serious insider threat incidents. The proposed model addresses the possible bias of detection results that can occur due to an inappropriate encoding process by employing the feature scaling and one-hot encoding techniques. Furthermore, the imbalance issue of the utilized dataset is also addressed utilizing the synthetic minority oversampling technique (SMOTE). Well known machine learning algorithms are employed to detect the most accurate classifier that can detect data leakage events executed by malicious insiders during the sensitive period before they leave an organization. We provide a proof of concept for our model by applying it on CMU-CERT Insider Threat Dataset and comparing its performance with the ground truth. The experimental results show that our model detects insider data leakage events with an AUC-ROC value of 0.99, outperforming the existing approaches that are validated on the same dataset. The proposed model provides effective methods to address possible bias and class imbalance issues for the aim of devising an effective insider data leakage detection system.
A Method to Compute the Schrieffer–Wolff Generator for Analysis of Quantum Memory
Authors: Kim Dong-Hwan , Lee Su-Yong , Jo Yonggi , Kim Duk Y. , Kim Zaeill , Jeong Taek
Published: September 2021

Quantum illumination uses entangled light that consists of signal and idler modes to achieve higher detection rate of a low-reflective object in noisy environments. The best performance of quantum illumination can be achieved by measuring the returned signal mode together with the idler mode. Thus, it is necessary to prepare a quantum memory that can keep the idler mode ideal. To send a signal towards a long-distance target, entangled light in the microwave regime is used. There was a recent demonstration of a microwave quantum memory using microwave cavities coupled with a transmon qubit. We propose an ordering of bosonic operators to efficiently compute the Schrieffer–Wolff transformation generator to analyze the quantum memory. Our proposed method is applicable to a wide class of systems described by bosonic operators whose interaction part represents a definite number of transfer in quanta.
One- and Two-Phase Software Requirement Classification Using Ensemble Deep Learning
Authors: Rahimi Nouf , Eassa Fathy , Elrefaei Lamiaa
Published: September 2021

Recently, deep learning (DL) has been utilized successfully in different fields, achieving remarkable results. Thus, there is a noticeable focus on DL approaches to automate software engineering (SE) tasks such as maintenance, requirement extraction, and classification. An advanced utilization of DL is the ensemble approach, which aims to reduce error rates and learning time and improve performance. In this research, three ensemble approaches were applied: accuracy as a weight ensemble, mean ensemble, and accuracy per class as a weight ensemble with a combination of four different DL models—long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), a gated recurrent unit (GRU), and a convolutional neural network (CNN)—in order to classify the software requirement (SR) specification, the binary classification of SRs into functional requirement (FRs) or non-functional requirements (NFRs), and the multi-label classification of both FRs and NFRs into further experimental classes. The models were trained and tested on the PROMISE dataset. A one-phase classification system was developed to classify SRs directly into one of the 17 multi-classes of FRs and NFRs. In addition, a two-phase classification system was developed to classify SRs first into FRs or NFRs and to pass the output to the second phase of multi-class classification to 17 classes. The experimental results demonstrated that the proposed classification systems can lead to a competitive classification performance compared to the state-of-the-art methods. The two-phase classification system proved its robustness against the one-phase classification system, as it obtained a 95.7% accuracy in the binary classification phase and a 93.4% accuracy in the second phase of NFR and FR multi-class classification.
Network Autoregressive Model for the Prediction of COVID-19 Considering the Disease Interaction in Neighboring Countries
Authors: Sioofy Khoojine Arash , Shadabfar Mahdi , Hosseini Vahid Reza , Kordestani Hadi
Published: September 2021

Predicting the way diseases spread in different societies has been thus far documented as one of the most important tools for control strategies and policy-making during a pandemic. This study is to propose a network autoregressive (NAR) model to forecast the number of total currently infected cases with coronavirus disease 2019 (COVID-19) in Iran until the end of December 2021 in view of the disease interactions within the neighboring countries in the region. For this purpose, the COVID-19 data were initially collected for seven regional nations, including Iran, Turkey, Iraq, Azerbaijan, Armenia, Afghanistan, and Pakistan. Thenceforth, a network was established over these countries, and the correlation of the disease data was calculated. Upon introducing the main structure of the NAR model, a mathematical platform was subsequently provided to further incorporate the correlation matrix into the prediction process. In addition, the maximum likelihood estimation (MLE) was utilized to determine the model parameters and optimize the forecasting accuracy. Thereafter, the number of infected cases up to December 2021 in Iran was predicted by importing the correlation matrix into the NAR model formed to observe the impact of the disease interactions in the neighboring countries. In addition, the autoregressive integrated moving average (ARIMA) was used as a benchmark to compare and validate the NAR model outcomes. The results reveal that COVID-19 data in Iran have passed the fifth peak and continue on a downward trend to bring the number of total currently infected cases below 480,000 by the end of 2021. Additionally, 20%, 50%, 80% and 95% quantiles are provided along with the point estimation to model the uncertainty in the forecast.
Variational Sparse Bayesian Learning for Estimation of Gaussian Mixture Distributed Wireless Channels
Authors: Kong Lingjin , Zhang Xiaoying , Zhao Haitao , Wei Jibo
Published: September 2021

In this paper, variational sparse Bayesian learning is utilized to estimate the multipath parameters for wireless channels. Due to its flexibility to fit any probability density function (PDF), the Gaussian mixture model (GMM) is introduced to represent the complicated fading phenomena in various communication scenarios. First, the expectation-maximization (EM) algorithm is applied to the parameter initialization. Then, the variational update scheme is proposed and implemented for the channel parameters’ posterior PDF approximation. Finally, in order to prevent the derived channel model from overfitting, an effective pruning criterion is designed to eliminate the virtual multipath components. The numerical results show that the proposed method outperforms the variational Bayesian scheme with Gaussian prior in terms of root mean squared error (RMSE) and selection accuracy of model order.
The Italian School Psychologists’ Role: A Qualitative Study about Professional Practices and Representations
Authors: Meroni Claudia , Fagnani Laura , Confalonieri Emanuela , Baventore Davide , Velasco Veronica
Published: September 2021

School psychologists’ relevance has been broadly affirmed. However, there is no shared definition of their professional role, and more efforts are needed to promote an organisational and whole-school approach. The present study aims to investigate practices and representations of Italian school psychologists, advance knowledge of the status and development of school psychology, and learn more about the approaches currently adopted in schools. A qualitative method was used and 11 focus groups with a total of 86 participants were carried out. Ad hoc instruments were defined. The results highlighted that school psychologists are more focused on building one-on-one relationships, whereas relationships with the organisation as a whole appear to be more difficult. However, participants reported a wide range of activities, targeted to both the individual and the organisation. Moreover, efforts to strengthen the relationships with school principals and the entire school community were described. Specific needs emerged and the necessity to better define the school psychologists’ role was reported by the participants. More efforts are needed to promote an organisational approach among Italian school psychologists and specific training should be offered.
Experimental Analysis of Modified DC-P&O Technique with Arm Controller for a Stand-Alone 40 W PV System
Authors: Singhal Ashish , Beniwal Narendra , Almutairi Khalid , Arockia Dhanraj Joshuva , Mostafaeipour Ali , Issakhov Alibek , Chaurasiya Prem , Goudarzi Hossein , Singh Vinod
Published: September 2021

The world is moving towards the generation of electricity with renewable energy sources (RES) due to the deterioration of the green environment and trying to replace non-renewable energy resources. The real-time results are achieved with the help of an arm controller, having good controller efficiency with the Waijung toolbox, compatible with MATLAB using STM32ST-link utility. In this paper, the authors are focused on areas such as easy to implement controller efficiency, and real-time solutions for modified direct-control perturbation & observation (DC-P&O) technique based on 32- bit ARM Cortex microcontroller (STM32F407VGT6) with embedded programming using Waijung blocksets, which offers very expected outcomes of the problem to make the stand-alone system efficient with fast-tracking. The observation setup is tested with a 40-watt photovoltaic (PV) panel with resistive load for achieving its stability. The designed algorithm enhances the efficiency of the controller by 84.48% for the real-time parameters of the PV panel at maximum power point (MPP) for a 57% duty ratio.
Smart City Management in the Context of Electricity Consumption Savings
Authors: Ptak Aleksandra
Published: September 2021

According to one definition, Smart City is an administrative unit that uses data and digital technology to improve the decision-making processes and well-being of its inhabitants. Smart Cities use modern, intelligent technologies to forecast future changes, optimize city functions, gain a better understanding of current conditions, and deliver solutions. To make the city smart, multiple connected sensors, smartphones and other IoT devices are needed. As part of the research conducted at the Technical University in Vienna, six key dimensions (categories) of smart cities have been identified and characterized: Smart Economy, Smart Mobility, Smart Environment, Smart People, Smart Living, and Smart Governance. The aim of the paper is to present the concept of the smart cities, the implemented innovative solutions and the issue of electricity saving in Smart Cities. The theoretical considerations were expanded of the presentation of selected smart city projects in Polish cities. The considerations were also supplemented with the results of research conducted on a group of respondents living in large and very large cities in Poland in order to their awareness of the concept of smart cities, awareness on smart projects implemented in their cities of residence, and the possibility of using intelligent technologies to achieve savings in electricity consumption.
Operational Parameters of a Diesel Engine Running on Diesel–Rapeseed Oil–Methanol–Iso-Butanol Blends
Authors: Čedík Jakub , Pexa Martin , Holúbek Michal , Mrázek Jaroslav , Valera Hardikk , Agarwal Avinash Kumar
Published: September 2021

This contribution focuses on utilizing blended biofuels of rapeseed oil and methanol with diesel. Rapeseed is one of the most cultivated energy crops in Europe, and its purpose in the blends is to increase the bio-content in test fuels. The purpose of methanol in the blends is to increase bio-content and compensate for the higher viscosity of the rapeseed oil. As methanol is almost insoluble in diesel and rapeseed oil, iso-butanol is used as a co-solvent. The fuel blends were tested in volumetric concentrations of diesel/rapeseed oil/methanol/iso-butanol 60/30/5/5, 50/30/10/10, and 50/10/20/20. Diesel was used as a reference. The measurements were performed on a turbocharged diesel engine Zetor 1204, loaded using the power-takeoff shaft of the Zetor Forterra 8641 tractor. In this paper, the effect of the blended fuels on performance parameters, engine efficiency, production of soot particles, and regulated and unregulated emissions are monitored and analyzed. It was found that engine power decreased by up to 27%, efficiency decreased by up to 5.5% at full engine load, emissions of NOX increased by up to 21.9% at 50% engine load, and production of soot particles decreased; however, the mean size of the particles was smaller.
Strategic Environmental Assessment in the Application of Preventive Protection for Wind Farm Noise—Case Study: Maestrale Ring Wind Farm
Authors: Josimović Boško D. , Cvjetić Aleksandar , Manić Božidar
Published: September 2021

Determining the spatial position of wind turbines is the initial and most important phase in the development of a wind farm project. In this sensitive phase, all potential problems that may arise in the later stages of project development should be prevented by means of spatial and urban planning instruments. This makes it possible to achieve maximum use of the potential of wind in a particular space and, thus, fulfil the technical and economic requirements of the project while respecting the goals of environmental protection in that same area, through preventive protection. Therefore, it is essential, even at the earliest planning and development stage of a wind farm project, for the requirements that are important for optimal spatial solutions to be balanced. In this process, strategic environmental assessment (SEA) is a support to the planning process and an invaluable instrument for finding optimal spatial solutions for the possible key spatial impacts of wind power with regard to noise, shadow flicker, ornithofauna and chiropterofauna. The weakness of SEA can be seen in its predominant application of expert qualitative methods that bring with them subjectivity, since they depend on expert knowledge and skills. This paper presents the aspect of noise impact assessment and its inclusion in the SEA for the Maestrale Ring wind farm in Serbia. The results of the research indicate how it is possible to achieve the principle of objectivity in the process of multicriteria expert evaluation by including the results of a partial impact assessment of the noise from wind farms, using results obtained from software modeling of the spatial dispersion of wind turbine noise in the SoundPlan 8.1 software package in the SEA process. These quantitative results predicting the noise level were used in a semi-quantitative method of multicriteria evaluation in the SEA through the definition of criteria to determine the ranking of impacts, which is elaborated in the paper. The results also show the significant of the contribution of applying a methodological approach based on a combination of qualitative and quantitative evaluation methods in SEA. These methods positively affect the application of the principle of preventive protection through the optimal selection of the number and position of wind turbines on one hand and the objectivity of drawing conclusions based on which strategic decisions are made in the final phase of the SEA process, on the other.