bluuebunny/crossref_metadata_2025 huggingface dataset:
Smartphone-Based Activity Recognition Using Multistream Movelets Combining Accelerometer and Gyroscope Data
Authors: Huang Emily J. , Yan Kebin , Onnela Jukka-Pekka
Published: March 2022

Physical activity patterns can reveal information about one’s health status. Built-in sensors in a smartphone, in comparison to a patient’s self-report, can collect activity recognition data more objectively, unobtrusively, and continuously. A variety of data analysis approaches have been proposed in the literature. In this study, we applied the movelet method to classify the activities performed using smartphone accelerometer and gyroscope data, which measure a phone’s acceleration and angular velocity, respectively. The movelet method constructs a personalized dictionary for each participant using training data and classifies activities in new data with the dictionary. Our results show that this method has the advantages of being interpretable and transparent. A unique aspect of our movelet application involves extracting unique information, optimally, from multiple sensors. In comparison to single-sensor applications, our approach jointly incorporates the accelerometer and gyroscope sensors with the movelet method. Our findings show that combining data from the two sensors can result in more accurate activity recognition than using each sensor alone. In particular, the joint-sensor method reduces errors of the gyroscope-only method in differentiating between standing and sitting. It also reduces errors in the accelerometer-only method when classifying vigorous activities.
Analysis of New Orthogonal Transforms for Digital Watermarking
Authors: Bogacki Piotr , Dziech Andrzej
Published: March 2022

The paper focuses on the application of new orthogonal transforms in digital watermarking. Novel types of transforms and their characteristics are presented. Potential methods for watermark embedding and recovery are also proposed. They assume embedding hidden information in the transform domains using the luminance channel of the original image. Image spectra are obtained by dividing the original image into smaller blocks that then are further processed by performing the forward transform operation. A watermark is embedded by modifying the spectral coefficients with relatively low values. Since there are various types of transforms, the latter process is realized in an adaptive manner. The proposed solutions were evaluated by measuring the level of visual distortion with respect to the total size of the inserted data. Additionally, the bit error rate (BER) in the recovery phase is also analyzed. The elaborated methods seem to be useful for applications in digital signal and image processing where high imperceptibility and low BER are of great importance. New orthogonal transforms were proved to be useful in watermarking tasks, and in some cases, they can even outperform the classic DCT approach.
Smart Cybersecurity Framework for IoT-Empowered Drones: Machine Learning Perspective
Authors: Aldaej Abdulaziz , Ahanger Tariq Ahamed , Atiquzzaman Mohammed , Ullah Imdad , Yousufudin Muhammad
Published: March 2022

Drone advancements have ushered in new trends and possibilities in a variety of sectors, particularly for small-sized drones. Drones provide navigational interlocation services, which are made possible by the Internet of Things (IoT). Drone networks, on the other hand, are subject to privacy and security risks due to design flaws. To achieve the desired performance, it is necessary to create a protected network. The goal of the current study is to look at recent privacy and security concerns influencing the network of drones (NoD). The current research emphasizes the importance of a security-empowered drone network to prevent interception and intrusion. A hybrid ML technique of logistic regression and random forest is used for the purpose of classification of data instances for maximal efficacy. By incorporating sophisticated artificial-intelligence-inspired techniques into the framework of a NoD, the proposed technique mitigates cybersecurity vulnerabilities while making the NoD protected and secure. For validation purposes, the suggested technique is tested against a challenging dataset, registering enhanced performance results in terms of temporal efficacy (34.56 s), statistical measures (precision (97.68%), accuracy (98.58%), recall (98.59%), F-measure (99.01%), reliability (94.69%), and stability (0.73).
Data-Driven Fault Diagnosis Techniques: Non-Linear Directional Residual vs. Machine-Learning-Based Methods
Authors: Cartocci Nicholas , Napolitano Marcello R. , Crocetti Francesco , Costante Gabriele , Valigi Paolo , Fravolini Mario L.
Published: March 2022

Linear dependence of variables is a commonly used assumption in most diagnostic systems for which many robust methodologies have been developed over the years. In case the system nonlinearities are relevant, fault diagnosis methods, relying on the assumption of linearity, might potentially provide unsatisfactory results in terms of false alarms and missed detections. In recent years, many authors have proposed machine learning (ML) techniques to improve fault diagnosis performance to mitigate this problem. Although very powerful, these techniques require faulty data samples that are representative of any fault scenario. Additionally, ML techniques suffer from issues related to overfitting and unpredictable performance in regions which are not fully explored in the training phase. This paper proposes a non-linear additive model to characterize the non-linear redundancy relationships among the system signals. Using the multivariate adaptive regression splines (MARS) algorithm, these relationships are identified directly from the data. Next, the non-linear redundancy relationships are linearized to derive a local time-dependent fault signature matrix. The faulty sensor can then be isolated by measuring the angular distance between the column vectors of the fault signature matrix and the primary residual vector. A quantitative analysis of fault isolation and fault estimation performance is performed by exploiting real data from multiple flights of a semi-autonomous aircraft, thus allowing a detailed quantitative comparison with state-of-the-art machine-learning-based fault diagnosis algorithms.
Exploring Artificial Neural Networks Efficiency in Tiny Wearable Devices for Human Activity Recognition
Authors: Lattanzi Emanuele , Donati Matteo , Freschi Valerio
Published: March 2022

The increasing diffusion of tiny wearable devices and, at the same time, the advent of machine learning techniques that can perform sophisticated inference, represent a valuable opportunity for the development of pervasive computing applications. Moreover, pushing inference on edge devices can in principle improve application responsiveness, reduce energy consumption and mitigate privacy and security issues. However, devices with small size and low-power consumption and factor form, like those dedicated to wearable platforms, pose strict computational, memory, and energy requirements which result in challenging issues to be addressed by designers. The main purpose of this study is to empirically explore this trade-off through the characterization of memory usage, energy consumption, and execution time needed by different types of neural networks (namely multilayer and convolutional neural networks) trained for human activity recognition on board of a typical low-power wearable device.Through extensive experimental results, obtained on a public human activity recognition dataset, we derive Pareto curves that demonstrate the possibility of achieving a 4× reduction in memory usage and a 36× reduction in energy consumption, at fixed accuracy levels, for a multilayer Perceptron network with respect to more sophisticated convolution network models.
Factors Influencing Attenuating Skill Decay in High-Risk Industries: A Scoping Review
Authors: Klostermann Marina , Conein Stephanie , Felkl Thomas , Kluge Annette
Published: March 2022

The infrequent use of skills relevant in non-routine situations in highly automated and high-risk industries is a major safety issue. The infrequent use of skills can lead to skill decay. Research on skill decay has a long history, but not much is known about the relevant factors and refresher interventions to attenuate skill decay in highly automated environments. In the present study, a scoping review was conducted to determine whether the well-known factors in skill decay research are also relevant for complex cognitive skill decay and to identify refresher interventions that are deemed effective for attenuating decay. A scoping review aims at identifying, summarizing, and mapping the body of literature on a given topic. Searches in electronic databases, including PsycArticles, PsyINFO, and Psyndex, via EBSCOhost and Web of Science and Google Scholar were conducted, and documents were analyzed regarding the research question, which resulted in n = 58 studies. The findings demonstrate the relevance of task characteristics and method-related (cognitive-based, behavioral-based training) and person-related factors (e.g., cognitive ability, experience, motivation) to mitigate decay. Additionally, the results demonstrate that minor refresher interventions are effective at attenuating complex cognitive skill decay. Implications for industry and training providers that aim to implement training and refresher interventions to attenuate skill decay in high-risk industries are provided. Researchers may use the information about the influences of person- and method-related factors, task characteristics, and refresher interventions presented in this scoping review as a starting point to conduct further empirical research by taking skill acquisition, retention, and transfer into account.
Multiple Imputation of Composite Covariates in Survival Studies
Authors: Clements Lily , Kimber Alan , Biedermann Stefanie
Published: March 2022

Missing covariate values are a common problem in survival studies, and the method of choice when handling such incomplete data is often multiple imputation. However, it is not obvious how this can be used most effectively when an incomplete covariate is a function of other covariates. For example, body mass index (BMI) is the ratio of weight and height-squared. In this situation, the following question arises: Should a composite covariate such as BMI be imputed directly, or is it advantageous to impute its constituents, weight and height, first and to construct BMI afterwards? We address this question through a carefully designed simulation study that compares various approaches to multiple imputation of composite covariates in a survival context. We discuss advantages and limitations of these approaches for various types of missingness and imputation models. Our results are a first step towards providing much needed guidance to practitioners for analysing their incomplete survival data effectively.
Ecological and Social Limitations for Mexican Dry Forest Restoration: A Systematic Review
Authors: Martínez-Garza Cristina , Ceccon Eliane , Méndez-Toribio Moisés
Published: March 2022

In Mexico, dry forests are one of the ecosystems in major need of restoration intervention. Here, we explored the ecological and social limitations on the restoration of Mexican dry forests from the perspective of restoration practitioners and researchers. We included three data sources: (i) projects included in a national evaluation (1979–2016), (ii) a systematic review of scientific literature (1979–2021), and (iii) restoration projects included in two governmental programs. The national evaluation and the systematic review coincided in identifying the establishment of plantings as the most important ecological limitation and low social participation as the most important social limitation. There were three times more publications addressing ecological limitations than those addressing social limitations. We did not find research to resolve the problems faced by practitioners related to invasive species, unpredictable climate, and poor soil quality. Governmental programs promoting the restoration of ecosystems need to include measurable indicators to document the socioecological limitations faced by local practitioners to restore Mexican dry forests.
Distribution of Phosphorus Fractions in Orchard Soils in Relation to Soil Properties and Foliar P Contents
Authors: Bibi Sumera , Irshad Muhammad , Mohiuddin Muhammad , Sher Sadaf , Tariq Muhammad Atiq Ur Rehman , Ng Anne Wai Man
Published: March 2022

Phosphorus (P) fractionation is the validation of the nature, solubility and relative bioavailability of P. A sequential P extraction was used to determine the distribution of plant-available P fractions in soils. The relationships of these P fractions to soil properties and foliar P contents were also determined. Results of this study showed substantial differences in soil properties among orchards. Higher amounts of soil organic matter (SOM), cation exchange capacity (CEC) and major plant nutrients were found under orchard soils when compared with control soil. Most of the soil variables varied among orchard species as loquat > citrus > guava. The orchard soil exhibited a slightly higher soil pH. Overall, the P fractions were higher in all types of orchard soils and lowered in the control soils. Among tree species, P fractions in soils were achieved as loquat > citrus > guava. The extracting agents differed for P in the order residual P > HCl-P > NaOH-P > NaHCO3-P > H2O-P. Mostly higher amounts of the P fractions were achieved in the topsoil. The average amount of extractable P was found significantly higher in those soils of fruit orchards where the total amount of P was actually higher. The higher r2 values between P fractions versus SOM, clay and CEC of soils predicted a strong interrelationship among these soil variables. Leaf N contents of loquat and guava trees were consistently higher, and leaf P contents varied as loquat > citrus > guava. Potassium and Ca contents were higher in citrus than in the other two species. Micronutrients were found as Fe > Zn > Mn > Cu in the leaves. Regression models indicated a sufficient relationship between Hedley P fractions and the foliar P contents in tree species. This study indicates that the above soil properties can be used to ascertain soil P fractions, and that can influence the bioavailability of P from orchard soils.
A Vision of Sustainable Design Concepts for Upgrading Vulnerable Coastal Areas in Light of Climate Change Impacts: A Case Study from Beirut, Lebanon
Authors: Wojtowicz-Jankowska Dorota , Bou Kalfouni Bahaa
Published: March 2022

Rapid urbanization combined with the effects of climate change has increased the vulnerability of poor urban communities to natural hazards, particularly to informal settlements located in coastal areas. Apart from socio-economic challenges, the effects of climate change threaten the very existence of these settlements. They are particularly vulnerable due to their poor structural quality and lack of adequate infrastructure to mitigate the consequences of any natural event. The article highlights an informal settlement belt, located on the coastline of the southern suburb of the capital Beirut. This area is an example of a complex urban, social, and economic structure. As a result, this research speculates on the possible sustainable design solutions and tested development strategies through urban, architecture, and landscape design proposals, derived from interventions that succeed in facing similar challenges compatible with the studied context. The aim is to advocate for comprehensive consideration and transformation of coastal informal settlements that can be used to encourage neutralizing the obvious challenges of climate change in urban settings. Therefore, planning specifications and quality aspects for future coastal informal settlements are proposed and extracted. The research involves analysis of qualitative data rather than quantitative data, as there is not much definite information available in terms of statistics, such as review of publications and grey literature. The scope of the study ranges from microscale improvement to providing usable and accessible city-wide ecological urban settings and sustainable spaces with new standards for coastal informal settlements as a potential backbone.
Towards a Model for Analyzing the Circular Economy in Ecuadorian Companies: A Conceptual Framework
Authors: Sucozhañay Gabriela , Vidal Iván , Vanegas Paúl
Published: March 2022

A successful implementation of a CE requires an appropriate comprehension of its conceptual framework and the identification of the elements that composed it. However, there is still no consensus on a unique CE definition, which makes it difficult for companies to adopt circular practices in their business models. In this context, this study proposes a CE framework based on four fundamental elements: principles, drivers, stakeholders and strategies, which was complemented by circular-organization-oriented metrics. Then, local case studies were used to assess the implementation of CE strategies in Ecuadorian companies. Among the identified elements, it was found that there are general action lines that facilitate the understanding of the CE. On the other hand, the majority of elements and metrics are oriented toward the production and end-of-life stages, while extraction, design, use, marketing and distribution are less considered. Furthermore, although environmental, economic and social aspects are considered, the latter are mostly limited to employment generation. Finally, for the local case studies it was observed that the current adoption of circular practices in companies derives from the incorporation of the sustainability approach instead of a structured and systematic implementation of CE strategies.
Sustainability Budgets: A Practical Management and Governance Method for Achieving Goal 13 of the Sustainable Development Goals for AI Development
Authors: Raper Rebecca , Boeddinghaus Jona , Coeckelbergh Mark , Gross Wolfgang , Campigotto Paolo , Lincoln Craig N.
Published: March 2022

Climate change is a global priority. In 2015, the United Nations (UN) outlined its Sustainable Development Goals (SDGs), which stated that taking urgent action to tackle climate change and its impacts was a key priority. The 2021 World Climate Summit finished with calls for governments to take tougher measures towards reducing their carbon footprints. However, it is not obvious how governments can make practical implementations to achieve this goal. One challenge towards achieving a reduced carbon footprint is gaining awareness of how energy exhaustive a system or mechanism is. Artificial Intelligence (AI) is increasingly being used to solve global problems, and its use could potentially solve challenges relating to climate change, but the creation of AI systems often requires vast amounts of, up front, computing power, and, thereby, it can be a significant contributor to greenhouse gas emissions. If governments are to take the SDGs and calls to reduce carbon footprints seriously, they need to find a management and governance mechanism to (i) audit how much their AI system ‘costs’ in terms of energy consumption and (ii) incentivise individuals to act based upon the auditing outcomes, in order to avoid or justify politically controversial restrictions that may be seen as bypassing the creativity of developers. The idea is thus to find a practical solution that can be implemented in software design that incentivises and rewards and that respects the autonomy of developers and designers to come up with smart solutions. This paper proposes such a sustainability management mechanism by introducing the notion of ‘Sustainability Budgets’—akin to Privacy Budgets used in Differential Privacy—and by using these to introduce a ‘Game’ where participants are rewarded for designing systems that are ‘energy efficient’. Participants in this game are, among others, the Machine Learning developers themselves, which is a new focus for this problem that this text introduces. The paper later expands this notion to sustainability management in general and outlines how it might fit into a wider governance framework.
Heat Load Profiles in Industry and the Tertiary Sector: Correlation with Electricity Consumption and Ex Post Modeling
Authors: Jesper Mateo , Pag Felix , Vajen Klaus , Jordan Ulrike
Published: March 2022

The accurate prediction of heat load profiles with a daily resolution is required for a broad range of applications, such as potential studies, design, or predictive operation of heating systems. If the heat demand of a consumer mainly originates from (production) processes independent of the ambient temperature, existing load profile prediction methods fail. To close this gap, this study develops two ex post machine learning models for the prediction of the heat demand with a daily resolution. The selected input features are commonly available to each consumer connected to public natural gas and electricity grids or operating an energy monitoring system: Ambient temperature, weekday, electricity consumption, and heat consumption of the last seven days directly before the predicted day. The study’s database covers electricity and natural gas consumption load profiles from 82 German consumers over a period of two years. Electricity and heat consumption correlate strongly with individual patterns for many consumers. Both shallow and deep learning algorithms from the Python libraries Scikit-Learn and Keras are evaluated. A Long Short-Term Memory (LSTM) model achieves the best results (the median of R2 is 0.94). The ex post model architecture makes the model suitable for anomaly detection in energy monitoring systems.
Dynamic Evolution and Regional Disparity in Carbon Emission Intensity in China
Authors: Yang Meng , Liu Yisheng , Tian Jinzhao , Cheng Feiyu , Song Pengbo
Published: March 2022

China’s carbon reductions are of great significance to the realization of global temperature control targets. Carbon emission intensity (CEI) represents the degree of coordination between emissions and economic development to some extent. Nevertheless, there is a paucity of research on its spatial–temporal evolution and regional differences. To fill the gap, this study exploits the Theil index to shed light on the characteristics of its spatial–temporal distribution and regional disparities in China during the period of 2000–2019, and constructs a multi-regional spatial index decomposition model to analyze the differences in its drivers. The results indicate that the decreasing CEI during the period of 2000–2019 shows a distinctive imbalance in spatial–temporal distribution. The gap between north and south is greater than that between east and west. The expansion of the Theil index based on CEI reveals a widening tendency of the mismatch between emissions and economic development among provinces. CEI disparity is mainly due to growing intraregional differences. For most provinces, the energy intensity effect is the essential driver of spatial differences regarding CEI, with the energy structure and the industrial structure effects gradually changing from promoting to inhibiting effects. The carbon emission factor effect has no significant fluctuation, but regional differences are distinct.
Exploration of Topic Classification in the Tourism Field with Text Mining Technology—A Case Study of the Academic Journal Papers
Authors: Chang I-Cheng , Horng Jeou-Shyan , Liu Chih-Hsing , Chou Sheng-Fang , Yu Tai-Yi
Published: March 2022

This study collects abstracts of SSCI tourism journal papers between 2010 and 2019 from the WoS (Web of Science) database and uses a novel method of topic classification to explore the vocabulary characteristics of the classified articles. The corpora of abstracts are given quantitative Term Frequency–Inverse Document Frequency (TF–IDF) weights. A hierarchical K-means cluster analysis is then performed to automatically classify the articles; co-word analysis techniques are used to show the characteristics of feature words for distinct clusters, titles, and the consistency of the classified articles. Based on the results for 5783 abstracts, cluster analysis classifies the number of K-means clusters into six categories: travel, culture, sustainability, model, behavior, and hotel. A cross-check method is applied to assess the consistency of the topic classifications, list titles and keywords of the documents with the three smallest distances in each category and apply a strategic diagram to present the features of the distinct categories.
Exploring Empirical Rules for Construction Accident Prevention Based on Unsafe Behaviors
Authors: Wang Han-Hsiang , Chen Jieh-Haur , Arifai Achmad Muhyidin , Gheisari Masoud
Published: March 2022

This paper is aimed at exploring rules for construction accident prevention based on unsafe behaviors. The literature review demonstrates a clear connection between construction accident prevention and unsafe behaviors, followed by a 2-year field investigation resulting in 2207 observations based on convenient sampling with 95% confidence and 5% limit of errors in the 50–50 category. There are 80.43% unsafe behaviors categorized into “Regulations for the Occupational Safety and Health Equipment and Measures”, where there are 66.37% of regulations and law VII violations, linking fall prevention with the most cases (94.48%) of Fall Protection and Structure Strengthening. The Apriori yields 13 association rules, where the top 3 rules show that 44.11% of the Passage and lighting category is linked to construction equipment inspections; 29.41% of the high-pressure gas category is linked to construction equipment inspections; 100% of the fire prevention category is linked to fire protection unsafe behavior. The findings clarify the association rules that can prevent workers from accidents in construction sites.
Sustainable Exploitation of Greek Rosmarinus officinalis L. Populations for Ornamental Use through Propagation by Shoot Cuttings and In Vitro Cultures
Authors: Kostas Stefanos , Kaplani Aikaterini , Koulaouzidou Efthymia , Kotoula Aikaterini-Angeliki , Gklavakis Evangelos , Tsoulpha Parthena , Hatzilazarou Stefanos , Nianiou-Obeidat Irini , Kanellis Angelos K. , Economou Athanasios
Published: March 2022

Rosmarinus officinalis L. belongs to the Lamiaceae family and is cultivated worldwide due to its diverse uses as an ornamental evergreen shrub in gardening, as well as a food seasoning and a natural medicine. The present research was conducted to study the morphological traits of seven wild-grown rosemary populations in Greece, as well as the propagation of two selected populations with the most desirable ornamental characteristics, by the use of shoot cuttings and in vitro cultures. From the study of the morphological traits of the seven populations, those with interesting features were grown in the areas of Amaliada and Piperia and, thus, were chosen for studying their propagation. Rooting of the shoot cuttings was carried out during the four seasons of the year, using potassium salt Indole-3-butyric acid (K-IBA) at concentrations of 0.5 and 1 g·L−1, in various substrates under the intermittent mist or fog system. It was found that the shoot cuttings rooted easily in all four seasons, but there were better results for the population of ‘Piperia’ in autumn, with 1 g·L−1 K-IBA (80%), and ‘Amaliada’ in spring, with 0.5 g·L−1 K-IBA (82.5%), while higher rooting percentages were achieved in the fog system, on a substrate consisting of perlite and peat, in a ratio of 2:1 (85%). For the in vitro cultures, shoot tips excised from the two selected populations were successfully disinfested by pre-soaking in an antioxidant solution and then, by sterilizing them in 0.6% (w/v) NaOCl, followed by transferring them onto a Murashige and Skoog (MS) nutrient medium. ‘Amaliada’ cultures proved to be the most productive population (2.1 shoots per explant), with the highest shoot formation frequency (91.6%), when cultivated on the MS nutrient medium without plant growth regulators. For ‘Piperia’ cultures, the highest shoot formation frequency (66.6%) was achieved on the MS nutrient medium supplemented with 0.25 or 0.5 mg·L−1 6-Benzyloaminopurine (BAP) and 0.1 mg·L−1 Indole-3-acetic acid (IAA). Spontaneous root formation frequency was noticed on the MS nutrient medium, containing 0.5 mg·L−1 BAP and 0.1 mg·L−1 IAA, for both ‘Amaliada’ and ‘Piperia’ cultures (50% and 41.6%, respectively) in a single stage, with root lengths of 7.1 and 5.3 cm, respectively. Rosemary plantlets, with roots formed in vitro after transplanting them in soilless substrate, were acclimatized adequately in the greenhouse environment (~70%).
“Cognition, Intelligence and Movement”: Extracurricular Physical Activity as a Promoter of Intelligence in Schoolchildren
Authors: Real-Pérez Mateo , Gavala-González Juan , Silva Mar Artacho , Fernández-García José Carlos
Published: March 2022

The main objective of this study was to assess the existence of significant relationships between motor capacity, academic performance and intelligence in a sample of 67 pre-adolescent children, between the ages of 8 and 11 years. Speed tests were carried out (10 × 5 m shuttle run and plate tapping test), and academic grades in Spanish Language and Literature, Mathematics, Natural Sciences, Foreign Language (English) and Physical Education were considered. The Raven test was administered to estimate the intelligence of the subjects. The results showed significant correlations, mainly between the motor capacities and intelligence variables but not with academic performance. Significant differences were also observed in subjects who participated in sports, with better results in motor capacities and intelligence tests compared to those who did not. The data reveal that engaging in intense physical activity and sports in general may improve academic performance.
Development of a New Residential Energy Management Approach for Retrofit and Transition, Based on Hybrid Energy Sources
Authors: Rahmani Khadidja , Ahriz Atef , Bouaziz Nahla
Published: March 2022

The building sector contributes to a significant part of growing global energy demand. Improving the energy efficiency of the existing building stock is a crucial strategy. Adopting the best energy retrofit strategy in a specific building is a hard task due to the multitude of possible combinations of retrofit measures and the management of different renewable energy sources in the building. However, significant efforts must be made to combine envelope retrofit with renewable energy to improve the energy efficiency of existing buildings and minimize the energy payback period and greenhouse gas (GHG) emissions. It also helps to achieve a successful transition to zero fossil fuel energy for houses and an existing housing stock free of greenhouse gas emissions. This paper is based on a systematic approach, which was applied to an Algerian case study “high-rise building/housing units” that represented southern Mediterranean countries. The percentage of energy consumption was identified; moreover, the performance of on-site hybrid exploitation of renewable energy systems such as photovoltaic cells, thermodynamic panels and small wind turbines was proven. Several actors have collaborated and a range of software types have been used (3D modelling, meteorological regeneration data, energy simulation of buildings, estimation of renewable energy production and statistical analysis). As a result, the building case study has become more efficient than before, whereas the payback time was 10 years. The economic and environmental analysis highlighted that it is possible to save energy demand by up to 51–75% for heating and up to 5–32% for cooling; electricity and hot water consumption was covered at 98% and 80%, respectively, whereas associated gas emissions are reduced by up to 252 tons per building, a reduction of 91%. The model of the platform designed is multi-disciplinary, facilitating collaboration with the various stakeholders to identify directions for successful interventions in pre-established cities. It is a solid framework for future research aimed at standardizing the approach to energy retrofitting and transitioning in existing buildings and is suitable for all types of residential buildings of the southern Mediterranean region such as Algeria, Tunisia and Morocco.
Not All Places Are Equal: Using Instagram to Understand Cognitions and Affect towards Renewable Energy Infrastructures
Authors: Vespa Mariangela , Kortsch Timo , Hildebrand Jan , Schweizer-Ries Petra , Volkmer Sara Alida
Published: March 2022

The research on people–place relations makes an important contribution to the understanding of the public responses to renewable energy technologies (RETs). Social media not only provides easy access to the sentiments and attitudes of online users towards RETs, but social media content can also shape the discourse, both on- and offline, about RETs. Hence, social media content analysis provides valuable insights into the public responses to RETs. However, as of now, only a small number of studies have investigated people–place relations in the context of the energy transition via online content. To address this lack of knowledge, this study aims to increase the understanding of people–place relations by investigating the relationship between the place scales mentioined in Instagram posts (categorized from local to planet) and the sentiments that are expressed in said posts, depending on different energy infrastructures (solar, wind, biomass, geothermal, powerlines, and renewable energy in general). Our analysis of 1500 Instagram posts shows that the place scales that are mentioned in the Instagram posts are related to some differences in the post emotionality, and that these effects differ across the different hashtags that are related to RETs. By using a sociocognitive approach, this study is the first to investigate Instagram posts in the context of the energy transition and people–place relationships.
Hybrid Domain Attention Network for Efficient Super-Resolution
Authors: Zhang Qian , Feng Linxia , Liang Hong , Yang Ying
Published: March 2022

Image SR reconstruction methods focus on recovering the lost details in the image, that is, high-frequency information, which exists in the region of edges and textures. Consequently, the low-frequency information of an image often requires few computational resources. At present, most of the recent CNN-based image SR reconstruction methods allocate computational resources uniformly and treat all features equally, which inevitably results in wasted computational resources and increased computational effort. However, the limited computational resources of mobile devices can hardly afford the expensive computational cost. This paper proposes a symmetric CNN (HDANet), which is based on the Transformer’s self-attention mechanism and uses symmetric convolution to capture the dependencies of image features in two dimensions, spatial and channel, respectively. Specifically, the spatial self-attention module identifies important regions in the image, and the channel self-attention module adaptively emphasizes important channels. The output of the two symmetric modules can be summed to further enhance the feature representation and selectively emphasize important feature information, which can enable the network architecture to precisely locate and bypass low-frequency information and reduce computational cost. Extensive experimental results on Set5, Set14, B100, and Urban100 datasets show that HDANet achieves advanced SR reconstruction performance while reducing computational complexity. HDANet reduces FLOPs by nearly 40% compared to the original model. ×2 SR reconstruction of images on the Set5 test set achieves a PSNR value of 37.94 dB.
Urinary Malondialdehyde (MDA) Concentrations in the General Population—A Systematic Literature Review and Meta-Analysis
Authors: Toto Antonio , Wild Pascal , Graille Mélanie , Turcu Veronica , Crézé Camille , Hemmendinger Maud , Sauvain Jean-Jacques , Bergamaschi Enrico , Guseva Canu Irina , Hopf Nancy B.
Published: March 2022

Oxidative stress has been associated with various inflammation-related human diseases. It is defined as an imbalance between the production and elimination of reactive oxygen species (ROS). ROS can oxidize proteins, lipids, and DNA, and some of these oxidized products are excreted in urine, such as malondialdehyde (MDA), which is considered a biomarker for oxidative damage of lipids. To interpret changes of this biomarker as a measure of oxidative species overproduction in humans, a background range for urinary MDA concentration in the general population is needed. We sought to establish urinary MDA concentration ranges for healthy adult populations based on reported values in the available scientific literature. We conducted a systematic review and meta-analysis using the standardized protocol registered in PROSPERO (CRD42020146623). EMBASE, PubMed, Web of Science, and Cochrane library databases were searched from journal inception up to October 2020. We included 35 studies (divided into 47 subgroups for the quantitative analysis). Only studies that measured creatinine-corrected urinary MDA with high-performance liquid chromatography (HPLC) with mass spectrometry (MS), fluorescence detection, or UV photometry were included. The geometric mean (GM) of urinary MDA concentration was 0.10 mg/g creatinine and 95% percentile confidence interval (CI) 0.07–0.12. Age, geographical location but not sex, and smoking status had a significant effect on urinary MDA concentrations. There was a significant increasing trend of urinary MDA concentrations with age. These urinary MDA values should be considered preliminary, as they are based on mostly moderate to some low-quality evidence studies. Although urinary MDA can reliably reflect excessive oxidative stress in a population, the influence of physiological parameters that affect its meaning needs to be addressed as well as harmonizing the chemical analytical methods.
The Beauty of Bacteriophage T4 Research: Lindsay W. Black and the T4 Head Assembly
Authors: Kuhn Andreas , Thomas Julie A.
Published: March 2022

Viruses are biochemically complex structures and mainly consist of folded proteins that contain nucleic acids. Bacteriophage T4 is one of most prominent examples, having a tail structure that contracts during the infection process. Intracellular phage multiplication leads to separate self-directed assembly reactions of proheads, tails and tail fibers. The proheads are packaged with concatemeric DNA produced by tandem replication reactions of the parental DNA molecule. Once DNA packaging is completed, the head is joined with the tail and six long fibers are attached. The mature particles are then released from the cell via lysis, another tightly regulated process. These processes have been studied in molecular detail leading to a fascinating view of the protein-folding dynamics that direct the structural interplay of assembled complexes. Lindsay W. Black dedicated his career to identifying and defining the molecular events required to form the T4 virion. He leaves us with rich insights into the astonishingly precise molecular clockwork that co-ordinates all of the players in T4 assembly, both viral and cellular. Here, we summarize Lindsay’s key research contributions that are certain to stimulate our future science for many years to come.
Identification and Characterization of vB_PreP_EPr2, a Lytic Bacteriophage of Pan-Drug Resistant Providencia rettgeri
Authors: Mencke Jaime L. , He Yunxiu , Filippov Andrey A. , Nikolich Mikeljon P. , Belew Ashton T. , Fouts Derrick E. , McGann Patrick T. , Swierczewski Brett E. , Getnet Derese , Ellison Damon W. , Margulieux Katie R.
Published: March 2022

Providencia rettgeri is an emerging opportunistic Gram-negative pathogen with reports of increasing antibiotic resistance. Pan-drug resistant (PDR) P. rettgeri infections are a growing concern, demonstrating a need for the development of alternative treatment options which is fueling a renewed interest in bacteriophage (phage) therapy. Here, we identify and characterize phage vB_PreP_EPr2 (EPr2) with lytic activity against PDR P. rettgeri MRSN 845308, a clinical isolate that carries multiple antibiotic resistance genes. EPr2 was isolated from an environmental water sample and belongs to the family Autographiviridae, subfamily Studiervirinae and genus Kayfunavirus, with a genome size of 41,261 base pairs. Additional phenotypic characterization showed an optimal MOI of 1 and a burst size of 12.3 ± 3.4 PFU per bacterium. EPr2 was determined to have a narrow host range against a panel of clinical P. rettgeri strains. Despite this fact, EPr2 is a promising lytic phage with potential for use as an alternative therapeutic for treatment of PDR P. rettgeri infections.
The Concern of COVID-19 Vaccine Safety Is behind Its Low Uptake among Patients with Diabetes Mellitus in Sudan
Authors: Omar Saeed M. , Khalil Rehana , Adam Ishag , Al-Wutayd Osama
Published: March 2022

Diabetic patients are vulnerable to developing severe complications and have a higher risk of death due to COVID-19 infection. Vaccination remains the mainstay during the current situation to mitigate the risks related to COVID-19 infection. Therefore, the aim of the current study was to assess the vaccination status and the factors associated with COVID-19 vaccine uptake among patients with diabetes mellitus (DM) in Sudan. A hospital-based cross-sectional study was conducted from January to February 2022 at Gadarif Hospital in eastern Sudan. Information on sociodemographics, the contracting of COVID-19 during the pandemic, beliefs toward COVID-19 vaccinations, and barriers related to COVID-19 vaccinations was obtained through an interview questionnaire among adult (≥18 years) patients with DM. Bivariate and multinomial logistic regression analyses performed. A total of 568 diabetic patients were enrolled, with a mean (SD) age of 53.07 (12.69) years. The majority of the participants were female (67.6%), urban residents (63.4%), uneducated (60.6%) and employed (73.2%). There was a history of COVID-19 in 97.4% of participants, and 29.2% of them had hypertension along with DM. About 31% received the vaccine, out of which 17.9% received the first dose, 13.2% received the second dose, and 0.2% received the third dose. Multinomial logistic regression analysis showed a significant association between belief in the safety of the COVID-19 vaccine and having had two doses of it (adjusted Odds ratio = 20.42, p < 0.001). The prevalence of COVID-19 infection was high, while the rate of COVID-19 vaccination uptake was low and inadequate among the participants. Appropriate health education and targeted interventions toward awareness of safety concerns are highly recommended.
Development of Flat Warts on the Cheeks after BioNTech-Pfizer BNT162b2 Vaccine: Is There a Correlation?
Authors: Cazzato Gerardo , Romita Paolo , Foti Caterina , Lobreglio Debora , Trilli Irma , Colagrande Anna , Ingravallo Giuseppe , Resta Leonardo
Published: March 2022

The SARS-CoV-2 pandemic has affected health systems across the globe, making the use of vaccines more urgent and topical than ever. Since the first months after the introduction of vaccinations, several reactions, both local and systemic, have been reported although they were mostly very mild and only rarely harbingers of more serious complications. We present a case of multiple flat warts onset over the cheeks in a patient after the second dose of mRNA BioNTech-Pfizer BNT162b2 vaccine, and we discuss the possible temporal association between the two events, also considering the patient’s antibody status.
Effect of Culling Management Practices on the Seroprevalence of Johne’s Disease in Holstein Dairy Cattle in Central Italy
Authors: Crociati Martina , Grispoldi Luca , Chalias Athanasios , Monaci Maurizio , Cenci-Goga Beniamino , Sylla Lakamy
Published: March 2022

A study was performed in Umbria, central Italy, to find out whether different culling strategies adopted by farms to control Johne’s disease (JD) infection exerted effects on the seroprevalence in dairy cattle. Fifty Fresian dairy herds in the Perugia and Assisi districts were visited and an audit of herd management was conducted. Among the 50 herds, 20 were selected for the consistency of management practices and, according to the culling strategy, two groups were created: group A (aggressive culling protocol, with average herd productive life <1100 days) and group B (lower culling rate, with productive life greater than 1500 days). The presence of antibodies to Mycobacterium avium subspecies paratuberculosis (Map) in the serum was determined using a commercial enzyme-linked immunosorbent assay (ELISA) kit. It was found that 3.3% (n = 14) of the cows of group B (n = 422, from 17 herds) were positive for Map antibodies, in comparison with 5.7% (n = 21) of the cows from group A (n = 366, from three herds). The odds ratio from multiple logistic regression (adjusted odds ratio 2.446, 95% confidence interval 0.412 to 14.525) showed that Johne’s disease prevalence in herds with a greater productive life was not higher than in herds with typical modern management characterized by more aggressive culling. This is a significant finding, indicating that aggressive culling may not be necessary. Current JD control recommendations are derived from data obtained in high-prevalence paratuberculosis areas (northern Europe, including northern Italy), while methods of information transfer to dairy farms in low-prevalence areas should be reassessed to ensure that the correct measures, including basic calving management and calf-rearing practices, are thoroughly implemented. Using the manufacturer’s suggested cut-off for a positive ELISA test and the sensitivity and specificity claimed, the overall true prevalence in Umbria dairy cattle was calculated as 7% (95% confidence interval 5.2% to 8.8%).
Development of a Chimeric Porcine Reproductive and Respiratory Syndrome Virus (PRRSV)-2 Vaccine Candidate Expressing Hypo-Glycosylated Glycoprotein-5 Ectodomain of Korean Lineage-1 Strain
Authors: Choi Hwi-Yeon , Kim Min-Sik , Kang Yeong-Lim , Choi Jong-Chul , Choi In-Yeong , Jung Sung-Won , Jeong Ji-Yun , Kim Min-Chul , Hwang Seong-Soo , Lee Sang-Won , Park Seung-Yong , Song Chang-Seon , Choi In-Soo , Lee Joong-Bok
Published: March 2022

Vaccination is a practical method to provide protection against porcine reproductive and respiratory syndrome virus (PRRSV), but current PRRSV vaccines show limited efficacy against divergent field strains. Lineage 1 PRRSV includes virulent strains such as NADC30 and MN184 and now has become one of the most prevalent viruses in Korea. Accordingly, there is an urgent need to develop a new vaccine for Korean lineage-1 strains. In this study, a vaccine candidate against Korean lineage-1 PRRSV, vCSL1-GP5-N33D, was developed by reverse genetics technology. vCSL1-GP5-N33D was designed as a hypo-glycosylated chimeric virus containing the glycoprotein 5 ectodomain region of the Korean lineage-1 wild-type strain. An inactivated vaccine of vCSL1-GP5-N33D was applied to a PRRS-endemic farm and elicited high serum virus neutralization (SVN) antibody titers. The vaccinated group induced SVN antibody titers of 4.40 (log2) ± 2.46, which were approximately 2-fold higher than those of the negative control at 8-weeks post-vaccination. Moreover, 60% of pigs in the vaccinated group displayed SVN antibody titers of ≥5 (log2), while none of the pigs in the negative control exhibited SVN antibody titers of ≥5 (log2). The overall results of the animal experiment suggest that the vCSL1-GP5-N33D inactivated vaccine is a promising vaccine candidate.