bluuebunny/crossref_metadata_2025 huggingface dataset:
Formation Tracking Control and Obstacle Avoidance of Unicycle-Type Robots Guaranteeing Continuous Velocities
Authors: Martinez Jose Bernardo , Becerra Hector M. , Gomez-Gutierrez David
Published: June 2021

In this paper, we addressed the problem of controlling the position of a group of unicycle-type robots to follow in formation a time-varying reference avoiding obstacles when needed. We propose a kinematic control scheme that, unlike existing methods, is able to simultaneously solve the both tasks involved in the problem, effectively combining control laws devoted to achieve formation tracking and obstacle avoidance. The main contributions of the paper are twofold: first, the advantages of the proposed approach are not all integrated in existing schemes, ours is fully distributed since the formulation is based on consensus including the leader as part of the formation, scalable for a large number of robots, generic to define a desired formation, and it does not require a global coordinate system or a map of the environment. Second, to the authors’ knowledge, it is the first time that a distributed formation tracking control is combined with obstacle avoidance to solve both tasks simultaneously using a hierarchical scheme, thus guaranteeing continuous robots velocities in spite of activation/deactivation of the obstacle avoidance task, and stability is proven even in the transition of tasks. The effectiveness of the approach is shown through simulations and experiments with real robots.
An Instance Segmentation and Clustering Model for Energy Audit Assessments in Built Environments: A Multi-Stage Approach
Authors: Arjoune Youness , Peri Sai , Sugunaraj Niroop , Biswas Avhishek , Sadhukhan Debanjan , Ranganathan Prakash
Published: June 2021

Heat loss quantification (HLQ) is an essential step in improving a building’s thermal performance and optimizing its energy usage. While this problem is well-studied in the literature, most of the existing studies are either qualitative or minimally driven quantitative studies that rely on localized building envelope points and are, thus, not suitable for automated solutions in energy audit applications. This research work is an attempt to fill this gap of knowledge by utilizing intensive thermal data (on the order of 100,000 plus images) and constitutes a relatively new area of analysis in energy audit applications. Specifically, we demonstrate a novel process using deep-learning methods to segment more than 100,000 thermal images collected from an unmanned aerial system (UAS). To quantify the heat loss for a building envelope, multiple stages of computations need to be performed: object detection (using Mask-RCNN/Faster R-CNN), estimating the surface temperature (using two clustering methods), and finally calculating the overall heat transfer coefficient (e.g., the U-value). The proposed model was applied to eleven academic campuses across the state of North Dakota. The preliminary findings indicate that Mask R-CNN outperformed other instance segmentation models with an mIOU of 73% for facades, 55% for windows, 67% for roofs, 24% for doors, and 11% for HVACs. Two clustering methods, namely K-means and threshold-based clustering (TBC), were deployed to estimate surface temperatures with TBC providing consistent estimates across all times of the day over K-means. Our analysis demonstrated that thermal efficiency not only depended on the accurate acquisition of thermal images but also relied on other factors, such as the building geometry and seasonal weather parameters, such as the outside/inside building temperatures, wind, time of day, and indoor heating/cooling conditions. Finally, the resultant U-values of various building envelopes were compared with recommendations from the American Society of Heating, Refrigerating, and Air-conditioning Engineers (ASHRAE) building standards.
Routing with Renewable Energy Management in Wireless Sensor Networks
Authors: Junior João , Lima Moysés , Balico Leandro , Pazzi Richard , Oliveira Horácio
Published: June 2021

In wireless sensor networks (WSNs), power consumption is an important aspect when designing routing protocols. When compared to other components of a sensor node, the power required by radio transmitters is responsible for most of the consumption. One way to optimize energy consumption is by using energy-aware protocols. Such protocols take into consideration the residual energy information (i.e., remaining battery power) when making decisions, providing energy efficiency through the careful management of energy consumption. In this work, we go further and propose a new routing protocol that uses not only the residual energy information, but also the available renewable energy information from renewable energy sources such as solar cells. We then present the Renewable Energy-Based Routing (REBORN) algorithm, an energy-aware geographic routing algorithm, capable of managing both the residual and the available energy. Our results clearly show the advantages and the efficiency achieved by our REBORN algorithm when compared to other proposed energy-aware approaches.
Multivariate Time Series Analysis of Temperatures in the Archaeological Museum of L’Almoina (Valencia, Spain)
Authors: Ramírez Sandra , Zarzo Manuel , García-Diego Fernando-Juan
Published: June 2021

An earlier study carried out in 2010 at the archaeological site of L’Almoina (Valencia, Spain) found marked daily fluctuations of temperature, especially in summer. Such pronounced gradient is due to the design of the museum, which includes a skylight as a ceiling, covering part of the remains in the museum. In this study, it was found that the thermal conditions are not homogeneous and vary at different points of the museum and along the year. According to the European Standard EN10829, it is necessary to define a plan for long-term monitoring, elaboration and study of the microclimatic data, in order to preserve the artifacts. With the aforementioned goal of extending the study and offering a tool to monitor the microclimate, a new statistical methodology is proposed. For this propose, during one year (October 2019–October 2020), a set of 27 data-loggers was installed, aimed at recording the temperature inside the museum. By applying principal component analysis and k-means, three different microclimates were established. In order to characterize the differences among the three zones, two statistical techniques were put forward. Firstly, Sparse Partial Least Squares Discriminant Analysis (sPLS-DA) was applied to a set of 671 variables extracted from the time series. The second approach consisted of using a random forest algorithm, based on the same functions and variables employed by the first methodology. Both approaches allowed the identification of the main variables that best explain the differences between zones. According to the results, it is possible to establish a representative subset of sensors recommended for the long-term monitoring of temperatures at the museum. The statistical approach proposed here is very effective for discriminant time series analysis and for explaining the differences in microclimate when a net of sensors is installed in historical buildings or museums.
Can a Semi-Active Energy Harvesting Shock Absorber Mimic a Given Vehicle Passive Suspension?
Authors: Reyes-Avendaño Jorge A. , Moreno-Ramírez Ciro , Gijón-Rivera Carlos , Gonzalez-Hernandez Hugo G. , Olazagoitia José Luis
Published: June 2021

Energy harvesting shock absorbers (EHSA) have made great progress in recent years, although there are still no commercial solutions for this technology. This paper addresses the question of whether, and under what conditions, an EHSA can completely replace a conventional one. In this way, any conventional suspension could be replicated at will, while recovering part of the wasted energy. This paper focuses on mimicking the original passive damper behavior by continuously varying the electrical parameters of the regenerative damper. For this study, a typical ball-screw EHSA is chosen, and its equivalent suspension parameters are tried to be matched to the initial damper. The methodology proposes several electrical control circuits that optimize the dynamic behavior of the regenerative damper from the continuous variation of a load resistance. The results show that, given a target damper curve, the regenerative damper can adequately replicate it when there is a minimum velocity in the damper. However, when the damper velocity is close to zero, the only way to compensate for inertia is through the introduction of external energy to the system.
Egocentric-View Fingertip Detection for Air Writing Based on Convolutional Neural Networks
Authors: Chen Yung-Han , Huang Chi-Hsuan , Syu Sin-Wun , Kuo Tien-Ying , Su Po-Chyi
Published: June 2021

This research investigated real-time fingertip detection in frames captured from the increasingly popular wearable device, smart glasses. The egocentric-view fingertip detection and character recognition can be used to create a novel way of inputting texts. We first employed Unity3D to build a synthetic dataset with pointing gestures from the first-person perspective. The obvious benefits of using synthetic data are that they eliminate the need for time-consuming and error-prone manual labeling and they provide a large and high-quality dataset for a wide range of purposes. Following that, a modified Mask Regional Convolutional Neural Network (Mask R-CNN) is proposed, consisting of a region-based CNN for finger detection and a three-layer CNN for fingertip location. The process can be completed in 25 ms per frame for 640×480 RGB images, with an average error of 8.3 pixels. The speed is high enough to enable real-time “air-writing”, where users are able to write characters in the air to input texts or commands while wearing smart glasses. The characters can be recognized by a ResNet-based CNN from the fingertip trajectories. Experimental results demonstrate the feasibility of this novel methodology.
Rice Seed Purity Identification Technology Using Hyperspectral Image with LASSO Logistic Regression Model
Authors: Liu Weihua , Zeng Shan , Wu Guiju , Li Hao , Chen Feifei
Published: June 2021

Hyperspectral technology is used to obtain spectral and spatial information of samples simultaneously and demonstrates significant potential for use in seed purity identification. However, it has certain limitations, such as high acquisition cost and massive redundant information. This study integrates the advantages of the sparse feature of the least absolute shrinkage and selection operator (LASSO) algorithm and the classification feature of the logistic regression model (LRM). We propose a hyperspectral rice seed purity identification method based on the LASSO logistic regression model (LLRM). The feasibility of using LLRM for the selection of feature wavelength bands and seed purity identification are discussed using four types of rice seeds as research objects. The results of 13 different adulteration cases revealed that the value of the regularisation parameter was different in each case. The recognition accuracy of LLRM and average recognition accuracy were 91.67–100% and 98.47%, respectively. Furthermore, the recognition accuracy of full-band LRM was 71.60–100%. However, the average recognition accuracy was merely 89.63%. These results indicate that LLRM can select the feature wavelength bands stably and improve the recognition accuracy of rice seeds, demonstrating the feasibility of developing a hyperspectral technology with LLRM for seed purity identification.
Design of a New Acoustic Logging While Drilling Tool
Authors: Zhang Kai , Tan Baohai , Zhang Wenxiu , Sun Yuntao , Zheng Jian , Su Yuanda , Liu Xutang , Wu Gaofu , Xin Shoutao
Published: June 2021

To obtain qualified logging while drilling (LWD) data, a new acoustic LWD tool was designed. Its overall design is introduced here, including the physical construction, electronic structure, and operation flowchart. Thereafter, core technologies adopted in this tool are presented, such as dominant exciting wave bands of dipole source, a sine wave pulse excitation circuit, broadband impedance matching, and an intellectualized active reception transducer. Lastly, we tested this tool in the azimuthal anisotropy module well, calibration well, and normal well, working in the model of the cable, sliding eye, and logging while drilling. Experiments showed that the core technologies achieved ideal results and that the LWD tool obtained qualified data.
Blockchain-IoT Sensor (BIoTS): A Solution to IoT-Ecosystems Security Issues
Authors: Gonzalez-Amarillo Carlos , Cardenas-Garcia  Cristian , Mendoza-Moreno  Miguel , Ramirez-Gonzalez Gustavo , Corrales Juan Carlos
Published: June 2021

Sensor devices that act in the IoT architecture perception layer are characterized by low data processing and storage capacity. These reduced capabilities make the system ubiquitous and lightweight, but considerably reduce its security. The IoT-based Food Traceability Systems (FTS), aimed at ensuring food safety and quality, serve as a motivating scenario for BIoTS development and deployment; therefore, security challenges and gaps related with data integrity are analyzed from this perspective. This paper proposes the BIoTS hardware design that contains some modules built-in VHDL (SHA-256, PoW, and SD-Memory) and other peripheral electronic devices to provide capabilities to the perception layer by implementing the blockchain architecture’s security requirements in an IoT device. The proposed hardware is implemented on FPGA Altera DE0-Nano. BIoTS can participate as a miner in the blockchain network through Smart Contracts and solve security issues related to data integrity and data traceability in an Blockchain-IoT system. Blockchain algorithms implemented in IoT hardware opens a path to IoT devices’ security and ensures participation in data validation inside a food certification process.
An Enhanced Smoothed L0-Norm Direction of Arrival Estimation Method Using Covariance Matrix
Authors: Paik Ji Woong , Lee Joon-Ho , Hong Wooyoung
Published: June 2021

An enhanced smoothed l0-norm algorithm for the passive phased array system, which uses the covariance matrix of the received signal, is proposed in this paper. The SL0 (smoothed l0-norm) algorithm is a fast compressive-sensing-based DOA (direction-of-arrival) estimation algorithm that uses a single snapshot from the received signal. In the conventional SL0 algorithm, there are limitations in the resolution and the DOA estimation performance, since a single sample is used. If multiple snapshots are used, the conventional SL0 algorithm can improve performance in terms of the DOA estimation. In this paper, a covariance-fitting-based SL0 algorithm is proposed to further reduce the number of optimization variables when using multiple snapshots of the received signal. A cost function and a new null-space projection term of the sparse recovery for the proposed scheme are presented. In order to verify the performance of the proposed algorithm, we present the simulation results and the experimental results based on the measured data.
Estimation of Respiratory Rate from Thermography Using Respiratory Likelihood Index
Authors: Takahashi Yudai , Gu Yi , Nakada Takaaki , Abe Ryuzo , Nakaguchi Toshiya
Published: June 2021

Respiration is a key vital sign used to monitor human health status. Monitoring respiratory rate (RR) under non-contact is particularly important for providing appropriate pre-hospital care in emergencies. We propose an RR estimation system using thermal imaging cameras, which are increasingly being used in the medical field, such as recently during the COVID-19 pandemic. By measuring temperature changes during exhalation and inhalation, we aim to track the respiration of the subject in a supine or seated position in real-time without any physical contact. The proposed method automatically selects the respiration-related regions from the detected facial regions and estimates the respiration rate. Most existing methods rely on signals from nostrils and require close-up or high-resolution images, while our method only requires the facial region to be captured. Facial region is detected using YOLO v3, an object detection model based on deep learning. The detected facial region is divided into subregions. By calculating the respiratory likelihood of each segmented region using the newly proposed index, called the Respiratory Quality Index, the respiratory region is automatically selected and the RR is estimated. An evaluation of the proposed RR estimation method was conducted on seven subjects in their early twenties, with four 15 s measurements being taken. The results showed a mean absolute error of 0.66 bpm. The proposed method can be useful as an RR estimation method.
Path Planning and Collision Risk Management Strategy for Multi-UAV Systems in 3D Environments
Authors: López Blanca , Muñoz Javier , Quevedo Fernando , Monje Concepción A. , Garrido Santiago , Moreno Luis E.
Published: June 2021

Multi-UAV systems are attracting, especially in the last decade, the attention of researchers and companies of very different fields due to the great interest in developing systems capable of operating in a coordinated manner in complex scenarios and to cover and speed up applications that can be dangerous or tedious for people: search and rescue tasks, inspection of facilities, delivery of goods, surveillance, etc. Inspired by these needs, this work aims to design, implement and analyze a trajectory planning and collision avoidance strategy for multi-UAV systems in 3D environments. For this purpose, a study of the existing techniques for both problems is carried out and an innovative strategy based on Fast Marching Square—for the planning phase—and a simple priority-based speed control—as the method for conflict resolution—is proposed, together with prevention measures designed to try to limit and reduce the greatest number of conflicting situations that may occur between vehicles while they carry out their missions in a simulated 3D urban environment. The performance of the algorithm is evaluated successfully on the basis of certain conveniently chosen statistical measures that are collected throughout the simulation runs.
Non-Destructive Analytical Investigation of Decorative Wallpapers Samples of the Nineteenth Century before Their Restoration
Authors: Costantini Ilaria , Castro Kepa , Rodriguez-Laso Maria Dolores , Madariaga Juan Manuel , Arana Gorka
Published: June 2021

In this work, decorative wallpapers (19th century) from an historical palace located in Oiartzun (Basque Country, Spain) were analyzed before their restoration. Micro-energy dispersive X-ray fluorescence spectroscopy, Raman spectroscopy, and attenuated total reflectance infrared spectroscopy were used to investigate the elemental and molecular composition of pigments, the presence of binders, and the state of conservation of the paper support. The aim of the investigation was trying to understand the possible degradation pathways and identify the raw materials in order to choose the best restoration protocol according to the original aspect of wallpapers. As stated from both the elemental distribution and the identification of mineral phases by Raman spectroscopy, the most used pigment was lead chromate. It was mixed with other pigments such as ultramarine blue, zinc chromate, hematite, and atacamite among others to obtain different shades and they were applied mixed with an animal glue. Brass, identified thanks to elemental micro-EDXRF maps, was employed as a shiny decorative element. In addition, a partial degradation of cellulose was detected due to its natural ageing, the acidic nature of lignin, and to a phenomenon of humidity of the walls. Probably the deposition of black particulate matter was the cause of the darkening of the painting surfaces.
Secure Video Surveillance Framework in Smart City
Authors: Li Hao , Xiezhang Tianhao , Yang Cheng , Deng Lianbing , Yi Peng
Published: June 2021

In the construction process of smart cities, more and more video surveillance systems have been deployed for traffic, office buildings, shopping malls, and families. Thus, the security of video surveillance systems has attracted more attention. At present, many researchers focus on how to select the region of interest (RoI) accurately and then realize privacy protection in videos by selective encryption. However, relatively few researchers focus on building a security framework by analyzing the security of a video surveillance system from the system and data life cycle. By analyzing the surveillance video protection and the attack surface of a video surveillance system in a smart city, we constructed a secure surveillance framework in this manuscript. In the secure framework, a secure video surveillance model is proposed, and a secure authentication protocol that can resist man-in-the-middle attacks (MITM) and replay attacks is implemented. For the management of the video encryption key, we introduced the Chinese remainder theorem (CRT) on the basis of group key management to provide an efficient and secure key update. In addition, we built a decryption suite based on transparent encryption to ensure the security of the decryption environment. The security analysis proved that our system can guarantee the forward and backward security of the key update. In the experiment environment, the average decryption speed of our system can reach 91.47 Mb/s, which can meet the real-time requirement of practical applications.
Health Condition Estimation of Bearings with Multiple Faults by a Composite Learning-Based Approach
Authors: Inyang Udeme , Petrunin Ivan , Jennions Ian
Published: June 2021

Bearings are critical components found in most rotating machinery; their health condition is of immense importance to many industries. The varied conditions and environments in which bearings operate make them prone to single and multiple faults. Widespread interest in the improvements of single fault diagnosis meant limited attention was spent on multiple fault diagnosis. However, multiple fault diagnosis poses extra challenges due to the submergence of the weak fault by the strong fault, presence of non-Gaussian noise, coupling of the frequency components, etc. A number of existing convolutional neural network models operate on a distinct feature that is not enough to assure reliable results in the presence of these challenges. In this paper, extended feature sets in three homogenous deep learning models are used for multiple fault diagnosis. This ensures a measure of diversity is introduced to the health management dataset to obtain complementary solutions from the models. The outputs of the models are fused through blending ensemble learning. Experiments using vibration datasets based on bearing multiple faults show an accuracy of 98.54%, with an improvement of 2.74% in the overall effectiveness over the single models. Compared with other technologies, the results show that this approach provides an improved generalized diagnostic capability.
Recent Advances in ZnO-Based Carbon Monoxide Sensors: Role of Doping
Authors: Pineda-Reyes Ana María , Herrera-Rivera María R. , Rojas-Chávez Hugo , Cruz-Martínez Heriberto , Medina Dora I.
Published: June 2021

Monitoring and detecting carbon monoxide (CO) are critical because this gas is toxic and harmful to the ecosystem. In this respect, designing high-performance gas sensors for CO detection is necessary. Zinc oxide-based materials are promising for use as CO sensors, owing to their good sensing response, electrical performance, cost-effectiveness, long-term stability, low power consumption, ease of manufacturing, chemical stability, and non-toxicity. Nevertheless, further progress in gas sensing requires improving the selectivity and sensitivity, and lowering the operating temperature. Recently, different strategies have been implemented to improve the sensitivity and selectivity of ZnO to CO, highlighting the doping of ZnO. Many studies concluded that doped ZnO demonstrates better sensing properties than those of undoped ZnO in detecting CO. Therefore, in this review, we analyze and discuss, in detail, the recent advances in doped ZnO for CO sensing applications. First, experimental studies on ZnO doped with transition metals, boron group elements, and alkaline earth metals as CO sensors are comprehensively reviewed. We then focused on analyzing theoretical and combined experimental–theoretical studies. Finally, we present the conclusions and some perspectives for future investigations in the context of advancements in CO sensing using doped ZnO, which include room-temperature gas sensing.
Optimization of Position and Number of Hotspot Detectors Using Artificial Neural Network and Genetic Algorithm to Estimate Material Levels Inside a Silo
Authors: Rhee Jeong Hoon , Kim Sang Il , Lee Kang Min , Kim Moon Kyum , Lim Yun Mook
Published: June 2021

To realize efficient operation of a silo, level management of internal storage is crucial. In this study, to address the existing measurement limitations, a silo hotspot detector, which is typically utilized for internal silo temperature monitoring, was employed. The internal temperature data measured using the hotspot detectors were used to train an artificial neural network (ANN) algorithm to predict the level of the internal storage of the silo. The prediction accuracy was evaluated by comparing the predicted data with ground truth data. We combined the ANN model with the genetic algorithm (GA) to improve the prediction accuracy and establish efficient sensor installation positions and number to proceed with optimization. Simulation results demonstrated that the best predictive performance (up to 97% accuracy) was achieved when the ANN structure was 9-19-19-1. Furthermore, the numbers of efficient sensors and sensors positions determined using the proposed ANN-GA technique were reduced from seven to five or four, thereby ensuring economic feasibility.
PVDF Based Pressure Sensor for the Characterisation of the Mechanical Loading during High Explosive Hydro Forming of Metal Plates
Authors: Tartière Jérémie , Arrigoni Michel , Nême Alain , Groeneveld Hugo , Van Der Veen Sjoerd
Published: June 2021

High explosive hydro forming (HEHF) is a suitable technique for large metal plate forming. Manufacturing stages of such a part requires an adapted design of explosive charge configurations to define the mechanical loading exerted on the part. This mechanical loading remains challenging to be experimentally determined but necessary for predictive numerical simulation in the design of parts to form. Providing that the actual mechanical impulse would allow the neglecting of the modelling of the detonation stage, this considerably increases the computational time. The present work proposes an experimental method for obtaining the exerted mechanical loading by HEHF on the part to form. It relies on the development of low-cost sensor based on a polyvinyliden fluorid (PVDF) gauge. In addition to it, an analytical approach based on shock physics is proposed for the sensor signal interpretation. The method considers the multi-layer aspect of the sensor and its intrusiveness with respect to waves propagation. Measurements were repeated to assess their relevance and the reproducibility by using steel and aluminium anvils in HEHF. Numerical modelling in 2D plane geometry of the experiments was performed with two commercial hydrocodes. The comparison of mechanical impulses shows an agreement in terms of chronology but a noticeable difference in terms of amplitude, explained by mesh size and numerical diffusion.
Fully Transparent and Sensitivity-Programmable Amorphous Indium-Gallium-Zinc-Oxide Thin-Film Transistor-Based Biosensor Platforms with Resistive Switching Memories
Authors: Jeon Hyeong-Un , Cho Won-Ju
Published: June 2021

This paper presents a fully transparent and sensitivity-programmable biosensor based on an amorphous-indium-gallium-zinc-oxide (a-IGZO) thin-film transistor (TFT) with embedded resistive switching memories (ReRAMs). The sensor comprises a control gate (CG) and a sensing gate (SG), each with a resistive switching (RS) memory connected, and a floating gate (FG) that modulates the channel conductance of the a-IGZO TFT. The resistive coupling between the RS memories connected to the CG and SG produces sensitivity properties that considerably exceed the limit of conventional ion-sensitive field-effect transistor (ISFET)-based sensors. The resistances of the embedded RS memories were determined by applying a voltage to the CG–FG and SG–FG structures independently and adjusting the compliance current. Sensors constructed using RS memories with different resistance ratios yielded a pH sensitivity of 50.5 mV/pH (RCG:RSG = 1:1), 105.2 mV/pH (RCG:RSG = 2:1), and 161.9 mV/pH (RCG:RSG = 3:1). Moreover, when the RCG:RSG = 3:1, the hysteresis voltage width (VH) and drift rate were 54.4 mV and 32.9 mV/h, respectively. As the increases in VH and drift rate are lower than the amplified sensitivity, the sensor performs capably. The proposed device is viable as a versatile sensing device capable of detecting various substances, such as cells, antigens, DNA, and gases.
Rhamnolipid Biosurfactants—Possible Natural Anticancer Agents and Autophagy Inhibitors
Authors: Semkova Severina , Antov Georgi , Iliev Ivan , Tsoneva Iana , Lefterov Pavel , Christova Nelly , Nacheva Lilyana , Stoineva Ivanka , Kabaivanova Lyudmila , Staneva Galya , Nikolova Biliana
Published: June 2021

Background/Aim: A number of biologically active substances were proved as an alternative to conventional anticancer medicines. The aim of the study is in vitro investigation of the anticancer activity of mono- and di-Rhamnolipids (RL-1 and RL-2) against human breast cancer. Additionally, the combination with Cisplatin was analyzed. Materials and Methods: Breast cell lines (MCF-10A, MCF-7 and MDA-MB-231) were treated with RLs and in combination with Cisplatin. The viability was analyzed using MTT assay, and investigation of autophagy was performed via acridine orange staining. Results: In contrast to the healthy cells, both tested cancer lines exhibited sensitivity to RLs treatment. This effect was accompanied by an influence on the autophagy-related acidic formation process. Only for the triple-negative breast cancer cell line (MDA-MB-231) the synergistic effect of the combined treatment (10 µM Cisplatin and 1 µg/mL RL-2) was observed. Conclusion: Based on studies on the reorganization of membrane models in the presence of RL and the data about a higher amount of lipid rafts in cancer cell membranes than in non-tumorigenic, we suggest a possible mechanism of membrane remodelling by formation of endosomes. Shortly, in order to have a synergistic effect, it is necessary to have Cisplatin andRL-2 as RL2 is a molecule inducingpositive membrane curvature.
It Takes a Couple to Tango: Antecedents to Collaborative Decision-Making
Authors: Koval Olena , Hansen Håvard
Published: June 2021

Existing research on vacation-related choices in couples concentrates on variables related to roles and influence in attempt to explain decision-making processes. However, as experienced from 2019–2021 COVID pandemic, travel-related decisions in couples may be characterized by higher complexity and risks, both in relation to health and economy. As the consequences of such decisions may affect both partners, the couples might benefit from a collaborative approach. This study investigates how certain known facilitators of group collaboration are associated with romantic couples’ perception of collaboration in the context of vacation choices. The data were collected by means of a cross sectional design, and 112 individuals from Norway who were in romantic relationships completed the survey. Multi-item scales were used and validated with factor analysis; the hypotheses were tested using multiple regression. Four of the five hypotheses were empirically supported, and shared experiences, flexibility, engagement, and partners’ support positively affected the perception of collaborative decision-making. No effect was found for role exchange. The results of this study may help to gain a better understanding of how couples make decisions together, and how firms can adapt their communication and service design to increase their appeal to this large tourism segment.
A Constrained Generalized Functional Linear Model for Multi-Loci Genetic Mapping
Authors: Huang Jiayu , Yang Jie , Gu Zhangrong , Zhu Wei , Wu Song
Published: June 2021

In genome-wide association studies (GWAS), efficient incorporation of linkage disequilibria (LD) among densely typed genetic variants into association analysis is a critical yet challenging problem. Functional linear models (FLM), which impose a smoothing structure on the coefficients of correlated covariates, are advantageous in genetic mapping of multiple variants with high LD. Here we propose a novel constrained generalized FLM (cGFLM) framework to perform simultaneous association tests on a block of linked SNPs with various trait types, including continuous, binary and zero-inflated count phenotypes. The new cGFLM applies a set of inequality constraints on the FLM to ensure model identifiability under different genetic codings. The method is implemented via B-splines, and an augmented Lagrangian algorithm is employed for parameter estimation. For hypotheses testing, a test statistic that accounts for the model constraints was derived, following a mixture of chi-square distributions. Simulation results show that cGFLM is effective in identifying causal loci and gene clusters compared to several competing methods based on single markers and SKAT-C. We applied the proposed method to analyze a candidate gene-based COGEND study and a large-scale GWAS data on dental caries risk.
The Deep Learning LSTM and MTD Models Best Predict Acute Respiratory Infection among Under-Five-Year Old Children in Somaliland
Authors: Hassan Mohamed Yusuf
Published: June 2021

The most effective techniques for predicting time series patterns include machine learning and classical time series methods. The aim of this study is to search for the best artificial intelligence and classical forecasting techniques that can predict the spread of acute respiratory infection (ARI) and pneumonia among under-five-year old children in Somaliland. The techniques used in the study include seasonal autoregressive integrated moving averages (SARIMA), mixture transitions distribution (MTD), and long short term memory (LSTM) deep learning. The data used in the study were monthly observations collected from five regions in Somaliland from 2011–2014. Prediction results from the three best competing models are compared by using root mean square error (RMSE) and absolute mean deviation (MAD) accuracy measures. Results have shown that the deep learning LSTM and MTD models slightly outperformed the classical SARIMA model in predicting ARI values.
An Integrative Approach to Assess the Environmental Impacts of Gold Mining Contamination in the Amazon
Authors: V. Capparelli Mariana , Cabrera Marcela , Rico Andreu , Lucas-Solis Oscar , Alvear-S Daniela , Vasco Samantha , Galarza Emily , Shiguango Lady , Pinos-Velez Veronica , Pérez-González Andrés , Espinosa Rodrigo , M. Moulatlet Gabriel
Published: June 2021

As the number of legal and illegal gold mining sites increases in the Andes–Amazonia region, integrative methods to evaluate the effects of mining pollution on freshwater ecosystems are of paramount importance. Here, we sampled water and sediments in 11 sites potentially affected by gold mining activities in the Napo province (Ecuador). The environmental impacts were evaluated using the following lines of evidence (LOEs): water physicochemical parameters, metal exposure concentrations, macroinvertebrate community response (AAMBI), and toxicity by conducting bioassays with Lactuca sativa and Daphnia magna. Dissolved oxygen and total suspended solids were under (<80%) and above (>130 mg/Ls) quality standards 65% of the sites. Ag, Al, As, Cd, Cu, Fe, Mn, Pb, and Zn in water and V, B, and Cr in sediments were detected above quality standards at sampled sites. Nine out of eleven sites were classified as having bad environmental quality based on the AAMBI. L. sativa seed germination in both water (37% to 70%) and sediment (0% to 65%) indicate significant toxicity. In five sites, neonates of D. magna showed a 25% reduction in survival compared to the control. Our integrated LOEs index ranked sites regarding their environmental degradation. We recommend environmental impact monitoring of the mining expansion at the Andes–Amazonia region using multiple LOEs.
Persistent Symptoms in Post-COVID-19 Patients Attending Follow-Up OPD at Sukraraj Tropical and Infectious Disease Hospital (STIDH), Kathmandu, Nepal
Authors: Bastola Anup , Nepal Richa , Shrestha Bikesh , Maharjan Kijan , Shrestha Sanjay , Chalise Bimal Sharma , Neupane Jenish
Published: June 2021

The long-term effects of COVID-19 among survivors is a matter of concern. This research aimed to study persistent symptoms in post-COVID-19 patients attending a follow-up clinic at a tertiary care hospital in Nepal. All patients, presenting to the outpatient clinic during the study duration of six weeks, with history of positive reverse transcriptase- polymerase chain reaction for severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) at least two weeks prior to presentation, were included. The duration of follow-up ranged from 15 till 150 days with the mean duration of 28 days after diagnosis of COVID-19. Of 118 patients, 43 (36.4%) had a history of mild COVID-19, 15 (12.8%) had moderate, and 60 (50.8%) had severe. At the time of presentation, 97 (82.2%) patients reported that they had at least one persistent/new symptom beyond two weeks from the diagnosis of COVID-19. Dyspnea, fatigue, chest heaviness, and cough were the commonest persistent complaints in 48 (40.7%), 39 (33.1%), 33 (28%), and 32 (27.1%) patients, respectively. The findings in our study highlight the need for extended monitoring of post-COVID-19 patients following discharge, in order to understand and mitigate long-term implications of the disease.
Directional Observation of Cold Dark Matter Particles (WIMP) in Light Target Experiments
Authors: Anokhina Anna , Gulyaeva Vasilisa , Khalikov Emil , Kurochkin Evgeny , Roganova Tatiana , Ursov Eduard , Vidulin Ivan
Published: June 2021

For the last 10 years, the search for dark matter (DM) was carried out taking into account the fact that the DM particles are WIMPs (Weakly Interacted Massive Particles) which were introduced in supersymmetric extensions of the Standard Model. Many experiments such as XENON1T, DarkSide, CRESST, etc. set the constraints on the WIMP-nucleon elastic interaction cross sections for different assumed WIMP masses. Methods for detecting WIMPs could play a special role, allowing one to determine the directions of the tracks of recoil nuclei and, therefore, to determine the preferred direction of the WIMP flux. In this work, we analyze the capabilities of such direct detection experiments through analyzing the lengths and directions of the tracks of recoil nuclei. Taking into account the existing experimental constraints, we conclude that the optimal target would be a lower density target containing nuclei of the CNO group, for example, liquid propane.
Regional Policy and Greenfield Investments in German Districts
Authors: Burger Martijn J. , Schalk Jelmer , Schiller Daniel , Stavropoulos Spyridon
Published: June 2021

Using data on greenfield investment in German districts from 2003 to 2010, we examine how regional development policies affect the decision of multinational corporations to locate facilities in Germany. We are interested in whether regional development policies accumulate to increase the attractiveness of a region and whether some policies are necessary to attract foreign investors. Applying count data models and geographic weighted regression, the results indicate that, on average, regional development policies increase the attractiveness of German districts for multinational firms. We find that place-based policies have the strongest effect on investments in the East German lagging regions. However, policies predominantly attract standardised types of investments that require considerable capital investments but not specialised location advantages.
On the Relationship of Viral Particles and Extracellular Vesicles: Implications for Viral Vector Technology
Authors: Metzner Christoph , Zaruba Marianne
Published: June 2021

Gene therapy vectors derived from different viral species have become a fixture in biomedicine, both for direct therapeutic intervention and as tools to facilitate cell-based therapies, such as chimeric antigen receptor-based immunotherapies. On the contrary, extracellular vesicles have only recently gained a massive increase in interest and, concomitantly, knowledge in the field has drastically risen. Viral infections and extracellular vesicle biology overlap in many ways, both with pro- and antiviral outcomes. In this review, we take a closer look at these interactions for the most prominent groups of viral vectors (Adenoviral, Adeno-associated and Retro/Lentiviral vectors) and the possible implications of these overlaps for viral vector technology and its biomedical applications.
Technoeconomic Analysis and Environmental Impact of Electric Vehicle Introduction in Taxis: A Case Study of Mexico City
Authors: Maciel Fuentes Daniel Arturo , González Eduardo Gutiérrez
Published: June 2021

In recent decades, urban air pollution has increased considerably in Mexico City, leading to harmful effects on the ecosystem. To reduce pollutant emissions, new sustainable technologies have been adopted in the transport sector. To date, no studies have conducted a technoeconomic analysis of the environmental impact of electric vehicles (EVs) in regard to taxis in Mexico. To address this gap in the research, this study aimed to perform a cost-environmental impact assessment of electric taxi introduction in Mexico City using the life-cycle cost (LCC) methodology and the greenhouse gas (GHG) emissions assessment. Furthermore, a sensitivity analysis was performed to identify parameters with the greatest influence on the LCC. The LCC of EVs was found to be larger than that of internal combustion vehicles (ICVs); the acquisition cost was identified as the greatest contributor to the total LCC, followed by the maintenance cost. Worldwide, mixed results have been reported due to differences in the use of local parameters and values. To promote EVs, it is necessary to reduce either acquisition costs or battery costs. The environmental analysis showed that there is only a slight reduction in GHG emissions with electric taxi introduction. Nevertheless, cleaner renewable energy sources must be adopted and considered in order to achieve a much greater reduction and take full advantage of the benefits of EVs.
The influence of dissolved CO₂ concentration and gas bubbles on CHO cells growth and metabolism
Authors: Eliseeva Vera Vyacheslavovna , Smirnov Aleksandr Alekseevich , Mazalyov Denis Alekseevich , Kuvshinov Viktor Andreevich
Published: June 2021

Chinese hamster ovary (CHO) cells cultured in bioreactors to produce various recombinant proteins for therapeutic applications. The concentration of carbon dioxide dissolved in the medium is one of the key components affecting the growth and productivity of CHO cells. Researching the effect of different concentrations рCO₂ on cells is necessary for optimization of cell culture conditions and increasing protein production. The review deals with research in this field.
Obtaining and studying the inclusion complex of ascorbic acid with hydroxypropyl-β-cyclodextrin
Authors: Baykova Ksenia Leonidovna , Lvov Egor Sergeevich , Tyukova Victoria Sergeevna
Published: June 2021

This paper describes a method for the production of inclusion complex of ascorbic acid with hydroxypropyl-β-cyclodextrin and its research using UV-spectrophotometry and differential scanning calorimetry.
Development of a lyophilized liposomal dosage form indolocarbazole derivative — LHS-1208
Authors: Gulyakin I. D. , Nikolaeva L. L. , Dmitrieva M. V. , Oborotova N. A. , O. L. Orlova O. L. Orlova , Polozkova A. P.
Published: June 2021

Aim. The aim of this work was to create a lyophilized liposomal dosage form of native hydrophobic antitumor compound from the group of indolocarbazole derivatives — LHS-1208. Materials and methods. Quantitative determination of the drug content was carried out on a Cary 100 spectrophotometer using a standard sample at λ = 320 ± 2 nm. The analysis of the average diameter of liposomes was carried out by the method of correlation spectroscopy of light scattering using devices Nicomp-380 nanosizer and Zetasizer Nano ZS zetasizer, using the latter, the zeta potential of the liposomal dispersion was also measured. To measure the pH of the solution, a HANNA pH 211 pH meter was used. The dynamic viscosity was measured on a Vibro Viscometer SV-10 viscometer. Lyophilization was carried out in an Edwards Minifast DO.2 freeze-drying chamber. Results. Experimental models of compositions of lyophilized liposomal dosage form LHS-1208 with various molar ratios of components were obtained and analyzed. TLC analysis using several solvent systems can be used to detect and identify LHS-1208 in a liposomal dosage form. According to the results of the study, a spectrophotometric method was developed for the quantitative determination of the LHS-1208 content in the composition of a liposomal dosage form. Based on the studies carried out, quality indicators were selected for standardization of lyophilized liposomal dosage form LHS-1208 and subsequent development of the draft regulatory documentation. Conclusion. As a result of the complex pharmaceutical research, the optimum composition of the components was determined, and the technology for production of liposomal dosage form LHS-1208 was developed.
Binder for colored asphalt based on light hydrogenated petroleum polymer resins
Authors: Kochetkova V.A.
Published: June 2021

The article provides an overview of the production of binders, sets out the requirements for the quality indicators of colored asphalt, the technology of production of binders, and also considers promising areas in the production of colorless binders. The purpose of the work was to create an affordable organic binder based on light hydrogenated petroleum polymer resins and use the developed binder for colored asphalt. To achieve this goal, we analyzed patents for the production of binders, on the basis of which the composition of raw materials was selected, and studied the physical and mechanical characteristics of the obtained samples on both hydrogenated and non-hydrogenated petroleum polymer resin.