bluuebunny/crossref_metadata_2025 huggingface dataset:
The Bacterial Gut Microbiota of Schoolchildren from High and Low Socioeconomic Status: A Study in an Urban Area of Makassar, Indonesia
Authors: Amaruddin Aldian I. , Hamid Firdaus , Koopman Jan Pieter R. , Muhammad Munawir , Brienen Eric A.T. , van Lieshout Lisette , Geelen Anoecim R. , Wahyuni Sitti , Kuijper Ed J. , Sartono Erliyani , Yazdanbakhsh Maria , Zwittink Romy D.
Published: June 2020

To understand the relationship between the gut microbiota and the health profile of Indonesians, it is important to elucidate the characteristics of the bacterial communities that prevail in this population. To this end, we profiled the faecal bacterial community of 140 Indonesian schoolchildren in urban Makassar. The core microbiota of Indonesian schoolchildren consisted of Bifidobacterium, Collinsella, and multiple members of the Lachnospiraceae and Ruminicoccaceae families, but the relative abundance of these taxa varied greatly among children. Socioeconomic status (SES) was the main driver for differences in microbiota composition. Multiple bacterial genera were differentially abundant between high and low SES children, including Bifidobacterium, Lactobacillus, Prevotella, and Escherichia-Shigella. In addition, the microbiota of high SES children was less diverse and strongly associated with body mass index (BMI). In low SES children, helminth infection was prevalent and positively associated with Olsenella, Enterohabdus, Lactobacillus, and Mogibacterium abundance, while negatively associated with relative abundance of Prevotella. Protozoa infection was also prevalent, and positively associated with Rikenellaceae, while it was negatively associated with the relative abundance of Romboutsia and Prevotella. In conclusion, Indonesian schoolchildren living in urban Makassar share a core microbiota, but their microbiota varies in diversity and relative abundance of specific bacterial taxa depending on socioeconomic status, nutritional status, and intestinal parasites infection.
Haplotype Diversity of NADPH-Cytochrome P450 Reductase Gene of Ophiocordyceps sinensis and the Effect on Fungal Infection in Host Insects
Authors: Xu Zixian , Zhu Yunguo , Xuan Lingyan , Li Shan , Cheng Zhou
Published: June 2020

Ophiocordyceps sinensis Berk. is a fungal parasite that parasitizes the larvae of Hepialidae and is used as a traditional Chinese medicine. However, it is not clear how O. sinensis infects its host. The encoding gene haplotype diversity and predicted function of the nicotinamide adenine dinucleotide phosphate (NADPH)-cytochrome P450 reductase (CPR) related to the fungal pathogenicity was analyzed for 219 individuals from 47 O. sinensis populations. Two NADPH CPR genes of O. sinensis were detected and their dominant haplotypes were widely distributed throughout the entire distribution range in Western China. Only 5.43% of all O. sinensis individuals possessed the specific private haplotypes of NADPH CPR-1 and CPR-2 genes. Bioinformatic analyses predicted that the phosphorylation sites, motifs, and domains of NADPH CPR of O. sinensis were different between those encoding by the dominant and private gene haplotypes. The one-to-one match fungus–host correspondence of the same individual suggested that the widely distributed O. sinensis with the dominant NADPH CPR gene haplotypes may strongly infect almost all host insects through a random infection by oral or respiratory pores. Conversely, O. sinensis with the specific private NADPH CPR gene haplotypes is likely to infect only a few corresponding host insects by breaching the cuticle, due to the changed NADPH CPR structure and function.
Retention of Contaminants Elements from Tailings from Lead Mine Washing Plants in Ceramics for Bricks
Authors: Suárez-Macías Jorge , Terrones-Saeta Juan María , Iglesias-Godino Francisco Javier , Corpas-Iglesias Francisco Antonio
Published: June 2020

Mining activity is essential for the social welfare of the population. However, this activity produces a series of mining waste. These mining wastes, if not properly treated, can produce significant environmental pollution. This study develops the incorporation of tailings from washing plants in ceramic materials for bricks in order to retain the contaminating elements in the ceramic matrix. To this end, firstly, a physical and chemical characterisation of the mining waste is carried out and different groups of samples are conformed with clay and mining waste. These conformed samples with mining waste are evaluated through different physical and mechanical tests typical in the ceramic industry, studying the variation of properties by the incorporation of the waste. In turn, the leachates from the groups of conformed samples are analyzed, confirming the retention of the contaminating elements of the mining waste in the ceramic matrix. The results of these tests showed that ceramics can be made for bricks with up to 90% mining waste, obtaining physical and mechanical properties acceptable regarding the regulations and retaining the contaminating elements in the ceramic matrix, as confirmed by the leachate tests.
Early-Late Devonian Post-Collision Extension Due to Slab Breakoff Regime along the Northern Margin of North China Craton: Implications from Muscovite 40Ar-39Ar Dating
Authors: Li Changhai , Li Shichao , Liu Zhenghong , Xu Zhongyuan , Dong Xiaojie , Shi Qiang , Wang Shijie , Feng Fan
Published: June 2020

Ordovician-Silurian subduction, Early Devonian arc-contient collision and followed post-collision extension are recorded in the north of the North China Craton. Most previous research has focused on the first two processes. Discussion on the post-collisional extension and its tetonic regime is still limited. In this study, 40Ar-39Ar muscovite ages obtained from the central part of the northern North China Craton were analyzed to shed light on the timing of post-collision extension. Garnet muscovite schist and muscovite quartz schist in the Jianshan Formation yielded 40Ar-39Ar muscovite plateau ages of 412 ± 3 Ma and 391 ± 3 Ma, respectively. Two other 40Ar-39Ar muscovite plateau ages (389 ± 2 Ma and 397 ± 2Ma) were obtained for two Mesoproterozoic monzogranites intruding into the Jianshan Formation. Based on previous research, the northern North China Craton underwent a collision event with Bainaimiao arc at c. 415 Ma, followed by post-collision extension in early Late Paleozoic. Therefore, combined with the newly acquired muscovite 40Ar-39Ar dating results, the Jianshan Formation might go through regional metamorphism at c. 412 Ma during the collision process. Subsequently, the Jianshan Formation and monzogranites intruding into it went through rapid exhumation along with metamorphism at c. 397–389 Ma in a post-collision extensional setting. The muscovite 40Ar-39Ar ages provide new markers for the exhumation history and the post-collisional extension setting during Early-Late Devonian in the study area. Furthermore, slab breakoff as the cause for this extensional setting is argued by the emplacement of the Early-Late Devonian alkaline rocks.
Alteration and Mineralization Products of the Zannone Giant Pockmark (Zannone Hydrothermal Field, Central Tyrrhenian Sea)
Authors: Conte Aida Maria , Di Bella Letizia , Ingrassia Michela , Perinelli Cristina , Martorelli Eleonora
Published: June 2020

The Zannone Giant Pockmark (ZGP) is a shallow-water (<−150 m) giant depression located on the shelf off Zannone Island (Pontine Archipelago, central Tyrrhenian Sea, Italy), hosting active hydrothermal vents. The ZGP seabed displays different fluid-venting morphologies (pockmarks, lithified pavements, mounds, and cone-shaped structures) and widespread bacterial communities. In this study, we analyzed ROV (Remote Operated Vehicle) images to gain information on seabed geology and the textural, mineralogical, and geochemical composition of authigenic crusts and gravel-sized clasts sampled close to active emissions. ROV images show authigenic dome-shaped crusts composed of native sulfur associated with barite, gypsum, amorphous silica, and secondary hydrothermal minerals (illite–montmorillonite). The gravel-sized clasts are mostly rhyolites strongly affected by hydrothermal alteration (Alteration Index > 88; depletion of some mobile elements and enrichment of some base metals), causing feldspar-destruction, silicification, formation of hydrothermal phyllosilicates, and precipitation of disseminated pyrite. More intense alteration implying the complete obliteration of the primary mineralogy or fabric is represented by quartz-pyrite samples. ZGP seabed morphology and petro-geochemical features of deposits point to the possible occurrence of a sulfide system linked to the degassing of magma similar to that feeding the Pleistocene products of Ponza Island.
Surface Degradation Mechanism on CH3NH3PbBr3 Hybrid Perovskite Single Crystal by a Grazing E-Beam Irradiation
Authors: Syafutra Heriyanto , Yun Jung-Ho , Yoshie Yuya , Lyu Miaoqiang , Takeda Sakura Nishino , Nakamura Masakazu , Wang Lianzhou , Jung Min-Cherl
Published: June 2020

To start a step such as some realization of minimized and integrated devices, it requires simply understanding the surface status of hybrid perovskite on the e-beam irradiation because many commercial semiconductor devices are performed with a surface patterning process using e-beam or etching gas. The surface status of CH3NH3PbBr3 (MAPbBr3) single crystal was studied after a grazing e-beam irradiation in an ultra-high vacuum. The prepared hybrid perovskite single crystal was irradiated by the 3 degree-grazing e-beam with energy of 15 kV for 10 min using a reflection high-electron energy diffraction technique. The e-beam irradiation on the MAPbBr3 hybrid perovskite single crystal induced the deformation from MAPbBr3 into MABr, Br2, and Pb on the surface. The gas phases of MABr and Br2 are depleted from the surface and the Pb element has remained on the surface. As a result of the e-beam irradiation, it formed a polycrystalline-like phase and Pb metal particles on the surface, respectively.
Enhanced Photocatalytic Activity and Stability in Hydrogen Evolution of Mo6 Iodide Clusters Supported on Graphene Oxide
Authors: Puche Marta , García-Aboal Rocío , Mikhaylov Maxim A. , Sokolov Maxim N. , Atienzar Pedro , Feliz Marta
Published: June 2020

Catalytic properties of the cluster compound (TBA)2[Mo6Ii8(O2CCH3)a6] (TBA = tetrabutylammonium) and a new hybrid material (TBA)2Mo6Ii8@GO (GO = graphene oxide) in water photoreduction into molecular hydrogen were investigated. New hybrid material (TBA)2Mo6Ii8@GO was prepared by coordinative immobilization of the (TBA)2[Mo6Ii8(O2CCH3)a6] onto GO sheets and characterized by spectroscopic, analytical, and morphological techniques. Liquid and, for the first time, gas phase conditions were chosen for catalytic experiments under UV–Vis irradiation. In liquid water, optimal H2 production yields were obtained after using (TBA)2[Mo6Ii8(O2CCH3)a6] and (TBA)2Mo6Ii8@GO) catalysts after 5 h of irradiation of liquid water. Despite these remarkable catalytic performances, “liquid-phase” catalytic systems have serious drawbacks: the cluster anion evolves to less active cluster species with partial hydrolytic decomposition, and the nanocomposite completely decays in the process. Vapor water photoreduction showed lower catalytic performance but offers more advantages in terms of cluster stability, even after longer radiation exposure times and recyclability of both catalysts. The turnover frequency (TOF) of (TBA)2Mo6Ii8@GO is three times higher than that of the microcrystalline (TBA)2[Mo6Ii8(O2CCH3)a6], in agreement with the better accessibility of catalytic cluster sites for water molecules in the gas phase. This bodes well for the possibility of creating {Mo6I8}4+-based materials as catalysts in hydrogen production technology from water vapor.
Using the Emission of Muonic X-rays as a Spectroscopic Tool for the Investigation of the Local Chemistry of Elements
Authors: Aramini Matteo , Milanese Chiara , Hillier Adrian D. , Girella Alessandro , Horstmann Christian , Klassen Thomas , Ishida Katsuo , Dornheim Martin , Pistidda Claudio
Published: June 2020

There are several techniques providing quantitative elemental analysis, but very few capable of identifying both the concentration and chemical state of elements. This study presents a systematic investigation of the properties of the X-rays emitted after the atomic capture of negatively charged muons. The probability rates of the muonic transitions possess sensitivity to the electronic structure of materials, thus making the muonic X-ray Emission Spectroscopy complementary to the X-ray Absorption and Emission techniques for the study of the chemistry of elements, and able of unparalleled analysis in case of elements bearing low atomic numbers. This qualitative method is applied to the characterization of light elements-based, energy-relevant materials involved in the reaction of hydrogen desorption from the reactive hydride composite Ca(BH4)2-Mg2NiH4. The origin of the influence of the band-structure on the muonic atom is discussed and the observed effects are attributed to the contribution of the electronic structure to the screening and to the momentum distribution in the muon cascade.
Zoonotic Abbreviata caucasica in Wild Chimpanzees (Pan troglodytes verus) from Senegal
Authors: Laidoudi Younes , Medkour Hacène , Latrofa Maria Stefania , Davoust Bernard , Diatta Georges , Sokhna Cheikh , Barciela Amanda , Hernandez-Aguilar R. Adriana , Raoult Didier , Otranto Domenico , Mediannikov Oleg
Published: June 2020

Abbreviata caucasica (syn. Physaloptera mordens) has been reported in human and various non-human primates including great apes. The identification of this nematode is seldom performed and relies on egg characterization at the coproscopy, in the absence of any molecular tool. Following the recovery of two adult females of A. caucasica from the feces of wild Senegalese chimpanzees, morphometric characteristics were reported and new data on the width of the esophagus (0.268–0.287 mm) and on the cuticle structure (0.70–0.122 mm) were provided. The molecular characterization of a set of mitochondrial (cox1, 16S rRNA, 12S rRNA) and nuclear (18S rRNA and ITS2) partial genes was performed. Our phylogenetic analysis indicates for the first time that A. caucasica is monophyletic with Physaloptera species. A novel molecular tool was developed for the routine diagnosis of A. caucasica and the surveillance of Nematoda infestations. An A. caucasica-specific qPCR targeting the 12S gene was assessed. The assay was able to detect up to 1.13 × 10−3 eggs/g of fecal matter irrespective of its consistency, with an efficiency of 101.8% and a perfect adjustment (R2 = 0.99). The infection rate by A. caucasica in the chimpanzee fecal samples was 52.08%. Only 6.19% of the environmental samples were positive for nematode DNA and any for A. caucasica. Our findings indicate the need for further studies to clarify the epidemiology, circulation, life cycle, and possible pathological effects of this infestation using the molecular tool herein developed.
Sensitivity and Selectivity of Two Commercially Available Media for Legionella spp. Recovery from Environmental Water Samples
Authors: Ditommaso Savina , Giacomuzzi Monica , Memoli Gabriele , Garlasco Jacopo , Zotti Carla M.
Published: June 2020

The quality control of culture media used for Legionella spp. isolation and enumeration is paramount to achieve a satisfactory degree of comparability among water testing results from different laboratories. Here, we report on a comparative assessment of the sensitivity and selectivity of MWY and BCYEα media supplied by two different manufacturers (i.e., Xebios Diagnostics GmbH and Oxoid Ltd) for the detection of Legionella spp. from environmental water samples. Even though our analysis showed an excellent agreement between the recovery rates of the four media tested (90.5%), the quantitative recovery of Legionella spp. colonies using Xebios media was significantly greater than that achieved by Oxoid media (P = 0.0054). Furthermore, the sensitivity of detection was significantly higher when samples were plated on MWY Xebios agar (P = 0.0442), while the selectivity of MWY appeared to be the same regardless of the manufacturer. Furthermore, MWYXebios agar favored the growth of much larger colonies compared to those observed on MWYOxoid agar. Finally, MWYXebios medium enhanced the recovery of non-pneumophila Legionella species. Collectively, our findings demonstrate that quality control is crucial to ensure high selectivity and sensitivity of the culture media used for the detection and enumeration of Legionella spp. from environmental water resources. As water remediation measures strictly depend on Legionella spp. recovery, culture protocol standardization, as well as quality control of the culture media, is essential to achieve intra- and interlaboratory reproducibility and accuracy.
The Effect of Natural Feline Coronavirus Infection on the Host Immune Response: A Whole-Transcriptome Analysis of the Mesenteric Lymph Nodes in Cats with and without Feline Infectious Peritonitis
Authors: Malbon Alexandra J. , Russo Giancarlo , Burgener Carole , Barker Emi N. , Meli Marina L. , Tasker Séverine , Kipar Anja
Published: June 2020

Feline infectious peritonitis (FIP) is a coronavirus-induced disease of cats, in which the immune system is known to play a crucial, but complex, role in the pathogenesis. This role is still incompletely understood, with involvement of both host and viral factors. To evaluate differential gene expression and pathway involvement in feline coronavirus (FCoV) infection and FIP, we applied next-generation RNA-sequencing of the mesenteric lymph nodes from cats with naturally-acquired FIP, as well as those with systemic FCoV infection without FIP, and those with neither. Viral infection was associated with upregulation of viral defenses regardless of the disease state, but to a greater degree in FIP. FIP was associated with higher pro-inflammatory pathway enrichment, whilst non-FIP FCoV-positive cats showed lower enrichment of humoral immunity pathways, below that of uninfected cats in the case of immunoglobulin production pathways. This host response is presumed to be protective. In FIP, downregulation of T cell-related processes was observed, which did not occur in non-FIP FCoV-positive cats. These results emphasize the importance of the host’s immune balance in determining the outcome of the FCoV infection.
Increasing Resistance and Changes in Distribution of Serotypes of Streptococcus agalactiae in Poland
Authors: Kaminska Dorota , Ratajczak Magdalena , Szumała-Kąkol Anna , Dlugaszewska Jolanta , Nowak-Malczewska Dorota M. , Gajecka Marzena
Published: June 2020

Streptococcus agalactiae is responsible for serious infections in newborn babies, pregnant women, and other patients. The aim of this study was to evaluate antimicrobial susceptibility, serotype distribution, and virulence determinants of the S. agalactiae isolates derived from clinical specimens considering the global increase of both antibiotic resistance and virulence. A total of 165 isolates were identified and serotyped by PCR techniques. Antimicrobial susceptibility was assessed by disk diffusion method, gradient diffusion method and VITEK® System. Virulence associated genes were investigated by PCR; ability to form biofilm was assessed using a microtiter plate assay. The highest observed MIC value for penicillin G was 0.12 µg/mL, seen in 8.5% of isolates. Resistance to erythromycin and clindamycin were found in 30.38% and 24.8% of the strains, respectively. The serotype III (32.73%), V (25.45%), and Ia (18.18%) were found as the most frequently represented. Previously unidentified strains in Poland, belonging to serotypes VI (three strains) and VII (one strain) were recognized. The presence of genes encoding various virulence factors as well as diverse ability to form biofilm were found. In conclusion, macrolide-resistance and decreased susceptibility to penicillin G were revealed signifying the increasing resistance among group B streptococci. Moreover, the presence of genes encoding various virulence factors and the ability to form biofilm were confirmed indicating their role in the pathomechanisms of the evaluated GBS infections.
Fraternity versus Parochialism: On Religion and Populism
Authors: Palaver Wolfgang
Published: June 2020

The relationship between populism and religion is complex because populists hijack religion but are often more interested in belonging than believing. This is one reason why there is a growing distance between populists and many leaders of mainline churches. To understand this complex field, we have to take social crises seriously and see how a static religion is, according to Henri Bergson, the first response to the precariousness of human life. This type of religion has led to closed societies leaning toward pseudospeciation and parochial altruism. Bergson, however, did not only describe static religion but also recognized dynamic religion leading to an open society. Jesus Christ’s Sermon on the Mount, with its call to love one’s enemy, is his key example. By going beyond Bergson, we can recognize dynamic religion as the mystic core of all world religions. Dynamic religion enables a universal fraternity, which is an essential element of every democracy in overcoming its populist temptations by respecting, internally, the rights of minorities and, externally, the universal human rights. Three examples from different religious backgrounds show how dynamic religion supports democracy through fraternity: the fraternal tradition in modern Catholicism, the Muslim philosopher S.B. Diagne and the Hindu M.K. Gandhi.
Editorial for the Special Issue “Remote Sensing of Atmospheric Components and Water Vapor”
Authors: Cachorro Victoria E. , Antón Manuel
Published: June 2020

The observation/monitoring of atmospheric components and water vapor in the atmosphere is today open to very different remote sensing techniques, most of them based on the radiation-matter interaction covering the full electromagnetic spectrum. This SI collects some papers regarding the retrieval, calibration, validation, analysis of data and uncertainties, as well as comparative studies on atmospheric gases and water vapor by remote sensing techniques, where different types of sensors, instruments, and algorithms are used or developed.
Using a Motion Sensor to Categorize Nonspecific Low Back Pain Patients: A Machine Learning Approach
Authors: Abdollahi Masoud , Ashouri Sajad , Abedi Mohsen , Azadeh-Fard Nasibeh , Parnianpour Mohamad , Khalaf Kinda , Rashedi Ehsan
Published: June 2020

Nonspecific low back pain (NSLBP) constitutes a critical health challenge that impacts millions of people worldwide with devastating health and socioeconomic consequences. In today’s clinical settings, practitioners continue to follow conventional guidelines to categorize NSLBP patients based on subjective approaches, such as the STarT Back Screening Tool (SBST). This study aimed to develop a sensor-based machine learning model to classify NSLBP patients into different subgroups according to quantitative kinematic data, i.e., trunk motion and balance-related measures, in conjunction with STarT output. Specifically, inertial measurement units (IMU) were attached to the trunks of ninety-four patients while they performed repetitive trunk flexion/extension movements on a balance board at self-selected pace. Machine learning algorithms (support vector machine (SVM) and multi-layer perceptron (MLP)) were implemented for model development, and SBST results were used as ground truth. The results demonstrated that kinematic data could successfully be used to categorize patients into two main groups: high vs. low-medium risk. Accuracy levels of ~75% and 60% were achieved for SVM and MLP, respectively. Additionally, among a range of variables detailed herein, time-scaled IMU signals yielded the highest accuracy levels (i.e., ~75%). Our findings support the improvement and use of wearable systems in developing diagnostic and prognostic tools for various healthcare applications. This can facilitate development of an improved, cost-effective quantitative NSLBP assessment tool in clinical and home settings towards effective personalized rehabilitation.
Uniformity Attentive Learning-Based Siamese Network for Person Re-Identification
Authors: Jeong Dasol , Park Hasil , Shin Joongchol , Kang Donggoo , Paik Joonki
Published: June 2020

Person re-identification (Re-ID) has a problem that makes learning difficult such as misalignment and occlusion. To solve these problems, it is important to focus on robust features in intra-class variation. Existing attention-based Re-ID methods focus only on common features without considering distinctive features. In this paper, we present a novel attentive learning-based Siamese network for person Re-ID. Unlike existing methods, we designed an attention module and attention loss using the properties of the Siamese network to concentrate attention on common and distinctive features. The attention module consists of channel attention to select important channels and encoder-decoder attention to observe the whole body shape. We modified the triplet loss into an attention loss, called uniformity loss. The uniformity loss generates a unique attention map, which focuses on both common and discriminative features. Extensive experiments show that the proposed network compares favorably to the state-of-the-art methods on three large-scale benchmarks including Market-1501, CUHK03 and DukeMTMC-ReID datasets.
Towards a Secure Thermal-Energy Aware Routing Protocol in Wireless Body Area Network Based on Blockchain Technology
Authors: Shahbazi Zeinab , Byun Yung-Cheol
Published: June 2020

The emergence of biomedical sensor devices, wireless communication, and innovation in other technologies for healthcare applications result in the evolution of a new area of research that is termed as Wireless Body Area Networks (WBANs). WBAN originates from Wireless Sensor Networks (WSNs), which are used for implementing many healthcare systems integrated with networks and wireless devices to ensure remote healthcare monitoring. WBAN is a network of wearable devices implanted in or on the human body. The main aim of WBAN is to collect the human vital signs/physiological data (like ECG, body temperature, EMG, glucose level, etc.) round-the-clock from patients that demand secure, optimal and efficient routing techniques. The efficient, secure, and reliable designing of routing protocol is a difficult task in WBAN due to its diverse characteristic and restraints, such as energy consumption and temperature-rise of implanted sensors. The two significant constraints, overheating of nodes and energy efficiency must be taken into account while designing a reliable blockchain-enabled WBAN routing protocol. The purpose of this study is to achieve stability and efficiency in the routing of WBAN through managing temperature and energy limitations. Moreover, the blockchain provides security, transparency, and lightweight solution for the interoperability of physiological data with other medical personnel in the healthcare ecosystem. In this research work, the blockchain-based Adaptive Thermal-/Energy-Aware Routing (ATEAR) protocol for WBAN is proposed. Temperature rise, energy consumption, and throughput are the evaluation metrics considered to analyze the performance of ATEAR for data transmission. In contrast, transaction throughput, latency, and resource utilization are used to investigate the outcome of the blockchain system. Hyperledger Caliper, a benchmarking tool, is used to evaluate the performance of the blockchain system in terms of CPU utilization, memory, and memory utilization. The results show that by preserving residual energy and avoiding overheated nodes as forwarders, high throughput is achieved with the ultimate increase of the network lifetime. Castalia, a simulation tool, is used to evaluate the performance of the proposed protocol, and its comparison is made with Multipath Ring Routing Protocol (MRRP), thermal-aware routing algorithm (TARA), and Shortest-Hop (SHR). Evaluation results illustrate that the proposed protocol performs significantly better in balancing of temperature (to avoid damaging heat effect on the body tissues) and energy consumption (to prevent the replacement of battery and to increase the embedded sensor node life) with efficient data transmission achieving a high throughput value.
Advantages of Highly Spherical Gold Nanoparticles as Labels for Lateral Flow Immunoassay
Authors: Byzova Nadezhda A. , Zherdev Anatoly V. , Khlebtsov Boris N. , Burov Andrey M. , Khlebtsov Nikolai G. , Dzantiev Boris B.
Published: June 2020

The use of lateral flow immunoassays (LFIAs) for rapid on-site testing is restricted by their relatively high limit of detection (LoD). One possible way to decrease the LoD is to optimize nanoparticle properties that are used as labels. We compare two types of Au nanoparticles: usual quasispherical gold nanoparticles (C-GNPs), obtained by the Turkevich–Frens method, and superspherical gold nanoparticles (S-GNPs), obtained by a progressive overgrowth technique. Average diameters were 18.6–47.5 nm for C-GNPs and 20.2–90.4 nm for S-GNPs. Cardiomarker troponin I was considered as the target analyte. Adsorption and covalent conjugation with antibodies were tested for both GNP types. For C-GNPs, the minimal LoD was obtained with 33.7 nm nanoparticles, reaching 12.7 ng/mL for covalent immobilization and 9.9 ng/mL for adsorption. The average diameter of S-GNPs varied from 20.2 to 64.5 nm, which resulted in a decrease in LoD for an LFIA of troponin I from 3.4 to 1.2 ng/mL for covalent immobilization and from 2.9 to 2.0 ng/mL for adsorption. Thus, we obtained an 8-fold decrease in LoD (9.9 to 1.2 ng/mL) by using S-GNPs. This effect can be related to more effective antibody immobilization and improved S-GNP optical properties. The obtained results can improve LFIAs for various practically significant analytes.
Low-Cost Conversion of Single-Zone HVAC Systems to Multi-Zone Control Systems Using Low-Power Wireless Sensor Networks
Authors: Jornet-Monteverde Julio Antonio , Galiana-Merino Juan José
Published: June 2020

This paper presents a novel approach to convert a conventional house air conditioning installation into a more efficient system that individually controls the temperature of each zone of the house through Wi-Fi technology. Each zone regulates the air flow depending on the detected temperature, providing energy savings and increasing the machine performance. Therefore, the first step was to examine the communication bus of the air conditioner and obtain the different signal codes. Thus, an alternative Controller module has been designed and developed to control and manage the requests on the communication bus (Bus–Wi-Fi gateway). A specific circuit has been designed to adapt the signal of the serial port of the Controller with the communication bus. For the acquisition of the temperature and humidity data in each zone, a Node module has been developed, which communicates with the Controller through the Wi-Fi interface using the Message Queuing Telemetry Transport (MQTT) protocol with Secure Sockets Layer / Transport Layer Security (SSL/TLS) certificates. It has been equipped with an LCD touch screen as a human-machine interface. The Controller and the Node modules have been developed with the ultra-low power consumption CC3200 microController of Texas Instruments and the code has been implemented under the TI-RTOS real-time operating system. An additional module based on the Raspberry Pi computer has been designed to create the Wi-Fi network and implement the required network functionalities. The developed system not only ensures that the temperature in each zone is the desired one, but also controls the fan velocity of the indoor unit and the opening area of the vent registers, which considerably improves the efficiency of the system. Compared with the single-zone system, the experiments carried out show energy savings between 75% and 94% when only one of the zones is selected, and 44% when the whole house is air-conditioned, in addition to considerably improving user comfort.
Comparative Study of Damage Detection Methods Based on Long-Gauge FBG for Highway Bridges
Authors: Chen Shi-Zhi , Feng De-Cheng , Han Wan-Shui
Published: June 2020

Damage detection of highway bridges is a significant part of structural heath monitoring. Conventional accelerometers or strain gauges utilized for damage detection have many shortcomings, especially their monitoring gauge length being too short, which would result in poor damage detection results. Under this circumstance, long-gauge FBG sensors as a novel optical sensor were developed to measure the macro-strain response of the structure. Based on this sensor, many derived damage detection methods were proposed. These methods exhibit various characteristics and have not been systematically compared. As a result, it is difficult to evaluate the state of the art and also leads to confusion for users to select. Therefore, a strict comparative study on three representative methods using long-gauge FBG was carried out. First, these methods’ theoretical backgrounds and formats were reformulated and unified for better comparison. Then, based on validated vehicle–bridge coupling simulation, these methods’ performances were tested through a series of parametric studies including various damage scenarios, vehicle types, speeds, road roughness and noise levels. The precision and reliability of three methods have been thoroughly studied and compared.
A Survey of IoT Security Based on a Layered Architecture of Sensing and Data Analysis
Authors: Mrabet Hichem , Belguith Sana , Alhomoud Adeeb , Jemai Abderrazak
Published: June 2020

The Internet of Things (IoT) is leading today’s digital transformation. Relying on a combination of technologies, protocols, and devices such as wireless sensors and newly developed wearable and implanted sensors, IoT is changing every aspect of daily life, especially recent applications in digital healthcare. IoT incorporates various kinds of hardware, communication protocols, and services. This IoT diversity can be viewed as a double-edged sword that provides comfort to users but can lead also to a large number of security threats and attacks. In this survey paper, a new compacted and optimized architecture for IoT is proposed based on five layers. Likewise, we propose a new classification of security threats and attacks based on new IoT architecture. The IoT architecture involves a physical perception layer, a network and protocol layer, a transport layer, an application layer, and a data and cloud services layer. First, the physical sensing layer incorporates the basic hardware used by IoT. Second, we highlight the various network and protocol technologies employed by IoT, and review the security threats and solutions. Transport protocols are exhibited and the security threats against them are discussed while providing common solutions. Then, the application layer involves application protocols and lightweight encryption algorithms for IoT. Finally, in the data and cloud services layer, the main important security features of IoT cloud platforms are addressed, involving confidentiality, integrity, authorization, authentication, and encryption protocols. The paper is concluded by presenting the open research issues and future directions towards securing IoT, including the lack of standardized lightweight encryption algorithms, the use of machine-learning algorithms to enhance security and the related challenges, the use of Blockchain to address security challenges in IoT, and the implications of IoT deployment in 5G and beyond.
A Combined Deep-Learning and Lattice Boltzmann Model for Segmentation of the Hippocampus in MRI
Authors: Liu Yingqian , Yan Zhuangzhi
Published: June 2020

Segmentation of the hippocampus (HC) in magnetic resonance imaging (MRI) is an essential step for diagnosis and monitoring of several clinical situations such as Alzheimer’s disease (AD), schizophrenia and epilepsy. Automatic segmentation of HC structures is challenging due to their small volume, complex shape, low contrast and discontinuous boundaries. The active contour model (ACM) with a statistical shape prior is robust. However, it is difficult to build a shape prior that is general enough to cover all possible shapes of the HC and that suffers the problems of complicated registration of the shape prior and the target object and of low efficiency. In this paper, we propose a semi-automatic model that combines a deep belief network (DBN) and the lattice Boltzmann (LB) method for the segmentation of HC. The training process of DBN consists of unsupervised bottom-up training and supervised training of a top restricted Boltzmann machine (RBM). Given an input image, the trained DBN is utilized to infer the patient-specific shape prior of the HC. The specific shape prior is not only used to determine the initial contour, but is also introduced into the LB model as part of the external force to refine the segmentation. We used a subset of OASIS-1 as the training set and the preliminary release of EADC-ADNI as the testing set. The segmentation results of our method have good correlation and consistency with the manual segmentation results.
SSD-TSEFFM: New SSD Using Trident Feature and Squeeze and Extraction Feature Fusion
Authors: Hwang Young-Joon , Lee Jin-Gu , Moon Un-Chul , Park Ho-Hyun
Published: June 2020

The single shot multi-box detector (SSD) exhibits low accuracy in small-object detection; this is because it does not consider the scale contextual information between its layers, and the shallow layers lack adequate semantic information. To improve the accuracy of the original SSD, this paper proposes a new single shot multi-box detector using trident feature and squeeze and extraction feature fusion (SSD-TSEFFM); this detector employs the trident network and the squeeze and excitation feature fusion module. Furthermore, a trident feature module (TFM) is developed, inspired by the trident network, to consider the scale contextual information. The use of this module makes the proposed model robust to scale changes owing to the application of dilated convolution. Further, the squeeze and excitation block feature fusion module (SEFFM) is used to provide more semantic information to the model. The SSD-TSEFFM is compared with the faster regions with convolution neural network features (RCNN) (2015), SSD (2016), and DF-SSD (2020) on the PASCAL VOC 2007 and 2012 datasets. The experimental results demonstrate the high accuracy of the proposed model in small-object detection, in addition to a good overall accuracy. The SSD-TSEFFM achieved 80.4% mAP and 80.2% mAP on the 2007 and 2012 datasets, respectively. This indicates an average improvement of approximately 2% over other models.
Multi-Objective Evolutionary Design of an Electric Vehicle Chassis
Authors: Luque Pablo , Mántaras Daniel A. , Maradona Álvaro , Roces Jorge , Sánchez Luciano , Castejón Luis , Malón Hugo
Published: June 2020

An iterative algorithm is proposed for determining the optimal chassis design of an electric vehicle, given a path and a reference time. The proposed algorithm balances the capacity of the battery pack and the dynamic properties of the chassis, seeking to optimize the tradeoff between the mass of the vehicle, its energy consumption, and the travel time. The design variables of the chassis include geometrical and inertial values, as well as the characteristics of the powertrain. The optimization is constrained by the slopes, curves, grip, and posted speeds of the different sections of the track. Particular service constraints are also considered, such as limiting accelerations due to passenger comfort or cargo safety. This methodology is applicable to any vehicle whose route and travel time are known in advance, such as delivery vehicles, buses, and race cars, and has been validated using telemetry data from an internal combustion rear-wheel drive race car designed for hill climb competitions. The implementation of the proposed methodology allows to reduce the weight of the battery pack by up to 20%, compared to traditional design methods.
Real-Time Identification of Irrigation Water Pollution Sources and Pathways with a Wireless Sensor Network and Blockchain Framework
Authors: Lin Yu-Pin , Mukhtar Hussnain , Huang Kuan-Ting , Petway Joy R. , Lin Chiao-Ming , Chou Cheng-Fu , Liao Shih-Wei
Published: June 2020

Real-time identification of irrigation water pollution sources and pathways (PSP) is crucial to ensure both environmental and food safety. This study uses an integrated framework based on the Internet of Things (IoT) and the blockchain technology that incorporates a directed acyclic graph (DAG)-configured wireless sensor network (WSN), and GIS tools for real-time water pollution source tracing. Water quality sensors were installed at monitoring stations in irrigation channel systems within the study area. Irrigation water quality data were delivered to databases via the WSN and IoT technologies. Blockchain and GIS tools were used to trace pollution at mapped irrigation units and to spatially identify upstream polluted units at irrigation intakes. A Water Quality Analysis Simulation Program (WASP) model was then used to simulate water quality by using backward propagation and identify potential pollution sources. We applied a “backward pollution source tracing” (BPST) process to successfully and rapidly identify electrical conductivity (EC) and copper (Cu2+) polluted sources and pathways in upstream irrigation water. With the BPST process, the WASP model effectively simulated EC and Cu2+ concentration data to identify likely EC and Cu2+ pollution sources. The study framework is the first application of blockchain technology for effective real-time water quality monitoring and rapid multiple PSPs identification. The pollution event data associated with the PSP are immutable.
OpCode-Level Function Call Graph Based Android Malware Classification Using Deep Learning
Authors: Niu Weina , Cao Rong , Zhang Xiaosong , Ding Kangyi , Zhang Kaimeng , Li Ting
Published: June 2020

Due to the openness of an Android system, many Internet of Things (IoT) devices are running the Android system and Android devices have become a common control terminal for IoT devices because of various sensors on them. With the popularity of IoT devices, malware on Android-based IoT devices is also increasing. People’s lives and privacy security are threatened. To reduce such threat, many researchers have proposed new methods to detect Android malware. Currently, most malware detection products on the market are based on malware signatures, which have a fast detection speed and normally a low false alarm rate for known malware families. However, they cannot detect unknown malware and are easily evaded by malware that is confused or packaged. Many new solutions use syntactic features and machine learning techniques to classify Android malware. It has been known that analysis of the Function Call Graph (FCG) can capture behavioral features of malware well. This paper presents a new approach to classifying Android malware based on deep learning and OpCode-level FCG. The FCG is obtained through static analysis of Operation Code (OpCode), and the deep learning model we used is the Long Short-Term Memory (LSTM). We conducted experiments on a dataset with 1796 Android malware samples classified into two categories (obtained from Virusshare and AndroZoo) and 1000 benign Android apps. Our experimental results showed that our proposed approach with an accuracy of 97 % outperforms the state-of-the-art methods such as those proposed by Nikola et al. and Hou et al. (IJCAI-18) with the accuracy of 97 % and 91 % , respectively. The time consumption of our proposed approach is less than the other two methods.
Comparing Person-Specific and Independent Models on Subject-Dependent and Independent Human Activity Recognition Performance
Authors: Scheurer Sebastian , Tedesco Salvatore , O’Flynn Brendan , Brown Kenneth N.
Published: June 2020

The distinction between subject-dependent and subject-independent performance is ubiquitous in the human activity recognition (HAR) literature. We assess whether HAR models really do achieve better subject-dependent performance than subject-independent performance, whether a model trained with data from many users achieves better subject-independent performance than one trained with data from a single person, and whether one trained with data from a single specific target user performs better for that user than one trained with data from many. To those ends, we compare four popular machine learning algorithms’ subject-dependent and subject-independent performances across eight datasets using three different personalisation–generalisation approaches, which we term person-independent models (PIMs), person-specific models (PSMs), and ensembles of PSMs (EPSMs). We further consider three different ways to construct such an ensemble: unweighted, κ -weighted, and baseline-feature-weighted. Our analysis shows that PSMs outperform PIMs by 43.5% in terms of their subject-dependent performances, whereas PIMs outperform PSMs by 55.9% and κ -weighted EPSMs—the best-performing EPSM type—by 16.4% in terms of the subject-independent performance.
Validation of Inertial Sensor to Measure Barbell Kinematics across a Spectrum of Loading Conditions
Authors: Abbott John C. , Wagle John P. , Sato Kimitake , Painter Keith , Light Thaddeus J. , Stone Michael H.
Published: June 2020

The aim of this study was to evaluate the level of agreement in measuring back squat kinematics between an inertial measurement unit (IMU) and a 3D motion capture system (3DMOCAP). Kinematic variables included concentric peak velocity (CPV), concentric mean velocity (CMV), eccentric peak velocity (EPV), eccentric mean velocity (EMV), mean propulsive velocity (MPV), and POP-100: a proprietary variable. Sixteen resistance-trained males performed an incrementally loaded one repetition maximum (1RM) squat protocol. A series of Pearson correlations, 2 × 4 RM ANOVA, Cohen’s d effect size differences, coefficient of variation (CV), and standard error of the estimate (SEE) were calculated. A large relationship existed for all variables between devices (r = 0.78–0.95). Between-device agreement for CPV worsened beyond 60% 1RM. The remaining variables were in agreement between devices with trivial effect size differences and similar CV magnitudes. These results support the use of the IMU, regardless of relative intensity, when measuring EMV, EPV, MPV, and POP-100. However, practitioners should carefully select kinematic variables of interest when using the present IMU device for velocity-based training (VBT), as certain measurements (e.g., CMV, CPV) do not possess practically acceptable reliability or accuracy. Finally, the IMU device exhibited considerable practical data collection concerns, as one participant was completely excluded and 13% of the remaining attempts displayed obvious internal error.