bluuebunny/crossref_metadata_2025 huggingface dataset:
Scaling Up: Molecular to Meteorological via Symmetry Breaking and Statistical Multifractality
Authors: Tuck Adrian F.
Published: February 2022

The path from molecular to meteorological scales is traced and reviewed, beginning with the persistence of molecular velocity after collision induces symmetry breaking, from continuous translational to scale invariant, associated with the emergence of hydrodynamic behaviour in a Maxwellian (randomised) population undergoing an anisotropic flux. An empirically based formulation of entropy and Gibbs free energy is proposed and tested with observations of temperature, wind speed and ozone. These theoretical behaviours are then succeeded upscale by key results of statistical multifractal analysis of airborne observations on horizontal scales from 40 m to an Earth radius, and on vertical scales from the surface to 13 km. Radiative, photochemical and dynamical processes are then examined, with the intermittency of temperature implying significant consequences. Implications for vertical scaling of the horizontal wind are examined via the thermal wind and barometric equations. Experimental and observational tests are suggested for free running general circulation models, with the possibility of addressing the cold bias they still exhibit. The causal sequence underlying atmospheric turbulence is proposed.
Model Analysis of Robotic Soft Arms including External Force Effects
Authors: Chen Zhi , Liu Zhong , Han Xingguo
Published: February 2022

Because robotic soft arms have a high power-to-weight ratio, low cost, and ease of manufacturability, increasing numbers of researchers have begun to focus on their characteristics in recent years. However, many urgent problems remain to be resolved. For example, soft arms are made of hyperelastic material, making it difficult to obtain accurate model predictions of the soft arm shape. This paper proposes a new modeling method for soft arms, combining the constant curvature model with Euler–Bernoulli beam theory. By combining these two modeling methods, we can quickly solve for the soft arm deformation under the action of an external force. This paper also presents an experimental platform based on a cable-driven soft arm to verify the validity of the proposed model. We carried out model verification experiments to test for different external effects. Experimental results show that the maximum error of our proposed soft arm deformation model is between 2.86% and 8.75%, demonstrating its effectiveness.
Is the Intestinal Bacterial Community in the Australian Rabbitfish Siganus fuscescens Influenced by Seaweed Supplementation or Geography?
Authors: Thépot Valentin , Slinger Joel , Rimmer Michael A. , Paul Nicholas A. , Campbell Alexandra H.
Published: February 2022

We recently demonstrated that dietary supplementation with seaweed leads to dramatic improvements in immune responses in S. fuscescens, a candidate species for aquaculture development in Asia. Here, to assess whether the immunostimulatory effect was facilitated by changes to the gut microbiome, we investigated the effects of those same seaweed species and four commercial feed supplements currently used in aquaculture on the bacterial communities in the hindgut of the fish. Since we found no correlations between the relative abundance of any particular taxa and the fish enhanced innate immune responses, we hypothesised that S. fuscescens might have a core microbiome that is robust to dietary manipulation. Two recently published studies describing the bacteria within the hindgut of S. fuscescens provided an opportunity to test this hypothesis and to compare our samples to those from geographically distinct populations. We found that, although hindgut bacterial communities were clearly and significantly distinguishable between studies and populations, a substantial proportion (55 of 174 taxa) were consistently detected across all populations. Our data suggest that the importance of gut microbiota to animal health and the extent to which they can be influenced by dietary manipulations might be species-specific or related to an animals’ trophic level.
Ca2+/Calmodulin-Dependent Protein Kinase II Inhibits Hepatitis B Virus Replication from cccDNA via AMPK Activation and AKT/mTOR Suppression
Authors: Kim Jumi , Kwon Hyeonjoong , Kalsoom Fadia , Sajjad Muhammad , Lee Hyun , Lim Jin , Jung Jaesung , Chwae Yong-Joon , Park Sun , Shin Ho-Joon , Kim Kyongmin
Published: February 2022

Ca2+/calmodulin-dependent protein kinase II (CaMKII), which is involved in the calcium signaling pathway, is an important regulator of cancer cell proliferation, motility, growth, and metastasis. The effects of CaMKII on hepatitis B virus (HBV) replication have never been evaluated. Here, we found that phosphorylated, active CaMKII is reduced during HBV replication. Similar to other members of the AMPK/AKT/mTOR signaling pathway associated with HBV replication, CaMKII, which is associated with this pathway, was found to be a novel regulator of HBV replication. Overexpression of CaMKII reduced the expression of covalently closed circular DNA (cccDNA), HBV RNAs, and replicative intermediate (RI) DNAs while activating AMPK and inhibiting the AKT/mTOR signaling pathway. Findings in HBx-deficient mutant-transfected HepG2 cells showed that the CaMKII-mediated AMPK/AKT/mTOR signaling pathway was independent of HBx. Moreover, AMPK overexpression reduced HBV cccDNA, RNAs, and RI DNAs through CaMKII activation. Although AMPK acts downstream of CaMKII, AMPK overexpression altered CaMKII phosphorylation, suggesting that CaMKII and AMPK form a positive feedback loop. These results demonstrate that HBV replication suppresses CaMKII activity, and that CaMKII upregulation suppresses HBV replication from cccDNA via AMPK and the AKT/mTOR signaling pathway. Thus, activation or overexpression of CaMKII may be a new therapeutic target against HBV infection.
Dendrimer Anion-Exchange Stationary Phase for Separation of Oligonucleotides
Authors: Studzińska Sylwia , Bocian Szymon , Kilanowska Anna , Buszewski Bogusław
Published: February 2022

Oligonucleotides are used in many research areas. Thus, there is a need for their successful separation methods. Ion-exchange chromatography is the most popular separation technique, but it has limitations for these compounds. For this reason, new stationary phases are developed in order to increase separation selectivity. This study aimed to apply a series of dendrimer anion exchangers with various bonded layers to separate oligonucleotides by using different mobile phases to find conditions that allow sufficient separation. The number of anion-exchange layers, type of salt, and pH significantly impacted the oligonucleotide analysis. The developed chromatographic method was characterized by adequate selectivity for oligonucleotides differing in sequence length. It is essential to underline that the number of bonded layers appeared to have a significant influence, and the three layers appeared optimal. Based on our results, it may be concluded that the dendrimer stationary phases can be successfully used as an alternative to commonly applied packing materials in ion-exchange chromatography.
Cranberry: Chemical Composition, Antioxidant Activity and Impact on Human Health: Overview
Authors: Nemzer Boris V. , Al-Taher Fadwa , Yashin Alexander , Revelsky Igor , Yashin Yakov
Published: February 2022

Cranberries are a rich source of bioactive compounds that comprise a healthy diet. Cranberry is abundant in nutritional components and many bioactive compounds that have antioxidant properties. Both American (Vaccinium macrocarpon) and European (Vaccinium oxycoccus) cranberry species are rich in polyphenols such as phenolic acids, anthocyanins and flavonoids, and is one of the few fruits that is high in proanthocyanidins, which is linked to many health benefits. The review systematizes information on the chemical composition of cranberry, its antioxidant effect, and the beneficial impact on human health and disease prevention after cranberry consumption, and in particular, its effect against urinary tract inflammation with both adults and children, cardiovascular, oncology diseases, type 2 diabetes, metabolic syndrome, obesity, tooth decay and periodontitis, Helicobacter pylori bacteria in the stomach and other diseases. Additional research needs to study cranberry proteomics profiling, polyphenols interaction and synergism with other biologically active compounds from natural ingredients and what is important in formulation of new functional foods and supplements.
Detecting Groups and Estimating F-Formations for Social Human–Robot Interactions
Authors: Pathi Sai Krishna , Kiselev Andrey , Loutfi Amy
Published: February 2022

The ability of a robot to detect and join groups of people is of increasing importance in social contexts, and for the collaboration between teams of humans and robots. In this paper, we propose a framework, autonomous group interactions for robots (AGIR), that endows a robot with the ability to detect such groups while following the principles of F-formations. Using on-board sensors, this method accounts for a wide spectrum of different robot systems, ranging from autonomous service robots to telepresence robots. The presented framework detects individuals, estimates their position and orientation, detects groups, determines their F-formations, and is able to suggest a position for the robot to enter the social group. For evaluation, two simulation scenes were developed based on the standard real-world datasets. The 1st scene is built with 20 virtual agents (VAs) interacting in 7 different groups of varying sizes and 3 different formations. The 2nd scene is built with 36 VAs, positioned in 13 different groups of varying sizes and 6 different formations. A model of a Pepper robot is used in both simulated scenes in randomly generated different positions. The ability for the robot to estimate orientation, detect groups, and estimate F-formations at various locations is used to determine the validation of the approaches. The obtained results show a high accuracy within each of the simulated scenarios and demonstrates that the framework is able to work from an egocentric view with a robot in real time.
Auditing the Representation of Female Versus Male Athletes in Sports Science and Sports Medicine Research: Evidence-Based Performance Supplements
Authors: Smith Ella S. , McKay Alannah K. A. , Kuikman Megan , Ackerman Kathryn E. , Harris Rachel , Elliott-Sale Kirsty J. , Stellingwerff Trent , Burke Louise M.
Published: February 2022

Although sports nutrition guidelines promote evidence-based practice, it is unclear whether women have been adequately included in the underpinning research. In view of the high usage rates of performance supplements by female athletes, we conducted a standardised audit of the literature supporting evidence-based products: β-alanine, caffeine, creatine, glycerol, nitrate/beetroot juice and sodium bicarbonate. Within 1826 studies totalling 34,889 participants, just 23% of participants were women, although 34% of studies included at least one woman. Across different supplements, 0–8% of studies investigated women exclusively, while fewer (0–2%) were specifically designed to compare sex-based responses. The annual publication of female-specific studies was ~8 times fewer than those investigating exclusively male cohorts. Interestingly, 15% of the female participants were classified as international/world-class athletes, compared with 7% of men. Most studies investigated performance outcomes but displayed poorer representation of women (16% of participants), whereas health-focussed studies had the greatest proportion of female participants (35%). Only 14% of studies including women attempted to define menstrual status, with only three studies (~0.5%) implementing best practice methodologies to assess menstrual status. New research should target the efficacy of performance supplements in female athletes, and future sports nutrition recommendations should specifically consider how well female athletes have contributed to the evidence-base.
Chronotype: A Tool to Screen Eating Habits in Polycystic Ovary Syndrome?
Authors: Barrea Luigi , Verde Ludovica , Vetrani Claudia , Savastano Silvia , Colao Annamaria , Muscogiuri Giovanna
Published: February 2022

Polycystic ovary syndrome (PCOS) is the most common endocrine disorders in women of reproductive age, whose lifestyle approach is an essential part of the treatment. Recently, chronotype, i.e., a trait that determines individual’s circadian preference in behavioral and biological rhythms, has been reported to play a role in determining nutrition preferences and the risk of developing chronic diseases. Thus, the aim of this study was to investigate if chronotype categories (morning, evening, and neither) could be used as tool to screen eating habits in women with PCOS. In this observational cross-sectional study, we assessed anthropometric measurements, lifestyle habits, chronotype categories, adherence to the Mediterranean Diet, dietary pattern, and metabolic parameters in 112 women with PCOS. Chronotype was classified as morning in 27.7%, evening in 42.9%, and neither in 29.5% of subjects. Women with PCOS with evening chronotype showed significantly higher percentages of grade I (p = 0.003) and grade II obesity (p = 0.001), did less regular exercise (p < 0.001), and most of them were smokers (p < 0.001) compared to those with neither and morning chronotypes. Women with PCOS with evening chronotype were significantly more insulin resistant (Homeostatic Model Assessment of Insulin Resistance (HoMA-IR) cut off > 2.5) than other two chronotypes (p < 0.001). Women with PCOS with evening chronotype had the lowest PREvención con DIetaMEDiterránea (PREDIMED) score, consumed more calories (p < 0.001), total (p < 0.001) and simple carbohydrates (p < 0.001), total fat (p < 0.001) and saturated fatty acids (p < 0.001), polyunsaturated fatty acids (p < 0.001) and n-6 polyunsaturated fatty acids (p < 0.001), and less fiber (p < 0.001) than women with PCOS with other chronotypes. In addition, women with PCOS with evening chronotype consumed less extra virgin olive oil (p = 0.001), legumes (p = 0.038), fish/seafood (p < 0.001), and tree nuts (p = 0.041) than women with PCOS of the other two chronotype categories and less red wine (p < 0.001) and more red/processed meat (p < 0.001) than women with PCOS with morning chronotype. In conclusion, in women with PCOS, evening chronotype has been associated with a most severe insulin resistance and unhealthiest eating habits. Thus, chronotype assessment could be an effective tool to screen the eating habits, and more generally the lifestyle, of women with PCOS.
Chicory: Understanding the Effects and Effectors of This Functional Food
Authors: Pouille Céline L. , Ouaza Souad , Roels Elise , Behra Josette , Tourret Melissa , Molinié Roland , Fontaine Jean-Xavier , Mathiron David , Gagneul David , Taminiau Bernard , Daube Georges , Ravallec Rozenn , Rambaud Caroline , Hilbert Jean-Louis , Cudennec Benoit , Lucau-Danila Anca
Published: February 2022

Industrial chicory has been the subject of numerous studies, most of which provide clinical observations on its health effects. Whether it is the roasted root, the flour obtained from the roots or the different classes of molecules that enter into the composition of this plant, understanding the molecular mechanisms of action on the human organism remains incomplete. In this study, we were interested in three molecules or classes of molecules present in chicory root: fructose, chlorogenic acids, and sesquiterpene lactones. We conducted experiments on the murine model and performed a nutrigenomic analysis, a metabolic hormone assay and a gut microbiota analysis, associated with in vitro observations for different responses. We have highlighted a large number of effects of all these classes of molecules that suggest a pro-apoptotic activity, an anti-inflammatory, antimicrobial, antioxidant, hypolipidemic and hypoglycemic effect and also an important role in appetite regulation. A significant prebiotic activity was also identified. Fructose seems to be the most involved in these activities, contributing to approximately 83% of recorded responses, but the other classes of tested molecules have shown a specific role for these different effects, with an estimated contribution of 23–24%.
Techniques for TPC Calibration: Application to Liquid Ar-TPCs
Authors: Maneira José
Published: February 2022

Large liquid argon TPCs are playing an increasingly important role in neutrino physics, and their calibration will be an essential component of their capability to reach the required performance and precision. Natural sources are extensively used but present limitations, since natural radioactivity from 39Ar is of low energy, and the rate of cosmic ray muons is low when the detectors are placed deep underground. Argon gas TPCs have been calibrated with ionizing laser beams for several decades, and more recently the technique has been further developed for use in liquid TPCs. Other recent ideas include the use of external neutron generators creating pulses that propagate into the detector. This paper reviews the development of the laser and neutron methods for the calibration of argon TPCs and describes their planned implementation in the upcoming DUNE experiment.
Azeotropic Distillation-Induced Self-Assembly of Mesostructured Spherical Nanoparticles as Drug Cargos for Controlled Release of Curcumin
Authors: Chen Long , Fu Xin , Lin Mei , Jiang Xingmao
Published: February 2022

Methods of large-scale controllable production of uniform monodispersed spherical nanoparticles have been one of the research directions of scientists in recent years. In this paper, we report an azeotropic distillation-induced evaporation self-assembly method as a universal method, and monodispersed hydrophobic ordered mesoporous silica nanospheres (MHSs) were successfully synthesized by this method, using triethoxymethylsilane (MTES) as the silica precursor and hexadecyl trimethyl ammonium bromide (CTAB) as the template. SEM and TEM images showed good monodispersity, sphericity, and uniform diameter. Meanwhile, SAXS and N2 adsorption–desorption measurements demonstrated a highly ordered lamellar mesostructure with a large pore volume. The model drug, curcumin was successfully encapsulated in MHSs for drug delivery testing, and their adsorption capacity was 3.45 mg g−1, which greatly improved the stability of curcumin. The release time when net release rate of curcumin reached 50% was extended to 6 days.
Durability Performance of Geopolymer Concrete: A Review
Authors: Wong Leong Sing
Published: February 2022

Geopolymer concrete is produced from the geopolymerization process, in which molecules known as oligomers integrate to form geopolymer networks with covalent bonding. Its production expends less thermal energy and results in a smaller carbon footprint compared to Ordinary Portland Cement (OPC) concrete. It requires only an alkaline activator to catalyze its aluminosilicate sources such as metakaolin and fly ash, to yield geopolymer binder for the geopolymerization to take place. Because of its eco-friendly technology and practical application, current research interest is mainly concentrated on the endurance of geopolymer concrete to resist heat and chemical aggressions. As such, it is pertinent for this review article to provide critical insight into the recent progress in research on the durability of geopolymer concrete. One significant outcome of the review is that the admixture of geopolymer concrete could be blended with additives such as micro-silica and fibers such as polypropylene fibers, to enhance its durability. The review on the durability aspects of geopolymer concrete showed that it had high compressive strength at an optimal elevated temperature, low to medium chloride ion penetrability, and high resistance to acid attack and abrasion. This makes geopolymer concrete a viable candidate to replace OPC concrete in the construction industry.
Single-Chain Mechanical Properties of Gelatin: A Single-Molecule Study
Authors: Qian Lu , Zhang Kai , Guo Xin , Zhou Junyu , Yu Miao
Published: February 2022

Gelatin is an important natural biological resource with a wide range of applications in the pharmaceutical, industrial and food industries. We investigated the single-chain behaviors of gelatin by atomic force microscopy (AFM)-based single-molecule force spectroscopy (SMFS), and found that gelatin exists as long chains by fitting with the M-FJC model. By comparing the single-chain elasticity in a nonpolar organic solvent (nonane) and DI water, it was surprising to find that there was almost no difference in the single-chain elasticity of gelatin in nonane and DI water. Considering the specificity of gelatin solubility and the solvent size effect of nonane molecules, when a single gelatin chain is pulled into loose nonane, dehydration does not occur due to strong binding water interactions. Gelatin chains can only interact with water molecules at high temperatures; therefore, no further interaction of single gelatin chains with water molecules occurred at the experimental temperature. This eventually led to almost no difference in the single-chain F–E curves under the two conditions. It is expected that our study will enable the deep exploration of the interaction between water molecules and gelatin and provide a theoretical basis and experimental foundation for the design of gelatin-based materials with more functionalities.
Natural-Fiber-Reinforced Chitosan, Chitosan Blends and Their Nanocomposites for Various Advanced Applications
Authors: Ilyas Rushdan , Aisyah Humaira , Nordin Abu , Ngadi Norzita , Zuhri Mohamed , Asyraf Muhammad , Sapuan Salit , Zainudin Edi , Sharma Shubham , Abral Hairul , Asrofi Mochamad , Syafri Edi , Sari Nasmi , Rafidah Mazlan , Zakaria Sharifah , Razman Muhammad , Majid Nuriah , Ramli Zuliskandar , Azmi Ashraf , Bangar Sneh , Ibrahim Rushdan
Published: February 2022

There has been much effort to provide eco-friendly and biodegradable materials for the next generation of composite products owing to global environmental concerns and increased awareness of renewable green resources. This review article uniquely highlights the use of green composites from natural fiber, particularly with regard to the development and characterization of chitosan, natural-fiber-reinforced chitosan biopolymer, chitosan blends, and chitosan nanocomposites. Natural fiber composites have a number of advantages such as durability, low cost, low weight, high specific strength, non-abrasiveness, equitably good mechanical properties, environmental friendliness, and biodegradability. Findings revealed that chitosan is a natural fiber that falls to the animal fiber category. As it has a biomaterial form, chitosan can be presented as hydrogels, sponges, film, and porous membrane. There are different processing methods in the preparation of chitosan composites such as solution and solvent casting, dipping and spray coating, freeze casting and drying, layer-by-layer preparation, and extrusion. It was also reported that the developed chitosan-based composites possess high thermal stability, as well as good chemical and physical properties. In these regards, chitosan-based “green” composites have wide applicability and potential in the industry of biomedicine, cosmetology, papermaking, wastewater treatment, agriculture, and pharmaceuticals.
Melt Conveying in Single-Screw Extruders: Modeling and Simulation
Authors: Marschik Christian , Roland Wolfgang , Osswald Tim A.
Published: February 2022

Numerous analyses have modeled the flow of polymer melts in the melt-conveying zones of single-screw extruders. While initial studies mainly provided exact analytical results for combined drag and pressure flows of Newtonian fluids, more recently developed, numerical methods seek to deepen the understanding of more realistic flow situations that include shear-thinning and non-isothermal effects. With the advent of more powerful computers, considerable progress has been made in the modeling and simulation of polymer melt flows in single-screw extruders. This work reviews the historical developments from a methodological point of view, including (1) exact analytical, (2) numerical, and (3) approximate methods. Special attention is paid to the mathematical models used in each case, including both governing flow equations and boundary conditions. In addition, the literature on leakage flow and curved-channel systems is revisited.
Data-Driven Machine Learning Intelligent Tools for Predicting Chromium Removal in an Adsorption System
Authors: Zafar Mohd , Aggarwal Ayushi , Rene Eldon R. , Barbusiński Krzysztof , Mahanty Biswanath , Behera Shishir Kumar
Published: February 2022

This study investigates chromium removal onto modified maghemite nanoparticles in batch experiments based on a central composite design. The effect of modified maghemite nanoparticles on the adsorptive removal of chromium was quantitatively elucidated by fitting the experimental data using artificial neural network (ANN) and adaptive neuro-fuzzy interference system (ANFIS) modeling approaches. The ANN and ANFIS models, relating the inputs, i.e., pH, adsorbent dose, and initial chromium concentration to the output, i.e., chromium removal efficiency (RE), were developed by comparing the predicted value with that of the experimental values. The RE of chromium ranged from 49.58% to 92.72% under the influence of varying pH (i.e., 2.6–9.4) and adsorbent dose, i.e., 0.8 g/L to 9.2 g/L. The developed ANN model fits the experimental data exceptionally well with correlation coefficients of 1.000 and 0.997 for training and testing, respectively. In addition, the Pearson’s Chi-square measure (χ2) of 0.0004 and 0.0673 for the ANN and ANFIS models, respectively, indicated the superiority of ANN over ANFIS. However, a small discrepancy in the predictability of the ANFIS model was observed owing to the fuzzy rule-based complexity and overtraining of data. Thus, the developed models can be used for the online prediction of RE onto synthesized maghemite nanoparticles with different sets of input parameters and it can also predict the operational errors in the system.
Eco-Anxiety and Pastoral Care: Theoretical Considerations and Practical Suggestions
Authors: Pihkala Panu
Published: February 2022

The environmental crisis is producing an increasing number of both physical and psychological impacts. This article studies the challenge of eco-anxiety for pastoral care, drawing from both interdisciplinary research and ecological theology. The aim is to help both practitioners and researchers to encounter eco-anxiety more constructively. The rapidly growing research about eco-anxiety and therapy is discussed in relation to pastoral care. The various forms of eco-anxiety are briefly analyzed. The role of the caregivers is discussed by using sources that study the challenges of therapists in relation to eco-anxiety. The existential depths of eco-anxiety are probed in the light of recent research and older existentialist theory. It is pointed out that the political character of ecological issues, especially climate change issues, causes many kinds of challenges for pastoral care. As the constructive conclusion of the article, various possibilities and resources for encountering eco-anxiety in pastoral care are discussed, along with the connections with wider pastoral theology. It is argued that pastoral care providers should engage in self-reflection about their own attitudes and emotions related to ecological issues, preferably with the support of trusted peers or mentors. Various organizational developments are also needed to support caregivers. Dialectical thinking is one tool that can help to navigate the complex dynamics related to environmental responsibility, eco-emotions, and questions of hope or hopelessness.
The Risky-Opportunity Analysis Method (ROAM) to Support Risk-Based Decisions in a Case-Study of Critical Infrastructure Digitization
Authors: Ardebili Ali Aghazadeh , Padoano Elio , Longo Antonella , Ficarella Antonio
Published: February 2022

Socio-ecologic, socio-economic, and socio-technical transitions are opportunities that require fundamental changes in the system. These will encounter matters associated with security, service adoption by end-users, infrastructure and availability. The purpose of this study is to examine and overcome the risks to take advantage of opportunities through the novel Risky-Opportunity Analysis Method (ROAM). A novel quantitative method is designed to determine when, after making some changes, the risks become acceptable so that the opportunity does not deviate from the objectives. The approach provided a quantitative evaluation of the possible changes in parallel with digitization, towards providing a green Service Supply Chain (SSC). The result of ROAM shows that the most cost-effective change to increase the resilience of the system is a solution (SMS) which is different from that identified by a TOPSIS multi-criteria method. Real-word decisions in change management should tackle the complexity of systems and uncertainty of events during and after transition through a careful analysis of the alternatives. A case-study was carried out to evaluate the alternatives of an ancillary service in the Payment Service Providers (PSP). The comparison of the ROAM results with the traditional TOPSIS of the case-study unveils the priority of the ROAM in practice when the alternatives are Risky-Opportunities. The existing risk assessment tools do not take advantage of risky opportunities. To this aim, the current article introduces the term Risky-Opportunity, and two indexes—Stress and Strain—of the alternatives that are designed to be employed in the new quantitative ROAM approach.
Ionospheric Nighttime Enhancements at Low Latitudes Challenge Performance of the Global Ionospheric Maps
Authors: Yang Yuyan , Liu Libo , Zhao Xiukuan , Xie Haiyong , Chen Yiding , Le Huijun , Zhang Ruilong , Tariq M. Arslan , Li Wenbo
Published: February 2022

In this study, two ionospheric nighttime enhancement (INE) events at low latitudes are selected to investigate their spatial features through the observations from Global Navigation Satellite System (GNSS) receivers and ionosondes. For the first time, we present the detailed spatial pictures of premidnight and postmidnight INEs under geomagnetically quiet conditions. The two INE events have the maximum extents of about 11° × 34° and 17° × 25° (longitude × latitude), respectively. Dramatic latitudinal and longitudinal features are revealed in the two INEs. We perform a comparison between the products of Global Ionospheric Maps (GIMs) and total electron content (TEC) measurement from GNSS receivers. However, GIMs fail to capture the TEC distribution during INEs owing to their limited spatial and temporal resolution. Considering the extent of INEs from the observations, the spherical harmonic (SH) expansion adopted by the GIM models needs to upgrade the degree and order to 36. The pixel-based methods developed from two GIM models are required to reduce their grid size for higher spatial resolution. The recommended time interval is shorter than 30 min. Among seven GIMs, CODG and JPLG maps generally have the best performance in reproducing the latitudinal structure of the ionosphere.
Using Ensemble-Based Systems with Near-Infrared Hyperspectral Data to Estimate Seasonal Snowpack Density
Authors: El Oufir Mohamed Karim , Chokmani Karem , El Alem Anas , Bernier Monique
Published: February 2022

Estimating the seasonal density of the snowpack has many financial and environmental benefits. Rapid assessment and daily monitoring of its evolution are therefore key to effective prevention. Traditionally, the physical characteristics of snow are measured directly in the field, which involves high costs and personnel mobilization. Hyperspectral imaging is a reliable and efficient technique to study and evaluate this physical property. The spectral reflectance of snow is partly defined by changes in its physical properties, particularly in the Near infrared (NIR) part of the spectrum. Recently, a hybrid snow density estimation model allowing retrieval of density from NIR hyperspectral data was developed, based on an a priori classification of snow samples. However, in order to obtain optimal density estimates with the Hybrid model (HM), the sources of classification and estimation error must be controlled. Following the same principle as the HM, an Ensemble-based system (EBS) was developed. This model reduces the number of misclassification errors produced by the HM. The general concept of EBS algorithms is based on the principle that obtaining more opinions before making a decision is part of human nature, especially when economic and environmental benefits are at stake. This approach has helped to reduce the risk of classification and estimation errors and to develop more robust density results. One hundred and fourteen snow samples collected during three winters (2018–2020) were used to calibrate and validate the EBS. The performance of the EBS was validated using an independent database and the results were satisfactory (R2 = 0.90, RMSE = 44.45 kg m−3, BIAS = 3.87 kg m−3 and NASH = 0.89).
Healthy Cattle Microbiome and Dysbiosis in Diseased Phenotypes
Authors: Khalil Alaa , Batool Aamina , Arif Sania
Published: February 2022

Cattle farming is an ancient practice, with roots in the early Neolithic era that has retained a major status in the food industry, with global beef market revenue amounting to $385.7B (as of 2018). Hence, cattle maintenance is essential for catering to the nutritional requirements of modern civilization. This extensive review aims to provide a holistic overview of the cattle microbiome, analyzing the native microbial composition within the respiratory tract, gastrointestinal tract, reproductive tract, and skin of cattle. The dysbiosis associated with various diseases such as bovine respiratory disease, bovine digital dermatitis, mastitis, Johne’s disease, uterine diseases (metritis and endometritis), and metabolic disorders (ruminal acidosis and ketosis) have been discussed. Moreover, various non-antibiotic microbial therapies including phage therapy, prebiotics, and probiotics have been examined as potential means to reduce disease-associated dysbiosis. In general, this review highlights the importance of the microbiome in the maintenance of health in cattle and its potential in alleviating bovine diseases, with an aim to enhance cattle health and production.
Automatic Dynamic Range Adjustment for Pedestrian Detection in Thermal (Infrared) Surveillance Videos
Authors: Oluyide Oluwakorede Monica , Tapamo Jules-Raymond , Walingo Tom Mmbasu
Published: February 2022

This paper presents a novel candidate generation algorithm for pedestrian detection in infrared surveillance videos. The proposed method uses a combination of histogram specification and iterative histogram partitioning to progressively adjust the dynamic range and efficiently suppress the background of each video frame. This pairing eliminates the general-purpose nature associated with histogram partitioning where chosen thresholds, although reasonable, are usually not suitable for specific purposes. Moreover, as the initial threshold value chosen by histogram partitioning is sensitive to the shape of the histogram, specifying a uniformly distributed histogram before initial partitioning provides a stable histogram shape. This ensures that pedestrians are present in the image at the convergence point of the algorithm. The performance of the method is tested using four publicly available thermal datasets. Experiments were performed with images from four publicly available databases. The results show the improvement of the proposed method over thresholding with minimum-cross entropy, the robustness across images acquired under different conditions, and the comparable results with other methods in the literature.
A Multimodal Sensory Apparatus for Robotic Prosthetic Feet Combining Optoelectronic Pressure Transducers and IMU
Authors: Fiumalbi Tommaso , Martini Elena , Papapicco Vito , Dell’Agnello Filippo , Mazzarini Alessandro , Baldoni Andrea , Gruppioni Emanuele , Crea Simona , Vitiello Nicola
Published: February 2022

Timely and reliable identification of control phases is functional to the control of a powered robotic lower-limb prosthesis. This study presents a commercial energy-store-and-release foot prosthesis instrumented with a multimodal sensory system comprising optoelectronic pressure sensors (PS) and IMU. The performance was verified with eight healthy participants, comparing signals processed by two different algorithms, based on PS and IMU, respectively, for real-time detection of heel strike (HS) and toe-off (TO) events and an estimate of relevant biomechanical variables such as vertical ground reaction force (vGRF) and center of pressure along the sagittal axis (CoPy). The performance of both algorithms was benchmarked against a force platform and a marker-based stereophotogrammetric motion capture system. HS and TO were estimated with a time error lower than 0.100 s for both the algorithms, sufficient for the control of a lower-limb robotic prosthesis. Finally, the CoPy computed from the PS showed a Pearson correlation coefficient of 0.97 (0.02) with the same variable computed through the force platform.
Brokerage System for Integration of LrWPAN Technologies
Authors: Balota Josiah E. , Kor Ah-Lian
Published: February 2022

The prevalent demand for remote data sharing and connectivity has catalysed the development of many wireless network technologies. However, low-power and low-rate wireless network technologies have emerged as the preferred choice (due to cheap procurement and maintenance cost, efficiency, and adaptability). Currently, these groups of wireless networks are adopted in homes, health, and business sectors. The increase in existing WSNs has resulted in the incompatibility of wireless network protocols and poses a problem that results in high acquisition or maintenance costs, increased complexity, reliability inadequacies in some instances, lack of uniformity within similar standards, and high energy consumption. To address this problem, we develop a novel machine-to-machine software-based brokerage application (known as JosNet) for interoperability and integration between Bluetooth LE, Zigbee, and Thread wireless network technologies. JosNet allows one network protocol to exchange data packets or commands with each other. In this paper, we present a novel working network brokerage model for a one-to-one network protocol to communication (e.g., from Zigbee to Bluetooth) or one-to-many network protocol communication (e.g., from Bluetooth to Zigbee, Thread, etc.) to securely send messages in a large-scale routing process for short or long-range connections. We also present a large-scale implementation of JosNet using a routing table for large areas. The results show an industry standard performance for end-to-end latency time and throughput.
A Review of Multisensor Data Fusion Solutions in Smart Manufacturing: Systems and Trends
Authors: Tsanousa Athina , Bektsis Evangelos , Kyriakopoulos Constantine , González Ana Gómez , Leturiondo Urko , Gialampoukidis Ilias , Karakostas Anastasios , Vrochidis Stefanos , Kompatsiaris Ioannis
Published: February 2022

Manufacturing companies increasingly become “smarter” as a result of the Industry 4.0 revolution. Multiple sensors are used for industrial monitoring of machines and workers in order to detect events and consequently improve the manufacturing processes, lower the respective costs, and increase safety. Multisensor systems produce big amounts of heterogeneous data. Data fusion techniques address the issue of multimodality by combining data from different sources and improving the results of monitoring systems. The current paper presents a detailed review of state-of-the-art data fusion solutions, on data storage and indexing from various types of sensors, feature engineering, and multimodal data integration. The review aims to serve as a guide for the early stages of an analytic pipeline of manufacturing prognosis. The reviewed literature showed that in fusion and in preprocessing, the methods chosen to be applied in this sector are beyond the state-of-the-art. Existing weaknesses and gaps that lead to future research goals were also identified.
Multi-Agent Reinforcement Learning Based Fully Decentralized Dynamic Time Division Configuration for 5G and B5G Network
Authors: Chen Xiangyu , Chuai Gang , Gao Weidong
Published: February 2022

Future network services must adapt to the highly dynamic uplink and downlink traffic. To fulfill this requirement, the 3rd Generation Partnership Project (3GPP) proposed dynamic time division duplex (D-TDD) technology in Long Term Evolution (LTE) Release 11. Afterward, the 3GPP RAN#86 meeting clarified that 5G NR needs to support dynamic adjustment of the duplex pattern (transmission direction) in the time domain. Although 5G NR provides a more flexible duplex pattern, how to configure an effective duplex pattern according to services traffic is still an open research area. In this research, we propose a distributed multi-agent deep reinforcement learning (MARL) based decentralized D-TDD configuration method. First, we model a D-TDD configuration problem as a dynamic programming problem. Given the buffer length of all UE, we model the D-TDD configuration policy as a conditional probability distribution. Our goal is to find a D-TDD configuration policy that maximizes the expected discount return of all UE’s sum rates. Second, in order to reduce signaling overhead, we design a fully decentralized solution with distributed MARL technology. Each agent in MARL makes decisions only based on local observations. We regard each base station (BS) as an agent, and each agent configures uplink and downlink time slot ratio according to length of intra-BS user (UE) queue buffer. Third, in order to solve the problem of overall system revenue caused by the lack of global information in MARL, we apply leniency control and binary LSTM (BLSTM) based auto-encoder. Leniency controller effectively controls Q-value estimation process in MARL according to Q-value and current network conditions, and auto-encoder makes up for the defect that leniency control cannot handle complex environments and high-dimensional data. Through the parallel distributed training, the global D-TDD policy is obtained. This method deploys the MARL algorithm on the Mobile Edge Computing (MEC) server of each BS and uses the storage and computing capabilities of the server for distributed training. The simulation results show that the proposed distributed MARL converges stably in various environments, and performs better than distributed deep reinforcement algorithm.
AFD-StackGAN: Automatic Mask Generation Network for Face De-Occlusion Using StackGAN
Authors: Jabbar Abdul , Li Xi , Assam Muhammad , Khan Javed Ali , Obayya Marwa , Alkhonaini Mimouna Abdullah , Al-Wesabi Fahd N. , Assad Muhammad
Published: February 2022

To address the problem of automatically detecting and removing the mask without user interaction, we present a GAN-based automatic approach for face de-occlusion, called Automatic Mask Generation Network for Face De-occlusion Using Stacked Generative Adversarial Networks (AFD-StackGAN). In this approach, we decompose the problem into two primary stages (i.e., Stage-I Network and Stage-II Network) and employ a separate GAN in both stages. Stage-I Network (Binary Mask Generation Network) automatically creates a binary mask for the masked region in the input images (occluded images). Then, Stage-II Network (Face De-occlusion Network) removes the mask object and synthesizes the damaged region with fine details while retaining the restored face’s appearance and structural consistency. Furthermore, we create a paired synthetic face-occluded dataset using the publicly available CelebA face images to train the proposed model. AFD-StackGAN is evaluated using real-world test images gathered from the Internet. Our extensive experimental results confirm the robustness and efficiency of the proposed model in removing complex mask objects from facial images compared to the previous image manipulation approaches. Additionally, we provide ablation studies for performance comparison between the user-defined mask and auto-defined mask and demonstrate the benefits of refiner networks in the generation process.
Query Optimization for Distributed Spatio-Temporal Sensing Data Processing
Authors: Li Xin , Yu Huayan , Yuan Ligang , Qin Xiaolin
Published: February 2022

The unprecedented development of Internet of Things (IoT) technology produces humongous amounts of spatio-temporal sensing data with various geometry types. However, processing such datasets is often challenging due to high-dimensional sensor data geometry characteristics, complex anomalistic spatial regions, unique query patterns, and so on. Timely and efficient spatio-temporal querying significantly improves the accuracy and intelligence of processing sensing data. Most existing query algorithms show their lack of supporting spatio-temporal queries and irregular spatial areas. In this paper, we propose two spatio-temporal query optimization algorithms based on SpatialHadoop to improve the efficiency of query spatio-temporal sensing data: (1) spatio-temporal polygon range query (STPRQ), which aims to find all records from a polygonal location in a time interval; (2) spatio-temporal k nearest neighbors query (STkNNQ), which directly searches the query point’s k closest neighbors. To optimize the STkNNQ algorithm, we further propose an adaptive iterative range optimization algorithm (AIRO), which can optimize the iterative range of the algorithm according to the query time range and avoid querying irrelevant data partitions. Finally, extensive experiments based on trajectory datasets demonstrate that our proposed query algorithms can significantly improve query performance over baseline algorithms and shorten response time by 81% and 35.6%, respectively.
Amputee Fall Risk Classification Using Machine Learning and Smartphone Sensor Data from 2-Minute and 6-Minute Walk Tests
Authors: Juneau Pascale , Baddour Natalie , Burger Helena , Bavec Andrej , Lemaire Edward D.
Published: February 2022

The 6-min walk test (6MWT) is commonly used to assess a person’s physical mobility and aerobic capacity. However, richer knowledge can be extracted from movement assessments using artificial intelligence (AI) models, such as fall risk status. The 2-min walk test (2MWT) is an alternate assessment for people with reduced mobility who cannot complete the full 6MWT, including some people with lower limb amputations; therefore, this research investigated automated foot strike (FS) detection and fall risk classification using data from a 2MWT. A long short-term memory (LSTM) model was used for automated foot strike detection using retrospective data (n = 80) collected with the Ottawa Hospital Rehabilitation Centre (TOHRC) Walk Test app during a 6-min walk test (6MWT). To identify FS, an LSTM was trained on the entire six minutes of data, then re-trained on the first two minutes of data. The validation set for both models was ground truth FS labels from the first two minutes of data. FS identification with the 6-min model had 99.2% accuracy, 91.7% sensitivity, 99.4% specificity, and 82.7% precision. The 2-min model achieved 98.0% accuracy, 65.0% sensitivity, 99.1% specificity, and 68.6% precision. To classify fall risk, a random forest model was trained on step-based features calculated using manually labeled FS and automated FS identified from the first two minutes of data. Automated FS from the first two minutes of data correctly classified fall risk for 61 of 80 (76.3%) participants; however, <50% of participants who fell within the past six months were correctly classified. This research evaluated a novel method for automated foot strike identification in lower limb amputee populations that can be applied to both 6MWT and 2MWT data to calculate stride parameters. Features calculated using automated FS from two minutes of data could not sufficiently classify fall risk in lower limb amputees.