bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Monocropping on Land Cover Transitions in the Wet Evergreen Agro-Ecological Zone of Ghana
Authors: Loh Seyram K. , Asubonteng Kwabena O. , Adanu Selase K.
Published: July 2022

The wet evergreen forest ecological zone of Ghana is undergoing notable structural changes, although most maps erroneously depict the area to be an intact forest. Several hectares of plantation agriculture such as rubber, oil palm, and cocoa are replacing the natural vegetation and habitats, thereby threatening indigenous biodiversity. This study aimed to assess the effects of tree monocrop proliferation on landscape transitions between 1986 and 2020 in the Wassa East District of Ghana. The ISODATA clustering technique was used to produce land cover category maps from Landsat images of 1986, 2002, and 2020. A post-classification change detection technique resulted in transition matrices which were used for the computation of land cover transition intensities over 34 years. The results showed that the landscape was dominated by forests in 1986. Still, the forest continuously declined by 34% (1% annually) in 2020 while all other land cover types increased in both periods with cocoa covering 23% oil palm covering 14%, and rubber covering 2% of the land area. These conversions in the land area are important, especially since the closed forests usually transition to monocrops through the intermediate process: food crop farming. This information is essential for decision making on land development and biodiversity conservation.
Micro-Mechanism of Uniaxial Compression Damage of Layered Cemented Backfill in Underground Mine
Authors: Chen Xinming , Zhang Haowen , Wu Yuping , Jiao Huazhe , Yang Liuhua , Wang Qinting , Zhang Wenxiang
Published: July 2022

Layering of filling body is common in large-scale filling areas. In this paper, the cement–sand ratio of 1:8 is based on the configuration of 68%, 70%, 72% and 74%; four concentrations; and filling layers of one-, two-, three- and four-layered cemented filling samples. Combined with a uniaxial compression test and two-dimensional particle flow software (PFC2D), the mechanical properties and failure modes were explored. The results show that the concentration can strengthen the uniaxial compressive strength of the filling body while increasing the filling times weakens its power; therefore, the constitutive damage model was constructed. It was found that the initial layered damage existed in the layered filling, and the total damage showed an upward trend of first fast and then slow; the main failure modes of one-layer and two-layer backfills showed prominent shear failure characteristics, and the three-layer and four-layer fillings were closer to tensile failure. From the crack generation to the specimen failure, there is a mutual conversion between different energies.
Plasma–Saline Water Interaction: A Systematic Review
Authors: Melo Tatiane Fonseca de , Rocha Lucas Cabral , Silva Rútilo Pereira , Pessoa Rodrigo Sávio , Negreiros Andreia Mitsa Paiva , Sales Júnior Rui , Tavares Moisés Bento , Alves Junior Clodomiro
Published: July 2022

Plasma–liquid interaction research has developed substantially in recent years due, mostly, to the numerous applications of cold atmospheric plasma (CAP). Plasma–liquid interactions are influenced by the concentrations of the ionic species present in the liquid environment, and few studies have paid attention to saline water, which generally mediates the reactions in many plasma applications. Therefore, the present review aims to explore the main results and the influence of variables on the modification of properties of saline water by CAP sources following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The searches were carried out in the Scopus, Science Direct, and Web of Science databases, resulting in the inclusion of 37 studies. The main effects of the interaction between CAP and saline water are (i) the production of reactive oxygen and nitrogen species (RONS); (ii) the increase in conductivity and decrease in pH, directly proportional to the increase in discharge voltage; (iii) and the effective area of interaction and the shortest distance between electrode and solution. Other effects are the localized evaporation and crystallization of salts, which make the interaction between plasma and saline water a promising field in the development of technologies for desalination and improvement of liquid properties.
Optimization of Process Variables in the Drilling of LM6/B4C Composites through Grey Relational Analysis
Authors: Rubi C. Sarala , Prakash J. Udaya , Čep Robert , Elangovan Muniyandy
Published: July 2022

The objective of this investigational analysis was to study the influence of process variables on the response during the drilling of LM6/B4C composite materials. Stir casting was employed to produce the LM6/B4C composites. A Vertical Machining Center (VMC) with a dynamometer was used to drill the holes and to record the thrust force. An L27 orthogonal array was used to carry out the experimental work. A grey relational analysis (GRA) was employed to perform optimization in order to attain the lowest Thrust Force (TF), Surface Roughness (SR) and Burr Height (BH). For minimal responses, the optimum levels of the process variables viz. the feed rate (F), spindle speed (S), drill material (D) and reinforcing percentage (R) were determined. The process variables in the drilling of the LM6/B4C composites were indeed optimized, according to confirmational investigations. The predicted Grey Relational Grade was 0.846, whereas the experimental GRG was 0.865, with a 2.2% error—indicating that the optimization process was valid.
The Research of “Products Rapidly Attracting Users” Based on the Fully Integrated Link Prediction Algorithm
Authors: Li Shugang , Wang Ziming , Zhang Beiyan , Zhu Boyi , Wen Zhifang , Yu Zhaoxu
Published: July 2022

One of the main problems encountered by social networks is the cold start problem. The term “cold start problem” refers to the difficulty in predicting new users’ friendships due to the limited number of links those users have with existing nodes. To fill the gap, this paper proposes a Fully Integrated Link Prediction Algorithm (FILPA) that describes the social distance of nodes by using “betweenness centrality,” and develops a Social Distance Index (SDI) based on micro- and macro-network structure according to social distance. With the aim of constructing adaptive SDIs that are suitable for the characteristics of a network, a naive Bayes (NB) method is firstly adopted to select appropriate SDIs according to the density and social distance characteristics of common neighbors in the local network. To avoid the risk of algorithm accuracy reduction caused by blind combination of SDIs, the AdaBoost meta-learning strategy is applied to develop a Fully Integrated Social Distance Index (FISDI) composed of the best SDIs screened by NB. The possible friendships among nodes will then be comprehensively presented using high performance FISDI. Finally, in order to realize the “products rapidly attracting users” in new user marketing, FILPA is used to predict the possible friendship between new users in an online brand community and others in different product circles.
Stability of a Bi-Jensen Functional Equation on Restricted Unbounded Domains and Some Asymptotic Behaviors
Authors: Bae Jae-Hyeong , Tareeghee Mohammad Amin , Najati Abbas
Published: July 2022

In this paper, we give some properties of the bi-Jensen functional equation and investigate its Hyers–Ulam stability and hyperstability. We construct a function which is bi-Jensen and is not continuous. Additionally, we investigate the Hyers–Ulam stability of the bi-Jensen functional equation on some restricted unbounded domains. Finally, we apply the obtained results to study some interesting asymptotic behaviors of bi-Jensen functions.
Structural Characterization of Maitotoxins Produced by Toxic Gambierdiscus Species
Authors: Murray J. Sam , Finch Sarah C. , Mudge Elizabeth M. , Wilkins Alistair L. , Puddick Jonathan , Harwood D. Tim , Rhodes Lesley L. , van Ginkel Roel , Rise Frode , Prinsep Michèle R.
Published: July 2022

Identifying compounds responsible for the observed toxicity of the Gambierdiscus species is a critical step to ascertaining whether they contribute to ciguatera poisoning. Macroalgae samples were collected during research expeditions to Rarotonga (Cook Islands) and North Meyer Island (Kermadec Islands), from which two new Gambierdiscus species were characterized, G. cheloniae CAWD232 and G. honu CAWD242. Previous chemical and toxicological investigations of these species demonstrated that they did not produce the routinely monitored Pacific ciguatoxins nor maitotoxin-1 (MTX-1), yet were highly toxic to mice via intraperitoneal (i.p.) injection. Bioassay-guided fractionation of methanolic extracts, incorporating wet chemistry and chromatographic techniques, was used to isolate two new MTX analogs; MTX-6 from G. cheloniae CAWD232 and MTX-7 from G. honu CAWD242. Structural characterization of the new MTX analogs used a combination of analytical chemistry techniques, including LC–MS, LC–MS/MS, HR–MS, oxidative cleavage and reduction, and NMR spectroscopy. A substantial portion of the MTX-7 structure was elucidated, and (to a lesser extent) that of MTX-6. Key differences from MTX-1 included monosulfation, additional hydroxyl groups, an extra double bond, and in the case of MTX-7, an additional methyl group. To date, this is the most extensive structural characterization performed on an MTX analog since the complete structure of MTX-1 was published in 1993. MTX-7 was extremely toxic to mice via i.p. injection (LD50 of 0.235 µg/kg), although no toxicity was observed at the highest dose rate via oral administration (155.8 µg/kg). Future research is required to investigate the bioaccumulation and likely biotransformation of the MTX analogs in the marine food web.
Effect of Bending Process on Microstructure, Mechanical Properties and Crack Formation of 5% Ni Steel
Authors: Niu Fengyong , He Jingshu , Liu Denghui , Zuo Xiurong , Cai Minghui
Published: July 2022

The 5% Ni steel is often used to make steel storage tanks to store liquefied natural gas (LNG). Herein, the microstructure and mechanical properties of 5% Ni steel samples during bending were studied through combining scanning electron microscopy, energy dispersive spectroscopy, optical microscopy, X-ray diffraction, and electron backscattered diffractometer methods with tensile tests. The outer and inner arcs underwent tensile and compressive stress, respectively, resulting in a severely deformed microstructure with a high density of dislocation, improving both the tensile and yield strengths. The ductility of the 5% Ni steel samples decreased significantly after bending due to the work hardening and dislocation accumulation. During bending, the shear bands occurred at the surface or subsurface, which were caused by strain localization. Amounts of “harder” grains with high TF and more orange and red KAM areas with high local strain at the outer and inner arcs produced a greater stress concentration than that of the mid-thickness, which can induce crack initiation and propagation due to the large deformation during bending.
Physical Treatment Reduces Trypsin Inhibitor Activity and Modifies Chemical Composition of Marama Bean (Tylosema esculentum)
Authors: Alabi Funmilola , Kiarie Elijah G. , Mnisi Caven Mguvane , Mlambo Victor
Published: July 2022

The utility of the marama bean (MB) as an alternative protein source to soybean (SB) can be limited by the high concentration of trypsin inhibitors (TI). The physical treatment of MB has the potential to ameliorate the antinutritional activities of TI and modify other chemical components. Thus, this study investigated the effects of physical treatments on the chemical components and trypsin inhibitor activity (TIA) of raw MB and SB. The bean substrates were subjected to each of the following treatment methods: (1) room temperature (20–22 °C) soaking for 24 h; (2) electric stove cooking at 100 °C for 10, 20, and 30 min; (3) steam autoclaving at a temperature of 110 °C and pressure of 7 pounds per square inch (psi), as well as a temperature of 121 °C and 7 psi for 5, 15, and 30 min; (4) pre-soaked autoclaving at 110 °C (7 psi) and 121 °C (17 psi) for 5, 15, and 30 min. Treated MB and SB had greater (p < 0.05) crude protein content than untreated samples. All the treatments (except 24 h soaking of MB) reduced (p < 0.05) the TIA and ash content. Marama and SB are similar in protein content, but their amino acids profile and TIA are quite different. Soaking for 24 h was less effective in reducing TIA in MB and SB, compared to the thermal methods, and it was detrimental to the ash and amino acids profile of the two beans. Soaking prior to autoclaving yielded beans with the lowest TI concentrations. In conclusion, thermal methods reduced the TI contents and modified the level of proximate components and amino acids profile of the beans.
Lipid Peroxidation in Algae Oil: Antagonist Effects of Natural Antioxidants
Authors: Santoro Ilaria , Russo Anna , Perri Enzo , Sindona Giovanni , Nardi Monica
Published: July 2022

Tandem mass spectrometry is proposed to check lipid oxidation, a free radical-mediated phenomenon which effects oxidative deterioration in polyunsaturated fatty acids. Antioxidants are used by the food industry to delay the oxidation process. This process can be controlled by antioxidants, which may occur as natural constituents of foods or may be intentionally added to products. Synthetic antioxidants such as BHT, BHA, and propyl gallate have been extensively used as antioxidants in the industry. The worldwide tendency to avoid or minimize the use of synthetic food additives has prompted the replacement of synthetic antioxidants with natural analogues. The entire process can be supported by the detection and characterization of the reacting species by suitable application of electrospray tandem mass spectrometry under collision-induced dissociation (ESI-CID-MS/MS). Natural antioxidants were tested in this study to check the oxidative stability of algae oil when adding the natural additive. Results were observed in algae oil in situ using electrospray mass spectrometry in tandem with collision-induced dissociation tandem mass spectrometry (ESI-CID-MS/MS) and the POBN spin trapper. The results indicate that alpha-tocopherol is a better antioxidant.
The Impact of the COVID-19 Epidemic on the Mood and Diet of Patients Undergoing Bariatric Surgery
Authors: Boniecka Iwona , Czerwonogrodzka-Senczyna Aneta , Sekuła Marzena , Zawodny Piotr , Szemitko Marcin , Sieńko Magdalena , Sieńko Jerzy
Published: July 2022

Limited social contacts, lack of professional activities, economic insecurity, and a sense of threat, as well as boredom during the COVID-19 pandemic, contributed to tension and stress. All of these increase the risk of an inappropriate diet. The aim of this cross-sectional study was to assess the impact of the COVID-19 pandemic on the mood and nutrition of patients undergoing bariatric surgery. A group of 312 patients (both before and after bariatric surgery) completed a questionnaire about their diet and mood during COVID-19 lockdown. About 70% of all respondents reacted to the epidemiological situation: irritability, anxiety about their own health, and eating without being hungry. A total of 74% of all of the subjects snacked between meals (especially sweets). The respondents who believed that obesity and its complications had a negative impact on the prognosis of the coronavirus infection had a statistically significant higher prevalence of health anxiety, feeling that important life issues were out of control, irritability, need for psychological support, and need for dietary consultation. Patients after bariatric surgery had e.g., a statistically significant lower incidence of feeling hungry, eating after meals, and eating fatty foods. The COVID-19 pandemic has been shown to negatively affect the mood and diet of bariatric patients, which may affect their health status and worsen the prognosis of COVID-19.
Precision Anti-Cancer Medicines by Oligonucleotide Therapeutics in Clinical Research Targeting Undruggable Proteins and Non-Coding RNAs
Authors: Bartolucci Damiano , Pession Andrea , Hrelia Patrizia , Tonelli Roberto
Published: July 2022

Cancer incidence and mortality continue to increase, while the conventional chemotherapeutic drugs confer limited efficacy and relevant toxic side effects. Novel strategies are urgently needed for more effective and safe therapeutics in oncology. However, a large number of proteins are considered undruggable by conventional drugs, such as the small molecules. Moreover, the mRNA itself retains oncological functions, and its targeting offers the double advantage of blocking the tumorigenic activities of the mRNA and the translation into protein. Finally, a large family of non-coding RNAs (ncRNAs) has recently emerged that are also dysregulated in cancer, but they could not be targeted by drugs directed against the proteins. In this context, this review describes how the oligonucleotide therapeutics targeting RNA or DNA sequences, are emerging as a new class of drugs, able to tackle the limitations described above. Numerous clinical trials are evaluating oligonucleotides for tumor treatment, and in the next few years some of them are expected to reach the market. We describe the oligonucleotide therapeutics targeting undruggable proteins (focusing on the most relevant, such as those originating from the MYC and RAS gene families), and for ncRNAs, in particular on those that are under clinical trial evaluation in oncology. We highlight the challenges and solutions for the clinical success of oligonucleotide therapeutics, with particular emphasis on the peculiar challenges that render it arduous to treat tumors, such as heterogeneity and the high mutation rate. In the review are presented these and other advantages offered by the oligonucleotide as an emerging class of biotherapeutics for a new era of precision anti-cancer medicine.
Using Dual Toll-like Receptor Agonism to Drive Th1-Biased Response in a Squalene- and α-Tocopherol-Containing Emulsion for a More Effective SARS-CoV-2 Vaccine
Authors: Short Kristopher K. , Lathrop Stephanie K. , Davison Clara J. , Partlow Haley A. , Kaiser Johnathan A. , Tee Rebekah D. , Lorentz Elizabeth B. , Evans Jay T. , Burkhart David J.
Published: July 2022

A diversity of vaccines is necessary to reduce the mortality and morbidity of SARS-CoV-2. Vaccines must be efficacious, easy to manufacture, and stable within the existing cold chain to improve their availability around the world. Recombinant protein subunit vaccines adjuvanted with squalene-based emulsions such as AS03™ and MF59™ have a long and robust history of safe, efficacious use with straightforward production and distribution. Here, subunit vaccines were made with squalene-based emulsions containing novel, synthetic toll-like receptor (TLR) agonists, INI-2002 (TLR4 agonist) and INI-4001 (TLR7/8 agonist), using the recombinant receptor-binding domain (RBD) of SARS-CoV-2 S protein as an antigen. The addition of the TLR4 and TLR7/8 agonists, alone or in combination, maintained the formulation characteristics of squalene-based emulsions, including a sterile filterable droplet size (<220 nm), high homogeneity, and colloidal stability after months of storage at 4, 25, and 40 °C. Furthermore, the addition of the TLR agonists skewed the immune response from Th2 towards Th1 in immunized C57BL/6 mice, resulting in an increased production of IgG2c antibodies and a lower antigen-specific production of IL-5 with a higher production of IFNγ by lymphocytes. As such, incorporating TLR4 and TLR7/8 agonists into emulsions leveraged the desirable formulation and stability characteristics of emulsions and can induce Th1-type humoral and cell-mediated immune responses to combat the continued threat of SARS-CoV-2.
Real-Time Access to Collisions between a Two-Soliton Molecule and a Soliton Singlet in an Ultrafast Fiber Laser
Authors: Li Junwen , Li Heping , Wang Zhuang , Zhang Zhiyao , Zhang Shangjian , Liu Yong
Published: July 2022

Optical solitons in ultrafast fiber lasers, as a result of dual balances between dispersion and nonlinearity as well as gain and loss, enable various soliton interactions. Soliton collisions are among the most intriguing soliton interactions, which fuel the understanding for particle-like properties of solitons. Here, we experimentally investigate the transient dynamics of collisions between a two-soliton molecule and a soliton singlet in a mode-locked fiber laser. By means of the dispersive Fourier transform technique, the evolving spectral interferograms of different collision scenarios are measured in real time. In particular, the “quasi-elastic” collision is observed, which shows that the soliton-molecule state remains unaltered after the collision and the group-velocity difference between the soliton molecule and the singlet is changed. It is directly demonstrated that a bond exchange occurs between the colliding solitons. By tuning the intra-cavity polarization controller, the dynamic processes of other collision outcomes, including the annihilation of a soliton in the soliton molecule as well as the formation of a stable unequally spaced soliton triplet, are also revealed. Our work facilitates a deeper understanding of soliton collision dynamics in ultrafast fiber lasers.
Foliar and Root Comparative Metabolomics and Phenolic Profiling of Micro-Tom Tomato (Solanum lycopersicum L.) Plants Associated with a Gene Expression Analysis in Response to Short Daily UV Treatments
Authors: Mannucci Alessia , Santin Marco , Vanhaelewyn Lucas , Sciampagna Maria Calogera , Miras-Moreno Maria Begoña , Zhang Leilei , Lucini Luigi , Quartacci Mike Frank , Van Der Straeten Dominique , Castagna Antonella , Ranieri Annamaria
Published: July 2022

Tomato (Solanum lycopersicum L.) is globally recognised as a high-value crop both for commercial profit and nutritional benefits. In contrast to the extensive data regarding the changes in the metabolism of tomato fruit exposed to UV radiation, less is known about the foliar and root metabolome. Using an untargeted metabolomic approach through UHPLC-ESI-QTOF-MS analysis, we detected thousands of metabolites in the leaves (3000) and roots (2800) of Micro-Tom tomato plants exposed to 11 days of short daily UV radiation, applied only on the aboveground organs. Multivariate statistical analysis, such as OPLS-DA and volcano, were performed to allow a better understanding of the modifications caused by the treatment. Based on the unexpected modulation to the secondary metabolism, especially the phenylpropanoid pathway, of which compounds were down and up accumulated respectively in leaves and roots of treated plants, a phenolic profiling was carried out for both organs. The phenolic profile was associated with a gene expression analysis to check the transcription trend of genes involved in the UVR8 signalling pathway and the early steps of the phenolic biosynthesis. The retention of the modifications at metabolic and phenolic levels was also investigated 3 days after the UV treatment, showing a prolonged effect on the modulation once the UV treatment had ceased.
Nanoparticles and Plant By-Products for Edible Coatings Production: A Case Study with Zinc, Titanium, and Silver
Authors: Tauferová Alexandra , Javůrková Zdeňka , Pospiech Matej , Koudelková Mikulášková Hana , Těšíková Karolína , Dordevic Dani , Dordevic Simona , Tremlová Bohuslava
Published: July 2022

For the development of functional edible packaging that will not lead to rejection by the consumer, it is needed to analyze the interactions between ingredients in the packaging matrix. The aim of this study was to develop edible chitosan-based coatings that have been enriched with red grape extracts, zinc, silver, and titanium nanoparticles. The organoleptic properties of the produced edible packaging were described by quantitative descriptive analysis and consumer acceptability was verified by hedonic analysis. By image analysis, color parameters in the CIELab system, opacity, Whiteness and Yellowness Index were described. The microstructure was described by scanning electron microscopy. The hedonic evaluation revealed that the addition of nanometals and their increasing concentration caused a deterioration in sample acceptability. The overall evaluation was higher than 5 in 50% of the samples containing nanometals. The addition of nanometals also caused statistically significant changes in L*, a*, and b* values. The sample transparency generally decreased with the increasing concentration of nanoparticle addition. Scanning electron microscopy showed, that the addition of nanometals does not disrupt the protective function of the packaging. From a sensory point of view, the addition of ZnO nanoparticles in concentrations of 0.05 and 0.2% appeared to be the most favorable of all nanometals.
Structural Analysis of Oxidized Sucrose and Its Application as a Crease-Resistant Crosslinking Agent
Authors: Feng Mao , Hu Xiaodong , Yin Yingting , Liang Yajing , Niu Jiarong , Yao Jinbo
Published: July 2022

Oxidized sucrose is a non-formaldehyde crosslinking agent with many applications in polymer crosslinking and modification, such as in the preparation of starch films and protein films. However, research on the structure of oxidized sucrose is lacking. In this paper, oxidized sucrose was synthesized through selective oxidation of sodium periodate. By LC-MS, FTIR, TGA, NMR, and HRMS analyses, it was shown that oxidized sucrose existed in the form of a hydrate, and the tetraaldehyde oxidized sucrose could isomerize into the form of two six-membered hemiacetal rings. The structure of oxidized sucrose was also verified by theoretical calculations. Furthermore, the diffusional properties of oxidized sucrose were investigated by the rolling-film method. Finally, it was found that oxidized sucrose used as a crosslinking agent could effectively improve the wrinkle recovery performance of cotton fabrics.
The Possibility of Silicon Purification by Metallurgical Methods: Part I
Authors: Serikkanov Abay , Pavlov Artem , Mukashev Bolat , Turmagambetov Tleuzhan , Kantarbayeva Dinara , Zholdybayev Kairat
Published: July 2022

This silicon purification research work has two stages and results will be provided in two articles due to the large scope of work. This paper provides the results of the first stage, concerning the metallurgical silicon purification. Silicon was purified by a combined method consisting of slag refining with new slag composition and acid leaching. In the first phase, the metallurgical grade silicon produced by the classical carbothermic reduction method was investigated. In the second phase, the metallurgical purification by slag refining was completed using slags of various new compositions. The purified silicon samples obtained after the melting processes were analyzed for concentrations of impurities. Finally, acid leaching treatment of the obtained silicon was done, followed by elemental analysis of purified silicon. According to our experience we can determine that obtained silicon purity is adequate for further stages of planned study.
Recycling of Lead Pastes from Spent Lead–Acid Batteries: Thermodynamic Constraints for Desulphurization
Authors: Xiong Yongliang
Published: July 2022

Lead–acid batteries are important to modern society because of their wide usage and low cost. The primary source for production of new lead–acid batteries is from recycling spent lead–acid batteries. In spent lead–acid batteries, lead is primarily present as lead pastes. In lead pastes, the dominant component is lead sulfate (PbSO4, mineral name anglesite) and lead oxide sulfate (PbO•PbSO4, mineral name lanarkite), which accounts for more than 60% of lead pastes. In the recycling process for lead–acid batteries, the desulphurization of lead sulfate is the key part to the overall process. In this work, the thermodynamic constraints for desulphurization via the hydrometallurgical route for recycling lead pastes are presented. The thermodynamic constraints are established according to the thermodynamic model that is applicable and important to recycling of lead pastes via hydrometallurgical routes in high ionic strength solutions that are expected to be in industrial processes. The thermodynamic database is based on the Pitzer equations for calculations of activity coefficients of aqueous species. The desulphurization of lead sulfates represented by PbSO4 can be achieved through the following routes. (1) conversion to lead oxalate in oxalate-bearing solutions; (2) conversion to lead monoxide in alkaline solutions; and (3) conversion to lead carbonate in carbonate solutions. Among the above three routes, the conversion to lead oxalate is environmentally friendly and has a strong thermodynamic driving force. Oxalate-bearing solutions such as oxalic acid and potassium oxalate solutions will provide high activities of oxalate that are many orders of magnitude higher than those required for conversion of anglesite or lanarkite to lead oxalate, in accordance with the thermodynamic model established for the oxalate system. An additional advantage of the oxalate conversion route is that no additional reductant is needed to reduce lead dioxide to lead oxide or lead sulfate, as there is a strong thermodynamic force to convert lead dioxide directly to lead oxalate. As lanarkite is an important sulfate-bearing phase in lead pastes, this study evaluates the solubility constant for lanarkite regarding the following reaction, based on the solubility data, PbO•PbSO4 + 2H+ ⇌ 2Pb2+ + SO42− + H2O(l).
Fleur de sel: How Does a Pinch of Suitable Choice Practices Value This Sustainable Natural Resource?
Authors: Ramos Jorge
Published: July 2022

As mechanized processes developed, small producers of traditional sea salt ceased to be competitive. However, when the valuable salt flower (a.k.a. fleur de sel, flower of salt) market appeared, it gave new breath to the activity of traditional salt pan production. Salt flower sensitivity and delicateness became a part of modern food habits. Its crystals present some grain differentiation and these can fulfill diversified consumer tastes. In cooking art, a regular fine flower of salt can be used to finish dishes, whereas a longer and thin grain known as ‘scale’ (a.k.a. écaille de fleur de sel in French) can be used for a more gourmet-like palate. Here a suitable method is presented to sort and grade flower of salt to satisfy different palates. The method of salt flower selection is based on four main characteristics, which should be considered: cleansing, moisture, color, and size. It is the grain size that contributes most to demand allocation. The results show that what is produced (supply) and the demand from customers do not exactly match. The tiniest types of salt flower are usually completely absorbed by the market, whereas the largest types have no market at all.
Clustering-Based Representation Learning through Output Translation and Its Application to Remote-Sensing Images
Authors: Li Qinglin , Li Bin , Garibaldi Jonathan M. , Qiu Guoping
Published: July 2022

In supervised deep learning, learning good representations for remote-sensing images (RSI) relies on manual annotations. However, in the area of remote sensing, it is hard to obtain huge amounts of labeled data. Recently, self-supervised learning shows its outstanding capability to learn representations of images, especially the methods of instance discrimination. Comparing methods of instance discrimination, clustering-based methods not only view the transformations of the same image as “positive” samples but also similar images. In this paper, we propose a new clustering-based method for representation learning. We first introduce a quantity to measure representations’ discriminativeness and from which we show that even distribution requires the most discriminative representations. This provides a theoretical insight into why evenly distributing the images works well. We notice that only the even distributions that preserve representations’ neighborhood relations are desirable. Therefore, we develop an algorithm that translates the outputs of a neural network to achieve the goal of evenly distributing the samples while preserving outputs’ neighborhood relations. Extensive experiments have demonstrated that our method can learn representations that are as good as or better than the state of the art approaches, and that our method performs computationally efficiently and robustly on various RSI datasets.
Towards Multiplexed and Multimodal Biosensor Platforms in Real-Time Monitoring of Metabolic Disorders
Authors: Chu Sung Sik , Nguyen Hung Anh , Zhang Jimmy , Tabassum Shawana , Cao Hung
Published: July 2022

Metabolic syndrome (MS) is a cluster of conditions that increases the probability of heart disease, stroke, and diabetes, and is very common worldwide. While the exact cause of MS has yet to be understood, there is evidence indicating the relationship between MS and the dysregulation of the immune system. The resultant biomarkers that are expressed in the process are gaining relevance in the early detection of related MS. However, sensing only a single analyte has its limitations because one analyte can be involved with various conditions. Thus, for MS, which generally results from the co-existence of multiple complications, a multi-analyte sensing platform is necessary for precise diagnosis. In this review, we summarize various types of biomarkers related to MS and the non-invasively accessible biofluids that are available for sensing. Then two types of widely used sensing platform, the electrochemical and optical, are discussed in terms of multimodal biosensing, figure-of-merit (FOM), sensitivity, and specificity for early diagnosis of MS. This provides a thorough insight into the current status of the available platforms and how the electrochemical and optical modalities can complement each other for a more reliable sensing platform for MS.
An IoT Machine Learning-Based Mobile Sensors Unit for Visually Impaired People
Authors: Dhou Salam , Alnabulsi Ahmad , Al-Ali A. R. , Arshi Mariam , Darwish Fatima , Almaazmi Sara , Alameeri Reem
Published: July 2022

Visually impaired people face many challenges that limit their ability to perform daily tasks and interact with the surrounding world. Navigating around places is one of the biggest challenges that face visually impaired people, especially those with complete loss of vision. As the Internet of Things (IoT) concept starts to play a major role in smart cities applications, visually impaired people can be one of the benefitted clients. In this paper, we propose a smart IoT-based mobile sensors unit that can be attached to an off-the-shelf cane, hereafter a smart cane, to facilitate independent movement for visually impaired people. The proposed mobile sensors unit consists of a six-axis accelerometer/gyro, ultrasonic sensors, GPS sensor, cameras, a digital motion processor and a single credit-card-sized single-board microcomputer. The unit is used to collect information about the cane user and the surrounding obstacles while on the move. An embedded machine learning algorithm is developed and stored in the microcomputer memory to identify the detected obstacles and alarm the user about their nature. In addition, in case of emergencies such as a cane fall, the unit alerts the cane user and their guardian. Moreover, a mobile application is developed to be used by the guardian to track the cane user via Google Maps using a mobile handset to ensure safety. To validate the system, a prototype was developed and tested.
Pedicle Screw Placement in Adolescent Idiopathic Scoliosis: A Comparison between Robotics Coupled with Navigation versus the Freehand Technique
Authors: Linden Gabriel S. , Ghessese Semhal , Cook Danielle , Hedequist Daniel J.
Published: July 2022

(1) Background: Robotics coupled with navigation (RAN) is a modern surgical platform shown to increase screw placement accuracy during pediatric scoliosis surgery. Our institution uses a technique which combines the RAN platform for apical pedicle screw placement and the freehand (FH) technique for terminal pedicle screw placement during scoliosis surgery (termed hybrid technique). We question if the complementary use of the RAN technology affects intraoperative outcomes, relative to the FH-only approach. (2) Methods: 60 adolescent idiopathic scoliosis (AIS) patients, ages 11–19 at surgery, who were operated on from 2019 through 2020 by a single surgeon, were retrospectively reviewed. Patients were separated by surgery type (hybrid RAN or FH), matched on demographic and surgical factors, and their intraoperative outcomes were compared statistically. (3) Results: Hybrid RAN patients had more screws placed (p = 0.01) and were of a higher BMI percentile (p = 0.005). Controlling for the number of screws placed, BMI%, and initial curve magnitude, there were no statistical differences in estimated blood loss per screw (p = 0.51), curve correction (p = 0.69), complications (p = 0.52), or fluoroscopy time (p = 0.88), between groups. However, operative time was two minutes longer per screw for hybrid RAN patients (p < 0.001). (4) Conclusions: Hybrid RAN surgeries took longer than FH, but yielded comparable effectiveness and safety as the FH technique during the initial RAN adoption phase.
A Pipeline for the Implementation and Visualization of Explainable Machine Learning for Medical Imaging Using Radiomics Features
Authors: Severn Cameron , Suresh Krithika , Görg Carsten , Choi Yoon Seong , Jain Rajan , Ghosh Debashis
Published: July 2022

Machine learning (ML) models have been shown to predict the presence of clinical factors from medical imaging with remarkable accuracy. However, these complex models can be difficult to interpret and are often criticized as “black boxes”. Prediction models that provide no insight into how their predictions are obtained are difficult to trust for making important clinical decisions, such as medical diagnoses or treatment. Explainable machine learning (XML) methods, such as Shapley values, have made it possible to explain the behavior of ML algorithms and to identify which predictors contribute most to a prediction. Incorporating XML methods into medical software tools has the potential to increase trust in ML-powered predictions and aid physicians in making medical decisions. Specifically, in the field of medical imaging analysis the most used methods for explaining deep learning-based model predictions are saliency maps that highlight important areas of an image. However, they do not provide a straightforward interpretation of which qualities of an image area are important. Here, we describe a novel pipeline for XML imaging that uses radiomics data and Shapley values as tools to explain outcome predictions from complex prediction models built with medical imaging with well-defined predictors. We present a visualization of XML imaging results in a clinician-focused dashboard that can be generalized to various settings. We demonstrate the use of this workflow for developing and explaining a prediction model using MRI data from glioma patients to predict a genetic mutation.
LiDAR-OSM-Based Vehicle Localization in GPS-Denied Environments by Using Constrained Particle Filter
Authors: Elhousni Mahdi , Zhang Ziming , Huang Xinming
Published: July 2022

Cross-modal vehicle localization is an important task for automated driving systems. This research proposes a novel approach based on LiDAR point clouds and OpenStreetMaps (OSM) via a constrained particle filter, which significantly improves the vehicle localization accuracy. The OSM modality provides not only a platform to generate simulated point cloud images, but also geometrical constraints (e.g., roads) to improve the particle filter’s final result. The proposed approach is deterministic without any learning component or need for labelled data. Evaluated by using the KITTI dataset, it achieves accurate vehicle pose tracking with a position error of less than 3 m when considering the mean error across all the sequences. This method shows state-of-the-art accuracy when compared with the existing methods based on OSM or satellite maps.
Performance Analysis of Wearable Dual-Band Patch Antenna Based on EBG and SRR Surfaces
Authors: Wajid Abdul , Ahmad Ashfaq , Ullah Sadiq , Choi Dong-you , Islam Faiz Ul
Published: July 2022

This paper presents the performance comparison of a dual-band conventional antenna with a split-ring resonator (SRR)- and electromagnetic bandgap (EBG)-based dual-band design operating at 2.4 GHz and 5.4 GHz. The compactness and dual-frequency operation in the legacy Wi-Fi range of this design make it highly favorable for wearable sensor network-based Internet of Things (IoT) applications. Considering the current need for wearable antennas, wash cotton (with a relative permittivity of 1.51) is used as a substrate material for both conventional and metamaterial-based antennas. The radiation characteristics of the conventional antenna are compared with the EBG and SRR ground planes-based antennas in terms of return loss, gain, and efficiency. It is found that the SRR-based antenna is more efficient in terms of gain and surface wave suppression as well as more compact in comparison with its two counterparts. The compared results are found to be based on two distinct frequency ranges, namely, 2.4 GHz and 5.4 GHz. The suggested SRR-based antenna exhibits improved performance at 5.4 GHz, with gains of 7.39 dbi, bandwidths of 374 MHz, total efficiencies of 64.7%, and HPBWs of 43.2 degrees. The measurements made in bent condition are 6.22 db, 313 MHz, 52.45%, and 22.3 degrees, respectively. The three considered antennas (conventional, EBG-based, and SRR-based) are designed with a compact size to be well-suited for biomedical sensors, and specific absorption rate (SAR) analysis is performed to ensure user safety. In addition, the performance of the proposed antenna under bending conditions is also considered to present a realistic approach for a practical antenna design.
Assessment of ZTD Derived from COSMIC Occultation Data with ECWMF, Radiosondes, and GNSS
Authors: Fu Naifeng , Jiang Mingbo , Li Fenghui , Guo Peng , Hou Chunping , Wu Mengjie , Wu Jianming , Wang Zhipeng , Kan Liang
Published: July 2022

Global Navigation Satellite System (GNSS) signals generate slant tropospheric delays when they pass through the atmosphere, which is recognized as the main source of error in many spatial geodetic applications. The zenith tropospheric delay (ZTD) derived from radio occultation data is of great significance to atmospheric research and meteorology and needs to be assessed in the use of precision positioning. Based on the atmPrf, sonPrf, and echPrf data from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) Data Analysis and Archiving Center (CDAAC) from 1 January to 31 December 2008 and 2012, we obtained the ZTDs of the radio occultation data (occZTD) and the corresponding radiosonde (sonZTD) and ECWMF data (echZTD). The ZTDs derived from ground-based global positioning system (GPS) observations from the International GNSS Service (IGS) were corrected to the lowest tangent point height of the matched radio occultation profile by the barometric height formula (gnsZTD). The statistical results show that the absolute values of the bias between occZTD and echZTD, sonZTD, or gnsZTD are less than 5 mm, and the standard deviations are approximately 20 mm or less, indicating that occZTD had significant accuracy in the GNSS positioning model even when the local spherical symmetry assumption error was introduced when the Abel inversion algorithm was used to obtain the refractive index profile of atmPrf. The effects of the horizontal/vertical matching resolution and the variation in the station height/latitude on the biases of occZTD and gnsZTD were analyzed. The results can be used to quantify the performance of radio occultation data for tropospheric delay error correction in dynamic high-precision positioning.
Classification Predictive Model for Air Leak Detection in Endoworm Enteroscopy System
Authors: Zazo-Manzaneque Roberto , Pons-Beltrán Vicente , Vidaurre Ana , Santonja Alberto , Sánchez-Díaz Carlos
Published: July 2022

Current enteroscopy techniques present complications that are intended to be improved with the development of a new semi-automatic device called Endoworm. It consists of two different types of inflatable cavities. For its correct operation, it is essential to detect in real time if the inflatable cavities are malfunctioning (presence of air leakage). Two classification predictive models were obtained, one for each cavity typology, which must discern between the “Right” or “Leak” states. The cavity pressure signals were digitally processed, from which a set of features were extracted and selected. The predictive models were obtained from the features, and a prior classification of the signals between the two possible states was used as input to different supervised machine learning algorithms. The accuracy obtained from the classification predictive model for cavities of the balloon-type was 99.62%, while that of the bellows-type was 100%, representing an encouraging result. Once the models are validated with data generated in animal model tests and subsequently in exploratory clinical tests, their incorporation in the software device will ensure patient safety during small bowel exploration.
Sensitivity Optimization and Experimental Study of the Long-Range Metal Detector Based on Chaotic Duffing Oscillator
Authors: Karimov Timur , Druzhina Olga , Vatnik Valerii , Ivanova Ekaterina , Kulagin Maksim , Ponomareva Veronika , Voroshilova Anzhelika , Rybin Vyacheslav
Published: July 2022

Sensors based on chaotic oscillators have a simple design, combined with high sensitivity and energy efficiency. Among many developed schemes of such sensors, the promising one is based on the Duffing oscillator, which possesses a remarkable property of demonstrating chaotic oscillations only in the presence of a weak sine wave at the input. The main goal of this research was to evaluate the maximal sensitivity of a practically implemented metal detector based on the Duffing oscillator and compare its sensitivity with conventional sensors. To achieve high efficiency of the Duffing-based design, we proposed an algorithm which performs a bifurcation analysis of any chaotic system, classifies the oscillation modes and determines the system sensitivity to a change in different parameters. We apply the developed algorithm to improve the sensitivity of the electronic circuit implementing the Duffing oscillator, serving as a key part of a three-coil metal detector. We show that the developed design allows detecting the presence of metal objects near the coils more reliably than the conventional signal analysis techniques, and the developed detector is capable of sensing a large metal plate at distances up to 2.8 of the coil diameter, which can be considered a state-of-the-art result.