bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparative Behavior of Viscose-Based Supercapacitor Electrodes Activated by KOH, H2O, and CO2
Authors: Breitenbach Stefan , Duchoslav Jiri , Mardare Andrei Ionut , Unterweger Christoph , Stifter David , Hassel Achim Walter , Fürst Christian
Published: February 2022

Activated carbons derived from viscose fibers were prepared using potassium hydroxide, carbon dioxide, or water vapor as activation agents. The produced activated carbon fibers were analyzed via scanning electron microscopy and energy dispersive X-ray spectroscopy, and their porosity (specific surface area, total pore volume, and pore size distribution) was calculated employing physisorption experiments. Activated carbon fibers with a specific surface area of more than 2500 m2 g−1 were obtained by each of the three methods. Afterwards, the suitability of these materials as electrodes for electrochemical double-layer capacitors (supercapacitors) was investigated using cyclic voltammetry, galvanostatic measurements, and electrochemical impedance spectroscopy. By combining CO2 and H2O activation, activated carbon fibers of high purity and excellent electrochemical performance could be obtained. A specific capacitance per electrode of up to 180 F g−1 was found. In addition, an energy density per double-layer capacitor of 42 W h kg−1 was achieved. These results demonstrate the outstanding electrochemical properties of viscose-based activated carbon fibers for use as electrode materials in energy storage devices such as supercapacitors.
Nutrition in Cystic Fibrosis—Some Notes on the Fat Recommendations
Authors: Strandvik Birgitta
Published: February 2022

Nutrition is important in cystic fibrosis (CF) because the disease is associated with a higher energy consumption, special nutritional deficiencies, and malabsorption mainly related to pancreatic insufficiency. The clinical course with deterioration of lung function has been shown to relate to nutrition. Despite general recommendation of high energy intake, the clinical deterioration is difficult to restrain suggesting that special needs have not been identified and specified. It is well-known that the CF phenotype is associated with lipid abnormalities, especially in the essential or conditionally essential fatty acids. This review will concentrate on the qualitative aspects of fat metabolism, which has mainly been neglected in dietary fat recommendations focusing on fat quantity. For more than 60 years it has been known and confirmed that the patients have a deficiency of linoleic acid, an n-6 essential fatty acid of importance for membrane structure and function. The ratio between arachidonic acid and docosahexaenoic acid, conditionally essential fatty acids of the n-6 and n-3 series, respectively, is often increased. The recently discovered relations between the CFTR modulators and lipid metabolism raise new interests in this field and together with new technology provide possibilities to specify further specify personalized therapy.
What Is the Evidence That Dietary Macronutrient Composition Influences Exercise Performance? A Narrative Review
Authors: Noakes Timothy David
Published: February 2022

The introduction of the needle muscle biopsy technique in the 1960s allowed muscle tissue to be sampled from exercising humans for the first time. The finding that muscle glycogen content reached low levels at exhaustion suggested that the metabolic cause of fatigue during prolonged exercise had been discovered. A special pre-exercise diet that maximized pre-exercise muscle glycogen storage also increased time to fatigue during prolonged exercise. The logical conclusion was that the athlete’s pre-exercise muscle glycogen content is the single most important acutely modifiable determinant of endurance capacity. Muscle biochemists proposed that skeletal muscle has an obligatory dependence on high rates of muscle glycogen/carbohydrate oxidation, especially during high intensity or prolonged exercise. Without this obligatory carbohydrate oxidation from muscle glycogen, optimum muscle metabolism cannot be sustained; fatigue develops and exercise performance is impaired. As plausible as this explanation may appear, it has never been proven. Here, I propose an alternate explanation. All the original studies overlooked one crucial finding, specifically that not only were muscle glycogen concentrations low at exhaustion in all trials, but hypoglycemia was also always present. Here, I provide the historical and modern evidence showing that the blood glucose concentration—reflecting the liver glycogen rather than the muscle glycogen content—is the homeostatically-regulated (protected) variable that drives the metabolic response to prolonged exercise. If this is so, nutritional interventions that enhance exercise performance, especially during prolonged exercise, will be those that assist the body in its efforts to maintain the blood glucose concentration within the normal range.
Factors Associated with Home Food Environment in Low-Income Overweight or Obese Pregnant Women
Authors: Chang Mei-Wei , Lin Chyongchiou J. , Lee Rebecca E. , Wegener Duane T.
Published: February 2022

Limited research has examined factors associated with home food availability. This study investigated the associations among demographics, body mass index category, stress, and home food availability among low-income overweight or obese pregnant women. This cross-sectional study enrolled 332 participants who were non-Hispanic black or white. We performed logistic regression modeling for unprocessed food, processed food, overall ultra-processed food, and three subcategories of ultra-processed food (salty snacks, sweet snacks and candies, and soda). Black women were less likely than white women to have large amounts of processed foods (OR = 0.56), salty snacks (OR = 0.61), and soda (OR = 0.49) available at home. Women with at least some college education or at least a college education were more likely to have large amounts of unprocessed food (OR = 2.58, OR = 4.38 respectively) but less likely to have large amounts of soda (OR = 0.44; OR = 0.22 respectively) available at home than their counterparts. Women with higher stress were less likely to have large amounts of unprocessed food available at home (OR = 0.58) than those with lower stress. Home food availability varied by race, education, and levels of stress in low-income overweight or obese pregnant women.
Maternal Pre-Pregnancy Nutritional Status and Physical Activity Levels and a Sports Injury Reported in Children: A Seven-Year Follow-Up Study
Authors: Kasović Mario , Štefan Lovro , Piler Pavel , Zvonar Martin
Published: February 2022

Objective: Our aim was to analyze dose–response associations between maternal pre-pregnancy body mass index and physical activity levels with childhood sports injury rates. Methods: Participants included pre-pregnant mothers (n = 4811) and their children at the age of 7 years (n = 3311). Maternal anthropometry (height, weight, and body mass index), time spent in physical activity, and education level were recorded. All sports injuries were defined as injuries reported in the past year by the children at the age of 7 years. Results: Children whose mothers were overweight/obese in the pre-pregnancy period were 2.04 (OR = 2.04, 95% CI = 1.12–3.71) times more likely to report a sports injury at the age of 7 years. Underweight mothers exhibited a 74% decrease in the odds of their children reporting a sports injury at follow-up (OR = 0.26, 95% CI = 0.10–0.68). Finally, an increase in maternal physical activity across the last three quartiles was associated with a lower odds of sports injuries. Conclusions: The risk of reporting a sports injury was greater for children whose mothers were overweight/obese in the pre-pregnancy period. However, there was a lower risk with both maternal underweight status and increasing minutes of physical activity.
The Influence of Roux-en-Y Gastric Bypass and Diet on NaCl and Sucrose Taste Detection Thresholds and Number of Circumvallate and Fungiform Taste Buds in Female Rats
Authors: Hyde Kellie M. , Blonde Ginger D. , Nisi A. Valentina , Spector Alan C.
Published: February 2022

Roux-en-Y gastric bypass (RYGB) in rats attenuates preference for, and intake of, sugar solutions. Additionally, maintenance on a high-fat diet (HFD) reportedly alters behavioral responsiveness to sucrose in rodents in short-term drinking tests. Due to the fact that the behavioral tests to date rely on the hedonic value of the stimulus to drive responsiveness, we sought to determine whether taste detection thresholds to sucrose and NaCl are affected by these manipulations as measured in an operant two-response signal detection paradigm. Female rats were maintained on HFD or chow for 10 weeks, at which point animals received either RYGB or SHAM surgery followed by a gel-based diet and then powdered chow. Upon recovery, half of the rats that were previously on HFD were switched permanently to chow, and the other rats were maintained on their presurgical diets (n = 5–9/diet condition × surgery group for behavioral testing). The rats were then trained and tested in a gustometer. There was a significant interaction between diet condition and surgery on NaCl threshold that was attributable to a lower value in RYGB vs. SHAM rats in the HFD condition, but this failed to survive a Bonferroni correction. Importantly, there were no effects of diet condition or surgery on sucrose thresholds. Additionally, although recent evidence suggests that maintenance on HFD alters taste bud number in the circumvallate papillae (CV) of mice, in a subset of rats, we did not find that diet significantly influenced taste pores in the anterior tongue or CV of female rats. These results suggest that any changes in sucrose responsiveness in intake/preference or hedonically oriented tests in rats as a function of HFD maintenance or RYGB are not attributable to alterations in taste sensitivity.
Eating Disorder Day Programs: Is There a Best Format?
Authors: Eshkevari Ertimiss , Ferraro Isabella , McGregor Andrew , Wade Tracey
Published: February 2022

The use of a Day Program (DP) format (i.e., intensive daily treatment with no overnight admission) has been shown to be an effective treatment for eating disorders (EDs). The disadvantages, however, include higher cost than outpatient treatment (including costs of meals and staff), greater disruption to patients’ lives, and the use of a highly structured and strict schedule that may interrupt the development of patients’ autonomy in taking responsibility for their recovery. This study investigated whether reducing costs of a DP and the disruption to patients’ lives, and increasing opportunity to develop autonomy, impacted clinical outcomes. Three sequential DP formats were compared in the current study: Format 1 was the most expensive (provision of supported dinners three times/week and extended staff hours); Format 2 included only one dinner/week and provision of a take-home meal. Both formats gave greater support to patients who were not progressing well (i.e., extended admission and extensive support from staff when experiencing feelings of suicidality or self-harm). Format 3 did not provide this greater support but established pre-determined admission lengths and required the patient to step out of the program temporarily when feeling suicidal. Fifty-six patients were included in the analyses: 45% were underweight (body mass index (BMI) < 18.5), 96.4% were female, 63% were given a primary diagnosis of anorexia nervosa (or atypical anorexia nervosa), and mean age was 25.57 years. Clinical outcomes were assessed using self-reported measures of disordered eating, psychosocial impairment, and negative mood, but BMI was recorded by staff. Over admission, 4- and 8-week post-admission, and discharge there were no significant differences between any of the clinical outcomes across the three formats. We can tentatively conclude that decreasing costs and increasing the opportunities for autonomy did not negatively impact patient outcomes, but future research should seek to replicate these results in other and larger populations that allow conclusions to be drawn for different eating disorder diagnostic groups.
Role of Vitamin C in Selected Malignant Neoplasms in Women
Authors: Markowska Anna , Antoszczak Michał , Markowska Janina , Huczyński Adam
Published: February 2022

Since the first reports describing the anti-cancer properties of vitamin C published several decades ago, its actual effectiveness in fighting cancer has been under investigation and widely discussed. Some scientific reports indicate that vitamin C in high concentrations can contribute to effective and selective destruction of cancer cells. Furthermore, preclinical and clinical studies have shown that relatively high doses of vitamin C administered intravenously in ‘pharmacological concentrations’ may not only be well-tolerated, but significantly improve patients’ quality of life. This seems to be particularly important, especially for terminal cancer patients. However, the relatively high frequency of vitamin C use by cancer patients means that the potential clinical benefits may not be obvious. For this reason, in this review article, we focus on the articles published mainly in the last two decades, describing possible beneficial effects of vitamin C in preventing and treating selected malignant neoplasms in women, including breast, cervical, endometrial, and ovarian cancer. According to the reviewed studies, vitamin C use may contribute to an improvement of the overall quality of life of patients, among others, by reducing chemotherapy-related side effects. Nevertheless, new clinical trials are needed to collect stronger evidence of the role of this nutrient in supportive cancer treatment.
Dietary Patterns and Their Association with Sociodemographic and Lifestyle Factors in Filipino Adults
Authors: de Juras Aileen R. , Hsu Wan-Chen , Hu Susan C.
Published: February 2022

Recent studies have investigated dietary patterns to assess the overall dietary habits of specific populations. However, limited epidemiological research has been conducted to explore the unique dietary intakes in low and middle-income countries. This study aims to examine the dietary patterns of Filipino adults and their association with sociodemographic and lifestyle factors. A total of 10,025 adults (≥20 years old) who participated in the 2013 Philippine National Nutrition Survey were included in the analysis. Dietary patterns were derived using factor analysis of 18 food groups from the dietary survey component. Six dietary patterns were identified, namely (1) rice; (2) cereal, milk, sugar, and oil; (3) fruits and miscellaneous food; (4) fish; (5) vegetables and corn; and (6) meat and beverage. Generalized ordered logistic regression analysis indicated that the dietary patterns were associated with different factors, specifically sex, age, educational attainment, marital status, employment status, household size, wealth quintile, smoking status, alcohol consumption, and physical activity. Our findings showed distinct dietary patterns among Filipino adults that were influenced by various sociodemographic and lifestyle parameters. The results of this study have valuable public health implications and the dietary patterns generated can further be used to analyze the link between diet and health outcomes.
More Insights about the Efficacy of Copper Ion Treatment on Mycobacterium avium subsp. paratuberculosis (MAP): A Clue for the Observed Tolerance
Authors: Tejeda Carlos , Steuer Pamela , Villegas Marcela , Reyes-Jara Angelica , Iranzo Esperanza C. , Umaña Reydoret , Salgado Miguel
Published: February 2022

Background: Scientific evidence is scarce for the antimicrobial effect of copper on bacteria characterized as more resistant. Using Mycobacterium avium subsp. paratuberculosis (MAP), a highly resistant microorganism, as a pathogen model, copper ion treatment has shown a significant bactericidal effect; however, the sustainability of MAP against copper toxicity was also reported in several studies. Accordingly, the present study aimed to evaluate the impacts of copper on MAP. Methodology: This study considered physicochemical properties and copper concentration in a buffer since it could modulate MAP response during the application of copper treatment. Results: Despite the efficacy of copper ions in significantly reducing the MAP load in Phosphate Buffered Saline, some MAP cells were able to survive. The copper concentration generated by the copper ion treatment device increased significantly with increasing exposure times. MAP bacterial load decreased significantly when treated with copper ions as the exposure times increased. An increase in pH decreased oxygen consumption, and an increase in conductivity was reported after treatment application. Conclusions: Even with higher concentrations of copper, the efficacy of MAP control was not complete. The concentration of copper must be a key element in achieving control of highly resistant microorganisms.
Trade-Off Asymmetric Profile for Extended-Depth-of-Focus Ocular Lens
Authors: Romero Lenny A. , Marrugo Andrés G. , Millán María S.
Published: February 2022

We explore the possibility of extending the depth of focus of an imaging lens with an asymmetric quartic phase-mask, while keeping the aberration within a relatively low level. This can be intended, for instance, for ophthalmic applications, where no further digital processing can take place, relying instead on the patient’s neural adaptation to their own aberrations. We propose a computational optimization method to derive the design-strength factor of the asymmetric profile. The numerical and experimental results are shown. The optical experiment was conducted by means of a modulo-2π phase-only spatial light modulator. The proposed combination of the asymmetric mask and the lens can be implemented in a single refractive element. An exemplary case of an extended-depth-of focus intraocular lens based on the proposed element is described and demonstrated with a numerical experiment.
An All-MRR-Based Photonic Spiking Neural Network for Spike Sequence Learning
Authors: Han Yanan , Xiang Shuiying , Zhang Yuna , Gao Shuang , Wen Aijun , Hao Yue
Published: February 2022

Photonic spiking neural networks (SNN) have the advantages of high power efficiency, high bandwidth and low delay, but limitations are encountered in large-scale integration. The silicon photonics platform is a promising candidate for realizing large-scale photonic SNN because it is compatible with the current mature CMOS platforms. Here, we present an architecture of photonic SNN which consists of photonic neuron, photonic spike timing dependent plasticity (STDP) and weight configuration that are all based on silicon micro-ring resonators (MRRs), via taking advantage of the nonlinear effects in silicon. The photonic spiking neuron based on the add-drop MRR is proposed, and a system-level computational model of all-MRR-based photonic SNN is presented. The proposed architecture could exploit the properties of small area, high integration and flexible structure of MRR, but also faces challenges caused by the high sensitivity of MRR. The spike sequence learning problem is addressed based on the proposed all-MRR-based photonic SNN architecture via adopting supervised training algorithms. We show the importance of algorithms when hardware devices are limited.
Effects of Different Inter-Row Soil Management and Intra-Row Living Mulch on Spontaneous Flora, Beneficial Insects, and Growth of Young Olive Trees in Southern Italy
Authors: Las Casas Giuseppina , Ciaccia Corrado , Iovino Valeria , Ferlito Filippo , Torrisi Biagio , Lodolini Enrico Maria , Giuffrida Alessio , Catania Roberto , Nicolosi Elisabetta , Bella Salvatore
Published: February 2022

Conservation agriculture (i.e., minimized soil disturbance and permanent soil covering) and living mulches represent two agroecological practices that can improve soil fertility, spontaneous flora, and beneficial insect communities. This research studied the effect of these practices in a young olive orchard in the Mediterranean area. Two Sicilian olive cultivars (‘Nocellara del Belice’ and ‘Nocellara etnea’) were used for the field experiment; inter-row minimum and zero tillage and four species of aromatic plants as living mulch along the row were tested. Spontaneous flora and beneficial insect communities, as well as tree growth, were monitored. The inter-row management did not influence the spontaneous flora dynamics. The species adopted for living mulch showed a very different degree of development and soil cover; 69 insect species (pollinators and predators) belonging to five orders (Hymenoptera, Lepidoptera, Diptera, Neuroptera, and Coleoptera) and 17 families were recorded. The growth of the olive trees was not affected by the conservative strategies.: In the inter-row, the growth of the spontaneous flora was limited by the high temperatures during the summer. Among the living mulch species, sage and lemongrass guaranteed an almost full soil cover, reducing the need for weed management along the row, as well as increasing the beneficial insects without influencing the young tree growth.
Ecologically Responsible Entrepreneurship and Its Contribution to the Green Economy’s Sustainable Development: Financial Risk Management Prospects
Authors: Osipov Vladimir S. , Krupnov Yuriy A. , Semenova Galina N. , Tkacheva Maria V.
Published: February 2022

This paper’s goal is to develop a scientific methodology of financial risk management of ecologically responsible entrepreneurship for the sustainable development of the green economy. The originality of this paper is due to the fact that, for the first time, the financial risks of the green economy are considered through the prism of its sustainability. The paper’s novelty is due to the modelling and quantitative measuring of the impact of the COVID-19 pandemic and the financial and economic crisis on the financial risks to the green economy’s sustainability, in addition to the development of precise quantitative recommendations for financial risk management of the green economy. This enables an increase in its sustainability and reduces ecological disproportion in regions of the world (reducing the differences in the green economy’s sustainability among regions of the world through the management of green investments). The paper’s contribution to the literature consists of specifying the theory of financial risks to the green economy. According to the specified fundamental provisions of this theory, the essence of the process of green economy development is clarified (the “black box” is opened) as the increase in its contribution to sustainable development. As the indicator of achieving this development, an increase in the green economy’s sustainability is offered. A new source of achieving the goal is proposed, consisting of a financial risk management of ecologically responsible entrepreneurship based on (private) green investments.
Model-Driven Impact Quantification of Energy Resource Redundancy and Server Rejuvenation on the Dependability of Medical Sensor Networks in Smart Hospitals
Authors: Silva Francisco Airton , Brito Carlos , Araújo Gabriel , Fé Iure , Tyan Maxim , Lee Jae-Woo , Nguyen Tuan Anh , Maciel Paulo Romero Martin
Published: February 2022

The spread of the Coronavirus (COVID-19) pandemic across countries all over the world urges governments to revolutionize the traditional medical hospitals/centers to provide sustainable and trustworthy medical services to patients under the pressure of the huge overload on the computing systems of wireless sensor networks (WSNs) for medical monitoring as well as treatment services of medical professionals. Uncertain malfunctions in any part of the medical computing infrastructure, from its power system in a remote area to the local computing systems at a smart hospital, can cause critical failures in medical monitoring services, which could lead to a fatal loss of human life in the worst case. Therefore, early design in the medical computing infrastructure’s power and computing systems needs to carefully consider the dependability characteristics, including the reliability and availability of the WSNs in smart hospitals under an uncertain outage of any part of the energy resources or failures of computing servers, especially due to software aging. In that regard, we propose reliability and availability models adopting stochastic Petri net (SPN) to quantify the impact of energy resources and server rejuvenation on the dependability of medical sensor networks. Three different availability models (A, B, and C) are developed in accordance with various operational configurations of a smart hospital’s computing infrastructure to assimilate the impact of energy resource redundancy and server rejuvenation techniques for high availability. Moreover, a comprehensive sensitivity analysis is performed to investigate the components that impose the greatest impact on the system availability. The analysis results indicate different impacts of the considered configurations on the WSN’s operational availability in smart hospitals, particularly 99.40%, 99.53%, and 99.64% for the configurations A, B, and C, respectively. This result highlights the difference of 21 h of downtime per year when comparing the worst with the best case. This study can help leverage the early design of smart hospitals considering its wireless medical sensor networks’ dependability in quality of service to cope with overloading medical services in world-wide virus pandemics.
The Challenge of Data Annotation in Deep Learning—A Case Study on Whole Plant Corn Silage
Authors: Rasmussen Christoffer Bøgelund , Kirk Kristian , Moeslund Thomas B.
Published: February 2022

Recent advances in computer vision are primarily driven by the usage of deep learning, which is known to require large amounts of data, and creating datasets for this purpose is not a trivial task. Larger benchmark datasets often have detailed processes with multiple stages and users with different roles during annotation. However, this can be difficult to implement in smaller projects where resources can be limited. Therefore, in this work we present our processes for creating an image dataset for kernel fragmentation and stover overlengths in Whole Plant Corn Silage. This includes the guidelines for annotating object instances in respective classes and statistics of gathered annotations. Given the challenging image conditions, where objects are present in large amounts of occlusion and clutter, the datasets appear appropriate for training models. However, we experience annotator inconsistency, which can hamper evaluation. Based on this we argue the importance of having an evaluation form independent of the manual annotation where we evaluate our models with physically based sieving metrics. Additionally, instead of the traditional time-consuming manual annotation approach, we evaluate Semi-Supervised Learning as an alternative, showing competitive results while requiring fewer annotations. Specifically, given a relatively large supervised set of around 1400 images we can improve the Average Precision by a number of percentage points. Additionally, we show a significantly large improvement when using an extremely small set of just over 100 images, with over 3× in Average Precision and up to 20 percentage points when estimating the quality.
Enhancing Channel Contention Efficiency in IEEE 802.15.4 Wireless Networks
Authors: Zhu Yi-Hua , Jia Luming , Zhang Yufan
Published: February 2022

Numerous Internet of Things (IoT) devices adopt the IEEE 802.15.4 standard, which targets low data rate wireless networks. With the explosive growth in the use of IoT devices, it is essential to design effective and efficient channel access schemes for the 802.15.4 networks. In order to improve channel contention efficiency (CCE), which is defined as the number of times of successfully gaining the channel per unit of backoff time whereby throughput is improved, the scheme of enhancing channel contention efficiency (ECCE) has been proposed to jointly optimize the three key parameters of macMinBe, macMaxBe and macMaxCsmaBackoffs in the carrier sense multiple access with collision avoidance (CSMA-CA) mechanism in the 802.15.4 standard. A novel Markov chain was developed to model the CSMA-CA mechanism, which yielded the expected number of failures in gaining the channel, the expected number of backoff periods and the expected number of backoffs when a node intended to transmit a packet. These statistics resulted in CCE. An optimization problem that maximized the CCE with respect to the above-mentioned three key parameters was formulated. The solution to the optimization problem led to the optimal parameter values, which were applied in the ECCE scheme. The simulation results show that the proposed ECCE scheme outperformed the CSMA-CA mechanism in terms of CCE, delay and throughput.
Automatic Object Detection Algorithm-Based Braille Image Generation System for the Recognition of Real-Life Obstacles for Visually Impaired People
Authors: Lee Dayeon , Cho Jinsoo
Published: February 2022

The global prevalence of visual impairment due to diseases and accidents continues to increase. Visually impaired individuals rely on their auditory and tactile senses to recognize surrounding objects. However, accessible public facilities such as tactile pavements and tactile signs are installed only in limited areas globally, and visually impaired individuals use assistive devices such as canes or guide dogs, which have limitations. In particular, the visually impaired are not equipped to face unexpected situations by themselves while walking. Therefore, these situations are becoming a great threat to the safety of the visually impaired. To solve this problem, this study proposes a living assistance system, which integrates object recognition, object extraction, outline generation, and braille conversion algorithms, that is applicable both indoors and outdoors. The smart glasses guide objects in real photos, and the user can detect the shape of the object through a braille pad. Moreover, we built a database containing 100 objects on the basis of a survey to select objects frequently used by visually impaired people in real life to construct the system. A performance evaluation, consisting of accuracy and usefulness evaluations, was conducted to assess the system. The former involved comparing the tactile image generated on the basis of braille data with the expected tactile image, while the latter confirmed the object extraction accuracy and conversion rate on the basis of the images of real-life situations. As a result, the living assistance system proposed in this study was found to be efficient and useful with an average accuracy of 85% a detection accuracy of 90% and higher, and an average braille conversion time of 6.6 s. Ten visually impaired individuals used the assistance system and were satisfied with its performance. Participants preferred tactile graphics that contained only the outline of the objects, over tactile graphics containing the full texture details.
Application of Fuzzy and Rough Logic to Posture Recognition in Fall Detection System
Authors: Pȩkala Barbara , Mroczek Teresa , Gil Dorota , Kepski Michal
Published: February 2022

Considering that the population is aging rapidly, the demand for technology for aging-at-home, which can provide reliable, unobtrusive monitoring of human activity, is expected to expand. This research focuses on improving the solution of the posture detection problem, which is a part of fall detection system. Fall detection, using depth maps obtained by the Microsoft Kinect sensor, is a two-stage method. We concentrate on the first stage of the system, that is, pose recognition from a depth map. For lying pose detection, a new hybrid FRSystem is proposed. In the system, two rule sets are investigated, the first one created based on a domain knowledge and the second induced based on the rough set theory. Additionally, two inference aggregation approaches are considered with and without the knowledge measure. The results indicate that the new axiomatic definition of knowledge measures, which we propose has a positive impact on the effectiveness of inference and the rule induction method reducing the number of rules in a set maintains it.
Deep Learning-Based Myoelectric Potential Estimation Method for Wheelchair Operation
Authors: Aihara Shimpei , Shibata Ryusei , Mizukami Ryosuke , Sakai Takara , Shionoya Akira
Published: February 2022

Wheelchair sports are recognized as an international sport, and research and support are being promoted to increase the competitiveness of wheelchair sports. For example, an electromyogram can observe muscle activity. However, it is generally used under controlled conditions due to the complexity of preparing the measurement equipment and the movement restrictions imposed by cables and measurement equipment. It is difficult to perform measurements in actual competition environments. Therefore, in this study, we developed a method to estimate myoelectric potential that can be used in competitive environments and does not limit physical movement. We developed a deep learning model that outputs surface myoelectric potentials by inputting camera images of wheelchair movements and the measured values of inertial sensors installed on wheelchairs. For seven subjects, we estimated the myoelectric potential during chair work, which is important in wheelchair sports. As a result of creating an in-subject model and comparing the estimated myoelectric potential with the myoelectric potential measured by an electromyogram, we confirmed a correlation (correlation coefficient 0.5 or greater at a significance level of 0.1%). Since this method can estimate the myoelectric potential without limiting the movement of the body, it is considered that it can be applied to the performance evaluation of wheelchair sports.
Improved Metaheuristics-Based Clustering with Multihop Routing Protocol for Underwater Wireless Sensor Networks
Authors: Mohan Prakash , Subramani Neelakandan , Alotaibi Youseef , Alghamdi Saleh , Khalaf Osamah Ibrahim , Ulaganathan Sakthi
Published: February 2022

Underwater wireless sensor networks (UWSNs) comprise numerous underwater wireless sensor nodes dispersed in the marine environment, which find applicability in several areas like data collection, navigation, resource investigation, surveillance, and disaster prediction. Because of the usage of restricted battery capacity and the difficulty in replacing or charging the inbuilt batteries, energy efficiency becomes a challenging issue in the design of UWSN. Earlier studies reported that clustering and routing are considered effective ways of attaining energy efficacy in the UWSN. Clustering and routing processes can be treated as nondeterministic polynomial-time (NP) hard optimization problems, and they can be addressed by the use of metaheuristics. This study introduces an improved metaheuristics-based clustering with multihop routing protocol for underwater wireless sensor networks, named the IMCMR-UWSN technique. The major aim of the IMCMR-UWSN technique is to choose cluster heads (CHs) and optimal routes to a destination. The IMCMR-UWSN technique incorporates two major processes, namely the chaotic krill head algorithm (CKHA)-based clustering and self-adaptive glow worm swarm optimization algorithm (SA-GSO)-based multihop routing. The CKHA technique selects CHs and organizes clusters based on different parameters such as residual energy, intra-cluster distance, and inter-cluster distance. Similarly, the SA-GSO algorithm derives a fitness function involving four parameters, namely residual energy, delay, distance, and trust. Utilization of the IMCMR-UWSN technique helps to significantly boost the energy efficiency and lifetime of the UWSN. To ensure the improved performance of the IMCMR-UWSN technique, a series of simulations were carried out, and the comparative results reported the supremacy of the IMCMR-UWSN technique in terms of different measures.
Hybrid Virtual Commissioning of a Robotic Manipulator with Machine Vision Using a Single Controller
Authors: Noga Marek , Juhás Martin , Gulan Martin
Published: February 2022

Digital twin (DT) is an emerging key technology that enables sophisticated interaction between physical objects and their virtual replicas, with applications in almost all engineering fields. Although it has recently gained significant attraction in both industry and academia, so far it has no unanimously adopted and established definition. One may therefore come across many definitions of what DT is and how to create it. DT can be designed for an existing process and help us to improve it. Another possible approach is to create the DT for a brand new device. In this case, it can reveal how the system would behave in given conditions or when controlled. One of purposes of a DT is to support the commissioning of devices. So far, recognized and used techniques to make the commissioning more effective are virtual commissioning and hybrid commissioning. In this article, we present a concept of hybrid virtual commissioning. This concept aims to point out the possibility to use real devices already at the stage of virtual commissioning. It is introduced in a practical case study of a robotic manipulator with machine vision controlled with a programmable logic controller in a pick-and-place application. This study presents the benefits that stem from the proposed approach and also details when it is convenient to use it.
The Immediate Carryover Effects of Peroneal Functional Electrical Stimulation Differ between People with and without Chronic Ankle Instability
Authors: Gottlieb Uri , Hoffman Jay R. , Springer Shmuel
Published: February 2022

Chronic ankle instability (CAI) is a common condition that may develop after an ankle sprain. Compared with healthy individuals, those with CAI demonstrate excessive ankle inversion and increased peroneal electromyography (EMG) activity throughout the stance phase of gait, which may put them at greater risk for re-injury. Functional electrical stimulation (FES) of targeted muscles may provide benefits as a treatment modality to stimulate immediate adaptation of the neuromuscular system. The present study investigated the effect of a single, 10 min peroneal FES session on ankle kinematics and peroneal EMG activity in individuals with (n = 24) or without (n = 24) CAI. There were no significant differences in ankle kinematics between the groups before the intervention. However, after the intervention, healthy controls demonstrated significantly less ankle inversion between 0–9% (p = 0.009) and 82–87% (p = 0.011) of the stance phase. Furthermore, a significant within-group difference was observed only in the control group, demonstrating increased ankle eversion between 0–7% (p = 0.011) and 67–81% (p = 0.006) of the stance phase after the intervention. Peroneal EMG activity did not differ between groups or measurements. These findings, which demonstrate that peroneal FES can induce ankle kinematics adaptations during gait, can help to develop future interventions for people with CAI.
Negative Index Metamaterial-Based Frequency-Reconfigurable Textile CPW Antenna for Microwave Imaging of Breast Cancer
Authors: Hossain Kabir , Sabapathy Thennarasan , Jusoh Muzammil , Lee Shen-Han , Rahman Khairul Shakir Ab , Kamarudin Muhammad Ramlee
Published: February 2022

In this paper, we report the design and development of a metamaterial (MTM)-based directional coplanar waveguide (CPW)-fed reconfigurable textile antenna using radiofrequency (RF) varactor diodes for microwave breast imaging. Both simulation and measurement results of the proposed MTM-based CPW-fed reconfigurable textile antenna revealed a continuous frequency reconfiguration to a distinct frequency band between 2.42 GHz and 3.2 GHz with a frequency ratio of 2.33:1, and with a static bandwidth at 4–15 GHz. The results also indicated that directional radiation pattern could be produced at the frequency reconfigurable region and the antenna had a peak gain of 7.56 dBi with an average efficiency of more than 67%. The MTM-based reconfigurable antenna was also tested under the deformed condition and analysed in the vicinity of the breast phantom. This microwave imaging system was used to perform simulation and measurement experiments on a custom-fabricated realistic breast phantom with heterogeneous tissue composition with image reconstruction using delay-and-sum (DAS) and delay-multiply-and-sum (DMAS) algorithms. Given that the MWI system was capable of detecting a cancer as small as 10 mm in the breast phantom, we propose that this technique may be used clinically for the detection of breast cancer.
Multi-Method Diagnosis of Blood Microscopic Sample for Early Detection of Acute Lymphoblastic Leukemia Based on Deep Learning and Hybrid Techniques
Authors: Abunadi Ibrahim , Senan Ebrahim Mohammed
Published: February 2022

Leukemia is one of the most dangerous types of malignancies affecting the bone marrow or blood in all age groups, both in children and adults. The most dangerous and deadly type of leukemia is acute lymphoblastic leukemia (ALL). It is diagnosed by hematologists and experts in blood and bone marrow samples using a high-quality microscope with a magnifying lens. Manual diagnosis, however, is considered slow and is limited by the differing opinions of experts and other factors. Thus, this work aimed to develop diagnostic systems for two Acute Lymphoblastic Leukemia Image Databases (ALL_IDB1 and ALL_IDB2) for the early detection of leukemia. All images were optimized before being introduced to the systems by two overlapping filters: the average and Laplacian filters. This study consists of three proposed systems as follows: the first consists of the artificial neural network (ANN), feed forward neural network (FFNN), and support vector machine (SVM), all of which are based on hybrid features extracted using Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM) and Fuzzy Color Histogram (FCH) methods. Both ANN and FFNN reached an accuracy of 100%, while SVM reached an accuracy of 98.11%. The second proposed system consists of the convolutional neural network (CNN) models: AlexNet, GoogleNet, and ResNet-18, based on the transfer learning method, in which deep feature maps were extracted and classified with high accuracy. All the models obtained promising results for the early detection of leukemia in both datasets, with an accuracy of 100% for the AlexNet, GoogleNet, and ResNet-18 models. The third proposed system consists of hybrid CNN–SVM technologies, consisting of two blocks: CNN models for extracting feature maps and the SVM algorithm for classifying feature maps. All the hybrid systems achieved promising results, with AlexNet + SVM achieving 100% accuracy, Goog-LeNet + SVM achieving 98.1% accuracy, and ResNet-18 + SVM achieving 100% accuracy.
Calculation of Capacitive-Based Sensors of Rotating Shaft Vibration for Fault Diagnostic Systems of Powerful Generators
Authors: Zaitsev Ievgen , Bereznychenko Victoriia , Bajaj Mohit , Taha Ibrahim B. M. , Belkhier Youcef , Titko Vladyslav , Kamel Salah
Published: February 2022

This paper presents the results of research and development of capacitive-based sensors of rotating shaft vibration for fault diagnostic systems of powerful turbines and hydro generators. It showed that diagnostic systems with special sensors are the key to increasing the reliability of powerful turbines and hydro generators. The application of sensors in monitoring systems was considered, and the requirements for the sensors used were analyzed. Structures of concentric capacitive-based sensors of rotating shaft vibration based on the measurement of the capacitance value from the distance to the metal surface were proposed. The design scheme was created for determining electrode dimensions of the rotating shaft vibration capacitive-based sensors with concentric electrodes, and analytical dependences were obtained. The calculation results allow the selection of optimal parameters of the active and guard electrodes. Analytical and computer simulation methods determined the response functions of the capacitive sensors. Analytical calculations and simulation results using 3D FEM were used to find the response functions of the sensors. The calculation of the characteristics of the capacitive-based sensors of rotating shaft vibration is presented. The study of the influence of fringe effects was carried out using the obtained results of the modeling and analytical calculations.
A Comprehensive Review on Time Sensitive Networks with a Special Focus on Its Applicability to Industrial Smart and Distributed Measurement Systems
Authors: Fedullo Tommaso , Morato Alberto , Tramarin Federico , Rovati Luigi , Vitturi Stefano
Published: February 2022

The groundbreaking transformations triggered by the Industry 4.0 paradigm have dramatically reshaped the requirements for control and communication systems within the factory systems of the future. The aforementioned technological revolution strongly affects industrial smart and distributed measurement systems as well, pointing to ever more integrated and intelligent equipment devoted to derive accurate measurements. Moreover, as factory automation uses ever wider and complex smart distributed measurement systems, the well-known Internet of Things (IoT) paradigm finds its viability also in the industrial context, namely Industrial IoT (IIoT). In this context, communication networks and protocols play a key role, directly impacting on the measurement accuracy, causality, reliability and safety. The requirements coming both from Industry 4.0 and the IIoT, such as the coexistence of time-sensitive and best effort traffic, the need for enhanced horizontal and vertical integration, and interoperability between Information Technology (IT) and Operational Technology (OT), fostered the development of enhanced communication subsystems. Indeed, established technologies, such as Ethernet and Wi-Fi, widespread in the consumer and office fields, are intrinsically non-deterministic and unable to support critical traffic. In the last years, the IEEE 802.1 Working Group defined an extensive set of standards, comprehensively known as Time Sensitive Networking (TSN), aiming at reshaping the Ethernet standard to support for time-, mission- and safety-critical traffic. In this paper, a comprehensive overview of the TSN Working Group standardization activity is provided, while contextualizing TSN within the complex existing industrial technological panorama, particularly focusing on industrial distributed measurement systems. In particular, this paper has to be considered a technical review of the most important features of TSN, while underlining its applicability to the measurement field. Furthermore, the adoption of TSN within the Wi-Fi technology is addressed in the last part of the survey, since wireless communication represents an appealing opportunity in the industrial measurement context. In this respect, a test case is presented, to point out the need for wirelessly connected sensors networks. In particular, by reviewing some literature contributions it has been possible to show how wireless technologies offer the flexibility necessary to support advanced mobile IIoT applications.