Authors:Tosin Adewumi, Sana Sabah Sabry, Nosheen Abid, Foteini Liwicki, Marcus Liwicki First page: 37 Abstract: We conduct relatively extensive investigations of automatic hate speech (HS) detection using different State-of-The-Art (SoTA) baselines across 11 subtasks spanning six different datasets. Our motivation is to determine which of the recent SoTA models is best for automatic hate speech detection and what advantage methods, such as data augmentation and ensemble, may have on the best model, if any. We carry out six cross-task investigations. We achieve new SoTA results on two subtasks—macro F1 scores of 91.73% and 53.21% for subtasks A and B of the HASOC 2020 dataset, surpassing previous SoTA scores of 51.52% and 26.52%, respectively. We achieve near-SoTA results on two others—macro F1 scores of 81.66% for subtask A of the OLID 2019 and 82.54% for subtask A of the HASOC 2021, in comparison to SoTA results of 82.9% and 83.05%, respectively. We perform error analysis and use two eXplainable Artificial Intelligence (XAI) algorithms (Integrated Gradient (IG) and SHapley Additive exPlanations (SHAP)) to reveal how two of the models (Bi-Directional Long Short-Term Memory Network (Bi-LSTM) and Text-to-Text-Transfer Transformer (T5)) make the predictions they do by using examples. Other contributions of this work are: (1) the introduction of a simple, novel mechanism for correcting Out-of-Class (OoC) predictions in T5, (2) a detailed description of the data augmentation methods, and (3) the revelation of the poor data annotations in the HASOC 2021 dataset by using several examples and XAI (buttressing the need for better quality control). We publicly release our model checkpoints and codes to foster transparency. Citation: Sci PubDate: 2023-09-22 DOI: 10.3390/sci5040037 Issue No:Vol. 5, No. 4 (2023)
Authors:Marija Šimat, Mateja Janković Makek, Maja Mičetić First page: 38 Abstract: The aim of this research is to present the effects of acupuncture treatment on morning blood glucose level (BGL) in type 2 diabetes mellitus (T2DM) patients, and to describe them by a predictive model. The morning BGL is measured after overnight fasting during a three-month long acupuncture treatment for two persons diagnosed with T2DM and is compared with the BGL of two persons in similar health conditions taking only metformin-based drugs. It is shown that the morning BGL is highly affected by each single acupuncture treatment and by the number of the already applied treatments. Significant lowering of BGL after each treatment is observed, as well as an overall BGL lowering effect, which is the result of the repeated acupuncture. The observed BGL reduction was found to be maintained during a follow-up performed a year after the acupuncture. The measured BGL dynamics curves are analyzed and described by a model. This model describes well all of the key features of the measured BGL dynamics and provides personal parameters that describe the BGL regulation. The model is used to simulate BGL regulation by acupuncture performed with different frequencies. It can be used generally to predict the effects of acupuncture on BGL and to optimize the time between two treatments. The results will enable a better understanding of acupuncture application in diabetes, and a prediction of its effects in diabetes treatment. Citation: Sci PubDate: 2023-09-26 DOI: 10.3390/sci5040038 Issue No:Vol. 5, No. 4 (2023)
Authors:Jamshed Bobokalonov, Yanhong Liu, Karley K. Mahalak, Jenni A. Firrman, Shiowshuh Sheen, Siyuan Zhou, LinShu Liu First page: 26 Abstract: Tomatoes are a perishable and seasonal fruit with a high economic impact. Carbon dioxide (CO2), among several other reagents, is used to extend the shelf-life and preserve the quality of tomatoes during refrigeration or packaging. To obtain insight into CO2 stress during tomato ripening, tomatoes at the late green mature stage were conditioned with one of two CO2 delivery methods: 5% CO2 for 14 days (T1) or 100% CO2 for 3 h (T2). Conventional physical and chemical characterization found that CO2 induced by either T1 or T2 delayed tomato ripening in terms of color change, firmness, and carbohydrate dissolution. However, T1 had longer-lasting effects. Furthermore, ethylene production was suppressed by CO2 in T1, and promoted in T2. These physical observations were further evaluated via RNA-Seq analysis at the whole-genome level, including genes involved in ethylene synthesis, signal transduction, and carotenoid biosynthesis. Transcriptomics analysis revealed that the introduction of CO2 via the T1 method downregulated genes related to fruit ripening; in contrast, T2 upregulated the gene encoding for ACS6, the enzyme responsible for S1 ethylene synthesis, even though there was a large amount of ethylene present, indicating that T1 and T2 regulate tomato ripening via different mechanisms. Quantitative real-time PCR assays (qRT-PCR) were used for validation, which substantiated the RNA-Seq data. The results of the present research provide insight into gene regulation by CO2 during tomato ripening at the whole-genome level. Citation: Sci PubDate: 2023-06-30 DOI: 10.3390/sci5030026 Issue No:Vol. 5, No. 3 (2023)
Authors:Gesualdo M. Zucco, Giuseppe Sartori First page: 27 Abstract: Malingering relates to intentionally pretending or exaggerating physical or psychologic symptoms to gain an external incentive, such as avoiding work, law prosecution or military service, or seeking financial compensation from insurance companies. Accordingly, various techniques have been developed in recent years by the scientific community to address this challenge. In this review, we discuss malingering within visual, auditory and olfactory domains, as well as in cognitive disorders and psychopathology. We provide a general, critical, narrative overview on the intermodal criteria for differential diagnosis, and discuss validated psychophysical tools and electrophysiology-based tests for its detection, as well as insights for future directions. Citation: Sci PubDate: 2023-07-06 DOI: 10.3390/sci5030027 Issue No:Vol. 5, No. 3 (2023)
Authors:Florian Butollo, Jana Flemming, Christine Gerber, Martin Krzywdzinski, David Wandjo, Nina Delicat, Lorena Herzog First page: 28 Abstract: Academic studies prior to the pandemic rather emphasized that the progression towards Industry 4.0 happened in an incremental manner. However, the extraordinary circumstances of the pandemic have led to considerable investments that were widely interpreted as a (generalized) digitalization push. However, little is known about the character of such investments and their effects. The goal of this contribution is to provide an empirically based overview of recent investment in digital technologies in six economic sectors of the German economy: mechanical engineering, chemicals, automotives, logistics, healthcare, and financial services. Based on 36 case studies and a survey at 540 companies, we investigate the following questions: 1. How much did the COVID-19 pandemic reduce existing obstacles for investments in digitalization measures' 2. Is there a universal digitalization push due to the COVID-19 pandemic that differs from the trajectory before the pandemic' The results show that the pandemic affected investment in an unequal manner. It was driven by the immediate need to sustain business operations through the virtualization of communication among employees and with external partners. However, there was less dynamism in shop-floor-related digitalization, as it was less related to epidemiological concerns and is more long-term in nature. Citation: Sci PubDate: 2023-07-07 DOI: 10.3390/sci5030028 Issue No:Vol. 5, No. 3 (2023)
Authors:Johannes Winter First page: 29 Abstract: For a long time, the challenge has been to provide products and services that precisely match the preferences, habits, and needs of users [...] Citation: Sci PubDate: 2023-07-12 DOI: 10.3390/sci5030029 Issue No:Vol. 5, No. 3 (2023)
Authors:Rui Cereja, Joana P. C. Cruz, Joshua Heumüller, Bernardo Vicente, Ana Amorim, Frederico Carvalho, Sara Cabral, Paula Chainho, Ana C. Brito, Inês J. Ferreira, Mário Diniz First page: 30 Abstract: Bivalves accumulate toxins produced by microalgae, thus becoming harmful for humans. However, little information is available about their toxicity to the bivalve itself. In the present work, the physiological stress and damage after the ingestion of toxic dinoflagellate species (Gymnodinium catenatum) and a diatom species (Skeletonema marinoi, which is non-toxic to humans but may be to grazers) in the oyster Magallana angulata are evaluated against a control treatment fed with the chlorophyte Tetraselmis sp. Oysters were exposed for two hours to a concentration of 4 × 104 cells/L of G. catenatum and 2 × 107 cells/L of S. marinoi. The biomarkers superoxide dismutase (SOD), catalase (CAT), glutathione S-Transferase, total Ubiquitin (Ubi) and Acetylcholinesterase (AchE) were assessed. The exposure of M. angulata to G. catenatum lead to a reduction in SOD and AchE activity and ubiquitin concentrations when compared to the control treatment. Moreover, it increased CAT activity in the adductor muscle, and maintained its activity in the other tissues tested. This may be related to the combination of reduced metabolism with the deployment of detoxification processes. S. marinoi also lead to a decrease in all biomarkers tested in the gills and digestive glands. Therefore, both species tested caused physiological alterations in M. angulata after two hours of exposure. Citation: Sci PubDate: 2023-07-17 DOI: 10.3390/sci5030030 Issue No:Vol. 5, No. 3 (2023)
Authors:Zacharias Frontistis, Grigoris Lykogiannis, Anastasios Sarmpanis First page: 31 Abstract: Among different biological methods used for advanced wastewater treatment, membrane bioreactors have demonstrated superior efficiency due to their hybrid nature, combining biological and physical processes. However, their efficient operation and control remain challenging due to their complexity. This comprehensive review summarizes the potential of artificial neural networks (ANNs) to monitor, simulate, optimize, and control these systems. ANNs show a unique ability to reveal and simulate complex relationships of dynamic systems such as MBRs, allowing for process optimization and fault detection. This early warning system leads to increased reliability and performance. Integrating ANNs with advanced algorithms and implementing Internet of Things (IoT) devices and new-generation sensors has the potential to transform the advanced wastewater treatment landscape towards the development of smart, self-adaptive systems. Nevertheless, several challenges must be addressed, including the need for high-quality and large-quantity data, human resource training, and integration into existing control system facilities. Since the demand for advanced water treatment and water reuse will continue to expand, proper implementation of ANNs, combined with other AI tools, is an exciting strategy toward the development of integrated and efficient advanced water treatment schemes. Citation: Sci PubDate: 2023-08-15 DOI: 10.3390/sci5030031 Issue No:Vol. 5, No. 3 (2023)
Authors:Hari M. Srivastava, Hare K. Nigam, Swagata Nandy First page: 32 Abstract: In this paper, we analyze the convergence problems of function g of Fourier series in Besov and generalized Zygmund norms using generalized Nörlund-Matrix (Np,qA) means of Fourier series. Convergence results are also compared by means of applications. Citation: Sci PubDate: 2023-08-22 DOI: 10.3390/sci5030032 Issue No:Vol. 5, No. 3 (2023)
Authors:Evangelos Bellos First page: 33 Abstract: Power plants constitute the main sources of electricity production, and the calculation of their efficiency is a critical factor that is needed in energy studies. The efficiency improvement of power plants through the optimization of the cycle is a critical means of reducing fuel consumption and leading to more sustainable designs. The goal of the present work is the development of semi-empirical models for estimating the thermodynamic efficiency of power cycles. The developed model uses only the lower and the high operating temperature levels, which makes it flexible and easily applicable. The final expression is found by using the literature data for different power cycles, named as: organic Rankine cycles, water-steam Rankine cycles, gas turbines, combined cycles and Stirling engines. According to the results, the real operation of the different cases was found to be a bit lower compared to the respective endoreversible cycle. Specifically, the present global model indicates that the thermodynamic efficiency is a function of the temperature ratio (low cycle temperature to high cycle temperature). The suggested equation can be exploited as a quick and accurate tool for calculating the thermodynamic efficiency of power plants by using the operating temperature levels. Moreover, separate equations are provided for all of the examined thermodynamic cycles. Citation: Sci PubDate: 2023-08-24 DOI: 10.3390/sci5030033 Issue No:Vol. 5, No. 3 (2023)
Authors:Laura Stefani, Goffredo Orlandi, Marco Corsi, Edoardo Falconi, Roberto Palazzo, Alessio Pellegrino, Pietro Amedeo Modesti First page: 34 Abstract: Background: Transplanted patients are frail individuals who may be affected by diastolic dysfunction, leading to a decrease in exercise tolerance. Previous studies have reported that certain ECG and echocardiographic parameters (such as the P-wave interval, PQ interval, P-wave dispersion, Tend-P interval, QTc interval, and strain) can support the diagnosis of diastolic dysfunction when the ejection fraction is preserved. This study aimed to examine the potential diagnostic contribution of specific ECG and deformation parameters in transplanted recipients, who are at a high risk of heart failure. Materials and Methods: A group of 33 transplanted subjects (17 renal and 16 liver) were categorized using two scores for heart failure with preserved ejection fraction (HFpEF). Additionally, they underwent evaluation based on ECG parameters (P-wave interval, PQ interval, Pwave dispersion, and Tend-P QTc) and echocardiographic deformation parameters (strain and twist). The Student’s t-test was used for statistical analysis. Results: The two scores identified different numbers of excludable and not excludable subjects potentially affected by HFpEF. The not excludable group presented ECG parameters with significantly higher values (P-wave, PQ interval, posterior wall diastole, and Tend-P, all with p ≤ 0.05) and significantly lower 4D strain and twist values (p < 0.05) Conclusions: There is evidence for a significant diagnostic contribution of additional ECG and echo strain parameters in an early phase of diastolic dysfunction in subjects potentially affected by HFpEF. Citation: Sci PubDate: 2023-08-25 DOI: 10.3390/sci5030034 Issue No:Vol. 5, No. 3 (2023)
Authors:Demetris Koutsoyiannis, Christian Onof, Zbigniew W. Kundzewicz, Antonis Christofides First page: 35 Abstract: The scientific and wider interest in the relationship between atmospheric temperature (T) and concentration of carbon dioxide ([CO2]) has been enormous. According to the commonly assumed causality link, increased [CO2] causes a rise in T. However, recent developments cast doubts on this assumption by showing that this relationship is of the hen-or-egg type, or even unidirectional but opposite in direction to the commonly assumed one. These developments include an advanced theoretical framework for testing causality based on the stochastic evaluation of a potentially causal link between two processes via the notion of the impulse response function. Using, on the one hand, this framework and further expanding it and, on the other hand, the longest available modern time series of globally averaged T and [CO2], we shed light on the potential causality between these two processes. All evidence resulting from the analyses suggests a unidirectional, potentially causal link with T as the cause and [CO2] as the effect. That link is not represented in climate models, whose outputs are also examined using the same framework, resulting in a link opposite the one found when the real measurements are used. Citation: Sci PubDate: 2023-09-13 DOI: 10.3390/sci5030035 Issue No:Vol. 5, No. 3 (2023)
Authors:Seyedmajid Hosseini, Mohsen Norouzi, Jian Xu First page: 36 Abstract: Strain sensors play a pivotal role in quantifying stress and strain across diverse domains, encompassing engineering, industry, and medicine. Their applicability has recently extended into the realm of wearable electronics, enabling real-time monitoring of body movements. However, conventional strain sensors, while extensively employed, grapple with limitations such as diminished sensitivity, suboptimal tensile strength, and susceptibility to environmental factors. In contrast, polymer-based composite strain sensors have gained prominence for their capability to surmount these challenges. The integration of carbon nanotubes (CNTs) as reinforcing agents within the polymer matrix ushers in a transformative era, bolstering mechanical strength, electrical conductivity, and thermal stability. This study comprises three primary components: simulation, synthesis of nanocomposites for strain sensor fabrication, and preparation of a comprehensive measurement set for testing purposes. The fabricated strain sensors, incorporating a robust polymer matrix of polyaniline known for its exceptional conductivity and reinforced with carbon nanotubes as strengthening agents, demonstrate good characteristics, including a high gauge factor, stability, and low hysteresis. Moreover, they exhibit high strain sensitivity and show linearity in resistance changes concerning applied strain. Comparative analysis reveals that the resulting gauge factors for composite strain sensors consisting of carbon nanotubes/polyaniline and carbon nanotubes/polyaniline/silicone rubber are 144.5 and 167.94, respectively. Citation: Sci PubDate: 2023-09-20 DOI: 10.3390/sci5030036 Issue No:Vol. 5, No. 3 (2023)
Authors:Anuradha Mathrani, Jian Wang, Ding Li, Xuanzhen Zhang First page: 14 Abstract: This paper draws upon the United Nations 2022 data report on the achievement of Sustainable Development Goals (SDGs) across the following four dimensions: economic, social, environmental and institutional. Ward’s method was applied to obtain clustering results for forty-five Asian countries to understand their level of progress and overall trends in achieving SDGs. We identified varying degrees of correlation between the four dimensions. The results show that East Asian countries performed poorly in the economic dimension, while some countries in Southeast Asia and Central and West Asia performed relatively well. Regarding social and institutional dimensions, the results indicate that East and Central Asian countries performed relatively better than others. Finally, in the environmental dimension, West and South Asian countries showed better performance than other Asian countries. The insights gathered from this study can inform policymakers of these countries about their own country’s position in achieving SDGs in relation to other Asian countries, as they work towards establishing strategies for improving their sustainable development targets. Citation: Sci PubDate: 2023-03-28 DOI: 10.3390/sci5020014 Issue No:Vol. 5, No. 2 (2023)
Authors:Eli D. Ethridge, Bahtiyar Efe, Anthony R. Lupo First page: 15 Abstract: Many previous studies of the occurrence of blocking anticyclones, their characteristics, and dynamics have defined the onset longitude using the one-dimensional zonal index type criterion proposed by Lejenas and Okland. In addition to examining the blocking event itself, the onset longitude was determined to start at the nearest five degrees longitude using the National Centers for Environmental Prediction/National Center for Atmospheric Research Reanalyses that were used to identify the events. In this study, each blocking event in the University of Missouri Blocking Archive was re-examined to identify an onset latitude, and this information was added to the archive. The events were then plotted and displayed on a map of the Northern or Southern Hemisphere using Geographic Information System (GIS) software housed at the University of Missouri as different colored and sized dots according to block intensity and duration, respectively. This allowed for a comparison of blocking events in the archive above to studies that used a two-dimensional index. Then the common onset regions were divided by phase of the El Nino and Southern Oscillation (ENSO), and the typical onset of intense and persistent blocking events could be examined. The results found a favorable comparison between the onset regions identified here and those found in previous studies that used a two-dimensional blocking index. Additionally, there was variability identified in the onset regions of blocking in both hemispheres by ENSO phase, including the location of more intense and persistent events. Citation: Sci PubDate: 2023-04-03 DOI: 10.3390/sci5020015 Issue No:Vol. 5, No. 2 (2023)
Authors:Sheng Wu, Dong-Sheng Jeng First page: 16 Abstract: Solute transport through porous media is usually described by well-established conventional transport models with the ability to account for advection, dispersion, and sorption. In this study, we further extend our previous one-dimensional model for solute transport in an unsaturated porous medium to two dimensions. The present model is based on a small-strain approach. The proposed model is validated with previous work. Both homogeneous landfill and pointed landfill conditions are considered. A detailed parametric study shows the differences between the present model and previous one-dimensional model. Citation: Sci PubDate: 2023-04-04 DOI: 10.3390/sci5020016 Issue No:Vol. 5, No. 2 (2023)
Authors:Silvia Brunoro, Lisa Mensi First page: 17 Abstract: The access to basic healthcare for people who are not registered in the national health system is nowadays a very urgent problem, both in Italy and in the rest of the world. Immigration and poverty are only some of the factors that make one of the primary rights of humanity—healthcare—not a right for everyone. The main problems, which have grown exponentially in the last decade, are at operational level, due to the lack of personnel (mostly volunteers) and the lack of spaces. This paper illustrates procedures and techniques for the design of a small emergency structure that can be moved and positioned in urban contexts. The first part consists of a deep analysis of the problem and of the state of the art of existing typologies. The second part is dedicated to the conceptual framework (requirements, conceptual model) and to the definition of the preliminary design for the new approach to basic non-conventional sanitary spaces. Finally, a virtual case study (project application) in Italy is presented. Citation: Sci PubDate: 2023-04-11 DOI: 10.3390/sci5020017 Issue No:Vol. 5, No. 2 (2023)
Authors:Antonio Sarasa-Cabezuelo First page: 18 Abstract: Violence involving firearms in the USA is a very important problem. As a consequence, a large number of crimes of this type are recorded every year. However, the solutions proposed have not managed to reduce the number of this type of crime. One of the cities with a large number of violent crimes is New York City. The number of crimes is not homogeneous and depends on the district where they occur. This paper proposes to study the information about the crimes in which firearms are involved with the aim of characterizing the factors on which the occurrence of this type of crime depends, such as the levels of poverty and culture. Since the districts are not homogeneous, the information has been analyzed at the district level. For this, data from the open data portal of the city of New York have been used and machine-learning techniques have been used. The results have shown that the variables on which they depend are different in each district. Citation: Sci PubDate: 2023-04-20 DOI: 10.3390/sci5020018 Issue No:Vol. 5, No. 2 (2023)
Authors:Raghav V. Anand, Maysam F. Abbod, Shou-Zen Fan, Jiann-Shing Shieh First page: 19 Abstract: The term “anesthetic depth” refers to the extent to which a general anesthetic agent sedates the central nervous system with specific strength concentration at which it is delivered. The depth level of anesthesia plays a crucial role in determining surgical complications, and it is imperative to keep the depth levels of anesthesia under control to perform a successful surgery. This study used electroencephalography (EEG) signals to predict the depth levels of anesthesia. Traditional preprocessing methods such as signal decomposition and model building using deep learning were used to classify anesthetic depth levels. This paper proposed a novel approach to classify the anesthesia levels based on the concept of time series feature extraction, by finding out the relation between EEG signals and the bi-spectral Index over a period of time. Time series feature extraction on basis of scalable hypothesis tests were performed to extract features by analyzing the relation between the EEG signals and Bi-Spectral Index, and machine learning models such as support vector classifier, XG boost classifier, gradient boost classifier, decision trees and random forest classifier are used to train the features and predict the depth level of anesthesia. The best-trained model was random forest, which gives an accuracy of 83%. This provides a platform to further research and dig into time series-based feature extraction in this area. Citation: Sci PubDate: 2023-05-05 DOI: 10.3390/sci5020019 Issue No:Vol. 5, No. 2 (2023)
Authors:Pantelis T. Nikolaidis, Konstantinos Havenetidis First page: 20 Abstract: Strenuous exercise, such as military training, is known to demand a high degree of physical performance and to cause injuries. The present study aimed to (a) monitor the incidence of soft tissue injuries (blisters, contusions, and lacerations) among cadets during Basic Combat Training (BCT), and (b) identify possible risk factors for these injuries. Participants were 315 first-grade cadets (women, n = 28; men, n = 287), recruited from the Hellenic Army Academy. Seven weeks of BCT resulted in an overall cadet injury rate of 24.1% (n = 76) with 13.7% being injured one time, whereas 10.4% of participants were injured 2–6 times. The incidence of injuries was 2.9 soft tissue injuries per 1000 training hours. The logistic regression model using sex, being an athlete, nationality, weight, height, body mass index, and percentage of body fat (BF) to predict soft tissue injury was not statistically significant (χ2(7) = 5.315, p = 0.622). The results of this study showed that BCT caused a large number of soft tissue injuries similar to the number reported for musculoskeletal injuries. In conclusion, following BCT, soft tissue injury characteristics (occurrence, severity, treatment) are similar to those applied in musculoskeletal injuries for Army cadets. However, risk factors such as sex, nationality, and BF have not been related to soft tissue injury prediction as previously shown for musculoskeletal injuries for the same sample group. Citation: Sci PubDate: 2023-05-06 DOI: 10.3390/sci5020020 Issue No:Vol. 5, No. 2 (2023)
Authors:Giovanna Ricci, Filippo Gibelli, Paolo Bailo, Anna Maria Caraffa, Maria Angela Casamassima, Ascanio Sirignano First page: 21 Abstract: Hoarding disorder (HD) is a recently recognized psychiatric condition, now classified under the category of obsessive-compulsive and related disorders in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). It leads to an unwarranted attachment to material possessions, such that the individual is unable to separate themselves from them. There is still a lack of awareness of the critical sociological implications of this disorder, which is too often considered a purely health-related issue. This article endeavors to frame hoarding disorder from a unique socio-criminological and legal perspective, proposing an alternative approach to HD that considers it not only as a mental disorder, but also as a genuine societal issue. We also explore potential avenues for protection, considering both the well-being of individuals with this mental disorder and the communities in which individuals suffering from HD reside. This paper presents a fresh perspective on HD, aiming to delineate its impact and significance as an affliction affecting both individuals and society at large. Citation: Sci PubDate: 2023-05-11 DOI: 10.3390/sci5020021 Issue No:Vol. 5, No. 2 (2023)
Authors:Jens Neuhüttler, Maximilian Feike, Janika Kutz, Christian Blümel, Bernd Bienzeisler First page: 22 Abstract: In recent years, a complex set of dynamic developments driven by both the economy and the emergence of digital technologies has put pressure on manufacturing companies to adapt. The concept of servitization, i.e., the shift from a product-centric to a service-centric value creation logic, can help manufacturing companies stabilize their business in such volatile times. Existing academic literature investigates the potential and challenges of servitization and the associated development of data-based services, so-called smart services, with a view to external market performance. However, with the increasing use of digital technologies in manufacturing and the development of internal smart services based on them, we argue that the existing insights on external servitization are also of interest for internal transformation. In this paper, we identify key findings from service literature, apply them to digital factory transformation, and structure them into six fields of action along the dimensions of people, technology, and organization. As a result, recommendations for designing digital factory transformation in manufacturing companies are derived from the perspective of servitization and developing internal smart services. Citation: Sci PubDate: 2023-05-16 DOI: 10.3390/sci5020022 Issue No:Vol. 5, No. 2 (2023)
Authors:Rupak Kumar Das, Anna Martin, Tom Zurales, Dale Dowling, Arshia Khan First page: 23 Abstract: Electroencephalography (EEG) is a mechanism to understand the brain’s functioning by analyzing brain electrical signals. More recently, it has been more commonly used in studies that are focused on the causation and effect of dementia. More tools are now available to gather EEG data. This brings about the challenge of understanding brain signals, which involves signal processing. Professionals with an electrical engineering background are very comfortable analyzing EEG data. Still, scientists in computer science and related fields need a source that can identify all the tools available and the process of analyzing the data. This paper deals specifically with the existing EEG data analysis tools and the processes involved in analyzing the EEG data using these tools. Furthermore, the paper goes in-depth into identifying the tools and the mechanisms of data processing techniques. In addition, it lists a set of definitions required for a better understanding of EEG data analysis, which can be challenging. The purpose of this paper is to serve as a reference for not only scientists that are new to EEG data analysis but also seasoned scientists that are looking for a specific data component in EEG and can go straight to the section of the paper that deals with the tool that they are using. Citation: Sci PubDate: 2023-06-01 DOI: 10.3390/sci5020023 Issue No:Vol. 5, No. 2 (2023)
Authors:Mai M. Awad, Randall B. Boone First page: 24 Abstract: Apis mellifera L. is considered one of the most important pollinators in nature. Unfortunately, in addition to other insect species, honey bee populations are decreasing at an alarming rate, urging researchers to investigate the causes and stressors that precipitated this decline. This study focuses on chemical stressors that are found to affect bee populations. We used pollen and honey samples to examine the variations in pesticides, selenium, and heavy metals in two different landscapes: urban and agricultural areas of northeastern Colorado, USA. Subsequently, we extrapolated the risks of these toxins’ residues to Apis spp. Based on the current literature, we found no spatial variations in metal and selenium concentrations in the pollen and honey samples collected from urban and agricultural areas. Moreover, we observed no spatial variations in pesticide concentrations in pollen and honey samples. Based on the previous literature and a comparison of the residues of heavy metals, selenium, and pesticides in our pollen and honey samples, we found that the heavy metal and selenium residues in some honey and pollen likely pose a severe health risk to honey bees. Although the levels of pesticide residues were below the documented thresholds of risk, we consider the possibility of synergistic chemical impacts. Our findings support future efforts to investigate the health risks associated with multiple-factor combinations. Citation: Sci PubDate: 2023-06-06 DOI: 10.3390/sci5020024 Issue No:Vol. 5, No. 2 (2023)
Authors:Lea M. Morath, Roger J. Guillory, Alexander A. Oliver, Shu Q. Liu, Martin L. Bocks, Galit Katarivas Levy, Jaroslaw W. Drelich, Jeremy Goldman First page: 25 Abstract: Platinum-containing stents are commonly used in humans with hypercholesterolemia, whereas preclinical stent evaluation has commonly been performed in healthy animal models, providing inadequate information about stent performance under hypercholesterolemic conditions. In this investigation, we used an ApoE−/− mouse model to test the impact of hypercholesterolemia on neointima formation on platinum-containing implants. We implanted 125 μm diameter platinum wires into the abdominal aortas of ApoE−/− and ApoE+/+ mice for 6 months, followed by histological and immunofluorescence examination of neointimal size and composition. It was found that ApoE−/− mice developed neointimas with four times larger area and ten times greater thickness than ApoE+/+ counterparts. Neointimas developed in the ApoE−/− mice also contained higher amounts of lipids quantified as having 370 times more coverage compared to ApoE+/+, a 3-fold increase in SMCs, and a 22-fold increase in macrophages. A confluent endothelium had regenerated in both mouse strains. The ApoE−/− mice experienced luminal reductions more closely resembling clinically relevant restenosis in humans. Overall, the response to platinum arterial implants was highly dependent upon the atherogenic environment. Citation: Sci PubDate: 2023-06-19 DOI: 10.3390/sci5020025 Issue No:Vol. 5, No. 2 (2023)
Authors:Caio Wolf Klein, Jéssica Kuntz Maykot, Enedir Ghisi, Liseane Padilha Thives First page: 1 Abstract: The objective of this study was to carry out the financial feasibility analysis of harvesting rainwater from permeable pavements in a city square. A case study was carried out in a square close to the beach in the city of Florianópolis, Brazil. Questionnaires were applied to pedestrians who circulate within the area. The square is to be implemented to promote sustainability and improve the user’s quality of life. From the rainfall data and the average daily water demand for irrigation of the square vegetation, the volume of rainwater to be harvested from the permeable pavement was calculated. The rainwater demand was estimated as 662 L/day. The implementation and operation costs of the pavement and irrigation systems were evaluated. The potential for potable water savings was 89.8%. The payback period was estimated as 347 months. This study showed that rainwater collected from permeable pavements is financially feasible and represents a promising technique. Citation: Sci PubDate: 2023-01-03 DOI: 10.3390/sci5010001 Issue No:Vol. 5, No. 1 (2023)
Authors:Amr A. El-Hanafy, Yasser M. Saad, Saleh A. Alkarim, Hussein A. Almehdar, Fuad M. Alzahrani, Mohammed A. Almatry, Vladimir N. Uversky, Elrashdy M. Redwan First page: 2 Abstract: With the increasing interest in the identification of differences between camel breeds over the last decade, this study was conducted to estimate the variability of milk production and composition of four Saudi camel breeds during different seasons. Milk records were taken two days per week from females of Majahem, Safra, Wadha, and Hamra breeds distributed over Saudi Arabia. The milk yield during winter indicated that the weekly average of the Wadha breed was significantly lower (27.13 kg/week) than Majahem and Hamra breeds. The Safra breed had the lowest milk yield (30.7 kg/week) during summer. During winter, the Hamra breed had a lower content of all analyzed milk components except proteins and was characterized by a lower pH than the milk of the other breeds. However, the Hamra breed had significantly higher contents of milk fat and lactose than the other breeds during summer, with the corresponding values of 3.87 and 4.86%, respectively. Milk collected during winter from Majahem, Safra, and Wadha breeds was characterized by a significant increase in all milk components and milk pH. Finally, the isoelectric focusing analysis revealed noticeable variability of casein purified from camel milk within the different Saudi breeds, with the highest significant value of 2.29 g per 100 mL recorded for the Wadha breed. Citation: Sci PubDate: 2023-01-06 DOI: 10.3390/sci5010002 Issue No:Vol. 5, No. 1 (2023)
Authors:Punya Mainali, Phadindra Wagle, Chasen McPherson, David. N. McIlroy First page: 3 Abstract: A signature of synaptic potentiation conductance has been observed in an α-Fe2O3/p-Si device fabricated using spin coating. The conductance of the device in dark conditions and illumination with a white light source was characterized as a function of the application of a periodic bias (voltage) with a triangular profile. The conductance of the device increases with the number of voltage cycles applied and plateaus to its maximum value of 0.70 μS under dark conditions and 12.00 μS under illumination, and this mimics the analog synaptic weight change with the action potential of a neuron. In the range of applied voltage from 0 V to 0.7 V, the conduction mechanism corresponds to trap-assisted tunneling (TAT) and in the range of 0.7–5 V it corresponds to the Poole–Frenkel emission (PFE). The conductance as a function of electrical pulses was fitted with a Hill function, which is a measure of cooperation in biological systems. In this case, it allows one to determine the turn-on threshold (K) of the device in terms of the number of voltage pulses, which are found to be 3 and 166 under dark and illumination conditions, respectively. The gradual conductance change and activation after a certain number of pulses perfectly mimics the synaptic potentiation of neurons. In addition, the threshold parameter extracted from the Hill equation fit, acting as the number of pulses for synaptic activation, is found to have programmability with the intensity of the light illumination. Citation: Sci PubDate: 2023-01-12 DOI: 10.3390/sci5010003 Issue No:Vol. 5, No. 1 (2023)
Authors:Cédric Sueur First page: 5 Abstract: Connectomics, which is the network study of connectomes or maps of the nervous system of an organism, should be applied and expanded to human and animal societies, resulting in the birth of the domain of socioconnectomics compared to neuroconnectomics. This new network study framework would open up new perspectives in evolutionary biology and add new elements to theories, such as the social and cultural brain hypotheses. Answering questions about network topology, specialization, and their connections with functionality at one level (i.e., neural or societal) may help in understanding the evolutionary trajectories of these patterns at the other level. Expanding connectomics to societies should be done in comparison and combination with multilevel network studies and the possibility of multiorganization selection processes. The study of neuroconnectomes and socioconnectomes in animals, from simpler to more advanced ones, could lead to a better understanding of social network evolution and the feedback between social complexity and brain complexity. Citation: Sci PubDate: 2023-01-30 DOI: 10.3390/sci5010005 Issue No:Vol. 5, No. 1 (2023)
Authors:Philip Q. Yang, Michaela LaNay Wilson First page: 6 Abstract: A global crisis generated by human-made climate change has added urgency to the need to fully understand human pro-environmental behaviors (PEBs) that may help slow down the crisis. Factors influencing personal and public PEBs may or may not be the same. Only a few studies have empirically investigated the determinants of personal and public PEBs simultaneously, but they contain major limitations with mixed results. This study develops a conceptual model for explaining both personal and public PEBs that incorporate demographic, socioeconomic, political, and attitudinal variables, and their direct and indirect effects. Using the latest available data from the 2010 General Social Survey and structural equation modeling (SEM), we tested the determinants of both personal and public PEBs in the United States. The results reveal that environmental concerns, education, and political orientation demonstrate similar significant impacts on both personal and public PEBs, but income, gender, race, urban/rural residency, region, and party affiliation have differential effects on these behaviors. Age, cohort, and religion have no significant effect on both types of behaviors. Our results confirm some existing findings; however, they challenge the findings of much of the literature. Citation: Sci PubDate: 2023-02-07 DOI: 10.3390/sci5010006 Issue No:Vol. 5, No. 1 (2023)
Authors:Ahmad Yaman Abdin, Claus Jacob First page: 7 Abstract: During the global Corona pandemic, the validity of science has been challenged by sections of the public, often for political gains [...] Citation: Sci PubDate: 2023-02-09 DOI: 10.3390/sci5010007 Issue No:Vol. 5, No. 1 (2023)
Authors:Shubashini Rathina Velu, Vinayakumar Ravi, Kayalvily Tabianan First page: 8 Abstract: The goal of the work is to enhance existing financial market forecasting frameworks by including an additional factor–in this example, a collection of carefully chosen tweets—into a long-short repetitive neural channel. In order to produce attributes for such a forecast, this research used a unique attitude analysis approach that combined psychological labelling and a valence rating that represented the strength of the sentiment. Both lexicons produced extra properties such 2-level polarization, 3-level polarization, gross reactivity, as well as total valence. The emotional polarity explicitly marked into the database contrasted well with outcomes of the innovative lexicon approach. Plotting the outcomes of each of these concepts against actual market rates of the equities examined has been the concluding step in this analysis. Root Mean Square Error (RMSE), preciseness, as well as Mean Absolute Percentage Error (MAPE) were used to evaluate the results. Across most instances of market forecasting, attaching an additional factor has been proven to reduce the RMSE and increase the precision of forecasts over lengthy sequences. Citation: Sci PubDate: 2023-02-15 DOI: 10.3390/sci5010008 Issue No:Vol. 5, No. 1 (2023)
Authors:Hartmut Hirsch-Kreinsen First page: 9 Abstract: This contribution deals with the diffusion of Industry 4.0 technologies and their consequences for work. Additionally, design options for work in Industry 4.0 are discussed. The following are outlined: First, since there are as yet no concrete future prospects for digital work, different development perspectives can be envisioned. Second, the development of Industry 4.0, therefore, has to be regarded as a design project. One theoretical basis for this is the “sociotechnical systems” approach. Third, this approach enables criteria for the design and implementation of human-oriented forms of digitized work to be systematically developed. The empirical basis of this contribution derives from research findings on the implementation of Industry 4.0 technologies and the development of digitized work in German industry. The research results are based on qualitative research methods such as company case studies and expert interviews. Citation: Sci PubDate: 2023-02-15 DOI: 10.3390/sci5010009 Issue No:Vol. 5, No. 1 (2023)
Authors:Sandeep Pratap Singh, Shamik Tiwari First page: 10 Abstract: Identity management describes a problem by providing the authorized owners with safe and simple access to information and solutions for specific identification processes. The shortcomings of the unimodal systems have been addressed by the introduction of multimodal biometric systems. The use of multimodal systems has increased the biometric system’s overall recognition rate. A new degree of fusion, known as an intelligent Dual Multimodal Biometric Authentication Scheme, is established in this study. In the proposed work, two multimodal biometric systems are developed by combining three unimodal biometric systems. ECG, sclera, and fingerprint are the unimodal systems selected for this work. The sequential model biometric system is developed using a decision-level fusion based on WOA-ANN. The parallel model biometric system is developed using a score-level fusion based on SSA-DBN. The biometric authentication performs preprocessing, feature extraction, matching, and scoring for each unimodal system. On each biometric attribute, matching scores and individual accuracy are cyphered independently. A matcher performance-based fusion procedure is demonstrated for the three biometric qualities because the matchers on these three traits produce varying values. The two-level fusion technique (score and feature) is implemented separately, and their results with the current scheme are compared to exhibit the optimum model. The suggested plan makes use of the highest TPR, FPR, and accuracy rates. Citation: Sci PubDate: 2023-03-01 DOI: 10.3390/sci5010010 Issue No:Vol. 5, No. 1 (2023)
Authors:Siegfried Hackel, Shanna Schönhals, Lutz Doering, Thomas Engel, Reinhard Baumfalk First page: 11 Abstract: This article depicts the role of the Digital Calibration Certificate (DCC) for an end-to-end digital quality infrastructure and as the basis for developments that are designated by the keyword “Industry 4.0”. Furthermore, it describes the impact the DCC has on increasing productivity in the manufacturing of products and in global trade. The DCC project is international in its scope. Calibration certificates document the measurement capability of a measurement system. They do this independently and by providing traceability to measurement standards. Therefore, they do not only play an important role in the world of metrology, but they also make it possible for manufacturing and commercial enterprises to exchange measurement values reliably and correctly at the national and at the international level. Thus, a DCC concept is urgently needed for the end-to-end digitalization of industry for the era of Industry 4.0 and for Medicine 4.0. A DCC brings about important advantages for issuers and for users. The DCC leads to the stringent, end-to-end, traceable and process-oriented organization of manufacturing and trading. Digitalization is thus a key factor in the field of calibration as it enables significant improvements in product and process quality. The reason for this is that the transmission of errors will be prevented, and consequently, costs will be saved as the time needed for distributing and disseminating the DCCs and the respective calibration objects will be reduced. Furthermore, it will no longer be necessary for the test equipment administration staff to update the data manually, which is a time-consuming, tedious and error-prone process. Citation: Sci PubDate: 2023-03-06 DOI: 10.3390/sci5010011 Issue No:Vol. 5, No. 1 (2023)
Authors:Pantelis T. Nikolaidis First page: 12 Abstract: Exercise testing has important applications for sport, exercise and clinical settings, providing valuable information for exercise prescription and diagnostics for health purposes. Often, exercise testing includes the participant’s maximal effort, and the testing score partially depends on whether the maximal effort has been exerted. In this context, motivation in exercise testing, including verbal encouragement and video presentation, plays a vital role in assessing participants. Professionals involved in exercise testing, such as exercise physiologists and sport scientists, should be aware of motivation’s role in performance during laboratory or field testing, especially using verbal encouragement. Motivation during exercise testing should be standardized and fully described in testing protocols. In this way, exercise testing would provide valid and reliable results for exercise prescription or other purposes (e.g., sport talent identification, athletes’ selection, education, research and rehabilitation). Citation: Sci PubDate: 2023-03-07 DOI: 10.3390/sci5010012 Issue No:Vol. 5, No. 1 (2023)
Authors:Amar Shukla, Rajeev Tiwari, Shamik Tiwari First page: 13 Abstract: Alzheimer’s Disease (AD) is becoming increasingly prevalent across the globe, and various diagnostic and detection methods have been developed in recent years. Several techniques are available, including Automatic Pipeline Methods and Machine Learning Methods that utilize Biomarker Methods, Fusion, and Registration for multimodality, to pre-process medical scans. The use of automated pipelines and machine learning systems has proven beneficial in accurately identifying AD and its stages, with a success rate of over 95% for single and binary class classifications. However, there are still challenges in multi-class classification, such as distinguishing between AD and MCI, as well as sub-stages of MCI. The research also emphasizes the significance of using multi-modality approaches for effective validation in detecting AD and its stages. Citation: Sci PubDate: 2023-03-21 DOI: 10.3390/sci5010013 Issue No:Vol. 5, No. 1 (2023)
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