Sci, Volume 5, Issue 2 (June 2023) – 12 articles

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article