The powertrain in combustion engine and electric vehicles requires a thermal management system to regulate the operating temperature of the under-hood components. The introduction of computer-controlled cooling system actuators (e.g., variable speed fans, pump, and valves) enables power savings over drive cycles. The radiator is typically sized for maximum heat rejection per environmental and vehicle thermal loading conditions. This paper explores the use of multiple radiators to adapt the cooling system operations to driving demands. A nonlinear multiple-input (i.e., fan array speed, pump, and outlet valve positions) thermal model is presented to predict system behavior. A stateflow controller has been designed and implemented to maintain the component temperature within a desired range (~80 °C). A series of experimental tests have been conducted to compare the proposed architecture’s performance against a single radiator design. A standard driving cycle featuring low (20 kW) and high (40 kW) heat loads was implemented in the laboratory for a vehicle starting from rest. The coolant temperature tracking, fan speeds, and fan power draw were studied over the representative operating cycle. The test results show a much faster warmup time (~10 min) and temperature tracking for the twin radiator experimental test as compared to the single radiator (~13 min). The net fan energy consumption was reduced by 4.6% with the twin radiator as opposed to the single-radiator configuration. Considering that engines usually operate at idle to medium loads, these findings can improve the powertrain’s overall performance. Full article

Studying the migration and diffusion of radionuclides plays an important role in emergency decision making and accident mitigation of floating nuclear power plants. Based on the CALPUFF model, this paper simulates the spatial distribution and concentration distribution of airborne radionuclides ¹³¹I diffusion under the conditions of sailing and power supply under LOCA (Loss-of-Coolant Accident) of the floating nuclear power plant, and the influence of four meteorological parameters, namely wind speed, cloudiness, temperature and air pressure, on the migration was analyzed using sensitivity analysis. The results show that the wind direction affects the diffusion direction of ¹³¹I, and the concentration of ¹³¹I decreases with the increase in the diffusion distance; under the same conditions, the radionuclides diffuses farther and the affected area is larger under the sailing condition. Wind speed is the dominant factor affecting the diffusion of radionuclides, followed by the cloud amount parameter, temperature parameter, and air pressure parameter. The research results can provide theoretical support for emergency responses to nuclear accidents in offshore floating nuclear power plants. Full article

The present investigation deals with some aspects of the diversity of fire ants (Hymenoptera: Formicidae) in their native range. The Red Imported Fire Ant Solenopsis invicta is native to the tropical and subtropical inland territories of South America. In Brazil, it mainly occurs around the Pantanal region and across the Paraguay river, a region composed of grasslands which are seasonally flooded. Recent studies have evidenced this fire ant species is gradually spreading to other regions of Brazil. In the present investigation, we surveyed the molecular diversity of S. invicta populations across fragments of Atlantic Forest in São Paulo, Brazil, using mtDNA COI haplotypes. Fire ant nests were sampled along the highways lining the northern and southern slope sides of the mountain range Serra do Mar, SP, Brazil. Four haplotypes were identified (H1–H4), which were assessed for similarity to deposited records by other authors, revealing that the haplotypes H1 and H2 are likely of foreign origin through recent reintroduction via a marine port to the south of the Serra do Mar mountain range. On the other hand, the haplotypes H3 and H4, predominating among the inland samples from the northern side of the mountain range, were most similar to previous records from more central regions of Brazil. Haplotypes clustered into distinct supergroups, further pointing to the occurrence of two separate expansion waves of S. invicta in the region. We suggest the obtained pattern indicates the mountain range may function as a geographical barrier deferring gene flow. Full article

Due to the inherent inter-class similarity and class imbalance of remote sensing images, it is difficult to obtain effective results in single-source semantic segmentation. We consider applying multi-modal data to the task of the semantic segmentation of HSR (high spatial resolution) remote sensing images, and obtain richer semantic information by data fusion to improve the accuracy and efficiency of segmentation. However, it is still a great challenge to discover how to achieve efficient and useful information complementarity based on multi-modal remote sensing image semantic segmentation, so we have to seriously examine the numerous models. Transformer has made remarkable progress in decreasing model complexity and improving scalability and training efficiency in computer vision tasks. Therefore, we introduce Transformer into multi-modal semantic segmentation. In order to cope with the issue that the Transformer model requires a large amount of computing resources, we propose a model, MFTransNet, which combines a CNN (convolutional neural network) and Transformer to realize a lightweight multi-modal semantic segmentation structure. To do this, a small convolutional network is first used for performing preliminary feature extraction. Subsequently, these features are sent to the multi-head feature fusion module to achieve adaptive feature fusion. Finally, the features of different scales are integrated together through a multi-scale decoder. The experimental results demonstrate that MFTransNet achieves the best balance among segmentation accuracy, memory-usage efficiency and inference speed. Full article

The article summarizes the state of the art in carbon-reinforced geopolymers. It takes into consideration various types of matrices and types of carbon fibers (CFs). The article shows the growing importance of this composite in the investigation conducted in recent years. Today, it is one of the most promising modern research areas, taking into account the decrease in the prices of CFs and their appearance on the market waste-based CFs, as well as research on new methods of producing CFs from sustainable precursors. The research methods applied in the article are critical analyses of the literature. The results of the literature analysis are discussed in a comparative context, including production methods and the influence of CFs on geopolymer properties. The potential applications for carbon fiber-reinforced geopolymer composites are shown. Additionally, the current research challenges for geopolymer composites reinforced by CFs are presented. Full article

Arterial stiffness is a major condition related to many cardiovascular diseases. Traditional approaches in the assessment of arterial stiffness supported by machine learning techniques are limited to the pulse wave velocity (PWV) estimation based on pressure signals from the peripheral arteries. Nevertheless, arterial stiffness can be assessed based on the pressure–strain relationship by analyzing its hysteresis loop. In this work, the capacity of deep learning models based on generative adversarial networks (GANs) to transfer pressure signals from the peripheral arterial region to pressure and area signals located in the central arterial region is explored. The studied signals are from a public and validated virtual database. Compared to other works in which the assessment of arterial stiffness was performed via PWV, in the present work the pressure–strain hysteresis loop is reconstructed and evaluated in terms of classical machine learning metrics and clinical parameters. Least-square GAN (LSGAN) and Wasserstein GAN with gradient penalty (WGAN-GP) adversarial losses are compared, yielding better results with LSGAN. LSGAN mean ± standard deviation of error for pressure and area pulse waveforms are 0.8 ± 0.4 mmHg and 0.1 ± 0.1 cm², respectively. Regarding the pressure–strain elastic modulus, it is achieved a mean absolute percentage error of 6.5 ± 5.1%. GAN-based deep learning models can recover the pressure–strain loop of central arteries while observing pressure signals from peripheral arteries. Full article

The diets of insular lizards are extremely varied, depending on the different environmental characteristics of each island population. This is particularly evident in the case of the populations of small coastal islets of the Balearic Islands, where the Balearic lizard, Podarcis lilfordi, is found. The study of trophic ecology carried out by means of traditional tools, such as morphological analysis of feces, has made it possible to detect numerous prey and nutritional elements. However, these methods are clearly insufficient, as some rare groups are not detected. It is also difficult to identify remains of marine subsidies or of foods contributed to these small islands by other predators, such as seabirds. The current study demonstrates the advantages of combining morphological diet analysis with the molecular study of individual feces samples obtained from the same populations. We obtained a greater diversity of prey groups using the combined methodologies, with each method identifying prey items that were not detected using the other method. Particularly, the study of diets at the molecular level identified plant species consumed by lizards that were, occasionally, not identified in morphological analyses. Conversely, the traditional morphological study of an equivalent number of fecal samples allowed for the identification of several prey groups that had not been detected in the molecular study. From this viewpoint, the advantages and disadvantages of each methodology are discussed. Full article

Climate heat waves occurring in urban centers are a serious threat to public health and wellbeing. Historically, most heat-related mortalities have arisen from excessive overheating of building interiors housing older occupants. This paper developed an approach that combines the results from building simulation and bioheat models to generate health-based limit criteria for overheating in long-term care homes (LTCHs) by which the body dehydration and core temperature of older residents are capped during overheating events. The models of the LTCHs were created for buildings representative of old and current construction practices for selected Canadian locations. The models were calibrated using measurements of indoor temperature and humidity acquired from monitoring the building interiors and the use of published building energy use intensity data. A general procedure to identify overheating events and quantify their attributes in terms of duration, intensity, and severity was developed and applied to LTCHs to generate the limit criteria. Comparing the limit criteria from the proposed and comfort-based methods showed evident differences. The proposed method predicted the overheating risk consistent with the overall thermal comfort during overheating events in contrast to the comfort-based methods. The new limit criteria are intended to be used in any study to evaluate overheating risk in similar buildings. Full article

Redox imbalance or oxidative stress that results from both environmental and genetic factors is observed in patients with schizophrenia. Therefore, identifying markers of oxidative stress in the early stages of psychosis and using antioxidant treatments as an adjuvant to antipsychotics has important implications. The reaction of p-N,N-dimethylaminobenzaldehyde (DMAB) with pyrrole moieties has been well studied for well over a century for use as a marker of oxidative stress dysregulation. Throughout this time, pyrroles have been investigated with varying veracity in urine extracts to identify elevated levels in patients diagnosed with schizophrenia. Since the 1960’s, various claims have been made with respect to what causes the colour change when DMAB is added to urine extracts. Whilst the substances from this reaction have not been fully elucidated, an objective look at most studies indicates that urobilinogen is likely to be one them. Urobilinogen has also been identified as a major interferent in our results. Both pyrroles and urobilinogen condense the DMAB reaction system (form condensation products) and are quite different. The urobilinogen detected in urine forms when gut microflora chemically reduces the bilirubin content of bile acids. In comparison, evidence suggests that the pyrrole fraction originates from the fragmentation of regulatory haem by reactive oxygen species (ROS) such as hydrogen peroxide and super and nitrous oxides. Clinical studies in our laboratories have established that pyrroles as a urine biomarker have specificity in detecting schizophrenia; however, caution must be applied as the readings are subject to interference by other DMAB active compounds that are present, such as urobilinogen. This review highlights the initial chemistry in isolating pyrroles and provides recommendations for standardised laboratory testing to ensure pyrroles are correctly measured and distinguished from other by-products. Full article

Confronted with unmanned surface vessel (USV) operations where GNSS signals are unavailable due to obscuration and other factors, a LiDAR SLAM-assisted fusion positioning method for USVs is proposed to combine GNSS/INS positioning with LiDAR-SLAM. When the USV works in wide-open water, the carrier phase differential GNSS/INS loosely coupled integration strategy is applied to fuse and calibrate the positioning data, and the positioning information of the USV is obtained through the coordinate conversion process. The system uses a dynamic switching strategy to enter to LiDAR-SLAM positioning when GNSS signals are not available, compensating the LiDAR data with precise angle information to ensure accurate and stable positioning. The experiments show that compared with the traditional Kalman filter and adaptive Kalman filter fusion algorithms, the positioning error is reduced by 55.4% and 43.5%. The velocity error is also limited by 78.2% and 57.9%. The standard deviation and the root mean square error are stable within 0.1 m, indicating that our method has better data stability, while the probability of positioning anomaly is effectively controlled. Full article

The need for transporting commodities has led to more and more freight vehicles on urban roads. Specific operational constraints of such vehicles could induce non-homogeneities in the smooth movement of traffic, especially at intersections where acceleration/deceleration events occur frequently. This leads to unnecessary wasted time for all vehicles, even in low to moderate traffic conditions. Hence, the literature reports different proposals to enhance the continuity of traffic at intersections. Among them, the green extension strategy has attracted researchers’ attention, owing to its simplicity, flexibility and practicality. In this paper, we propose a new approximate probabilistic model for the expected waiting/wasted time of all vehicles at an intersection with green time extension in low to moderate traffic conditions. Accordingly, the optimal green extension interval that minimizes the total expected waiting time can then be determined in different conditions. The proposed analysis needs few pieces of information (as opposed to microsimulation models conventionally employed to analyze such systems) and is therefore, suitable for quickly deciding on the optimal strategy based on the current situation in a dynamic environment. We have validated our approximate analysis with simulations in the VISSIM simulation tool. Full article

Accurate and reliable estimation of the axial compression capacity can assist engineers toward an efficient design of circular concrete-filled steel tube (CCFST) columns, which are gaining popularity in diverse structural applications. This study proposes a novel methodology based on computational intelligence for estimating the compression capacity of CCFST. Accordingly, a conventional artificial neural network (ANN) is hybridized with a metaheuristic algorithm called the seeker optimization algorithm (SOA). Utilizing information such as the column’s length, compressive strength of ultra-high-strength concrete, and the diameter, thickness, yield stress, and ultimate stress of the steel tube, the capacity of the column is predicted through non-linear calculations. In addition to the SOA, the future search algorithm (FSA) and social ski driver (SSD) are used as comparative benchmarks. The prediction results showed that the SOA-ANN can learn and predict the compression capacity pattern with high accuracy (relative error < 2.5% and correlation > 0.99). Also, this model outperformed both benchmark hybrids (i.e., FSA-ANN and SSD-ANN). Apart from accuracy, the configuration of the SOA-ANN is simpler owing to the smaller population recruited for the optimization task. An explicit formula for the proposed model is developed, which, owing to its observed efficiency, can be reliably applied to CCFST columns for the early estimation of the compression capacity. Full article

With the widespread use of computers, the amount of malware has increased exponentially. Since dynamic detection is costly in both time and resources, most existing malware detection methods are based on static features. However, existing static methods mainly rely on single feature types of malware, while few pay attention to multi-feature fusion. This paper presents a novel multi-feature extraction and fusion method to effectively detect malware variants by combining binary and opcode features. We propose a stacked convolutional network to capture the temporal and discontinuity information in the function call of the binary file from malware. Additionally, we adopt the triangular attention algorithm to extract code-level features from assembly code. Additionally, these two extracted features are aligned and fused by the cross-attention, which could provide a stable feature representation. We evaluate our method on two different datasets. It achieves an accuracy of 0.9954 on the Kaggle Malware Classification dataset and an accuracy of 0.9544 on a large real-world dataset. To optimize our detection model, we conduct in-depth discussions on different feature extractors and multi-feature fusion strategies. Moreover, a visualized attention module in our model is provided to explain its superiority in the opcode feature extraction. An experimental analysis is performed against five baseline deep learning models and five state-of-the-art malware detection models, which reveals that our strategy outperforms competing approaches in all evaluation circumstances. Full article

Experimental and clinical data suggest a tight link between hearing and cognitive functions under both physiological and pathological conditions. Indeed, hearing perception requires high-level cognitive processes, and its alterations have been considered a risk factor for cognitive decline. Thus, identifying common pathogenic determinants of hearing loss and neurodegenerative disease is challenging. Here, we focused on redox status imbalance as a possible common pathological mechanism linking hearing and cognitive dysfunctions. Oxidative stress plays a critical role in cochlear damage occurring during aging, as well as in that induced by exogenous factors, including noise. At the same time, increased oxidative stress in medio-temporal brain regions, including the hippocampus, is a hallmark of neurodegenerative disorders like Alzheimer’s disease. As such, antioxidant therapy seems to be a promising approach to prevent and/or counteract both sensory and cognitive neurodegeneration. Here, we review experimental evidence suggesting that redox imbalance is a key pathogenetic factor underlying the association between sensorineural hearing loss and neurodegenerative diseases. A greater understanding of the pathophysiological mechanisms shared by these two diseased conditions will hopefully provide relevant information to develop innovative and effective therapeutic strategies. Full article

Background and Objectives: As an adjunct to postoperative multimodal analgesic regimens, pregabalin has been reported in reducing postoperative acute pain and opioid consumption. However, there is only a small amount of evidence for preemptive pregabalin in patients undergoing cancer-related surgery. This systematic review was conducted to integrate high-quality evidence to evaluate the preemptive analgesic effects of pregabalin in cancer-related surgery. Materials and Methods: Seven electronic databases were searched in a combination of subject terms and free words. Efficacy and safety of preemptive pregabalin on postoperative pain for cancer-related surgery were evaluated by assessing resting and dynamic pain scores postoperatively, cumulative morphine equivalent consumption, time to first analgesic request, hemodynamic parameters, and the safety indicators. Results: Thirteen trials were incorporated for quantitative synthesis. The pooled results showed administration of pregabalin preoperatively is clinically significant for improving resting (weighted mean difference (WMD), −1.53 cm; 95% CI, −2.30 to −0.77) and dynamic (WMD, −1.16 cm; 95% CI, −2.22 to −0.11) pain severity scores at 2 h postoperatively and prolonging time to first analgesic request (WMD, 2.28 h; 95% CI, 0.79 to 3.77) in cancer-related surgery. Preemptive pregabalin was also statistically effective in some other pain indicators but would increase the risk of pregabalin-related side effects after surgery. Conclusions: Our findings do not support the administration of pregabalin in doses larger than 300 mg when put in cancer-related surgery. Taken together, more high-quality research particularly focused on the optimal dosages and timing of pregabalin in cancer-related surgery is needed in the future to establish stronger evidence for therapeutic effects. Full article

Solid-state supercapacitors (SSCs) consist of porous carbon electrodes and gel-polymer electrolytes and are used in novel energy storage applications. The current study aims to simulate the impedance of SSCs using a clearly defined equivalent circuit (EC) model with the ultimate goal of improving their performance. To this end, a conventional mathematical and a physicochemical model were adapted. The impedance was measured by electrochemical impedance spectroscopy (EIS). An EC consisting of electrical elements was introduced for each modeling approach. The mathematical model was purely based on a best-fit method and utilized an EC with intuitive elements. In contrast, the physicochemical model was motivated by advanced theories and allowed meaningful associations with properties at the electrode, the electrolyte, and their interface. The physicochemical model showed a higher approximation ability (relative error of 3.7%) due to the interface impedance integration in a more complex circuit design. However, this model required more modeling and optimization effort. Moreover, the fitted parameters differed from the analytically calculated ones due to uncertainties in the SSC’s microscale configuration, which need further investigations. Nevertheless, the results show that the proposed physicochemical model is promising in simulating EIS data of SSCs with the additional advantage of utilizing well-reasoned property-based EC elements. Full article

The Neil–Gehrels Swift Observatory has added extensively to our understanding of small bodies in our solar system through its capabilities to rapidly respond to short-live events such as outbursts and collisions, through its near-ultraviolet coverage, and by its ability to track time-dependent changes through monitoring campaigns. These capabilities have enabled many significant studies, including the onset and evolution of different sources of water in comet C/2009 P1 (Garradd), the unprecedented changes in the rotation period of comet 41P/Tuttle–Giacobini–Kresák, near-UV spectroscopic observations of asteroids that can help us understand how their properties evolve over time, and the first observations of the aftermath of a collision between a 100 m sized asteroid and the large primitive asteroid 596 (Scheila). In this review paper, we will highlight some of the observational results of Swift-UVOT in the field of small-body research. Full article

We aimed to find agreement between diagnoses obtained through standardized (SDI) and non-standardized diagnostic interviews (NSDI) for schizophrenia and Bipolar Affective Disorder (BD). Methods: A systematic review with meta-analysis was conducted. Publications from 2007 to 2020 comparing SDI and NSDI diagnoses in adults without neurological disorders were screened in MEDLINE, ISI Web of Science, and SCOPUS, following PROSPERO registration CRD42020187157, PRISMA guidelines, and quality assessment using QUADAS–2. Results: From 54231 entries, 22 studies were analyzed, and 13 were included in the final meta-analysis of kappa agreement using a mixed-effects meta-regression model. A mean kappa of 0.41 (Fair agreement, 95% CI: 0.34 to 0.47) but high heterogeneity (β = 92%) were calculated. Gender, mean age, NSDI setting (Inpatient vs. Outpatient; University vs. Non-university), and SDI informant (Self vs. Professional) were tested as predictors in meta-regression. Only SDI informant was relevant for the explanatory model, leaving 79% unexplained heterogeneity. Egger’s test did not indicate significant bias, and QUADAS–2 resulted in “average” data quality. Conclusions: Most studies using SDIs do not report the original sample size, only the SDI-diagnosed patients. Kappa comparison resulted in high heterogeneity, which may reflect the influence of non-systematic bias in diagnostic processes. Although results were highly heterogeneous, we measured a fair agreement kappa between SDI and NSDI, implying clinicians might operate in scenarios not equivalent to psychiatry trials, where samples are filtered, and there may be more emphasis on maintaining reliability. The present study received no funding. Full article

Ionic polymer–metal composites (IPMCs) have attracted attention in recent years due to their integration of actuation and sensing functions. As one of the main sensing functions of IPMCs, humidity sensing has been of consistent interest in wearable health monitors and artificial skin. However, there are still some technical challenges in that classical IPMCs have poor humidity sensing performance due to their dense surface electrode, and IPMCs are damaged easily due to an electrode/membrane mismatch. In this work, through the spraying and electrodepositing process, we developed an efficient method to rapidly prepare a Au-shell-Ag-NW (silver nanowire)-based IPMC with high strength, low surface resistance and excellent humidity sensing performance. Meanwhile, we optimized the preparation method by clarifying the influence of solvent type and electrodepositing time on the performance of the Au-shell-Ag-NW-based IPMC, thus effectively improving the humidity sensing effect and strength of the IPMC. Compared with previous research, the humidity electrical response (~9.6 mV) of the Au-shell-Ag-NW-based IPMC is at least two orders of magnitude higher than that of the classical IPMC (~0.41 mV), which is mainly attributed to the sparse gap structure for promoting the exchange of water molecules in the environment and Nafion membrane, a low surface resistance (~3.4 Ohm/sq) for transmitting the signal, and a seamless connection between the electrode and Nafion membrane for fully collecting the ion charges in the Nafion membrane. Additionally, the Au-shell-Ag-NW-based IPMC could effectively monitor the human breathing process, and the humidity sensing performance did not change after being exposed to the air for 4 weeks, which further indicates that the Au-shell-Ag-NW-based IPMC has good application potential due to its efficient preparation technology, high stability and good reproducibility. Full article

The depletion of oil reserves and concerns about the environmental impact of the use and incorrect disposal of mineral lubricants have been promoting the development of bio-based lubricants. In this study, biolubricants obtained from fatty acids of castor oil were synthesized by esterification (>wt.%93), epoxidation (>wt.%92), and oxirane ring opening reactions using water (>wt.%92) or 2-ethylhexanol (>wt.%94) as nucleophilic agents. The frictional characteristics of the synthesized samples were obtained through tribological tests performed in a four-ball tester and compared with a commercial mineral oil. The sample obtained through oxirane ring opening with water showed the best frictional performance (FC = 0.0699 ± 0.0007) among the prepared samples, with equivalent wear rate (WSD = 281.2 ± 5.54 μm) and ca. 20% lower friction coefficient when compared to the commercial mineral oil, indicating its great potential for replacing mineral fossil oils. Full article

Phosphorus is a key factor controlling eutrophication processes. Out of all the parts of the lake ecosystem, the biggest pool of this element (more than 90%) is stored in the surficial layers of bottom sediment. Hence, the bottom sediment’s ability to trap and release P is very important in analyzing a lake ecosystem’s function, particularly when the lake is subjected to restoration. Studies were carried out on Lake Kortowskie (87.2 ha in area, maximum depth 17.2 m), restored in 1956 using the hypolimnetic withdrawal method. The sediment cores were taken at two research stations—experimental (max. depth 17.2 m) and control (max. depth 15.7 m). Experiments were made in laboratory conditions. The results showed that phosphorus adsorption in the bottom deposits of Lake Kortowskie was multilayered. The Freundlich, BET, and Freundlich-Langmuir adsorption models best fit the experimental data. Adsorption isotherms were concave, suggesting that P adsorption effectiveness is higher for higher P concentrations in ambient water. Full article

We investigate some of the fundamental features of the interaction of a mixture of coherent states, namely, a Kaleidoscope states mixture, with two-level atoms in the Jaynes–Cummings model framework. We begin our analysis by calculating the von Neumann entropy of the field, which is determined with the help of the virtual atom method. The oscillations appearing in the entropy indicate a state of purity greater than the initial state, i.e., a purification of the initial state due to a transfer of coherence from the atom to the field. In this oscillatory region, we obtain a negative Wigner function that hints at a (noisy) multiple Schrödinger cat. Full article

In an era ruled by data and information, engineers need new tools to cope with the increased complexity of industrial operations. New architectural models for industry enable open communication environments, where workflows can play a major role in providing flexible and dynamic interactions between systems. Workflows help engineers maintain precise control over their factory equipment and Information Technology (IT) services, from the initial design stages to plant operations. The current application of workflows departs from the classic business workflows that focus on office automation systems in favor of a manufacturing-oriented approach that involves direct interaction with cyber-physical systems (CPSs) on the shop floor. This paper identifies relevant industry-related challenges that hinder the adoption of workflow technology, which are classified within the context of a cohesive workflow lifecycle. The classification compares the various workflow management solutions and systems used to monitor and execute workflows. These solutions have been developed alongside the Eclipse Arrowhead framework, which provides a common infrastructure for designing systems according to the microservice architectural principles. This paper investigates and compares various solutions for workflow management and execution in light of the associated industrial requirements. Further, it compares various microservice-based approaches and their implementation. The objective is to support industrial stakeholders in their decision-making with regard to choosing among workflow management solutions. Full article

Oxidative stress and endothelial dysfunction have been shown to play crucial roles in the pathophysiology of COVID-19 (coronavirus disease 2019). On these grounds, we sought to investigate the impact of COVID-19 on lipid peroxidation and ferroptosis in human endothelial cells. We hypothesized that oxidative stress and lipid peroxidation induced by COVID-19 in endothelial cells could be linked to the disease outcome. Thus, we collected serum from COVID-19 patients on hospital admission, and we incubated these sera with human endothelial cells, comparing the effects on the generation of reactive oxygen species (ROS) and lipid peroxidation between patients who survived and patients who did not survive. We found that the serum from non-survivors significantly increased lipid peroxidation. Moreover, serum from non-survivors markedly regulated the expression levels of the main markers of ferroptosis, including GPX4, SLC7A11, FTH1, and SAT1, a response that was rescued by silencing TNFR1 on endothelial cells. Taken together, our data indicate that serum from patients who did not survive COVID-19 triggers lipid peroxidation in human endothelial cells. Full article

Head and neck squamous cell carcinoma (HNSCC) represents a heterogeneous group of neoplasms whose histological derivation comes from the mucous membranes lining the epithelium: the oral cavity, the larynx, the hypopharynx, the nasopharynx, and the oropharynx. The etiopathogenetic mechanisms involving tumor genesis including the alteration of cell proliferation, apoptosis, invasion, migration, and death may involve alterations in the expression of microRNA (miR). To date there have been no systematic reviews with meta-analysis conducted specifically on the role of miR-195 in HNSCC; therefore, our hypothesis was to evaluate if the aberrant expression of miR-195 in HNSCC tissues may represent a prognostic biomarker of survival through the hazard ratio (HR) and relative risk (RR) analysis. The systematic review was designed according to the PRISMA indications; in total, three electronic databases were consulted (PubMed, Scopus, Cochrane Central Trial) including Google Scholar and the gray literature, and a combination of keywords was used such as miR-195 AND HNSCC, microRNA AND HNSCC and miR-195. The meta-analysis and trial sequential analysis were performed using RevMan 5.41 software and TSA software (Cochrane Collaboration, Copenhagen, Denmark). This search identified 1592 articles and, at the end of the selection process, three articles were included. The results of the meta-analysis reported an aggregated risk ratio for overall survival (OS) between the expression of miR-195 at the highest and lowest of 0.36 and 6, respectively, 95% CI: [0.25, 0.51]. Heterogeneity was evaluated through Chi² = 0.05 df = 2 (p = 0.98) and the Higgins index I² = 0%. The test for the overall effect was Z = 5.77 (p < 0.00001). The forest plot was in favor of higher OS in patients with high miR-195 expression. Full article

The increase in fetal requirements of long-chain polyunsaturated fatty acids (LCPUFAs) during pregnancy alters maternal fatty acid metabolism, and therefore, fatty acid desaturase (FADS) gene polymorphisms may change blood fatty acid composition or concentration differently during pregnancy. We investigated the relationship between a FADS1 single-nucleotide polymorphism (SNP) and maternal serum LCPUFA levels in Japanese pregnant women during the first and third trimesters and at delivery. Two hundred and fifty-three pregnant women were included, and fatty acid compositions of glycerophospholipids in serum (weight %) and the FADS1 SNP rs174547 (T/C) were analyzed. LCPUFAs, including arachidonic acid (ARA) and docosahexaenoic acid (DHA), significantly decreased from the first to the third trimester of pregnancy. Furthermore, DHA significantly decreased from the third trimester of pregnancy to delivery. At all gestational stages, linoleic acid (LA) and α-linolenic acid were significantly higher with the number of minor FADS1 SNP alleles, whereas γ-linolenic acid and ARA and the ARA/LA ratio were significantly lower. DHA was significantly lower with the number of minor FADS1 SNP alleles only in the third trimester and at delivery, suggesting that genotype effects become more obvious as pregnancy progresses. Full article

Feline coronavirus (FCoV) is widely circulating among domestic cats (Felis catus). The zoonotic origin of the emerged severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and the biological characteristics of CoVs, including the ability to cross interspecies barriers, facilitate its emergence in different animals, including cats’ populations. The current study is the first to report the serological investigation on the presence of FCoV and SARS-CoV-2 in domestic cats living with COVID-19-positive owners in the UAE. A total of 83 sera were collected from domestic cats living with COVID-19-positive owners (by RT-qPCR). The cats were sampled during the period between February and May 2022 in Al-Ain and Abu Dhabi Cities, UAE. Detection of FCoV and SARS-CoV-2 was carried out by enzyme-linked immunosorbent assay (ELISA). FCoV antibodies were detected in 54 samples (65%). The frequencies of FCoV were significantly higher in purebred cats (48%; 40/83) and in the cat group with outdoor access (49.4%; 41/83). SARS-CoV-2 seroprevalence in collected sera revealed 8 samples (9.6%) with positive results. Four samples (4.8%) showed positive results for both FCoV and SARS-CoV-2 antibodies. In conclusion, FCoV is widely circulating within cats’ populations involved in the study. The antibodies for SARS-CoV-2 were detected in cats’ populations but at a low prevalence rate. COVID-19-positive people should avoid close contact with their cats. Future serological testing of large cats’ populations is crucial for providing a good understanding of COVID-19 dynamics in cats. Full article