The quality of healthcare services is a very important indicator of social welfare in developed and emerging countries. Good quality levels are critical to ensuring patient safety, improving health outcomes, increasing patient satisfaction, optimizing resource utilization, and meeting regulatory requirements. The stable and sustainable operation of medical institutions urgently requires the establishment of a reliable and effective medical quality performance evaluation platform. However, the complexity of the evaluation process, involving numerous evaluators and the consideration of different subjective and objective criteria, poses significant challenges and essentially constitutes a group multi-criteria decision-making problem. This article uses the overall medical environment of the institution, medical service attitude, medical security, administrative service measures, etc. as measurement standards to deal with this challenge. It proposes a medical service quality measurement model suitable for both qualitative and quantitative analysis. In view of the heterogeneity of information, the two-tuple fuzzy language calculation method is introduced to enable evaluators to skillfully manage diverse information and effectively avoid potential information omission problems in the subjective evaluation integration process. The effectiveness and feasibility of the model were demonstrated by applying it to the operational framework of a medical institution in central Taiwan.

Mixing is considered as a critical process parameter (CPP) during process development due to its significant influence on reaction selectivity and process safety. Nevertheless, mixing issues are difficult to identify and solve owing to their complexity and dependence on knowledge of kinetics and hydrodynamics. In this paper, we proposed an optimization methodology using Computational Fluid Dynamics (CFD) based compartmental modelling to improve mixing and reaction selectivity. More importantly, we have demonstrated that through the implementation of surrogate-based optimization, the proposed methodology can be used as a computationally non-intensive way for rapid process development of reaction unit operations. For illustration purpose, reaction selectivity of a process with Bourne competitive reaction network is discussed. Results demonstrate that we can improve reaction selectivity by dynamically controlling rates and locations of feeding in the reactor. The proposed methodology incorporates mechanistic understanding of the reaction kinetics together with an efficient optimization algorithm to determine the optimal process operation and thus can serve as a tool for quality-by-design (QbD) during product development stage.

One of the challenges in Content-Based Image Retrieval (CBIR) is to reduce the semantic gaps between low-level features and high-level semantic concepts. In CBIR, the images are represented in the feature space and the performance of CBIR depends on the type of selected feature representation. Late fusion also known as visual words integration is applied to enhance the performance of image retrieval. The recent advances in image retrieval diverted the focus of research towards the use of binary descriptors as they are reported computationally efficient. In this paper, we aim to investigate the late fusion of Fast Retina Keypoint (FREAK) and Scale Invariant Feature Transform (SIFT). The late fusion of binary and local descriptor is selected because among binary descriptors, FREAK has shown good results in classification-based problems while SIFT is robust to translation, scaling, rotation and small distortions. The late fusion of FREAK and SIFT integrates the performance of both feature descriptors for an effective image retrieval. Experimental results and comparisons show that the proposed late fusion enhances the performances of image retrieval.

The results of an LA-ICP-MS U-Pb dating and Hf isotope study of zircon crystals separated from small eclogite xenoliths found in Devonian kimberlites within the Prypyat horst, Ukraine, are reported. The studied area is located in the Central Belarusian Suture Zone, which represents a Paleoproterozoic belt extending along the boundary between the Sarmatian and Fennoscandian segments of the East European Platform. Four laser ablation sites on two zircon grains yielded Paleoproterozoic U-Pb ages between 1954 ± 24 Ma and 1735 ± 54 Ma. In contrast, three of four Hf sites revealed negative εHf values and Paleoarchean to Mesoarchean model ages, excluding the possibility that the eclogite xenoliths represent segments of a Paleoproterozoic subducted slab or younger mafic melts crystallized in the subcontinental lithospheric mantle. A single laser ablation Hf spot yielded a positive εHf value (+3) and a Paleoproterozoic model age. Two models for eclogite origin can be proposed. The first foresees the extension of the Archean lower-crustal or lithospheric roots beneath the Central Belarus Suture Zone for over 200 km from the nearest known outcrop of Archean rocks. The second model is that the Central Belarus Suture Zone represents a rifted-out fragment of the Kola-Karelian craton that was accreted to Sarmatia before the actual collision of these two segments of Baltica.

Intrinsically Disordered Proteins (IDPs) play crucial roles in signal transduction, cell differentiation, and transcriptional regulation. Many disrupted genes associated with mental disorders are identified as IDPs, and emerging evidence suggests the functional role of IDPs in neuropsychiatric disorders. However, few studies comprehensively explore the functional association between protein disorder properties and different mental diseases. To address this gap, we collected disrupted gene sets for seven mental diseases (MDD, SCZ, BP, ID, AD, ADHD, ASD) and a control gene set from normal brains using DisGeNET and GeneCards databases. The state-of-the-art predictor IUPred2A was then utilized to calculate the disorder properties , followed by a thorough comparison between the disordered proteins in mental diseases and healthy controls. Functional enrichment analysis and protein-protein interaction networks were conducted to investigate the IDPs’ functional roles in psychiatric diseases. Additionally, we demonstrated the role of IDPs in protein binding formations through a case study of alpha-synuclein using protein docking. Our findings reveal that differentially expressed proteins in mental disorders, especially in ASD and ADHD, exhibit more IDPs than those in healthy controls. IDPs in psychiatric disorders are significantly enriched in neurodevelopmental pathways and play an important role in in gene expression regulation. Our results indicate distinct functional patterns of disorder proteins among mental diseases and healthy controls. When comparing the IDPs between ASD and ADHD, we observed that IDPs in ADHD are more likely linked to the synaptic signaling and regulation, while IDPs in ASD are more tendentiously related to diverse and complex biological processes. Our work offers valuable insights into the important functions of IDPs in psychiatric disorders, enhancing our understanding of the molecular mechanisms involved in psychiatric disorders.

A sedentary lifestyle associated with unregulated diets rich in high-calorie foods have contributed to the great prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) latterly, with up to 60% in high-risk population and 25% in the general population. The absence of specific pharmacological strategies for this syndrome represents one of the major problems in the management of MASLD patients. Lifestyle interventions and adherence to a healthy diet are the main cornerstones of current therapies. The identification of nutraceuticals useful in the treatment of MASLD appears to be one of the most promising strategies for the development of new effective and safe treatments for this disease. The onion, one of the most widely studied foods in the field of nutraceuticals, serves as an inexhaustible reservoir of potent compounds with various beneficial effects. The following preliminary study analyses, mediating in silico studies, the iteration of a library of typical onion compounds with 3-hydroxy-3-methylglutaryl-coenzyme A reductase, Liver receptors X α and β as well as Peroxisome proliferator-activated receptors α and γ. In this study fort the first time promising smart molecules from the onion that could have a beneficial action in MASLD patients were identified.

The EASY-NET network program (NET-2016-02364191 - Effectiveness of Audit and Feedback (A&F) strategies to improve health practice and equity in various clinical and organizational set-tings), piloted a new and more structured A&F strategy. This study compared the effectiveness of the new strategy versus the periodic publication of indicators alone in improving the appropri-ateness and timeliness of emergency health interventions in patients with Acute Miocardial In-farction (AMI) and ischemic stroke in the Lazio Region. Efficacy was evaluated according to a prospective quasi-experimental design of pre-post (2021-2022) intervention type with control group. The exposed are hospitals in the Lazio Region where professional teams voluntarily par-ticipated in the intervention. The control group is involved only in the usual reporting activities. Effectiveness analysis was conducted at the patient level. Regional health information systems were used to calculate process and outcome indicators. The effectiveness of the intervention was assessed with Difference In Difference models, comparing pre-post intervention periods between exposures and controls. Estimates were calculated in terms of the difference in percentage points (pp) between absolute risks. Sixteen facilities for the IMA pathway and 13 for the stroke pathway joined the intervention. The intervention reduced the proportion of 30-days readmissions after a hospitalization for ischemic stroke by 0.54 pp in the exposed patients with a significant difference of -3.80 pp (IC95% -6.57; -1.03; 5453 patients, 63.7% cases) in the exposed compared to controls. There were no statistically significant differences attributable to the implemented A&F interven-tion in the other indicators considered. The results show, for the first time in Italy, the impact of A&F interventions in an emergency setting using aggregated data from hospitals involved in the Lazio Region emergency network.

The agronomic characteristics of Cenchrus purpureus were assessed through a randomized block experimental design with seven accessions (Taiwan grass, King grass, Elephant grass, Merkeron, CT-115, Purpule grass and Maralfalfa) as treatments and four replications research was carried out at the Instituto Tecnológico Superior de Tantoyuca, in 2019 and 2020, under rainfed conditions. Sowing density was established at 36 tillers in 25 m2. Green and dry matter yield, daily rates of dry matter accumulation in leaves, leaf/stem ratio, height, leaf area index and cutting frequency were assessed. Rainfall amount, temperature, relative humidity and global radiation levels were recorded to assess their effect on the evaluated variables. One-way ANOVA and multivariate analysis techniques, such as multiple linear regression (MLR), Clustering, and Principal component analysis (PCA) with the Statistica V7 software, were used. The leaf area index was greatest (p < 0.05) on the dry season. Green and dry matter yield were greatest in Maralfalfa (p < 0.05). Variation in the results is attributed to weather phenomena; nonetheless, Elephant grass and Maralfalfa showed the highest yields. Quality parameters must be evaluated to identify those grasses with the best attributes.

Clustering is a crucial at the same time challenging task in several application domains. It is important to incorporate the optimum feature finding into our clustering algorithms for getting better prediction accuracy but this is difficult when there is no or little information about the importance or relevance of features. To tackle this task in an efficient manner we employ the natural evolution process inherent in genetic algorithms (GA) to find the optimum features for clustering for the healthy aging dataset. In order to empirically verify the findings, genetic algorithms were combined with a number of clustering algorithms including parti-tional, density based as well as agglomerative. A variant of the popular KMeans algorithm, named KMeans++ gave the best performance on all performance metrics when combined with GA.

Autonomous mobile robots are essential to the industry, and human-robot interactions are becoming more common nowadays. These interactions require that the robots navigate scenarios with static and dynamic obstacles in a safely manner, avoiding collisions. This paper presents a physical implementation of a method for dynamic obstacle avoidance using a Long Short-Term Memory (LSTM) neural network that obtains information from the mobile robot’s LiDAR for it to be capable of navigating through scenarios with static and dynamic obstacles while avoiding collisions and reaching its goal. The model is implemented using a TurtleBot3 mobile robot within an OptiTrack Motion Capture (MoCap) system for obtaining its position at any given time. The user operates the robot through these scenarios, recording its LiDAR readings, target point, position inside the MoCap system, and its linear and angular velocities, all of which serve as the input for the LSTM network. The model is trained on data from multiple user-operated trajectories across five different scenarios, outputting the linear and angular velocities for the mobile robot. Physical experiments prove that the model is successful in allowing the mobile robot to reach the target point in each scenario while avoiding the dynamic obstacle, with a validation accuracy of 98.02%.

Machine Learning deals with creating algorithms capable of learning from the provided data. These systems have a wide range of applications and can also be a valuable tool for scientific research which in recent years has been focused on finding new diagnostic techniques for particle accelerator beams. In this context SPARC_LAB is positioned, a facility located at the Frascati National Laboratories of INFN, where the progress of beam diagnostics is one of the main developments of the entire project. With this in mind, you aim to present the design of two neural networks aimed at predicting the spot size of the electron beam of the plasma-based accelerator at SPARC_LAB, which powers an undulator for the generation of x-ray Free Electron Laser (XFEL). Data-driven algorithms use two different data preprocessing techniques, namely autoencoder neural network and PCA. With both approaches, the predicted measurements can be obtained with an acceptable margin of error and most importantly without activating the accelerator, thus saving time, even compared to a simulator that can produce the same result but much more slowly. The goal is to lay the groundwork for creating a digital twin of linac and conducting virtualized diagnostics using an innovative approach.

In Chuanming Zong’s program to attack Hadwiger’s covering conjecture, which is a longstanding open problem from Convex and Discrete Geometry, it is essential to estimate covering functionals of convex bodies effectively. Recently, He et al. [J. Optim. Theory Appl. 2023, 196, 1036–1055.] and Yu et al. [Mathematics, 2023, 11, 2000] provided two deterministic global optimization algorithms having high computational complexity for this purpose. Since satisfactory estimations of covering functionals will be sufficient in Zong’s program, we propose a stochastic global optimization algorithm based on CUDA and provide an error estimation for the algorithm. The accuracy of our algorithm is tested by comparing numerical and exact values of covering functionals of convex bodies including the Euclidean unit disc, the three-dimensional Euclidean unit ball, the regular tetrahedron, and the regular octahedron. We also present estimations of covering functionals for the regular dodecahedron and the regular icosahedron.

The core motivation for this study emanates from Voronin S.'s seminal work on the universality of the zeta function. This universality positions the zeta function as a pivotal element in the conceptual framework of global artificial intelligence, suggesting its potential as a foundational component. A critical barrier to harnessing this promising universality has been the absence of efficient algorithms capable of actualizing this concept.

Conventional methods for pathogen detection in water rely on time-consuming enrichment steps followed by biochemical identification strategies, which require assay times ranging from 24 hours to up to a week. However, in recent years, significant efforts have been made towards the development of biosensing technologies enabling rapid and close-to-real-time detection of waterborne pathogens. In previous studies, we developed a plastic optical fiber (POF) immunosensor using an optoelectronic configuration consisting of a U-Shape probe connected to an LED and a photodetector. Bacterial detection was evaluated with the immunosensor immersed in a bacterial suspension in water with a known concentration. Here, we report on the sensitivity of a new optoelectronic configuration consisting of two POF U-Shape probes, one as the reference and the other as the immunosensor, for the detection of Escherichia coli. In addition, another way of detection was tested where the sensors were calibrated in the air, before immersed in bacterial suspension and then read in the air, making the immunosensor more sensitive and with a faster detection time. This new sensor detected the presence of E. coli at 104 CFU/mL in less than 10 minutes. Currently sub 10 minutes is faster than previous studies using fiber-optic based biosensors. It is also a much simpler and quicker methodology when compared with conventional laboratory technology.

Identifying skeletal remains has been and will remain a challenge for forensic doctors and forensic anthropologists, especially in disasters with multiple victims or skeletal remains in an advanced stage of decomposition. This study proposes a machine learning method to determine gender starting from morphometric analysis of L1-L5 lumbar vertebrae in a modern Romanian population. The purpose of the present study was to observe whether by using the ML method there is a good predictability of gender in forensic identification based on parameters obtained from the metric analysis of the lumbar spine specific to the Romanian population. This paper offers two models of ML, RF and XGB, each with its own characteristics, and presenting different performance, random forest having the best. For both, we used two metrics (accuracy and roc_auc), the latter being the most used to highlight model performance. The L1-L5 lumbar vertebrae exhibit sexual dimorphism and can be used in gender estimation. Machine learning is more accurate in determining gender than discriminatory function analysis.

Perceptual and statistical evidence has underlined voice characteristics of individuals affected by genetic syndromes different from that of normophonic subjects. In this paper we propose a procedure for the systematic collection of such pathological voices and the development of AI-based automatic tools to support differential diagnosis. Guidelines are provided concerning most suitable recording devices, vocal tasks and acoustical parameters, in order to simplify, speed up and make the whole procedure homogenous and replicable. The proposed procedure was applied to a set of 56 subjects, affected by Costello syndrome (CS), Down syndrome (DS), Noonan syndrome (NS) and Smith-Magenis syndrome (SMS). The whole database has been divided into three groups, respectively called: paediatric subjects (PS, individuals < 12 years of age), female adults (FA) and male adults (MA) subjects. In line with literature results, the Kruskal-Wallis test and post-hoc analysis with Dunn-Bonferroni test highlighted several significant differences in acoustical features not only between healthy subjects and patients, but also across syndromes within PS, FA and MA groups. Machine learning provided for the PS group a k-nearest neighbour classifier with 75% accuracy, for the FA group a support vector machine (SVM) model with 84% accuracy and for the MA group a SVM model with 97% accuracy. These preliminary results suggest that the proposed procedure, based on acoustical analysis and AI, might be helpful for an effective non-invasive automatic characterization of genetic syndromes.

Object detection is a popular image processing technique, widely used in numerous applications for detecting and locating objects in images or videos. While being one of the fastest algorithms for object detection, Single-Shot Multibox Detection (SSD) networks are also computationally very demanding, which limits their usage in real-time edge applications. Even though the SSD post-processing algorithm is not the most complex segment of the overall SSD object detection network, it is still computationally demanding and can become a bottleneck with respect to processing latency and power consumption, especially in edge applications with limited resources. When using hardware accelerators to accelerate backbone CNN processing, the SSD post-processing step implemented in software can become critical for high-end applications where high frame rates are required, as this paper shows. To overcome this problem, we propose Puppis, an architecture for hardware acceleration of SSD post-processing algorithm. As experiments will show, our solution will lead to an average SSD post-processing speedup of 34.42 when compared with a software implementation. Furthermore, execution of a complete SSD network will be on average 45.39 times faster than software implementation when the proposed Puppis SSD hardware accelerator is used together with some existing CNN accelerators.

Weeds are a permanent component of anthropogenic ecosystems, which requires strict control to avoid the accumulation of their long–lasting seeds in the soil. With high crop infestation, many elements of crop production technologies (fertilization, productive varieties, growth stimulators, etc.) turn out to be practically meaningless due to high yield losses. Intensive use of chemical herbicides (CH) has led to undesirable consequences: contamination of soil and wastewater, accumulation of their residues in the crop, the emergence of CH-resistant populations of weeds. In this regard, the development of environmentally friendly CH with new mechanisms of action is relevant. Natural phytotoxins of plant or microbial origin may be directly explored in herbicidal formulations (biorational CH) or indirectly as scaffolds for nature-derived CH. This review considers: 1) the main current trends in the development of CH that may be important for enhancement of biorational herbicides; 2) advances in the development and practical application of natural compounds for weed control; 3) the use of phytotoxins as prototypes of synthetic herbicides. Some modern approaches such as computational methods of virtual screening and design herbicidal molecules, development of modern formulations, determination of molecular targets are stressed as important to make exploration of natural compounds more effective.

The present work aims to characterize the mechanical behavior of a new composite material for the conservation and development of the vast historical and architectural heritage that is particularly vulnerable to environmental and seismic actions. The new composite consists of natural hydraulic lime (NHL) -based mortar, reinforced by sisal short fibers randomly oriented in the mortar matrix. The NHL-based mortar ensures the chemical-physical compatibility with the original feature of the historical masonry structures (mostly in stone and clay) aiming to pursue both the effectiveness and durability of the intervention. The use of vegetable fibers (i.e. the sisal one) is an exciting challenge for the construction industry since they require a lower degree of industrialization for their processing, and therefore, their costs are also low, as compared to the most common synthetic/metal fibers. Beams of sisal-composite sizing 160x40x40 mm3 with a central notch are tested in three-point bending, aiming to evaluate both their bending strength and fracture energy. Also, tensile tests and compressive tests were performed on the composite samples, while water retention test and slump test were performed on the fresh mix. Finally, the tensile tests on the Sisal strand were carried out to evaluate the tensile strength of both strand and wire. A final comparison with unreinforced mortar specimens shows that the proposed composite ensures great workability and good performances in term of ductility and strength and it can be considered a promising alternative to the classic fiber-reinforcing systems.

Human metapneumovirus (HMPV) is an important cause of upper and lower respiratory tract disease in individuals of all ages. It is estimated that most individuals will be infected by HMPV by the age of 5 years old. Despite this burden of disease, there remains caveats in our knowledge of virus global genetic diversity due to a lack of HMPV sequencing, particularly at whole genome scale. The purpose of this study was to create a simple and robust approach for HMPV whole genome sequencing to be used for genomic epidemiological studies. To design our assay, all available HMPV full length genome sequences were downloaded from the NCBI GenBank database and used to design four primer sets to amplify long, overlapping amplicons spanning the viral genome and, importantly, specific to all known HMPV subtypes. These amplicons were then pooled and sequenced on an Illumina iSeq; however the approach is suitable to other common NGS platforms. We demonstrate the utility of this method using a representative subset of clinical samples and examine these sequences using a phylogenetic approach. Here we present an amplicon-based method for the whole genome sequencing of HMPV from clinical extracts that can be used to better inform genomic studies of HMPV epidemiology and evolution.

The aims of this work were to examine the new electrode, based on polymer bacteria modified aluminum electrode for simultaneous production of electricity and degradation of phenol. This electrode is based aluminum modified by bacteria inserted in the polymer matrix, developed in situ on the surface. This electrode, designated subsequently by bacteria-polymer-aluminum, Showed stable response and was characterized with voltametric methods, as cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The experimental results revealed that the prepared electrode could be a feasible for degradation of hazardous phenol pollutants. The sensor was successfully applied to the determination of phenol in a real sample with satisfactory results.

Various peptides or non-structural amino acids are recognized by their specific target proteins and perform biological role in various pathways in vivo. Understanding the interactions between target protein and peptides (or non-structural amino acids) provides key information on the molecular interactions, which can be potentially translated to the development of novel drugs. However, it is experimentally challenging to determine the crystal structure of protein-peptide complexes. To obtain structural information on substrate recognition of peptide-recognizing enzyme, X-ray crystallographic studies were performed using triglycine (Gly-Gly-Gly) as main-chain of peptide. The crystal structure of Parengyodontium album Proteinase K in complex with triglcyine was determined at 1.4 Å resolution. Two different bound conformations of triglycine were observed at the substrate recognition site. The triglycine backbone forms stable interactions with β5-α4 and α5-β6 loops of main-chain. One of the triglycine-binding conformations was identical with the binding mode of a peptide-based inhibitor from a previously reported crystal structure of Proteinase K. Triglycine has potential application X-ray crystallography to identify substrate recognition sites in peptide binding enzymes.

Confidence interval of is an interval corresponding to a specified confidence and including the true value. It can be used to describe the precision of a statistical quantity and quantify its uncertainty. Although the principle of maximum entropy (POME) has been used for a variety of applications in hydrology, the confidence intervals of the POME quantile estimators have not been available. In this study, the calculation formulas of asymptotic variances and confidence intervals of quantiles based on POME for Gamma, Pearson type 3 (P3) and Extreme value type 1 (EV1) distributions were derived. Monte Carlo Simulation experiments were performed to evaluate the performance of derived formulas for finite samples. Using four data sets for annual precipitation at the Weihe River basin in China, the derived formulas were applied for calculating the variances and confidence intervals of precipitation quantiles for different return periods and the results were compared with those of the methods of moments (MOM) and of maximum likelihood (ML) method. It is shown that POME yields the smallest standard errors and the narrowest confidence intervals of quantile estimators among the three methods, and can reduce the uncertainty of quantile estimators

Widely used in hydraulics, the Colebrook equation for flow friction relates implicitly to the input parameters; the Reynolds number, Re and the relative roughness of inner pipe surface, ε/D with the output unknown parameter; the flow friction factor, λ; λ=f(λ, Re, ε/D). In this paper, a few explicit approximations to the Colebrook equation; λ≈f(Re, ε/D), are generated using the ability of artificial intelligence to make inner patterns to connect input and output parameters in explicit way not knowing their nature or the physical law that connects them, but only knowing raw numbers, {Re, ε/D}→{λ}. The fact that the used genetic programming tool does not know the structure of the Colebrook equation which is based on computationally expensive logarithmic law, is used to obtain better structure of the approximations which is less demanding for calculation but also enough accurate. All generated approximations are with low computational cost because they contain a limited number of logarithmic forms used although for normalization of input parameters or for acceleration, but they are also sufficiently accurate. The relative error regarding the friction factor λ, in best case is up to 0.13% with only two logarithmic forms used. As the second logarithm can be accurately approximated by the Padé approximation, practically the same error is obtained also using only one logarithm.

This paper presents a thermal runaway prognosis scheme based on the big-data platform and entropy method for battery systems in electric vehicles. It can simultaneously realize the diagnosis and prognosis of thermal runaway caused by the temperature fault through monitoring battery temperature during vehicular operations. A vast quantity of real-time voltage monitoring data was collected in the National Service and Management Center for Electric Vehicles (NSMC-EV) in Beijing to verify the effectiveness of the presented method. The results show that the proposed method can accurately forecast both the time and location of the temperature fault within battery packs. Furthermore, a temperature security management strategy for thermal runaway is proposed on the basis of the Z-score approach and the abnormity coefficient is set to make real-time precaution of temperature abnormity.

The article explores the task of making higher education more profession-oriented. In this context, we consider the technology of structuring and matching professional activities and content of professional education curricula with the help of ontology. This technology employs intelligent analysis of labor market and educational content matching with the aim to organize educational programs and verify professional competences based on their ontological properties. The article also considers development of a professional training cognitive map that can help design the student’s personalized educational trajectory factoring in the given parameters.

Naturally fermented dairy products are an important component of the human diet. They are a valuable source of nutrients as well as vitamins and minerals. Their importance as a source of probiotic bacterial strains should not be overlooked. A number of studies highlights the positive effects of species of the Lactobacillacea on the intestinal microbiota, the overall homeostasis of the body, as well as a complementary treatment for some diseases. However, data on the effects on intestinal epithelial cells of postmetabolites released by bacteria are incomplete. This is likely due to the fact that these effects are species and strain specific. In the present study, we investigated the effects of postmetabolites produced by a pre-selected candidate probiotic strain Limosilactobacillus fermentum on HT-29 intestinal epithelial cells. Our data showed a pronounced proliferative effect, evaluated by flow cytometry, quantification of cell population, and determination of mitotic index. This was accompanied by stabilization of the cell monolayer, measured by increase of TEER (trans epithelial electric resistance) and reorganization of actin filaments. The data obtained are a clear indication of the positive effects, that the products secreted by L. fermentum strain 53 have on the intestinal epithelial cells.

This study evaluated batch fermentation modes, namely, separate hydrolysis and fermentation (SHF), Quasi-simultaneous saccharification and fermentation (Q-SSF), and simultaneous saccharification and fermentation (SSF), and fermentation conditions, i.e., enzyme and yeast loadings, nutrient supplementation and sterilization, on high titer bioethanol production from SPORL-pretreated Douglas-fir forest residue without detoxification. The result indicated Q-SSF and SSF were obviously superior to SHF operation in terms of ethanol yield. The enzyme loading showed a strong positive correlation between enzyme loading and the ethanol yield. The nutrient supplementation and sterility was not necessary for ethanol production from SPORL-pretreated Douglas-fir. The yeast loading showed no significant influence on the ethanol yield for typical SSF conditions. The terminal ethanol titer of 43.2 g/L, or 75.1% theoretical based on glucose, mannose, and xylose theoretical was achieved when SSF was conducted at the condition of following: whole slurry solids loading of 15%, enzyme loading of 20 FPU/g glucan, 1.8 g/kg (wet) yeast loading, without nutrition supplementation and sterilization, at 38°C, on shake flask at 150 rpm for 96h. It is believed that with mechanical mixing, enzyme loading can be substantially reduced with affect ethanol yield by using a long fermentation time.

This review paper aims to understand the current processing plant maintenance systems and further identify on-stream phased array corrosion mapping (PACM) to reduce turnaround maintenance (TAM) activity during plant operations. Reducing the TAM duration and extending the TAM interval are common goals of most researchers. Thus, a detailed review was performed to understand the maintenance systems and the problems faced. Furthermore, a review of the current PACM application and the possibility of applying it during on-stream inspection was also per-formed. PACM has better detectability for localized corrosion, and the results can be obtained for a range of thicknesses, which is the main advantage of this method. The challenge is to apply it to a surface with high temperatures, owing to the greater probe contact and the limitation of ultrasonic properties. This literature review provides a more precise direction for future research to evaluate PACM on piping that can be utilized in inspection during plant operation at elevated temperatures. Detecting and monitoring corrosion growth without shutdown.

Sulfite and kraft pulping are two principal methods of industrial delignification of wood. In recent decades, those have been considered as possibilities to pretreat recalcitrant wood lignocellulosics for enzymatic hydrolysis of polysaccharides and subsequent fermentation of obtained sugars to valuable bioproducts. Current work compares chemistry and technological features of two different cooking processes in the preparation of polysaccharide substrates for deep sacchari-fication with P. verruculosum glycosyl hydrolases. Bleached kraft and sulfite pulps were subjected to hydrolysis with enzyme mixture of high xylanase, cellobiohydrolase and β-glucosidase activities at a dosage of 10 FPU/g of dry pulp and fiber concentration of 2.5, 5 and 10%. HPLC was used to analyze soluble sugars after hydrolysis and additional acid inversion of oligomers to monosaccharides. Kraft pulp demonstrated higher pulp conversion after 48 h (74-99%), mostly resulted from deep xylan hydrolysis. Sulfite-pulp hydrolysates, obtained in similar conditions, due to higher hexose concentration (more than 50 g/L), had higher fermentability for industrial strains producing alcohols, microbial protein or organic acids. Along with saccharification, enzymatic modification of non-hydrolysed residues occurred, which led to decreased degree of polymerization and composition changes in two industrial pulps. As a result, crystallinity of kraft pulp increased by 1.3%, which opens possibilities for obtaining new types of cellulosic products in pulp and paper industry. The high adaptability and controllability of enzymatic and fermentation processes creates prospects for the modernization of existing factories.

The urgency to address the adverse impacts of climate change on livelihoods and ecosystems has seen an increase in global driven initiatives. However, shifting vulnerabilities associated with land use resource based adaptation and maladaptive feedback loops they create have been given low attention. Policy discourses that frame adaptation as a local responsibility and bias towards reducing industrial Greenhouse gas (GHG) emissions at the expense of Agricultural emissions across scale are thought to account for the undesirable situation. This calls for a reflective policy framework and climate policy innovation. We provide counter arguments using Drivers, Pressure, State, Impact, Response (DPSIR) model and telecoupling principles to suggest use of resilience as an integrative lens in visualising the proposal. Using a case study on resource constrained smallholder dairy production systems, western Kenya, we analyse the critical issues in the context of decision making and environmental externalities. The effect of price risks on dairy cattle feeding strategies and ultimately carbon footprints and ecoefficiencies were examined through methane simulation and gross margin analysis (GM). The lowest ecoefficiency was associated with exclusively local coping strategies i.e. Maize Stover (Ms), while the highest ecoefficiency was observed in feeding strategies that utilise external resources and/or legume fodders. We conclude that management of externalities need to capture institutional, economic processes and incentive systems, as well as organizational and policy coherence to shape the interests and behaviour of individual land user. In particular, policy innovation should focus on price and market risks as critical factors that mediate actor decision making at implementation level as they impact GHG emissions which transcend individual decision boundaries.

The rationale of this study is to explore underlying sustainability assessment constructs in the context of WCSM implementation as a key focus by manufacturing industries in the journey of achieving world-class status in their manufacturing operations. This study primarily intends to collect primary data from 143 plant managers and supervisors through the structured questionnaires at select manufacturing companies in south India on the basis of area-cum-purposive sampling technique. The survey questionnaire is designed exclusively to reflect the sustainability assessment dimensions in terms of social, economic and environmental issues to examine the impetus of WCSM implementation on the competitive advantage of manufacturing companies. The empirical findings of this study clearly revealed that sustainable manufacturing practices, sustainability programs, creation of sustainable products, safety for employee, community and product, operational efficiency and integration of sustainability goals in day-to-day manufacturing activities were considered to be the key indicators by the manufacturing firms in their never-ending journey towards becoming the world class. Moreover, the WCSM practices & techniques adopted by sample manufacturers have a substantial positive influence on the achievement of competitive advantage in their manufacturing and business operations.

The Shimensi super-large tungsten polymetallic deposit is located in Late Jurassic- Early Cretaceous Porphyry-Skarn tungsten ore belt in the south Yangtze metallogenic belt. There are 3 types of mineralization: veinlet-disseminated type, thick quartze vein type and hydrothermal crypto-explosive breccia type. Based on geological studies, this paper presents new petrographic, microthermometric, laser Raman spectroscopic and hydrogen and oxygen isotope research of the fluid inclusions from the deposit. The results show that there are five different types of fluid inclusions: liquid-rich inclusions, vapor-rich inclusions, pure liquid inclusions, pure vapor inclusions, and fluid inclusions containing a solid crystal. The homogenization temperatures of the fluid inclusion range from 140℃-270℃, the salinities are 3%-5% NaCleq and the densities of ore-forming fluid range from 0.64 g/cm3 to 0.99 g/cm3. For the analyses of Laser Raman spectroscopic, the ore-forming fluids can be approximated by a Ca2+-Na+-SO42--Cl- fluid system with small amount of CO2, CH4 and N2. Otherwise, the datas of the pressure, pH and Eh show a fluid metallogenic environment of low pressure, weak acid and weak reduction. The values of the homogenization temperature in 3 types of orebodies suggest that the mineralization is characterized by the decrease of the temperature under the condition of the fluid immiscibility. The H-O isotope values are interpreted to indicate that the ore-forming fluids are mainly composed of magmatic water, and also the meteoric water was added with the process of the magma rising.

Electric propulsion systems have emerged as a disruptive technological approach towards achieving sustainable and climate-neutral aviation. To expand the operational envelope of such propulsion units in terms of altitude and velocity, an enclosing duct and counter-rotating rotors to enhance efficiency can be utilized. In this study, an iterative CFD-based design tool developed for these novel propulsion systems is utilized to design a reference engine. A substantial reduction in discrepancies between initial specifications, subsequent CFD simulations, and experimental investigations compared to conventional design tools relying on empirical formulations can be demonstrated using a classic rotor-stator configuration. This allows the CFD-based tool to be validated for designing scalable counter-rotating fan engines. Furthermore, to facilitate the transferability of research on such propulsion systems, a validated manufacturing process for composite blades is presented. This effort aims to make state-of-the-art technology accessible to smaller research projects, promoting the widespread adoption of electric propulsion technology in the aviation sector.

Four 1,10-phenanthroline derivatives (1-4) have been synthesized as potential telomeric DNA binders, three substituted in their chains with thiosemicarbazones (TSCs), the neutral compound (1) and two ionic derivatives (PF6¯ and BF4¯ salts, 2 and 3, respectively), and a fourth corresponding to a 4-phenylthiazole derivative (4). Quadruplex and dsDNA interactions have been preliminarily studied, especially for the neutral derivative 1, by FRET-based DNA melting assays, equilibrium dialysis (both competitive and non-competitive), circular dichroism and viscosity titrations. The TSC derivatives bind and stabilize the telomeric Tel22 quadruplex more efficiently than dsDNA, with an estimated 24-fold selectivity determined by equilibrium dialysis for compound 1. In addition, cytotoxic activity against different tumor cells (PC-3, DU145, HeLa, MCF-7 and HT29), and two non-tumor cell lines (HFF-1 and RWPE-1) has been evaluated. Except for the 4-phenylthiazole derivative, which was inactive, the compounds show moderate cytotoxic properties, with the salts displaying lower IC50 values, compared to the neutral TSC. However, the neutral derivative was the only compound that exhibited a modest selectivity in the case of prostate cells (tumor PC-3 versus healthy RWPE-1). Cell cycle analysis and Annexin V/PI assays revealed that the compounds can produce cell death by apoptosis, an effect that has proven similar to that demonstrated by other known 1,10-phenanthroline G4 ligands endowed with antitumor properties, such as PhenDC3 or PhenQE8.

In order to create a smart mine, it is necessary to digitize the existing emulsion pumping station. Existing research does not achieve this task, this paper designs and manufactures a high-pressure, high-flow, high-water-based mining digital controlled unloading valve (abbreviated as HLHMDCUV), which achieves good results in both theoretical analysis and experimental testing. In order to obtain the accurate dynamic characteristics, this valve was analyzed by simulation using AMESim. Based on the dynamic performance analysis and the flow field analysis inside the valve body, the structure has been optimized to improve the performance of HLHMDCUV. The results show that the HLHMDCUV has a better dynamic characteristics, which were also demonstrated in the experiment. At the same time, the HLHMDCUV is easy to disassemble and maintain. This paper provides a theoretical basis for the design of such valves, which is conducive to the subsequent promotion of intelligent mine transformation.

There is voluminous literature on Food Security in Africa. This study explicitly considers the spatio-temporal factors in addition to the usual FAO-based metrics in modeling and understanding the dynamics of food security and nutrition across the African continent. To better understand the complex trajectory and burden of food insecurity and nutrition in Africa, it is crucial to consider space-time factors when modeling and interpreting food security. The spatio-temporal anova model was found to be superior(employing statistical criteria) to the other there models from the spatio-temporal interaction domain models. The results of the study suggest that dietary supply adequacy, food stability, and consumption status are positively associated with severe food security, while average food supply and environmental factors have negative effects on Food Security and Nutrition. The findings also indicate that severe food insecurity and malnutrition are spatially and temporally correlated across the African continent. Spatio-temporal modeling and spatial mapping are essential components of a comprehensive practice to reduce the burden of severe food insecurity. likewise, any planning and intervention to improve the average food supply and environment to promote sustainable development should be regional instead of one size fit all.

This study focused on the development and implementation of an educational simulation program based on Korean Triage and Acuity Scale (KTAS) for nurses in emergency medical centers who completed KTAS training. We also examined its educational effects based on the evaluation of clinical decision-making ability, job satisfaction, and customer orientation. The study participants were 30 nurses in the emergency medical center of a general hospital. Data were collected from May 3 to 24, 2017, and analyzed using SPSS 22.0. There was a significant difference in the mean scores in clinical decision-making ability, job satisfaction, and customer orientation before and after simulation education. In other words, emergency nurses who received KTAS-based simulation education program improved their clinical decision making ability, job satisfaction, and customer orientation. Based on the results of this study, it is expected that it can be used for KTAS education, and it was found that simulation-based education is a useful learning method for triage nurses in emergency medical center.

This study aims to verify the time-variant feature of American ginseng (AG) continuous cropping obstacles and to explore the factors impeding continuous cropping. We verified the feature with a plant-soil feedback pot experiment and then investigated the factors by comparing the properties of control soils that had not been previously used for growing ginseng (CS) with those of soils with a 10-year-crop-rotation cycle following the growth of AG (RS). It’s found that the survival rate of AG in RS was lower than that in CS. The RS had lower pH, available potassium content, and urease activity. Additionally, p-coumaric, p-hydroxybenzoic, vanillic, caffeic, and cinnamic acid levels were lower in RS than in CS, but salicylic acid levels showed the opposite pattern. RS had higher Rhodanobacter and lower Acidothermus, Sphingomonas relative abundances in bacterial community. It’s also found that many bacteria were substantially correlated with phenolic acids and soil physiochemical properties. Results indicate that even after 10-year crop rotation, the negative effects of prior continuous cropping of AG has not been eliminated. The growth of AG can be affected negatively with deterioration of soil physicochemical properties and with lower levels of phenolic acids which promote pathogen reproduction. Probiotics reduction also weighs. Moreover, biotic factors are interrelated with abiotic ones. Therefore, it can be inferred that the comprehensive change of soil properties is the main obstacle for continuous cropping.

Reducing sedentary behaviours can help prevent non-communicable diseases, particularly among young adolescents with long term illnesses or disabilities (LTID). Much of young people’s voluntary sedentary time is related to screen-time behaviours (STBs) such as TV viewing, playing computer games, and using the computer for other activities. Although public health data on adolescents’ STB is growing, information about adolescents with LTID is currently lacking in a European context. The purpose of this study is to compare time on STBs between adolescents with and without LTID in European Countries through the HBSC 2013/14 study. Young adolescents (n = 61,329; boys 47.8%) from 15 European countries reported the time spent on TV viewing, playing computer games, and using the computer for other purposes on weekdays and the weekend. STBs were dichotomised based on international recommendations of less than 2 h per day, and Chi-square tests of independence were performed to investigate differences. STB time was combined to produce a sum score as dependent variable in multiple analysis of covariance with age and family affluence as covariates. There were statistically significant differences in computer gaming among boys and other computer use among girls for both weekdays and weekends, whereby adolescents with LTID reported higher use. In addition, both boys and girls with LTID spent more time on STBs than their same sex peers without LTID (Boys, F = 28.17, p < 0.001; Girls, F = 9.60, p = 0.002). The results of this study indicate a need for preventive strategies to address high levels of STB among young adolescents with LTID and reduce the risk of poor health outcomes associated with higher levels of sedentary behaviour.

The research was undertaken to study the way deformation behaves in ultrafine-grained (UFG) conducting Al-Zr alloys doped with Sc, Hf, and Yb. All in all, 8 alloys were studied with zirconium partially replaced by Sc, Hf, and/or Yb. Doping elements (X = Zr, Sc, Hf, Yb) in the alloys total 0.4 wt.%. The choice of doping elements is conditioned by possible precipitation of Al3X particles with L12 structure while annealing these alloys. Such particles provide higher thermal stability of a nonequilibrium UFG microstructure. Initial coarse-grained samples were obtained by induction casting. The UFG microstructure in the alloys was formed by Equal Channel Angular Pressing (ECAP) at 225°C. Superplasticity tests were carried out at temperatures ranging from 300 to 500 °C and strain rates varying between 3.310-4 and 3.310-1 s-1. The highest values of elongation to failure are observed in Sc-doped alloys. A UFG Al-0.2%Zr-0.1%Sc-0.1%Hf alloy has maximum ductility: at 450 °C and a strain rate of 3.310-3 s-1, relative elongation to failure reaches 765%. At the onset of superplasticity, stress–strain curves are characterized by a stage of homogeneous strain and a long stage of localized plastic flow. The dependence of homogeneous strain (eq) on test temperature in UFG Sc-doped alloys is increasing uniformly, which is not the case for other UFG alloys with eq(T) dependence peaking at 350-400 оС. Strain rate sensitivity coefficient of flow stress m is small and does not exceed 0.26-0.3 at 400-500 °C. In UFG alloys containing no scandium, m coefficient is observed to go down to 0.12-0.18 at 500 оС. It has been suggested that lower m values are driven by intensive grain growth and pore formation in large Al3X particles, which develop specifically at the ingot crystallization stage.

Tumor associated macrophages (TAMs) in the tumor microenvironment (TME) promote immune evasion, cancer cell proliferation, and metastasis. Ongoing research is focused on finding ways to prevent tumor growth by inhibiting TAM polarization, which has shown a correlation with unfavorable prognosis in clinical studies. Pancreatic adenocarcinoma upregulated factor (PAUF) is a protein secreted from pancreatic cancer (PC) and acts as a TME modulator that affects the TME by acting on not only cancer cells but also stromal cells and immune cells. Tumor cells can evade the immune system by PAUF binding to Toll-like receptor (TLR) in monocytes, as this research shows. In this study, the examination centered around the recruitment of human monocytes by PAUF and the subsequent differentiation into macrophages. In an in vitro chemotaxis assay, PAUF induced chemotactic migration of TLR2-mediated monocytes. In addition, PAUF induced differentiation of monocytes into M2 macrophages, which was verified based on expressing surface markers and cytokines and morphological analysis. The inhibition of T cell proliferation and function was observed in differentiated M2 macrophages. To conclude, these findings indicate that PAUF functions as a promoter of cancer progression by regulating the recruitment and differentiation of macrophages within TMEs, ultimately causing immunosuppression.

The objective of the present contribution was to design and characterize resveratrol (RSV) and tocopherol (TOC) loaded 11S quinoa seed protein nanocomplexes. Firstly, molecular docking was performed to describe the probable binding sites between protein and ligands. Isothermal titration calorimetry allowed to obtain the thermodynamic parameters that described the molecular interactions between RSV or TOC with the protein. 11S globulin intrinsic fluorescence spectra showed a quenching effect exerted by RSV and TOC, demonstrating protein-bioactive compounds interactions. Stern-Volmer, Scatchard and Förster resonance energy transfer models application confirmed static quenching and allowed to obtain parameters that described the 11S-RSV or 11S-TOC complexation process. Secondly, protein aggregation induced by bioactive compounds interactions was confirmed by dynamic light scattering and atomic force microscopy with diameters <150 nm, detected by both techniques. Finally, it was found that antioxidant capacity of single 11S globulin did not decrease, meanwhile was additive for 11S-RSV. These nanocomplexes could constitute a real platform for the design of nutraceutical products.

Drug resistance in melanoma is a major hindrance in cancer therapy. Growth hormone (GH) plays a pivotal role in contributing to resistance to chemotherapy. Knocking down or blocking the GH receptor has been shown to sensitize the tumor cells to chemotherapy. Extensive studies have demonstrated that exosomes, a subset of extracellular vesicles, play an important role in drug resistance by transferring key factors to sensitize cancer cells to chemotherapy. In this study, we explore how GH modulates exosomal cargoes from melanoma cells and their role in drug resistance. We treated the melanoma cells with GH, doxorubicin, and GHR antagonist, pegvisomant, and analyzed the exosomes released. Additionally, we administered these exosomes to recipient cells. The GH-treated melanoma cells released exosomes with elevated levels of ABC transporters (ABCC1 and ABCB1), N-cadherin and MMP2, enhancing drug resistance and migration in recipient cells. GHR antagonism reduced these exosomal levels, restoring drug sensitivity and attenuating migration. Overall, our findings highlight a novel role of GH in modulating exosomal cargoes that drive chemoresistance and metastasis in melanoma. This understanding provides insights to the mechanisms of GH in melanoma chemoresistance and suggests GHR antagonism as a potential therapy to overcome chemoresistance in melanoma treatment.

Civil construction is one of the oldest known activities in humanity, reports indicate that builders of the Roman Empire already sought the reuse of materials. Currently, considering the depletion of the supply of natural resources, the recycling of solid waste from construction and demolition (CDW), not only provides new products, but also offers ecological and economic alternative. In this context, this research seeks new variables for the disposal of waste from construction and demolition having in the composition of artificial finishing stones an odd possibility to be analyzed. In this research was developed and analyzed a new composite from the construction and demolition waste using as a binder an orthophthalic polyester resin. The residue was sieved and separated by granulometry by the simplex centroid method, the best compacted mixture was determined statically by ANOVA and the Tukey test. The residue was characterized by X-ray fluorescence and the resin by infrared spectroscopy by Fourier transform. Artificial rock plates were produced with 85% residue and 15% resin, by mass, by vibro system, compression and vacuum, later were cut for mechanical, physical and chemical tests. Analysis was performed by scanning electron microscopy of fractured compositions as well as grain sizes. The artificial stone with the best results presented density of 2,256 g/cm3, water absorption of 0.69%, apparent porosity of 1.55%; bending strength of 34.74 MPa and compression of 111.96 MPa; good results in the alterability and thermal tests. In this satisfactory scenario, the use of this waste in the composition of artificial rocks is promising because it directly meets the concept of sustainable development; replacing the concept of end-of-life of the linear economy, by new circular flows of reuse, restoration and renovation, in an integrated process the circular economy; as well as the quality of the final product with properties similar to the artificial rocks already marketed.

Transport systems are essential in the path towards sustainable urbanisation and the transition to more sustainable living. Recently, European cities have undergone substantial changes, owing to which suburbanisation is posing new challenges. Suburban areas are often more affordable in terms of housing, but their neighbourhoods tend to be car-oriented. This leads to higher commuter costs, immobility, transport and time poverty, pollution, and accident rates and a lack of social interaction. To offer sustainable mobility options to citizens, we must comprehensively understand their specific mobility practices, together with their personal characteristics, and the built environment. This study centred on the Barcelona Metropolitan Region, which has a public transport network that covers the entire area. The aim of this study was to examine the relationship between travel behaviour, transport mode use, individual characteristics, and place of residence, using detailed information sources. Herein, we used data from the annual travel survey conducted in the Barcelona region from 2018 to 2021, together with land-use and other information sources. Our findings suggest that transport policies have encouraged sustainable mobility practices, particularly in the centre of Barcelona. Despite the positive results, considerable disparities exist between the inner and outer city, with notably decreased sustainable mobility practices in the latter. Our results indicate that promoting sustainable mobility policies requires further changes in transport, city, and land-use planning that consider equity, the socioeconomic profile of citizens, and mixed urban planning.

Leakage is important in groundwater cycle research and water resource management, the key is to construct the spatial distribution of aquitard. Influenced by natural and anthropogenic factors, the density of boreholes restricts the accuracy of aquitard structure. This paper explores the K-Nearest Neighbor (KNN) method to construct multi-layer aquitard structure in the hinterland of Songnen Plain, Northeast China, with limited boreholes, and compare it with Inverse Distance Weight (IDW) and Ordinary Kriging (OK) methods to evaluate the leakage. The KNN needs to first identify the eigenvalue-k of each layer, which is used to enhance the trainset to obtain the testset with a larger amount of sample data enhanced, and to make a prediction to obtain the 3-dimensional structure of aquitard. KNN is more accurate than the IDW and OK. The aquitrad structure obtained by the KNN is different from the IDW and OK, the thickness difference is 3.53%-54.00%, the area difference is 2.28%-23.91%, and the difference in leakage can be up to 24.51%. KNN also has a high degree of identification of aquitard edges, which can help the prevention of groundwater pollution. The results provide guidance for water balance analysis, borehole engineering design, water resources management and exploitation.

The architectural configuration of the space plays a crucial role in the acoustics of Neonatal Intensive Care Units (NICUs). The design of the environment, the materials used in construction, and the organisation of noise sources within the room significantly influence the noise levels present in these critical areas. For this research, noise levels found in two hospitals, with different architectural configurations and sizes but similar construction materials, have been analyzed. Data were recorded at 1-second intervals over a period of more than 24 hours. The data collected in these hospitals confirm the magnitude of the noise problem in NICUs, highlighting the urgent need to address it effectively to ensure an appropriate and safe environment for the recovery of neonates. Good architectural planning can help mitigate noise, while poor configuration can exacerbate acoustic problems, negatively affecting both patients and healthcare staff. It is crucial to involve architects, acoustic engineers, and healthcare staff in the design and renovation of NICUs to create spaces that not only meet medical needs but also consider the auditory well-being of neonates.

Reducing under-five mortality is a key indicator of a country's overall development, yet in Angola, understanding the factors contributing to hospital deaths in this vulnerable demographic remains incomplete despite improvements in healthcare infrastructure and public health policies. With one of the highest under-five mortality rates in Sub-Saharan Africa, Angola grapples with significant challenges, including malaria, malnutrition, pneumonia, neonatal conditions, and intestinal infectious diseases, as leading causes of death among children. This study aimed to identify factors associated with these hospital deaths in children aged 28 days to five years admitted to DBPH in Luanda between May 2022 and June 2023. Utilizing a hospital-based case-control design, a total of 1020 children were studied, with 340 cases of hospital deaths. Notably, distal and intermediate determinants emerged as primary predictors of hospital mortality, revealing significant associations with maternal education status, maternal alcohol consumption during pregnancy, short hospital stays (≤24h), poor nutritional status, short interbirth intervals, maternal age ≤19 years, and maternal age ≥35 years. These findings underscore the preventable nature of most under-five hospital deaths and emphasize the urgent need to address social inequities and enhance the quality of primary healthcare services to effectively mitigate child mortality.

Fractional calculus gained a lot of attention in the last couple of years. Researchers discovered that processes in various fields follow rather fractional dynamics than ordinary integer-ordered dynamics, meaning the corresponding differential equations feature non-integer valued derivatives. There are several arguments for why this is the case, one of them being that fractional derivatives’ inherit spatiotemporal memory and/or the ability to express complex naturally occurring phenomena. Another popular topic nowadays is machine learning, i.e., learning behavior and patterns from historical data. In our ever-changing world with ever-increasing amounts of data, machine learning is a powerful tool for data analysis, problem-solving, modeling, and prediction. It further provides many insights and discoveries in various scientific disciplines. As these two modern-day topics provide a lot of potential for combined approaches to describe complex dynamics, this article reviews combined approaches of fractional derivatives and machine learning from the past, puts them into context, and thus provides a list of possible combined approaches and the corresponding techniques. Note, however, that this article does not deal with neural networks, as there already is profound literature on neural networks and fractional calculus. We sorted past combined approaches from the literature into three categories, i.e., preprocessing, machine learning &amp; fractional dynamics, and optimization. The contributions of fractional derivatives to machine learning are manifold as they provide powerful preprocessing and feature augmentation techniques, can improve physically informed machine learning, and are capable of improving hyperparameter optimization. Thus, this article serves to motivate researchers dealing with data-based problems, to be specific machine learning practitioners, to adopt new tools and enhance their existing approaches.

Background: Older adults with bipolar disorder (OABD) are individuals aged 50 years and older with bipolar disorder (BD). People with BD may have fewer coping strategies or resilience. A long course of the disease, such as in OABD, could impact the development of resilience strategies, but this remains under-researched in OABD. Therefore, this study aims to assess resilience levels within the OABD and explore associated factors, hypothesizing that resilience could improve psychosocial functioning, wellbeing and quality of life of OABD patients. Methods: This study sampled 33 OABD patients from the OABD cohort at the Bipolar and Depressive Disorders Unit of Hospital Clinic of Barcelona. This was an observational, descriptive and cross-sectional study. Demographic and clinical variables as well as psychosocial functioning, resilience and cognitive reserve were analyzed. Resilience was measured using the CD-RISC-10. Non-parametric tests were used for statistical analysis. Results: The average CD-RISC-10 score was 25.67 points (SD 7.87). Resilience negatively correlated with the total number of episodes (p = 0.034), depressive episodes (p = 0.001), and the FAST (p < 0.001). Participants with normal resilience had a lower FAST (p = 0.046), a higher CRASH (p = 0.026), and more EOBD (p = 0.037) compared to those with low resilience. Conclusions: OABD individuals may exhibit lower resilience levels which correlate with more psychiatric episodes, particularly the number of depressions and worse psychosocial functioning and cognitive reserve. Better understanding and characterization of resilience could aid in early identification of patients requiring additional support to foster resilience and enhance OABD management.

The paper explores the Kullback-Leibler divergence formalism (KLDF) applied to the stable MG1 queue manifold. It explores the analytic forms of state probabilities and their maximization based on entropy functionals, subject to normalization and mean value constraints. The credibility of KLDF is justified through consistency axioms, and the application of Rényi's and Tsallis's formalisms on a stable M/G/1 queue is examined, resulting in novel state probabilities and insights into information theory of Queue Learning.

The present study shows a methodology for analyzing and designing a cylindrical packed-bed reactor considering stationary and dynamic models. The design comprises the reactor's stationary and dynamic governing differential equations for mass and heat transfer under multi-dimensional approaches. The results included simulation of concentration, temperature, and reaction rate profiles via the 1-D and 2-D differential equations solution with FlexPDE software. The analysis was complemented with a scaled 3-D dynamic model implemented in COMSOL Multiphysics. Both FlexPDE and COMSOL Multiphysics relied on the finite element technique to solve the governing differential equations. The simulated concentration and temperature profiles from both FlexPDE and COMSOL models were compared to experimental data gathered from literature (specifically from a Fischer-Tropsch process to produce low-molecular-weight hydrocarbons in a configuration of cylindrical packed-bed reactors). Simulated concentration and temperature profiles from the 2-dimensional dynamic model and the COMSOL model were in good agreement with the trend observed in experimental data. Finally, the predicted reaction rate profiles from the COMSOL model and the 2-dimensional dynamic model followed the temperature trend, thus reflecting the temperature dependence of the reaction.

The main purpose of this paper is to present a comprehensive view of the application mathematical models in the designing and implementing SSCM beside to solving problems and making decision. The research questions are: what mathematical models are used for designing and implementing sustainable supply chain management, how to use them, which industries implemented in, what modules of SSCM depth in and finally finding the gaps of researches. The methodology of research is Systematic Literature review through peer review papers which are published in high ranking journals. In this paper, First, we search all papers through scientific data bases like Scopus, science direct, MDPI, Springer, Google Scholar, then, screening papers based on the criteria such as subject of paper, journals impact factor which is published in-should be peer review journal- and relative content of the papers. Finally, we selected 245 papers with three steps screening through 2806 papers that they have enough quality and relative to our research goals for context analysis. Through context analysis, first we categorized the information of the papers and drew the current situation of researches in the framework of our topic. Then, we evaluate and compare the goals of sustainability and current situation and found the gapes, then, offered new suggestions like implementing SSCMs models in pollutant industries like casting industry, Heavy industry, coal Industry and so on. On the other hand, there are gaps in researches in some modules of SSCM such as packaging, designing products, etc.

This manuscript employs a surface stress-driven nonlocal model to explore the combined effects of long-range interaction and surface energy on higher vibration modes of functionally graded nanobeams. The nanobeam theory, based on Bernoulli-Euler kinematics, incorporates surface effects such as surface elasticity, surface residual stresses, surface density, and rotary inertia. Hamilton's principle is applied to derive the governing equation with size-dependent considerations. The main outcomes of a parametric investigation, considering four different kinematic boundary conditions (Cantilever, Simply-Supported, Clamped-Pinned, and Doubly-Clamped) while varying the nonlocal parameter and material gradient index, are presented and discussed. Additionally, the normalized natural frequencies for the second, third, fourth and fifth modes of vibrations are provided and analyzed for each case under study. The results underscore the model's effectiveness in capturing surface energy effects on the overall dynamic behavior of functionally graded Bernoulli-Euler nanobeams, offering a cost-effective approach for designing and optimizing nano-scaled structures.

Keywords: xylose reductase; cashew apple bagasse; bioprocesses; fluid dynamics; xylitol.

Molecular analysis of growing teratoma syndrome has not been extensively studied. Here, we report a 14-year-old boy with a growing mass during treatment for a mixed germ cell tumor of the pineal region. Tumor markers were negative; thus, growing teratoma syndrome was suspected. A radical resection via the occipital transtentorial approach was performed, and histopathological examination revealed a teratoma with malignant features. Methylation classifier analysis confirmed the diagnosis of teratoma, and DMRT1 loss and 12p gain were identified by copy number variation analysis, potentially elucidating the cause of growth and malignant transformation of the teratoma. The patient remains in remission after intense chemoradiation treatment as a high-risk germ cell tumor.

Contaminants of Emerging Concern (CECs), a new category of contaminants in limelight, is a major issue of global concern. The pervasive nature of CECs and its harmful effects, such as cancer, reproductive disorders, neurotoxicity etc., make the situation alarming. The perilous nature of CECs lies in the fact that even very small concentration of CECs can cause great impacts on living beings. It also has a nature of bioaccumulation. Thus, it is the ultimate need to have efficient sensors for detection of CECs to ensure safe living environment. Electrochemical sensors are an efficient platform for CEC detection as they are highly selective, sensitive, stable, reproducible, and prompt, and can detect very low concentrations of analyte. Major classes of CECs are pharmaceuticals, illicit drugs, personal care products, endocrine disruptors, newly registered pesticides, and disinfection by-products. This review focusses on CECs, including their sources and pathways, health effects caused by them, and electrochemical sensors as reported in literatures under each category for the detection of major CECs.

Nutritional epigenomics is exceptionally important because describes the complex interactions among food compounds and epigenome modifications. A healthy diet can improve the quality of life and, alleviate the progression and symptomatology of many complex diseases such as osteoarthritis (OA). Phytonutrients or bioactive compounds, which are secondary metabolites of plants, can protect against OA by suppressing the expression of inflammatory and catabolic mediators, and modulating epigenetic changes in DNA methylation, histone or chromatin remodelling of key inflammatory genes and noncoding RNAs. The combination of natural epigenetic modulators is crucial because their additive and synergistic effects, safety and therapeutic efficacy, and lower adverse effects than conventional pharmacology in the treatment of OA. In this review, we have summarised the chondroprotective properties of bioactive compounds and nutraceuticals for the management, treatment, or prevention of OA in both human and animal [1]studies. Some of them have been considered as natural epigenetic modulators that can modify the activity of various epigenetic factors and, alter the expression of genes related to inflammation and cartilage destruction. However, this complex pathology with inflammatory mediators has been little studied at the nutriepigenomic level. Further research is needed towards bioactive compounds as epigenetic modulators in OA, likewise, determine their potential value for future clinical applications in OA patients.

In this investigation, Cu-Ni alloys for possible fixed dental prostheses (FDPs), with different aluminum additions were produced using vacuum atmosphere. Annealed alloys (He atmosphere) with low aluminum concentration showed a coarse dendritic structure, while samples with high aluminum concentration showed a refined dendritic structure. Structural defects analyses have shown a relative low vacancy concentrations. Hardness value increases approximately five times for the alloyed samples compared with the as-cast and unalloyed samples. Compression test results have shown a noticeable strengthening improvement, mainly in samples heat treated with 10 at % Al, while samples with 5 at % Al presented also acceptable resistance. Results are discussed in terms of dislocation mobility and hardening precipitation mechanism.

Water hyacinth (Eichhornia crassipes) is a potential accumulator of water pollutants in aquatic ecosystems, and its presence in water systems can affect water quality. This study used different field measurements and laboratory tests of Lake Water to determine the impact of water hyacinth phytoremediation capacity. A total of eight sampling stations used for the two lakes; Lake Koka and Lake Ziway. Sampling stations were selected from sites infested with water hyacinth (low, medium and high) and non-water hyacinth aquatic plants during wet and dry seasons to compare the effects of plants on water quality in the two lakes. All the sampled stations had various human interventions. The water samples were tested for the selected physico-chemical properties namely phosphate, nitrate, pH, electrical conductivity (EC), Biological oxygen demand (BOD5), temperature, and heavy metals (Chromium (Cr), Lead (Pb), Cadmium (Cd), Zinc (Zn), and Copper (Cu). These water quality variables were compared by means of ANOVA. Despite the COD of Lake Ziway, this study found no significant (p &gt; 0.05) variation in the concentrations of Cu, EC, pH and temperature between wet and dry seasons in either lake. Variations in Zn concentration and other physico-chemical parameters (EC, BOD, COD, nitrate, phosphate) between low, medium and high levels of water hyacinth were significant in both lakes (p&lt;0.05). Water hyacinth has shown significant phytoremediation nature during wet and dry seasons. The lowest average heavy metal, phosphate, and nitrate concentrations; and significant pH and temperature variations were observed in Lakes Koka and Lake Ziway, among water hyacinth and other grass-infested sites.

The cultivation of berry crops by the type of "intensive plantings" is an economic and scientific approach in modern horticulture. The principles of forming a red currant assortment adapted to mechanized harvesting are considered. The bush should be compact for mechanized harvesting and have a straight-growing or slightly spreading shape. The morphological structure of the bush is a feature determined by the genotype. The ripening period of berries and the mechanical parameters of berries assess the effectiveness of the berry harvesting process. With the help of agrotechnical techniques, the mechanical parameters of berries cannot be improved and are de-termined by weather, soil, water, and climatic factors. The present study was conducted in the 2021-2022 season using 14 red currant genotypes of different geographical and genetic origins to assess cultivars' suitability for machine harvesting. In most of the studied cultivars, berry quality indicators, the parameters of separation force (Fs) and crushing force (Fc) decreased by the time of biological maturity of the genotype. Several cultivars have shown a non-simultaneous decrease in Fs and Fc. A minor limiting feature determines the duration of harvesting. The high correlation of Fs and Fc (R=0.75-0.85) allows us to predict the most significant period for the high-quality operation of a mechanized harvester. There was no dependence of the strength of the attachment of berries to the peduncle on the thickness of the skin of the berries. Jonkheer Van Tets, Red Lake, Rovada, Rolan, Vika, Asya, and Niva can be attributed to technological cultivars as sources of compact and erect bush habit, Jonkheer Van Tets, Rovada, Rolan, Vika, and Asya are recommended for mechanized harvesting.

In the present work freezing and melting characteristics of water seeded with chemically functionalized graphene nano-platelets in a vertical cylindrical capsule was experimentally studied. The volume percentage of functionalized graphene nano-platelets was varied from 0.1% to 0.5% with an interval of 0.1%. The stability of the synthesised samples were carried out by zeta potential distribution. The thermal conductivity of the nanocomposite samples were experimentally measured using transient hot wire method. A maximum enhancement of ~24% in the thermal conductivity was observed for the 0.5% volume percentage in the liquid state while a ~53% enhancement in the solid state. Freezing and melting behaviour of water dispersed with graphene nanoplatelets were carried out using a cylindrical stainless steel capsule in a constant temperature bath. The bath temperatures considered for studying freezing characteristics were considered to be −6 °C and −10 °C, while to study the melting characteristics the bath temperature was set as 31 °C and 36 °C. The freezing and melting time decreased for all the test conditions when the volume percentage of GnP increased. The freezing rate was enhanced by ~ 43% and ~32% for the bath temperatures of −6 °C and −10 °C respectively at 0.5 vol % of graphene loading. The melting rate was enhanced by ~42% and ~63% for the bath temperature of 31 °C and 36 °C respectively at 0.5 vol % of graphene loading.

By using the Sala-i-Martin version of extreme bounds analysis, which is based on the cumulative density function, cross-sectional evidence is presented on the robust determinants of the infection and case mortality rates of Covid-19. The results show that, for most part, different factors determine the infection and mortality rates in developed and developing countries. However, common factors are also apparent, including the risk environment, the number of tests per million and the percentage of population over 65. Notwithstanding the possibility that the official data represent a partial reflection of the truth, it is concluded that the results presented in this study provide useful information that may aid decision-making in the fight against the pandemic.

Aims: The aim of our work was to demonstrate the possibility, in carefully selected cases, to perform operations under local anesthesia with a benefit to the patient and savings for national health care. Method: 123 patients with breast cancer underwent quadrantectomy or wide tumorectomy and sentinel lymph node removal under local anesthesia in out-patient surgery. Results: Of the 123 patients, 9 had micro-metastases and 4 had macro-metastases and only three of this had metastasis in other lymph nodes. Conclusions: Our results demonstrate that it is possible in selected cases to operate on breast cancer in out-patient surgery, with an advantage for the patient and savings for national health care.

Idiopathic granulomatous mastitis (IGM) is a rare condition characterised by chronic inflammation and granuloma formation in the breast. The aetiology of IGM is unclear. By focusing on the protein-coding regions of the genome, where most disease-related mutations often occur, whole-exome sequencing (WES) is a powerful approach for investigating rare and complex conditions, like IGM. We report WES results on paired blood and tissue samples from eight IGM patients. Samples were processed using standard genomic protocols. Somatic variants were called with two analytical pipelines: nf-core/sarek with Strelka2 and GATK4 with Mutect2. Our WES study of eight patients did not find evidence supporting a clear genetic component. The discrepancies between variant calling algorithms, along with the considerable genetic heterogeneity observed amongst the eight IGM cases, indicate that common genetic drivers are not readily identifiable. With only three genes, CHIT1, CEP170, and CTR9, recurrently altered in multiple cases, the genetic basis of IGM remains uncertain. The absence of validation for somatic variants by Sanger sequencing raises further questions about the role of genetic mutations in the disease. These observations suggest that IGM may be influenced by non-genetic factors, such as environmental triggers or immune system dysregulation, rather than being primarily a genetic disorder.

The level of development of the innovation factor plays a crucial role in supporting the high-quality development of China's economy. To advance the development of regional innovation factor allocation efficiency along the Belt and Road. This article introduces the super-efficiency SBM-DEA-Malmquist model for the static and dynamic analysis of innovation factor allocation efficiency in 17 provinces along the Belt and Road from 2012 to 2021. The Moran index model analyzes the spatial correlation. The results show that: (1) The overall innovation factor allocation efficiency along the Belt and Road is not high, and there are obvious differences among different regions. The eastern region is the highest compared to other regions. (2) According to the efficiency decomposition results, pure technical efficiency is the main reason for the low innovation factor allocation efficiency. (3) Through the Malmquist index and decomposition index, it is found that pure technical efficiency and scale efficiency are key factors in improving technical efficiency. (4) The analysis of spatial correlation reveals a strong spatial agglomeration feature among the provinces along the Belt and Road. The innovation factor allocation efficiency is mainly manifested in the L-L type. Finally, according to the analysis results, suggestions and policies are put forward to improve the innovation factor allocation efficiency in the regions along the Belt and Road.

Aquatic environments are generally heterogeneous in space and time, and the marine organisms have their ways response to those environmental heterogeneities. Cutlassfish (Trichiurus spp.) once is one of the “Chinese Four Major Marine Fish Species” along the coast of northwest Pacific Ocean. There are some comments on the cutlassfish (T. spp.) name identification. In this study, three cutlassfish species, namely, the largehead hairtail (T. japonicas) in East China Sea (ECS), Chinese short-tailed hairtail (T. brevis), and South China Sea (SCS) cutlassfish (T. nanhaiensis), have been identified in the northwest Pacific Ocean. This work integrated 115 studies in cutlassfish population dynamics from 24 sites across 8 countries in the world. The biomass distribution of T. japonicas in the ECS and T. nanhaiensis in SCS, was found to be highly correlated with water temperature and salinity. Spatiotemporal heterogeneity of cutlassfish is found to correlate with both genetic and environmental characteristics in the northwest Pacific Ocean (e.g., density dependency, primary productivity and climate ocean oscillation). Closely related species coexisting in the same shelf region (e.g., T. japonicus and T. nanhaiensis in ESC) often differ in diet and microhabitat. The difference of skull phenology between T. nanhaiens and T. brevis is found to correlate with species separation and environmental heterogeneity. With the implications for the design of ecological research programs on cutlassfish, our study found that the effect of temporal changes outweighs (to some extent) the effect of spatial changes on cutlassfish in those shelf regions.

This study addresses the enhancement of material efficiency and reduction of brittleness in timber-to-concrete adhesive connections for beam-type timber and timber-concrete composite panels. The research explores the potential benefits of adding longitudinal timber ribs to Cross-Laminated Timber (CLT) beam-type panels. Three groups of flexure-tested specimens were analysed: (1) timber panels (1400 mm × 400 mm) with two 100 mm thick CLT panels and two 60 mm thick CLT panels reinforced with 150x80 mm timber ribs; (2) eight specimens (600 mm × 100 mm × 150 mm) with CLT members (600 mm × 100 mm × 100 mm) connected to a 50 mm concrete layer using granite chips and Sikadur-31 (AB) epoxy adhesive; (3) six CLT panels (1400 mm × 400 mm × 50 mm) bonded to a 50 mm concrete layer, with two panels containing polypropylene microfibers and two panels incorporating polyethene dowels for mechanical connection. Specimens were subjected to three-point bending tests and analysed using the transformed section method, γ-method, and finite element method with ANSYS 2023R2 software. Results indicated a 53% increase in load-carrying capacity for ribbed CLT panels with no additional material consumption, a 24.8%-41.1% increase for CLT panels strengthened with a concrete layer, and improved ductility and prevention of disintegration in timber-concrete composites with polypropylene microfibers.

Even despite rapid advances in colorectal surgery, morbidity and mortality rates in elective gastrointestinal surgery play a significant role. For decades, there have been tempestuous discussions on preventive measures to minimize the risk of anastomotic dehiscence. When mechanical bowel preparation before elective procedure, one of the key hypotheses, has been introduced into practice, it was assumed that it would decrease number of infectious complications and anastomotic dehiscence. The advancements in antibiotic treatment supported concomitant administration of oral antibiotics and mechanical bowel preparation. In the prospective study conducted at our clinic, we performed left-side colorectal procedures without the prior mechanical preparation. All patients enrolled in the study underwent the surgery and had been observed in the 3rd Surgical Clinic, Faculty of Medicine, Comenius University in Bratislava, Slovakia, from January 2019 to January 2020. As a control group we used similar group of patients with MBP. Our observed group included 87 patients with tumors in the left part of their large intestine (lineal flexure, descendent colon, sigmoid colon, and rectum). Dixon laparoscopic resection was performed in 26 patients. Sigmoid laparoscopic resection was performed in 27 patients. In 12 patients, the procedure was started laparoscopically but had to be converted due to adverse anatomical conditions. The conservative approaches mostly included Dixon resections (19 patients), sigmoid colon resection (5 patients) and left side hemicolectomy (6 patients), and Miles´ tumor resection with rectal amputation (4 patients). Our study highlighted the fact that MBP does not have the unequivocal benefit for patients with colorectal infection, which has an impact on development of anastomotic dehiscence.

ABSTRACT Introduction: Traditional penile venous surgery and enhancement have been controversial. Based on De Novo penile fibrovascular assembly, we report on penile venous stripping (PVS) and factual penile girth enhancement (FPGE). Methods: From 2013 to 2023, 18 men underwent PVS and FPGE, all confirmed with veno-occlusive dysfunction (VOD). PVS involves the stripping of erection-related veins. FPGE was performed along the tunica albuginea bilaterally; then, the two tunic defects were repaired with a 70.0x30.0 mm venous stripe and covered with either autologous venous walls (AVW, n=10) or Surgiform (SF, n=8). The penile girth was measured, and radio-opaque contrast was used. The abridged 5-item version of the International Index of Erectile Function (IIEF-5) score and the Erection Hardness Scale (EHS) were used yearly during follow-up via the Internet. Results: The follow-up period was 5.3±1.5 years. Radiopacity was consistently enhanced. Although there was indifference between AVW and SF, there was a significant improvement (both P＜0.01) in IIEF-5 and EHS scores (9.7±2.8 vs. 20.8±2.3; 1.7±0.6 vs. 3.2±0.2, respectively). Both the diameters of the glans and penile shaft increased (28.0±2.3mm and 28.3±2.1mm vs 35.3±2.2mm and 36.3±2.1mm respectively). The satisfaction rate was 81.3% (13/16). Conclusions: This novel combination of PVS and FPGE is promising, although a larger sample size is needed for further validation.

Saudi Arabian cities have experienced a rapid expansion of their urban areas - particularly their city centers - over the last four decades. This has resulted to an increase in vehicular usage, which has adversely affected the daily walking experience of the residents. Walkability has several positive effects on people's health as well as the environment in cities. In addition to being a means of transportation, it also helps people create a sense of place, enhancing their image ability to be easily understood as a legible urban structure, and deepen their emotional bond with the city. Using the medium-sized Saudi Arabian city of Onaizah as a case study, this study looks into the feasibility of creating urban walking corridors to encourage users to walk more. According to Saudi Arabia's Vision 2030, which emphasizes the value of sustainable urban development and raising quality of life for residents, the study addresses walkability as a generator to maintain and improve the urban scene of the city center. Geographic Information Systems (GIS) was used to analyze the data and generate the urban corridors in the city center. The results showed the possibility of improving walking in Onaizah through three urban corridors to the city center, measuring 1,401 meters, 1,283 meters, and 1,507 meters, respectively. These corridors represent urban, commercial and heritage corridors, giving an opportunity for planners and decision makers to challenge prioritize pedestrian connectivity and enhance the physical environment, leading to sustainable urban development. This research paper used various criteria analysis methods to provide a comprehensive assessment of various factors to conclude these urban corridors; for example, assessments of land use, transportation and environmental considerations. The study aligns with Saudi Arabia's vision 2030 by promoting walking and enhancing the city's overall walkability while offering Onaizah society by creating and suggesting urban corridors to the city center a sustainable and livable urban environment.

Extracellular vesicles (EVs) carry diverse cargo of DNA, RNA, proteins, and lipids. They are crucial mediators for regulating various functions through interactions with the host. Recent investigations suggest that EVs or their constituent proteins derived from probiotics could improve the effectiveness of probiotic strains in disease management compared to probiotic strains themselves. This enhanced effec-tiveness arises from their innate antimicrobial properties, which can specifically target pathogenic mol-ecules or cells, in addition to their significant ability to regulate immune responses. Significantly, previous studies have highlighted the inhibitory potential of Lactobacillus crispatus PMC201 against Mycobacterium tuberculosis. In this study, we focused on optimizing isolation conditions for studying extracellular vesicles and proteins from anti-TB potent strains. We provide analysis results based on food-grade media (FGM) optimization and isolation methods. Nanoparticle tracking analysis (NTA) was used to determine se-cretion patterns. Proteomic analysis was performed by liquid chromatography and tandem mass spec-trometry (LC-MS/MS). A total of 64 proteins were identified in the L. crispatus PMC201 strain, particularly enriched in s-layer and splX. Questions remain about their relevance to anti-TB. This anti-tuber strain has potential as a therapeutic agent, given its significant role in intercellular killing. Further studies are needed to investigate its mechanisms of action.

The objective was to conduct a scoping review and produce a publicly available database characterizing the design and reporting elements of the literature on dietary added sugars and select health outcomes. Relevant studies published from 1990 – 2021 were identified to create a database containing information on study and population characteristics, reported added sugars source and concentrations, diet energy balance, total energy intake, and outcome measures related to body composition, obesity, cardiovascular disease, and diabetes mellitus. There were 245 publications identified, 22% of which describe interventions and 78% describe observational studies. Publications pertaining to added sugars have risen dramatically since 2010, led by studies primarily assessing body composition (36%) or cardiovascular health (32%), including adults (65%), measuring liquid-only sources of added sugars (56%). Over 65% of studies reported total energy intake, 61% controlled for total energy intake in the design and analysis, and fewer than 5% of studies reported the energy balance of subjects. There has been a significant increase in research on added sugars since 2010, with substantial heterogeneity across all facets of methodology – study designs, exposures and outcomes of interest, terminology, and reporting of dietary intake data – thus limiting the ability to synthesize evidence in this scope of literature. This evidence map highlights gaps and important areas for improvement to strengthen the state of research to better inform future policies and dietary recommendations on added sugars.

The goal of our article was to systematize studies that investigated the impact of living conditions in large national regions on individual alcohol consumption. We sought publications on the research topic in PubMed, Google Scholar, OpenGrey, Crossref and eLibrary databases from the moment they were created until December 31, 2021. After removal of duplicates and unsuitable publications, and full-text review of remaining published sources, the final sample included 81 publications. The majority of ultimately selected papers were published after 2010, represented the United States of America, and considered samples of children and youths, either the younger population or the general adult population. High quality was characteristic for 19 studies, while satisfactory quality was exhibited by 46 publications. The most consistent associations with individual alcohol consumption were revealed for the legislative environment (especially for integral scales and indices), alcohol pricing policy, the prevalence of alcohol consumption and binge drinking in the population, and unemployment rate. Among the shortcomings of the reviewed articles, we should mention the prevailing assessment of the impact of only one type of regional characteristics, as well as the lack of studies analyzing the interaction of environmental influence at the territorial level of different scales. Protocol PROSPERO CRD42021234874.

Selection is a fundamental interaction element in Virtual Reality (VR) and 3D user interfaces (UIs). Raycasting, one of the most common object selection techniques, has been known to have difficulties in selecting small or distant objects. Meanwhile, recent advancements in computer vision technology have enabled seamless vision-based hand tracking in consumer VR headsets, enhancing accessibility to freehand mid-air interaction and highlighting the need for further research in this area. This study proposed a new technique called TouchView which utilizes a virtual panel with a modern adaptation of the Through-the-lens metaphor to improve freehand selection for VR UI. TouchView enables faster and less demanding target selection by allowing direct touch interaction with the magnified object proxies reflected on the panel view. Repeated-measures ANOVA on the results of a follow-up user experiment on multitarget selection with 23 participants showed that TouchView outperformed the current market-dominating freehand raycasting technique, Hybrid Ray in terms of task performance, perceived workload, and preference. The user behavior was also analyzed to understand the underlying reasons for these improvements. The proposed technique can be used in VR UI applications to enhance the selection of distant objects, especially for cases with frequent view shifts.

Background and Objectives. To examine trends in the links between induced abortion and subsequent mental health problems for U.S. women approaching completed fertility. Materials and Methods. Panel data on a nationally-representative cohort of 3,842 ever-pregnant women assessed at mean ages of 15, 22, 28 and 37 years from the National Longitudinal Study of Adolescent to Adult Health (Add Health) were examined for pregnancy outcome-related mental health disorders, with extensive adjustment for confounders including sociodemographic dif-ferences, mental health history, and unwanted pregnancies. Outcomes were defined using pub-lished diagnostic criteria or validated index for depression, anxiety disorder, post-traumatic stress disorder (PTSD), and suicide ideation (affective problems); alcohol abuse, drug abuse, opioid abuse, and cannabis abuse (substance abuse problems); and summary counts of affective problems, substance abuse problems and total disorders. Results were adjusted for abortion under-reporting using cross-wave analysis. Results. The relative risk of mental health disorder with exposure to induced abortion followed an inverted “V” pattern as women aged to midlife, increasing from Wave III (age 22; IRR 1.41) to Wave IV (age 28; IRR 1.67), then dropping sharply by Wave V (age 37; IRR 1.20). The population at-tributable fraction of mental health disorder due to abortion dropped from 18.1% at Wave IV to 8.0% at Wave V. Unlike previous Waves, at Wave V mental health risk was more strongly reduced by exposure to childbirth (IRR 0.70, 95% CI .63, .78, p < .001). Reduced relative metrics at Wave V were not due to reduced prevalence of mental health disorders for ever-aborting women, which increased from Wave IV to Wave V by 1.7%, but increased much more, by 20.5%, for never-aborting women. Conclusion. At midlife approaching completed fertility, differential negative psychological out-comes for ever-aborting U.S. women remained elevated but reduced from younger ages.

Contrasting results have suggested that the prevalence and intensity of post abortion emotional distress (PAD) either diminishes rapidly or persists over time. Few studies have followed women for longer than five years. This study examines retrospective population data on 226 ever aborting U.S. women aged 41-45, at an average of 20 years following the abortion, with extensive measures of PAD. Most post abortive women (55.2%) experienced little or no distress from their abortion(s) and 74.6% were confident it had been the right decision, but 44.8% reported moderate (20.7%) to high (24.1%) distress related to their abortion(s), including 37.7% who reported negative emotions; 31.2% “frequent feelings of loss, grief or sadness;” 24.6% “frequent thoughts, dreams, or flashbacks;” 23.2% for whom thoughts and feelings about their abortion “negatively interfered with life, work, or relationships.” Time since the abortion (mean 20 years, range 0-31 years) was not correlated with PAD (r = -.02, p .71). For the minority of women who experienced it, PAD did not diminish but persisted for decades. An estimated 7.5 million U.S. women currently experience persistent high PAD. Further research to specify risk factors and possible interventions for postabortion distress would be of value for this group of women.

In the last decade, a higher level of reliability became a compulsory demand when it comes to modern DC-DC converters. This work addresses the main reliability metrics: the failure rate λλ and MTBF of an output capacitor bank used within a high current low voltage buck converter due to many studies have shown that the output capacitor bank was demonstrated being the most critical component within the converter. Many authors treated this issue, usually by doing reliability predictions. The majority of the papers use only one specific Standard Prediction to solve the problem. Herein calculation was done using both the older standard MIL-HDBK-217 and the latest one Telcordia SR-332, providing a benchmark comparison between the two which is a helpful tool for the output capacitor selection in the early stage design. Military standard was well accepted for decades in order to reliability prediction even on industrial electronics and is still used today under a critical manner because no more update after the latest version MIL-HDBK-217F - Notice 2 released in 1995. Since then, newer prediction standards had appeared in the electronics reliability market. Over time, this standard was mostly used but it does not accurately model the reliability because lack of taking account of mission profile. The above-mentioned newer standard – i.e., Telcordia SR-332 is trying to compensate also the lack of the newest component technology in the older standard (which is the first standard released on the market) supplying useful design data for design engineers who use the so-called “design with the reliability in mind” concept. Within the paper were established the environmental condition for the passive components by means of a PoL (Point of load) buck converter that is used for both calculation methods. The influence of temperature and several specific concepts like reference conditions, operating conditions, ripple, and internal self-heating were taking account in order to display the results. The temperature for the capacitor’s capsule needed in π_T stress factor calculation was derived by PSPICE simulation. High fidelity and dedicated SPICE models provided by the manufacturer were used for MOSFETs, polymer electrolytic, and MLCC capacitors that compose the converter.

This study explores innovative strategies to enhance hands-on learning experiences in a Database Management Systems (DBMS) course while addressing challenges related to software installation complexities and financial barriers. The investigation focuses on an online course format catering to students majoring in information systems, healthcare administration, business administration, and marketing. Traditional software installation hurdles, especially for complex systems like Microsoft Access and MySQL, can impede students' engagement and access. To overcome these challenges, the study proposes leveraging cloud-based solutions and clientless remote desktop gateways like Apache Guacamole. Results indicate that these approaches effectively eliminate software installation obstacles, ensuring seamless access to essential tools for all students. The study underscores the importance of innovative technological solutions in promoting inclusive and impactful learning environments in higher education.

Background: Sexual dysfunction is the most common side effect encountered by cervical cancer patients during and after treatment, and rehabilitation strategies are needed to improve their quality of life. A variety of rehabilitation strategies have been reported in previous studies, varying by country, race and even continent. However, the common rehabilitation strategies used by cervical cancer survivors in Tanzania and their outcomes are unknown. The study aimed to unfold sexual rehabilitation strategies and outcome practiced by cervical cancer survivors who experienced sexual dysfunction during and after treatment in North-western Tanzania. Methods: Descriptive qualitative study were employed. Phenomenology study design used with a sample of 7 participants and data corrected via in-depth interview guided by 11 questions. Audiotaped and transcribed to get main themes and sub themes through deductive analysis. Results: Most participants reported inability to achieve an orgasm and low libido and dyspareunia after treatment. Most participants were eagerly to have their sexual activity recovered. However, they were not familiar with any rehabilitation strategy after the experience of sexual dysfunction. Even though, some of the participants attempted various interventions, but all were unsatisfactory leading to a significant decline in sexual interest and activity. Conclusion: Cervical cancer survivors are not familiar with any sexual rehabilitation strategies. Health education concerning various sexual rehabilitation strategies should be provided to survivors during and even after treatment.

This study focused on the stories of women planters in the implementation of Taranuman Project in the Third District of Albay. Particularly, this explores and documents the experiences, challenges, and successes of women planters involved. By understanding their stories, this undertaking shed light on the motivations and aspirations of women planters, the barriers they have experienced, and their strategies to achieve success. The researchers used descriptive-qualitative research and made use of in-depth interviews and focus group discussions (FGDs). From the collection of the respondents’ narratives, three (3) recurring themes emerged on the motivation and aspirations of women planters in Taranuman Project; Taranuman para sa Pagkasararo (Social cohesion, belonging and unity), Taranuman para sa Pangkabuhayan (Economic empowerment), and Taranuman para sa Karapatang Pampulitika (Political rights, engagement and participation). This study also revealed major challenges in its implementation including infertility of the soil, the distant location of the lands to be cultivated for the project, and the insufficiency in the supply of tools and equipment. However, despite the emerging problems, strategies to counter the difficulties were also formulated. Even so, the researchers strongly recommend that implementers should expand the Taranuman Project in all of the district’s municipalities, enhance women planters' financial and communication skills through training, maintain close partnerships with agencies for project support, encourage beneficiary communication, and develop comprehensive strategies to overcome ongoing challenges and ease beneficiaries’ burdens.

The article presents a methodology for modeling the impact of factors of both on-farm activity and environmental factors on the resulting indicators of an international company. The dataset includes the company's profit, revenue, valuation, share price, and market share from 2012 through 2022. This empirical period is optimal for such a type of modeling. Modeling the impact of carbon emissions factors on the resulting indicators is considered based on complex systems theory. Endogenous model variables include indicators of the development of the international company. Predefined model variables reflect the influence of both the external environment and internal factors on the development indicators of an international company. An approach of picture fuzzy rough sets based on time series of endogenous and exogenous variables can provide an opportunity to analyze and consider the consequences of feedback changes in the systems of which they are a part. Based on the results of the analysis of the model construction, it is concluded that picture fuzzy rough sets can be an excellent way to model interdependent social processes.

Paclitaxel is a natural, highly lipophilic anti proliferative drug widely used in medicine. We have studied the release of tritium-labeled paclitaxel (³H-PTX) from matrices destined for the coating of vascular stents and produced by the electrospinning method from the solutions of polycaprolactone (PCL) with paclitaxel (PTX) in hexafluoisoropropanol (HFIP) and/or solutions of PCL with PTX and human serum albumin (HSA) in HFIP or HIFP-dimethyl sulphoxide (DMSO) blend. The release of PTX has been shown to depend on the solvent and the composition of electrospinning solution, as well as the composition of the surrounding medium, particularly the concentration of free PTX and PTX-binding biomolecules present in human serum. It was shown that 3D matrices can completely release PTX without weight loss. Two-phase PTX release from optimized 3D matrices was obtained: ~27% of PTX was released in the first day, another 8% were released over the next 26 days. Wherein ~2.8%, ~2.3%, and ~0.25% of PTX was released on day 3, 9, and 27, respectively. Considering PTX toxicity, the rate of its diffusion through the arterial wall, and the data obtained the minimum cytostatic dose of the drug in the arterial wall will be maintained for at least three months.

Gas leakage can occur for various reasons, including a gas leak in the distribution line, damaged regulators, service connection failures, fluctuations in inlet gas pressure, and other factors leading to millions in losses to the state. The objective is to develop a general and intelligent model for tracking and monitoring LPG distribution based on the Internet of Things and Business Intelligence technology. Devices such as sensors and actuators reduce risks and prevent accidents. IoT can also access automatic control of machines and infrastructure, and sensors’ data helps make intelligent decisions. The quantitative approach is used in the systematic review methodology based on the PRISMA statement; the architecture design is based on analytical research that studies the feasibility of a measure through empirical evidence; and the architecture description uses a qualitative approach. The Y.4908 Standard evaluates an IoT network’s interoperability, usability, and security. A targeted group of thirty IT professionals was surveyed to assess the Business Intelligence model. Forty scientific papers were selected for analysis. A model with IoT components and a four-level business intelligence system was found. At the overall average level, 49% strongly agree, 38% agree, 12% neither agree nor disagree, and 1% disagree. In other words, the model has an overall average approval rating of 87%.

A millimeter-wave microstrip leaky-wave antenna array based on the substrate-integrated waveguide (SIW) and the complementary split-ring resonator (CSRR) is proposed in this paper. The slow wave characteristics of the SIW are enhanced by etching two rows of symmetrical CSRR-type slots on the surface of the SIW layer. The magnetic coupling between the CSRR-SIW layer and equally spaced circular patches array on the upper layer realizes the radiation of electromagnetic energy. In the 23~30 GHz (relative bandwidth 26.4%) operating frequency band, the horizontal polarization radiation beam scanning from -62° to -2° is realized, with a peak gain of 9.6~11.7 dBi, and the cross-polarization ratio is more than 43.8 dB.

Aiming at the difficult problem of broadband oscillation localization in power systems, the intel-ligent localization method of oscillation source based on the digital twin is proposed and the os-cillation source localization system based on the digital twin is constructed. Firstly, a digital twin-based oscillation source localization method and system architecture are proposed. Fur-thermore, an intelligent positioning method of oscillation source based on data-driven and mechanism fusion is proposed. It includes three steps: oscillation signal preprocessing, oscillation modal analysis and oscillation source localization. For the oscillation signal preprocessing, the generative adversarial imputation network is used to repair the missing samples, and the su-per-resolution technique is used to realize the super-resolution measurement of broadband os-cillation. In the oscillation modal analysis, the spectrum of the oscillation signal is extracted using the fast Fourier transform. To accurately locate the oscillation source, the branch potential energy is used as input to the data-driven model, such as LSTM and CNN. Finally, an oscillation source localization system is developed based on the digital twin workshop CloudPSS-XStudio, which can locate the oscillation source quickly and accurately.

Brain-computer interfaces (BCI) from electroencephalography (EEG) provide a practical approach to support human-technology interaction. In particular, motor imagery (MI) is a widely-used BCI paradigm that guides the mental trial of motor tasks without physical movement. Here, we present a deep learning methodology, named Kernel-based Regularized EEGNet (KREEGNet), leveled on Centered Kernel Alignment and Gaussian Functional Connectivity, explicitly designed for EEG-based MI classification. The approach proactively tackles the challenge of intra-subject variability brought on by noisy EEG records and the lack of spatial interpretability within end-to-end frameworks applied for MI classification. KREEGNet is a refinement of the widely accepted EEGNet architecture, featuring an additional kernel-based layer for regularized Gaussian functional connectivity estimation based on CKA. The superiority of KREEGNet is evidenced by our experimental results from binary and multi-class MI classification databases, outperforming the baseline EEGNet and other state-of-the-art methods. Further exploration of our model’s interpretability is conducted at individual and group levels, utilizing classification performance measures and pruned functional connectivities. Our approach is a suitable alternative for interpretable end-to-end EEG-BCI based on deep learning.

Cardiovascular diseases remain the leading cause of mortality worldwide. Although new therapies are actively being developed and used for cardiovascular pathologies, these attempts have not significantly decreased mortality rates. Regenerative medicine has made enormous progress and set promising approaches over the past half-century. However, since autologous (donor-derived) vascular grafts are lacking, an alternative prosthesis must be constructed for cardiovascular disease patients. In vascular tissue manufacturing and regenerative medicine, scientists seek to improve this significant clinical challenge using bio-fabrication techniques combining additive manufacturing, biomaterials science, and advanced cellular biology. In the last few decades, many improvements and changes in various approaches have helped develop bioengineered concepts that reflect native blood vessels’ structure and function. However, numerous challenges must be overcome to clinically translate the next generation of tissue-engineered vascular transplants. This review provides update on the cell sources, scaffold essential for cardiovascular tissue engineering, and tissue engineering approaches as prospective options for curative therapy for blood vessel disease.

Background: Process mining (PM) exploits event logs to obtain meaningful information about the processes that produced them. As the number of applications developed on cloud infrastructures is increasing, it becomes important to study and discover their underlying processes. However, many current PM technologies face challenges in dealing with complex and large event logs from cloud applications, especially when they have little structure (e.g., clickstreams). Methods: Using Design Science Research, this paper introduces a new method, called Cloud Pattern API – Process Mining (CPA-PM), that enables discovering and analyzing cloud-based application processes using PM in a way that addresses many of these challenges. CPA-PM exploits a new application programming interface (API), with an R implementation, for creating repeatable scripts that preprocess event logs collected from such applications. Results: Applying CPA-PM to a case with real and evolving event logs related to the trial process of a Software-as-a-Service cloud application led to useful analyses and insights, with reusable scripts. Conclusion: CPA-PM helps producing executable scripts for filtering event logs from clickstream and cloud-based applications, where the scripts can be used in pipelines while minimizing the need for error-prone and time-consuming manual filtering.

Cardiovascular disease (CVD) may sometimes unexpected loss of life. It affects the heart and blood vessels of body. CVD plays an important factor of life since it may cause death of human. It is necessary to detect early of this disease for securing patients life. In this chpter two exclusively different methods are proposed for detection of heart disease. The first one is Pattern Recognition Approach with grammatical concept and the second one is machine learning approach. In the syntactic pattern recognition approach initially ECG wave from different leads is decomposed into pattern primitive based on diagnostic criteria. These primitives are then used as terminals of the proposed grammar. Pattern primitives are then input to the grammar. The parsing table is created in a tabular form. It finally indicates the patient with any disease or normal. Here five diseases beside normal are considered. Different Machine Learning (ML) approaches may be used for detecting patients with CVD and assisting health care systems also. These are useful for learning and utilizing the patterns discovered from large databases. It applies to a set of information in order to recognize underlying relationship patterns from the information set. It is basically a learning stage. Unknown incoming set of patterns can be tested using these methods. Due to its self-adaptive structure Deep Learning (DL) can process information with minimal processing time. DL exemplifies the use of neural network. A predictive model follows DL techniques for analyzing and assessing patients with heart disease. A hybrid approach based on Convolutional Layer and Gated-Recurrent Unit (GRU) are used in the paper for diagnosing the heart disease.

The paper describes problems with the current additive manufacturing chain before considering additive manufacturing as part of a modern manufacturing chain. Additive manufacturing can be used for near net-shape for finishing, for repair or for adding special features which cannot be made with traditional manufacturing. This paper describes how STEP-NC deals with these different scenarios in terms of accuracy, multi-material and variation of slice direction. The possibilities of multi-material objects also raises questions about the design of such objects and how these need to be handled by an advanced controller. The paper also describes non-planar slicing. Curved direction and cylindrical direction are shown to improve the accuracy of curved structure additive manufacturing. STEP-NC using boundary representation has better capability of depicting complex internal structures for additive processes. By using exact model of the final product represented by STEP-NC, the paper demonstrates improvements in data size reduction, slicing accuracy, and precise manipulation of internal structure.

Worldwide, there is a significant increase in the use of unmanned aerial vehicles (UAVs) by emergency services. They offer a lot of possibilities during the rescue operation. Such a wide application for various purposes and environments causes many threats related to their use. To minimize the risks associated with conducting air operations with UAVs, the SORA methodology will be important. Due to its level of detail, it is a methodology adapted to civilian use. In this article, the authors' team will try to develop guidelines and directions for adapting SORA to the requirements of the operational work of emergency services. Thus following article aims to present the most important risks related to conducting operations with the use of UAVs by First Responders (FRs) and to show the sample risk analysis performed for this type of operation on the example of the ASSISTANCE project. The paper describes, on the one hand, possibilities offered by UAVs in crisis or disaster management and step-by-step Specific Operations Risk Assessment (SORA) and on the other hand, presents possible threats, consequences and methods of their mitigation during FRs missions.

We shall discuss three methods of inverse Laplace transforms. A Sinc-Thiele approxi- mation, a pure Sinc, and a Sinc-Gaussian based method. The two last Sinc related methods are exact methods of inverse Laplace transforms which allow us a numerical approximation using Sinc methods. The inverse Laplace transform converges exponentially and does not use Bromwich contours for computations. We apply the three methods to Mittag-Leffler functions incorporating one, two, and three parameters. The three parameter Mittag-Leffler function represents Prabhakar’s function. The exact Sinc methods are used to solve fractional differential equations of constant and variable differentiation order.

Abstract: Insecure and portable devices in the smart city’s Internet of Things (IoT) network are increasing at an incredible rate. Various distributed and centralized platforms against cyber-attacks have been implemented in recent years, but these platforms are inefficient due to their constrained levels of storage, high energy consumption, the central point of failure, underutilized resources, high latency, and etc. In addition, the current architecture confronts the problems of scalability, flexibility, complexity, monitoring, managing & collecting of IoT data, and defend against cyber-threats. To address these issues, the author presents distributed and decentralized Blockchain-Software Defined Networking (SDN) based energy-optimized architecture for IoT in smart cities. Thus, SDN continuous observing, controlling, managing IoT devices activities and detect possible attacks in the network; Blockchain provides adequate security & privacy against cyber-attacks, reduces the central point of failure issues; Network Function Virtualization (NFV) are used to saving energy, load balancing, as well as increasing the lifetime of the entire network. Also, we introduce a Cluster Head Selection (CHS) algorithm to reduce the energy consumption in the presented model. Finally, we analyze the performance using various parameters (e.g. throughput, response time, gas consumption, communication overhead) and demonstrating the result that provides higher throughput, lower response time, lower gas consumption than existing works for smart cities.

In this manuscript, an automated framework dedicated to design space exploration and design optimization studies is presented. The framework integrates a set of numerical simulation, computer-aided design, numerical optimization, and data analytics tools using scripting capabilities. The tools used are open-source and freeware, and can be deployed on any platform. The main feature of the proposed methodology is the use of a cloud-based parametrical computer-aided design application, which allows the user to change any parametric variable defined in the solid model. We demonstrate the capabilities and flexibility of the framework using computational fluid dynamics applications; however, the same workflow can be used with any numerical simulation tool (e.g., a structural solver or a spread-sheet) that is able to interact via a command line interface or using scripting languages. We conduct design space exploration and design optimization studies using quantitative and qualitative metrics, and to reduce the high computing times and computational resources intrinsic to these kinds of studies, concurrent simulations and surrogate-based optimization are used.

Modern Monte-Carlo-based rendering systems still suffer from the computational complexity involved in the generation of noise-free images, making it challenging to synthesize interactive previews. We present a framework suited for rendering such previews of static scenes using a caching technique that builds upon a linkless octree. Our approach allows for memory-efficient storage and constant-time lookup to cache diffuse illumination at multiple hitpoints along the traced paths. Non-diffuse surfaces are dealt with in a hybrid way in order to reconstruct view-dependent illumination while maintaining interactive frame rates. By evaluating the visual fidelity against ground truth sequences and by benchmarking, we show that our approach compares well to low-noise path traced results, but with a greatly reduced computational complexity allowing for interactive frame rates. This way, our caching technique provides a useful tool for global illumination previews and multi-view rendering.

Polymer-based piezoelectric biomaterials have already proven their relevance for tissue engineering applications. Further, the morphology of the scaffolds plays also an important role in cell proliferation and differentiation. The present work reports on poly(hydroxybutyrate-co-hydroxyvalerate) (PHBV), a biocompatible, biodegradable and piezoelectric biopolymer that has been processed in different morphologies, including films, fibres, microspheres and 3D scaffolds. Further, the corresponding magnetically active PHBV-based composites were also produced. The effect of the morphology on physico-chemical, thermal, magnetic and mechanical properties of pristine and composites samples was evaluated, as well as their cytotoxicity. It was observed that the morphology does not strongly affect the properties of the pristine samples but the introduction of cobalt ferrites induces changes in the degree of crystallinity that could affect the applicability of prepared biomaterials. Young modulus is dependent of the morphology and also increases with the addition of cobalt ferrites. Both, pristine and PHBV/cobalt ferrite composite samples are no cytotoxic, indicating their suitability for tissue engineering applications.

This paper presents novel feature descriptors and classification algorithms for automated scoring of HER2 in Whole Slide Images (WSI) of breast cancer histology slides. Since a large amount of processing is involved in analyzing WSI images, the primary design goal has been to keep the computational complexity to the minimum possible level and to use simple, yet robust feature descriptors that can provide accurate classification of the slides. We propose two types of feature descriptors that encode important information about staining patterns and the percentage of staining present in ImmunoHistoChemistry (IHC) stained slides. The first descriptor is called a characteristic curve which is a smooth non-increasing curve that represents the variation of percentage of staining with saturation levels. The second new descriptor introduced in this paper is an LBP feature curve which is also a non-increasing smooth curve that represents the local texture of the staining patterns. Both descriptors show excellent interclass variance and intraclass correlation, and are suitable for the design of automatic HER2 classification algorithms. This paper gives the detailed theoretical aspects of the feature descriptors and also provides experimental results and comparative analysis.

The RPL protocol is a routing protocol for low power and lossy networks. In such a network, energy is a very scarce resource; so many studies are focused on minimizing global energy consumption. End-to-end latency is another important performance indicator of the network, but existing research tends to focus more on energy consumption and ignore the end-to-end delay of data transmission. In this paper, we propose a kind of energy equalization routing protocol to maximize the surviving time of the restricted nodes so that the energy consumed by each node is close to each other. At the same time, a multi-path forwarding route is proposed based on the cache utilization. The data is sent to the sink node through different parent nodes at a certain probability, not only by selecting the preferred parent node, thus avoiding buffer overflow and reducing end-to-end delay. Finally, the two algorithms are combined to accommodate different application scenarios. The experimental results show that the proposed three improved schemes improve the reliability of the routing, extend the lifetime of the network, reduce the end-to-end delay, and reduce the number of DAG reconfiguration.