Continuous casting is the most common method for producing steel into semi-finished shapes like billets or slabs. Throughout this process, steel experiences mechanical and thermal stresses, which influence its mechanical properties. During continuous casting, decreased formability in steel components leads to crack formation and failure. One reason for this phenomenon is the appearance of the soft ferrite phase during cooling. However, it is unclear under which conditions this ferrite is detrimental to the formability. In the present research, we investigated what microstructural changes decrease the formability of microalloyed steels during continuous casting. We studied the hot compression behaviour of microalloyed steel over temperatures ranging from 650 °C to 1100 °C and strain rates of 0.1 s-1 to 0.001 s-1 using a Gleeble 3800® device. We examined microstructural changes at various deformation conditions using microscopy. Furthermore, we implemented a physically-based model to describe the deformation of austenite and ferrite. The model describes the work hardening and dynamic restoration mechanisms, i.e., discontinuous dynamic recrystallisation (dDRX) in austenite and dynamic recovery (DRV) in ferrite and austenite. The model considers the stress, strain, and strain rate distribution between phases by describing the dynamic phase transformation during the deformation in iso-work conditions. Increasing the strain rate below the transformation temperature improves hot ductility by reducing DRV and strain concentration in ferrite. Due to limited grain boundary sliding, the hot ductility improves at lower temperatures (<750 °C). In the single-phase domain, DRX improves the hot ductility provided that fracture occurs at strains in which DRX advances. However, at very low strain rates, the ductility decreases due to prolonged time for grain boundary sliding and crack propagation.

AliAmbra is a project developed to explore and promote high-quality catches of the Amvrakikos Gulf GP to Artas’ wider regions. In addition, this project aimed to implement an integrated plan of action, to form a business identity with high-added value and achieve integrated business services adapted to the special characteristics of the area. The action plan for this project was to actively search for new markets, create a collective identity for the products, promote their quality and added value, engage in gastronomes and tasting exhibitions, dissemination and publicity actions, as well as enhance the quality of the products and markets based on the customer needs. The primary focus of this publication is to observe and analyze the data retrieved from various tasting exhibitions of the AliAmbra project, with a target goal of improving customer experience and product quality.

The formation of atroposelective biaryl compounds in plants and fungi is well understood; however, the polyketide aglycone synthesis and dimerization in bacteria remain unclear. Thus, the biosynthetic gene cluster (BGC) responsible for antibacterial setomimycin production from Streptomyces nojiriensis JCM3382 was examined in comparison with the BGCs of spectomycin, julichromes, lincolnenins, and huanglongmycin. Setomimycin BGC includes post-PKS assembly/cycling enzymes StmD (C-9 ketoreductase), StmE (aromatase), and StmF (thioesterase) as key components. The heterodimeric TcmI-like cyclases StmH and StmK are proposed to aid in forming the setomimycin monomer. In addition, StmI (P-450) is predicted to catalyze biaryl coupling of two monomeric setomimycin units, with StmM (ferredoxin) specific to setomimycin BGC. The roles of StmL and StmN, part of the nuclear transport factor 2 (NTF-2)-like protein family and unique to setomimycin BGCs, could particularly interest biochemists and combinatorial biologists. α-Glucosidase, a key enzyme in type 2 diabetes, hydrolyzes carbohydrates into glucose, thereby elevating blood glucose levels. This study aimed to assess the α-glucosidase inhibitory activity of EtOAc extracts of JCM 3382 and setomimycin. The JCM 3382 EtOAc extract and setomimycin exhibited greater potency than the standard inhibitor, acarbose, with IC50 values of 285.14 ± 2.04 μg/mL and 231.26 ± 0.41 μM, respectively. Kinetic analysis revealed that setomimycin acts as a competitive inhibitor of α-glucosidase. Molecular docking demonstrated two hydrogen bonds with maltase-glucoamylase chain A residues Thr205 and Lys480 (binding energy = −6.8 kcal·mol⁻¹), two π-π interactions with Trp406 and Phe450, and one π-cation interaction with Asp542. Residue-energy analysis highlighted Trp406 and Phe450 as key in setomimycin's binding to maltase-glucoamylase. These findings suggest that setomimycin is a promising candidate for further enzymological research and potential antidiabetic therapy.

Background: Physical activity (PA) has paramount importance for achieving good health and an active lifestyle among university students. Quality physical education (QPE) program ensures the attainment of sustainability in PA. Motivation can be considered a critical factor in promoting PA in students. However, motivation alone cannot bring inclusivity unless quality aspects are not being incorporated thoughtfully into physical education (PE) programs. Aims: As such this study aimed at examining three research questions: i) do the motive of PA participation can be enhanced through the quality provision of PE programs in university settings ii) does the quality provision of PE help promote sustainable PA among adolescents? and iii) whether or not this carry-over process is influenced by gender. Methods: This cross-sectional study recruited N = 610 (male = 467) & female = 143) university students. Students who studied PE as one of their course subjects were only included in this study. They participated in > 27 different sports and had a mean age of 20.76 (SD = 3.47) years and a mean of 4.70 (SD = 4.93) years of athletic experience. Descriptive (mean, SD) statistics, Pearson’s correlation, and regression analysis were used to meet the objectives. To examine the variables’ differences in gender, data were then computed using a 2 (Gender) by 2 (Type of Sport: Individual vs. Team) MANCOVA, with the covariate being age. Results: The factors provided sound reliability scores and highlighted positive correlations between QPE and exercise needs satisfaction. However, the factors lacked establishing correlations with PA. The perceived competence for exercise needs satisfaction reported differences in sexes, with the male being scored higher. Furthermore, QPE did not show any significant differences in gender. In comparison to females, males showed higher mean scores in the PA. Conclusion: The provision of QPE is identified as a pivotal factor in determining the competence of exercise satisfaction among students. QPE provision was adequate in the university, however, it could not promote PA among females as expected.

The development of low-cost, open-source Remotely Operated Vehicle (ROV) systems has provided almost unrestricted access for researchers looking to monitor the marine environment in ever greater resolution. Sampling microbial communities from the marine environment, however, still usually relies on Niskin-bottle sampling (ROV or CTD based), a method which introduces an inaccuracy and variability that is incompatible with metatranscriptomic analysis. Here, we describe a versatile, easily-replicated platform which achieves in situ mRNA preservation, via the addition of RNAlater to filtered microbial cells, to enhance ROV or CTD functionality.

Background: The antisense oligonucleotide against APOC3 mRNA volanesorsen was recently introduced to treat Familial Chylomicronemia Syndrome (FCS). Cases of decreased platelet count are reported among patients treated with volanesorsen. The aim of the study is to evaluate platelet function and thrombin generation (TG) assessment in FCS patients receiving volanesorsen. We performed a cross-sectional study on FCS patients treated with volanesorsen. Methods: changes in PLC were assessed from baseline to Tw12 and Tw36. To assess TG, samples were processed by CAT (with PPP-reagent LOW). The results were expressed by the trombogram graphic (thrombin variation over time); lagTime; Endogenous Thormbin Potential(ETP); peak; time to reach peak(ttpeak), startTail e velocity Index. Platelet aggregation was assessed by testing different agonists using turbidimetry method. Results: Four FCS patients and 4 matched healthy controls were included in the present study. Changes in PLC at Tw12 was 30% and 34% at T36w. Thrombin generation results show values in normal range (for patients and controls respectively lagTime:10,42±4,40 and 9,25±0,99; ttPeak:14,33± 4,01 and 13,10±0,67; startTail:32,13±3,54 and 29,46±1,69; velocityIndex:20,21±3,63 and 33,05±13,21; ETP 599,80±73,47 and 900,2±210,99; peak value 76,84±1,07 and 123,30±39,45) and no significant difference between cases and controls. Platelet aggregation test showed values in range with no significant difference with healthy controls Conclusions: Our study showed for the first time that no significant changes in the general haemostasis assessed by TG and in platelet function have been observed in FCS patients receiving volanesorsen.

This study introduces an integrated method for managing process risks in a Business Process Reengineering (BPR) project, using Robust Data Envelopment Analysis (RDEA) and machine learning (ML). The goal is to prioritize risks based on three standard factors of PFMEA: Severity, Occurrence, and Detection (S-O-D), and incorporating two additional factors (Breakdown Cost and Breakdown Duration) seen as undesirable outputs. The model also accounts for the effect of uncertainty on expert-estimated values by applying disturbance percentages in the linear PFMEA-RDEA model. A machine learning model is proposed to predict new values if partial or total modifications have been made to the processes. The approach was implemented in an au-tomotive sector company, and the results showed the impact of uncertainty on values by com-paring different approaches such as RPN, PFMEA-DEA, and PFMEA-RDEA. A new reduced risk categorization was achieved, who allowed for decision-makers to focus on necessary actions for reengineering.

Genetic variability in rice breeding program plays the very crucial role. It provides outstanding pool of superior alleles governing better agronomic and quality characters through association mapping. For the understanding of population structure and genetic relationship among the different rice lines is indispensable prior to setting of correlation among dynamic alleles and traits. In the present investigation, genetic diversity and population structure of 116 rice accessions by using 64 polymorphic SSR markers was targeted for the evaluation of the genetic relatedness and diversity. Genotyping assessment based on SSR markers revealed a total of 225 alleles, with an average PIC value of 0.755. The germplasm lines were classified into three distinct subgroups through population structure analysis, utilizing both model and distance-based approaches. AMOVA analysis showed that 11% of the total variation could be attributed to differences between groups, while the remaining 89% of the variation was likely due to differences within groups. The study suggests that the population structure and genetic relatedness should be considered when working with the core collection of 116 rice germplasm lines for association mapping, aiming to establish marker-trait associations.

From physics to the social sciences, information is now seen as a fundamental component of reality. However, a form of information seems still underestimated, perhaps precisely because it is so pervasive that we take it for granted: the information encoded in the very environment we live in. We still do not fully understand how information takes the form of cities, and how our minds deal with it in order to learn about the world, make daily decisions, and take part in the complex system of interactions we create as we live together. This paper addresses three related problems that need to be solved if we are to understand the role of environmental information: (1) the physical problem: how can we preserve information in the built environment? (2) The semantic problem: how do we make environmental information meaningful? And (3) the pragmatic problem: how do we enact environmental information in our daily lives? Attempting to devise a solution to these problems, we introduce a three-layered model of information in cities, namely environmental information in physical space, environmental information in semantic space, and the information enacted by interacting agents. We propose forms of calculating entropy in these different layers, and apply these measures to archetypal urban cases and simulated scenarios. Our results suggest that ordered spatial structures and diverse land use patterns encode information, and that aspects of physical and semantic information affect coordination in interaction systems.

Brain pathological changes impair cognition early in disease etiology. There is an urgent need for understanding aging-linked mechanisms of early memory loss to develop therapeutic strategies and prevent the development of cognitive impairment. Tusc2, also named Fus1, is a mitochondrial-resident protein regulating Ca2+ fluxes to and from mitochondria impacting overall health. We reported that Tusc2-/- female mice develop chronic inflammation and age prematurely, causing age- and gender-dependent spatial memory deficits at 5 m.o. Therefore, we investigated Tusc2-dependent mechanisms of memory impairment in mice, comparing changes in resident and brain-infiltrating immune cells. Interestingly, Tusc2-/- female mice demonstrated proinflammatory increase in astrocytes, IFN-γ expression in CD4+T cells and Granzyme-B in CD8+T cells. We also found fewer FOXP3+ T-regulatory cells and Ly49G+ NK and Ly49G+ NKT cells in female Tusc2-/- brain, suggesting a weakened anti-inflammatory response. Moreover, Tusc2-/- hippocampi exhibited Tusc2- and sex-specific protein changes associated with brain plasticity, including mTOR activation, and Calbindin and CamKII dysregulation affecting intracellular Ca2+ dynamics. Overall, data suggest that dysregulation of Ca2+-dependent processes and proinflammatory brain microenvironment heighten in Tusc2-/- mice could underlie cognitive impairment. Thus, strategies to modulate the brain’s Tusc2-, Ca2+, and mitochondria-dependent pathways should be explored to improve cognitive health.

Ghana, blessed with abundant solar resources and has strategically invested in solar photovoltaic (PV) technologies to diversify its energy mix and reduce the environmental impacts of traditional energy technologies. The 50 MW solar PV installation by the Bui Power Authority exemplifies the nation's dedication to utilizing clean energy for sustainable growth. This study seeks to close the knowledge gap by providing a detailed analysis of the system's performance under different weather conditions, particularly on days with abundant sunshine and those with cloudy skies. The research consists of one-year monitoring data of the climatic and AC energy output fed into the grid. This data was used to analyze PV performance on each month's sunniest and cloudiest days. The results show that the total amount of AC energy output fed into the grid each month on the sunniest day varies between 229.3 MWh in December and 278.0 MWh in November, while the total amount of AC energy output fed into the grid each month on the cloudiest day varies between16.1 MWh in August and 192.8 MWh in February. Also, the percentage variation in energy produced between the sunniest and cloudiest days within a month ranges from 16.9% (December) to 94.1% (August). The reference and system yield analysis showed that the PV plant has a high conversion efficiency of 91.3%; however, only the sunniest and overcast days had an efficiency of 38% and 92%, respectively. The BPA plant's performance can be enhanced by using this analysis to identify erratic power generation on sunny days and schedule timely maintenance to keep the plant's performance from deteriorating. Optimizing a solar PV system's design, installation, and operation can significantly improve its AC energy output, performance ratio, and capacity factor on sunny and cloudy days.

Nowadays, animal breeders look for effective innovations to care animal health. Use of probiotics, postbiotics, parabiotics and the other beneficial natural substances are dominated. Mundticin-like substance EM 41/3 is thermo-stable bacteriocin (postbiotic) with a broad antimicrobial spectrum. In this study its effect in broiler rabbits was assessed. Rabbits represent food-derived animal and they are also suitable animal model. The parameters tested were analyzed by validated methods. Administration of MLS EM 41/3 produced by non-autochthonous strain Enterococcus mundtii EM 41/3 lead to significant increase of parameter immunity-phagocytic activity, while it did not influence microbiota composition. It also did not influence blood biochemistry and no oxidative stress was noted. Moreover, higher growth parameters were noted and also stimulated hydrolytic activity. Finally, no negative influence on rabbit meat quality was noted. Postbiotic MLS EM 41/3 administration in feeding strategy seems to be contributing fact for rabbit health status maintaining.

Quantitative analysis of human gait is critical for early discovery, progressive tracking, and rehabilitation of neurological and musculoskeletal disorders, such as Parkinson’s Disease, stroke, and Cerebral Palsy. Gait analysis typically involves estimating gait characteristics, such as spatio-temporal gait parameters and gait health indicators (e.g., step time, length, symmetry, and balance). Traditional methods of gait analysis involve the use of cameras, wearables, and force plates, but are limited in operational requirements when applied in daily life - such as direct line-of-sight, carrying devices, and dense deployment. This paper introduces a novel approach for gait analysis by passively sensing footstep-induced floor vibrations during walking using vibration sensors mounted on the floor surface. Our approach is non-intrusive, scalable, and perceived as more privacy-friendly, making it suitable for continuous gait health monitoring in daily life. We developed a floor vibration-based gait analysis framework to estimate various gait parameters that are used as standard metrics in medical practices, including temporal parameters (step, stride, stance, swing, double-support, single-support time) and spatial parameters (step length, width, angle, stride length), and extracts gait health indicators (cadence/walking speed, left-right symmetry, gait balance, initial contact types). The main challenge is that floor vibrations are distinct across different floor types. To address this challenge, our approach develops floor-adaptive algorithms to extract floor-specific features and is designed to be generalizable to various settings, including homes, hospitals, and eldercare facilities. We evaluate our approach through real-world walking experiments with 20 adults with 12,231 labeled gait cycles across concrete and wooden floors. Our results show 90.5% (RMSE 0.08s), 71.3% (RMSE 0.38m), and 92.3% (RMSPE 7.7%) accuracy in estimating temporal, spatial parameters, and gait health indicators, respectively.

Image processing is a computationally intensive task that can benefit significantly from parallel techniques. In this study, we evaluate the efficiency of image processing using parallel techniques compared to sequential techniques. We use Dask to parallelize the loading and processing of images and compare the execution times for various image processing techniques, including smoothing, edge detection, rotation, and thresholding. The results show that parallel processing significantly reduces execution time compared to sequential processing.

Real-time image processing is crucial in applications such as video surveillance, diagnostic medicine, and autonomous vehicle systems. Parallel computing is essential to address the demands of real-time processing, but challenges persist in its practical implementation. This review analyzes parallel computing techniques applied to real-time image processing, examining parallel architectures and algorithms, identifying effective applications, and evaluating challenges and limitations. The results of this study will contribute to the development of more efficient solutions for real-time image processing. Keywords: Parallel computing, real-time image processing, video surveillance, diagnostic medicine, autonomous vehicle systems.

Over the last decade, the prevalence of health issues has increased by approximately 29.1%, putting a substantial strain on healthcare services. This has accelerated the integration of machine learning in healthcare, particularly following the COVID-19 pandemic. The utilization of machine learning in healthcare has grown significantly, however many present approaches are unsuitable for real-world implementation due to high memory footprints and lack of interpretability. We introduce DeClEx, a pipeline designed to address these issues. DeClEx ensures that data mirrors real-world settings by incorporating gaussian noise and blur and employing autoencoders to learn intermediate feature representations. Subsequently, our convolutional neural network, paired with spatial attention, provides comparable accuracy to state of the art pre-trained models while achieving threefold improvement in training speed. Furthermore, we provide interpretable results using explainable AI techniques. We integrate denoising and deblurring, classification and explainability in a single pipeline called DeClEx.

One of the needs of the teaching and learning processes is the lack of information during the process, such as the emotions of students. Emotions influence learning, the ability to process information and accurately understand what we are dealing with. They profoundly affect students’ academic engagement and performance. One new way of getting such information is monitoring students' emotions. This research aims to expose 6 groups of secondary school students to video recordings for further analysis and categorization of their emotions. A model of emotional recognition (ER) during classroom learning was designed, with a custom-made code, from recorded videos to images to identify faces, action units (AUs) and consequently the emotions of each of the students shown on the screen. In addition, the ER model was optimized, from detecting few students to many students during a class. Emotions were then analysed according to the academic year, subject, and comparing the emotions at the beginning and at the end of the class. The results indicate the presence of a variety of emotions in the classroom and we found significant differences in the presence of some emotions depending on the time of class, subject and academic year, although there are no clear patterns. We discussed how emotions influence students' academic performance, and we suggest future research focus on teachers utilizing tools like this to improve student well-being and performance.

In this contribution we present work on rapid thermal annealing of as-electrodeposited thin films of Cu2ZnSnS4. The treatment was carried out in cold wall tubular reactor in dynamic conditions with variations of temperature, speed and time of the specific elements of the process. The effect of annealing was investigated by X-ray diffractometry, Raman scattering and Scanning Electron Microscopy (SEM). The phase composition of the films in dependence on conditions of treatment was analysed and have been shown that in slow and prolonged high-temperature process the low temperature binaries react completely and only Kesterite and ZnS is left. In addition, structural investigations by XRD have shown gradual decrease of crystallite sizes when the temperature level and duration of the high-temperature segment increases and respectively increase in the strain, due to formation of the phases in non-equilibrium conditions. In opposite, when the speed of dynamic segments in the process decreases the both crystallite size and strain of the Kesterite decreases non-monotonically. The grain sizes of Kesterite, presented by SEM investigations, have shown increase when the temperature and the duration increase, respectively the speed decreases with exemption at higher temperature near 750 C. The set of experiments gave idea by scrupulous analysis of Raman data to elucidate the track for fine manipulating of the defects in the structure of CZTS thin films having in mind the dependences of the ratios of Q = I287/I303 and Q’= I338/(I366+I374) on the process variables.

Bifunctional catalysts consisting of mono- or bimetallic nanoparticles (NPs) and zeolite supports received considerable attention due to excellent catalytic properties in numerous reactions including direct and indirect biomass processing. Here, we discuss major approaches to the preparation of NPs in zeolites, concentrating on methods allowing the best interplay (synergy) between metal and acid sites which is normally achieved for small NPs well-distributed through zeolite. We focus on modification of zeolites to provide structural integrity and controlled acidity which can be accomplished by incorporation of certain metal ions or elements. The other modification avenue is the adjustment of the zeolite morphology including creation of numerous defects for the NP entrapment and designed porosity. In this review we also provide examples of synergy between metal and acid sites and emphasize that without density functional theory calculations many assumptions about interactions between active sites stay unvalidated. Finally, we describe the most interesting examples of direct and indirect biomass (waste) processing for the last five years.

Current proposals on the temporal sequence in the processing of emotional visual stimuli are partially incompatible with growing empirical data. In the majority of them, the initial evaluation structures (IES) postulated to be in charge of the earliest detection of emotional stimuli (i.e., salient for the individual), are high order structures (i.e., those receiving visual inputs after several synapses). Thus, their latency of response cannot account for the first visual cortex response to emotional stimuli (peaking 80 ms in humans). Additionally, these proposed structures lack the necessary infrastructure to locally analyze the visual features of the stimulus (shape, color, motion, etc.) that define a stimulus as emotional. In particular, the amygdala is defended as the cornerstone IES also in humans, and cortical areas such as the ventral prefrontal cortex or the insula have been proposed as well to intervene in this initial evaluation process. The present review describes several first-order brain structures (i.e., receiving visual inputs after one synapsis), and second order structures (two synapses) that may complement the former, that accomplish with both prerequisites: presenting response latencies compatible with the observed activity at the visual cortex and possessing the necessary architecture to rudimentarily analyze in situ relevant features of the visual stimulation. The visual thalamus, and particularly the lateral geniculate nucleus (LGN), a first-order thalamic nucleus that actively processes visual information, is a good candidate to be the core IES, with the complementary action of the thalamic reticular nucleus (TRN). This LGN-TRN tandem could be supported, also in an ascending, initial evaluation phase, by the pulvinar, a second order thalamic structure, and first-order extra-thalamic nuclei (superior colliculus and certain nuclei of pretectum and the accessory optic system). In sum, the visual thalamus, scarcely studied in relation to emotional processing, is a serious candidate to be the missing link in early emotional evaluation and, in any case, is worth exploring in future research.

Named herein “CytroCell”, the cellulosic material obtained via hydrodynamic cavitation of citrus processing waste in water is cellulose of low crystallinity, high porosity, good water holding capacity and good dispersibility in water. These properties, here demonstrated for the first time for lemon and grapefruit CytroCell, open the route to mass scale production of a useful functional material from a cheap and abundant biowaste. The process, indeed, does not require any pre-treatment of the raw material, and does not use acid, alkali, chemical oxidants or enzymes.

Over the past decades, research has escalated on the use of polylactic acid (PLA) as replacement for petroleum-based polymers. This is due to its valuable properties, such as renewability, biodegradability, biocompatibility and good thermomechanical properties. Despite possessing good mechanical properties comparable to conventional petroleum-based polymers, PLA suffers from some shortcomings such as low thermal resistance, heat distortion temperature and rate of crystallization, thus different fillers have been used to overcome these limitations. In the frame work of environmentally friendly processes and products, there has been growing interest on the use of cellulose nanomaterials viz. cellulose nanocrystals (CNC) and nanofibers (CNF) as natural fillers for PLA towards advanced applications other than short-term packaging and biomedical. Cellulosic nanomaterials are renewable in nature, biodegradable, eco-friendless and they possess high strength and stiffness. In the case of eco-friendly processes, various conventional processing techniques, such as melt extrusion, melt-spinning, and compression moulding, have been used to produce PLA composites. This review addresses the critical factors in the manufacturing of PLA-cellulosic nanomaterials by using conventional techniques and recent advances needed to promote and improve the dispersion of the cellulosic nanomaterials. Different aspects, including morphology, mechanical behavior and thermal properties, as well as comparisons of CNC- and CNF reinforced PLA, are also discussed

As the world’s population age, Cognitive Frailty (CF) is becoming one of the most serious health problems and elucidating its biological mechanisms along with prevention and treatments becomes increasingly important also considering the associated health costs. This pilot study aims to investigate the potential cognitive and neuropsychological effects of a 5-week treatment with a therapy based on the regenerative properties of ozone (O3) in a cohort of patients stratified according to CF. We also study the potential effects of O3 on blood-based biomarkers indicative of specific biological systems that may be altered in CF. Seventy-five older persons have been recruited and randomly assigned to receive the active treatment (150cc of oxygen-O2-O3 mixture at the concentration of 30ug of O3 per cc of O2) or O2, or the placebo (air) for 5 weeks. The primary endpoint is a change in the scores of clinical scales from baseline (T0) to weeks 3 (T3), 9 (T9), 15 (T15) after treatment. The secondary endpoint is a change in biomarkers levels resulting from transcriptomics, proteomics and metabolomic patterns at the same times. This study will evaluate whether an intervention with the O2-O3 therapy could have potential cognitive, neuropsychological and biological effects on CF.

The strong competition in the automotive industry has required manufacturers to implement lean production, both with methods and techniques specific to Industry 4.0. At the same time, universities must provide graduates with specific skills for applying these new production methods and techniques. In this context, a Lean Learning Factory was developed in the Pitesti University Center, which allows students to learn, experiment, and research, in an environment similar to that of enterprises, the new Lean Manufacturing methods and techniques, and Industry 4.0. The research presented in this study had as its objective identifying the minimum number of repetitions necessary to train operators who serve two differently organized workstations, one classic, the other including digital techniques, but on which the same assembly operation is performed. Several indicators were considered in the analysis, such as the number of errors, the number of stops, the effective duration of the work cycle, and the percentage ratio between the standard duration of cyclical activities and the effective duration of the work cycle. The evolution of these indicators was mathematically modelled by regression functions, using the least squares method. The obtained results also highlight the usefulness of applying the DOJO method as a Lean manufacturing-specific learning technique and the efficiency of implementing digital techniques in work organization.

Oligopeptide transporters (OPTs) are a group of membrane localized proteins that have a broad range of substrate transport capabilities and contribute to numerous biological processes. However, limited information has been reported on OPTs in higher plants. In this study, a comprehensive analysis of the OPT gene family in Medicago truncatula was performed. A total of 26 OPT genes (MtOPT01-MtOPT26) have been identified in the Medicago truncatula genome. Phylogenetic analyses indicated that MtOPTs consisted of two distinct subgroups, 12 MtOPTs belonged to the peptide transport subgroup (PT-OPT) based on their predicted amino acid sequences containing the two highly conserved motifs (NPG and KIPPR) and 14 MtOPTs belonged to yellow stripe subgroup (YS-OPT). The MtOPTs distributed on each of 8 chromosomes in Medicago truncatula. Sequence analysis verified that MtOPTs significant similar to those in other plants. The copy number of MtOPTs was low and the multiply of MtOPTs was simple relatively. Gene structure analysis showed that most of the MtOPTs have various numbers of introns. The multiple of MtOPTs might play different biological roles which were supported by the fact that MtOPTs have a distinct tissue-specific expression pattern. The data obtained in this study will help to better understand the complexity of the MtOPTs gene family and provide new evidence for the function and evolution of the OPT gene family in higher plants.

Superior fiber quality is one of the most important objectives in cotton breeding. To detect the genetic basis underlying fiber quality, an F2 population containing 413 plants was constructed by crossing Jifeng 914 and Jifeng 173, both of which have superior fiber quality, with Jifeng 173 being better. Five fiber quality traits were investigated in the F2, F2:3, F2:4, and F2:5 populations. Quantitative trait loci (QTL) mapping was conducted based on a high-density genetic map containing 11,488 single nucleotide polymorphisms (SNPs) and spanning 4,202.12 cM in length. Transgressive segregation patterns and complex correlations in the five tested traits were observed. A total of 108 QTLs were found, including 27 for fiber length (FL), 16 for fiber strength (FS), 24 for micronaire (MC), 17 for fiber uniformity (FU), and 24 for fiber elongation rate (FE). Chromosome A7 contained 12 QTL, ranking the first. No QTL was found on chromosome D1 and D11. Each QTL contributed 1.98%–21.45% to the phenotypic variation (PV), including 13 major effect QTLs that contributed more than 10% toward PV. Two QTLs could be repeatedly detected in three populations, including qFL-D3-2 in F2, F2:4, and F2:5 with 9.18%–21.45% of PV, qFS-A11-1 in F2:3, F2:4 and F2:5 with 6.05%–10.41% of PV. Another seven stable QTLs could be detected in two populations, including four major effect QTLs: qFL-A12-3, qFS-D10-2, qMC-D6-2, and qMC-D8-1. Fourteen QTL-overlapping regions were found, which might explain the complex correlations among the five phenotypic traits. Four regions on chromosome A11, D3, D6, and D10 covered by both stable and major effect QTLs are promising for further fine mapping. The genomic regions of the two QTLs detected in three populations and the four major effect QTLs contain 810 genes. Gene functional analysis revealed that the annotated genes are mainly involved in protein binding and metabolic pathways. Fifteen candidate genes in the qFL-D3-2 region are highly expressed in fiber or ovules during fiber initiation, elongation, secondary cell wall thickening, or maturation stages. qRT-PCR revealed that Ghir_D03G005440.1 and Ghir_D03G011310.1 may play a role in promoting fiber initiation, Ghir_D03G006470.1 may be beneficial for promoting fiber elongation. This study provides more information for revealing the molecular genetic basis underlying cotton fiber quality.

In the constantly growing field of wireless communication, antenna selection has a considerable influence on system performance, efficiency, and dependability. This paper gives a thorough comparison of patch antennas and dipole antennas, two of the most common forms used in wireless communication. Patch antennas are distinguished by their low profile, ease of production, and integration with microwave electronics, as opposed to dipole antennas, which have become known for their simplicity, omnidirectional emission patterns, and broad application. The discussion begins with an explanation of the basic concepts, design considerations, and operational characteristics of patch and dipole antennas. Key performance criteria including bandwidth, gain, radiation efficiency, polarization, and impedance matching are assessed using theoretical considerations and actual applications. Case studies from modern wireless applications, such as mobile communication, Wi-Fi networks, and Internet of Things devices, are used to demonstrate the strengths and limits of each antenna type in real-world circumstances.The comparative literature review additionally explores advanced strategies for improving the performance of patch and dipole antennas, such as the use of metamaterials, reconfigurable components, and new substrate materials. Comparative models and experimental findings illustrate the trade-offs between size, cost, and performance, assisting engineers and researchers in determining the best antenna type for certain applications.

Fundus image registration plays a crucial role in the clinical evaluation of ocular diseases, such as diabetic retinopathy and macular degeneration, necessitating meticulous monitoring. The alignment of multiple fundus images enables the longitudinal analysis of patient progression, widening the visual scope, or augmenting resolution for detailed examinations. Currently, prevalent methodologies rely on feature-based approaches for fundus registration. However, certain methods exhibit high feature point density, posing challenges in matching due to point similarity. This study introduces a novel fundus image registration technique integrating U-Net for the extraction of feature points employing FIVES for its training and evaluation, a novel and large dataset for blood vessels segmentation, prioritizing point distribution over abundance. Subsequently, the method employs medial axis transform and pattern detection to obtain feature points characterized by the Fast Retina Keypoint (FREAK) descriptor, facilitating matching for transformation matrix computation. Assessment of the vessel segmentation achieves 0.7559 for Intersection Over Union (IoU), while evaluation on the Fundus Image Registration Dataset (FIRE) demonstrates the method’s comparative performance against existing methods, yielding a registration error of 0.596 for area under the curve, refining similar earlier methods and suggesting promising performance comparable to prior methodologies.

Cognitive function, arthrogryposis, obstetrics palsy, children, motor disorder

The purpose of this review is to elucidate the actual threat of the most prevalent mycotoxins in agricultural commodities and human/animal food/feed for appearance of foodborne ailments and diseases. The underestimated hazard of joint mycotoxin uptake of animals or humans is critically discussed with regards to synergistic or additive interaction between some target mycotoxins. The real toxicity of target mycotoxin combinations as it happens in practice is evaluated and possible lower limit values or control measures are suggested in such cases. Some critical points in adequate risk assessment, hygiene control and regulation of mycotoxns are discussed. The efficiency of current mycotoxin regulations and control measures is evaluated in regards to human/animal health hazard. The risk assessment in the case of multiple mycotoxin exposure of humans/animals via the food/feed or agricultural commodities is evaluated and some suggestions are proposed in such cases. Appropriate control measures and food safety issues throughout the food supply chain are proposed in order to prevent target foodborne diseases. Some preventive measures and possible veterinary hygiene control or risk assessment are proposed in some practical cases of foodborne diseases for preventing mycotoxin contamination of animal products designed for human consumption and to avoid possible public health issues.

This paper assessed rice farmers' perceptions of agricultural and irrigation practices through their choices. A survey was carried out among 278 rice farmers on five irrigation schemes in three agro-ecological zones of Togo, viz. the savannah region, the forest region and the coastal region. The sample of random respondents was selected from a list of rice farmers provided by the managers of these irrigated schemes. Data collected through face-to-face interviews using kobocollect software were analyzed with R 4.2 software, and the Pearson test was validated at the 5% level. The results showed that the socio-economic characteristics of the rice growers, such as access to agricultural credit, experience, non-agricultural activities, membership of a rice growers' cooperative, gender, level of education and training in growing irrigated rice, and farm labor, significantly influenced the choice of agronomic practices and irrigation management. Characteristics such as experience, training and hired farm labor enhanced paddy yields, while access to agricultural credit was unproductive. Chemical clearing combined with stump removal, tiller ploughing, transplanting and high plant density are the agricultural practices that increased paddy yields, while tiller mudding and basal mineral fertilization were inefficient. In the same way, water distribution by demand, water depth above the soil of less than 5 cm, use of water-saving technologies and farmer's knowledge of how to improve water and fertilizer use-efficiency also helped to boost paddy yields. For sustainable rice production in the context of labor shortages and the financial resources required, future studies will focus on the effectiveness of motorized mudding and agricultural credits for rice farmers. Furthermore, considering the crucial role of training in the adopting of good agricultural and irrigation practices, future research will study strategies for wide diffusion of knowledge to rice producers.

The design complexity of the new generation of civil aero-engines results in higher demands on engines’ components, higher component temperature, higher heat generation, and, finally, critical thermal management issues. This paper will propose a methodological approach to create physics-based models for heat loads developed by sources as well as a systematic sensitivity analysis to identify the effects of design parameters on the thermal behaviour of civil aero-engines. The ranges and levels of heat loads generated by heat sources (e.g., accessory gearbox, bearing, pumps, etc.) and heat absorption capacity of heat sinks (e.g., engine fuel, oil, and air) are discussed systematically. The practical research challenges for thermal management system design and development for the new and next generation of turbofan engines will then be addressed through a sensitivity analysis of the heat load values as well as the heat sinks flow rates. The potential solutions for thermal performance enhancements of propulsion systems will be proposed and discussed accordingly.

Malnutrition is a serious problem with negative impact on the quality of life and the evolution of our patients, contributing to an increase in morbidity, hospital stay, mortality and health spending. Early identification is fundamental to implement the necessary therapeutic actions involving adequate nutritional support to prevent or reverse malnutrition. This review presents two complementary methods of fighting malnutrition: nutritional screening and nutritional assessment. Nutritional risk screening is conducted using simple, quick-to-perform tools and is the first line of action in detecting at-risk patients. It should be implemented systematically and periodically on admission to hospital or residential care, as well as on an outpatient basis for patients with chronic conditions. Once patients with a nutritional risk have been detected they should undergo a more detailed nutritional assessment to identify and quantify the type and degree of malnutrition. This should include health history and clinical examination, dietary history, anthropometric measurements, evaluation of the degree of aggression determined by the disease, functional assessment and, whenever possible, some method of measuring body composition.

We study different aspects of an exactly solvable master equation (ME). Its simplicity allows one direct access to the details of its workings, highlighting significant features and peculiarities. The existence of phase transitions is unveiled.

Our natural language approach concerns syntactic analysis using a dedicated Javascript library - wink-nlp - and semantic analysis based on Prolog programming language, facilitated by another Javascript library - tau-prolog - that allows defining logical programs, declaring rules and checking for goals inside Javascript language. Firstly, our program splits the original text into sentences, than into tokens and identifies each part of the sentence, dynamically maps entities into Prolog rules, then check the spelling accordingly to the Definite Clause Grammar (DCG) by querying the pre-defined program for initial goals (the sentence itself). Basically, we let the parser infer its own rules from the syntactic point of view, then check the grammar from a semantic perspective against the DCG inside the same work flow or pipeline of steps.The provided article combine the usage of wink-nlp and tau-prolog packages for natural language processing (NLP) and understanding (NLU).

Gene or genome editing (GE) revises, removes, or replaces a mutated gene at the DNA level; it is a tool. Gene therapy (GT) offsets mutations by introducing a "normal" version of the gene into the body while the diseased gene remains in the genome; it is a medicine. So far, no in vivo GE product has been approved, as opposed to 22 GT products approved by the FDA. However, several are under development, and the FDA anticipates their fast entry, as evidenced by the FDA's actions of issuing GE-specific guidelines. The potential of GE in treating diseases far supersedes any other modality due to the recognized role of genes in most human diseases. While GT products add missing genes, GE tools can overcome many aberrations. However, GE presents many safety challenges, including off-target impact, delivery consistency, and long-term effects of gene-fixing. However, most of these concerns will be resolved once the FDA begins approving them, leading to hundreds of products capable of treating untreatable diseases.

The Sharing Economy (SE) has dawn great attention from several stakeholders in society in the last five years. While business actors are interested in financial opportunities to meet consumer needs, new business models, the academia and governmental organizations are concerned with potential unintended effects on the society and environment. In the process of making a clearer comprehension of the SE phenomenon, researchers have identified that, despite its notable global growth, there still persists a lack of a more solid ground in understanding its origins and respective mechanisms under which it has been evolving over time as a category. In this research, we address the problematics of the origins and ascendency of the SE by examining the process by which the SE is arising as a new category, searching for conceptual clarification and pinpointing the legitimacy granted by key stakeholders. Our guiding research questions are: (1) how the SE was formed and evolved as a market category; and (2), as a market category, is the SE legitimate? Additionally, we attempt to identify the nature of the SE as a category. To answer these questions, we conducted an historical analysis of the expression SE and its equivalents. This paper deepens the discussion about the nature of the SE by providing evidence that (i) the SE has predominantly been formed by emergence processes, comprising social movement, similarity clustering and truce components. It is the combination of all these aforementioned processes that renders the SE a special case of market category formation, which, in turn, has been allowing communication, entrepreneurship, regulation and research about what really is the SE, and despite the evident lack of agreements regarding both the label and its content; (ii) there is a generalized legitimacy granted to the SE by a vast number of stakeholders, even though still lacking on the consolidation of socio-political legitimation, and (iii) the nature of the SE seems to fall in a metaphorical approach, particularly, the notion of radial categories.

Fractional flow reserve (FFR) is well recognized as a gold standard measure for the estimation of functional coronary stenosis. Technological progressions in image processing have empowered the reconstruction of three-dimensional models of the coronary arteries via both non-invasive imaging modalities, such as Coronary Computed Tomography Angiography (CCTA), and invasive coronary angiography or intravascular imaging. The application of computational fluid dynamics (CFD) techniques to coronary 3D anatomical models allows the virtual evaluation of the hemodynamic significance of a coronary lesion with high diagnostic accuracy in comparison to the invasive FFR using pressure wires. These opportunities providing hemodynamic information based on anatomy have given rise to a new era of functional angiography and coronary imaging. However, further validations are needed to overcome several scientific and computational challenges before these methods are applied in everyday clinical practice. This review aims to present all the data on the studies and methodologies that have been presented for the virtual estimation of FFR, and the diagnostic execution of the virtual tools for the hemodynamic assessment of coronary lesions compared to the invasive FFR.

We study Cartan-Schouten metrics, explore invariant dual connections, and propose them as models for Information Geometry. Based on the underlying Riemannian barycenter and the biinvariant mean of Lie groups, we subsequently propose a new parametric mean for data science and machine learning which comes with several advantages compared to traditional tools such as the arithmetic mean, median, mode, expectation, least square method, maximum likelihood, linear regression. We call a metric on a Lie group, a Cartan-Schouten metric, if its Levi-Civita connection is biinvariant, so every 1-parameter subgroup through the unit is a geodesic. Except for not being left or right invariant in general, Cartan-Schouten metrics enjoy the same geometry as biinvariant metrics, since they share the same Levi-Civita connection. To bypass the non-invariance apparent drawback, we show that Cartan-Schouten metrics are completely determined by their value at the unit. We give an explicit formula for recovering them from their value at the unit, thus making them much less computationally demanding, compared to general metrics on manifolds. Furthermore, Lie groups with Cartan-Schouten metrics are complete Riemannian or pseudo-Riemannian manifolds. We give a complete characterization of Lie groups with Riemannian or Lorentzian Cartan-Schouten metrics. Cartan-Schouten metrics are in abundance on 2-nilpotent Lie groups. Namely, on every 2-nilpotent Lie group, there is a 1-1 correspondence between the set of left invariant metrics and that of Cartan-Schouten metrics.

Energy and information are fundamental concepts that play crucial roles in the foundations of science, each contributing to different aspects of our understanding of the natural world. Here we encounter that Fisher' measure is able to link them. We also investigate some interesting relations between Fisher's information, Poisson's distribution, and thermal quantifiers in a grand canonical ensemble. These connections permit one to visualize interesting novel artifacts in statistical mechanics. We clearly see them emerge in discussing the ideal gas. For example, we encounter that the Poisson distribution is intrinsically involved in the physics of the ideal gas, and we are able to give a Fisher-estimation of the difference in informational content between the grand canonical and canonical ensembles.

We attempt to re-express Nernst law in terms of a suitable information measure (IM). This is done on the basis of adapting the idea of purity to a thermal Gibbs environment, which yields what we might call a ``purity'' (and symbolize it by the symbol $D$). We further apply the properly generalization of this $D$-IM to a classical scenario. This generalization turns our to have interesting consequences, when used in conjunction with the classical Shannon entropy $S$. These consequences are related to Nernst law.

Face detection is an important problem in computer vision because it enables a wide range of applications, such as facial recognition and analysis of human behavior. The problem is challenging because of the large variations in facial appearance across different individuals and different lighting and pose conditions. One way to detect faces is to utilize a highly advanced face detection method, such as RetinaFace, which uses deep learning techniques to achieve high accuracy in various datasets. However, even the best face detectors can produce false positives, which can lead to incorrect or unreliable results. In this paper, we propose a method for reducing false positives in face detection by using information from a depth map. A depth map is a two-dimensional representation of the distance of objects in an image from the camera. By using the depth information, the proposed method is able to better differentiate between true faces and false positives. The authors evaluate their method on a combined dataset of 549 images, containing a total of 614 upright frontal faces. The results show that the proposed method is able to significantly reduce the number of false positives without sacrificing the overall detection rate. This indicates that the use of depth information can be a useful tool for improving face detection performance.

We present large eddy simulations of a mid-latitude open ocean front using a modified state of the art computational fluid dynamics code. We investigate the energy and information fluxes at the submesoscale/small-scale range in the absence of any atmospheric forcing. We find submesoscale conditions (Ro∼1, Ri∼1) near surface, within baroclinic structures related with partially imbalanced frontogenetic activity. Near surface, the simulations show a significant scale coupling on scales larger than ∼103(m). This is manifested as a strong direct energy cascade and intense mutual communication between scales, where the latter was evaluated using an estimator based on Mutual Information Theory. At scales smaller than ∼103(m) the results show near-zero energy flux, however, at this scale range, the estimator of mutual communication still shows values corresponding with a significant level of communication between them. This fact motivates to investigate the nature of the self-organized turbulent motion at this scale range with weak energetic coupling but where communication between scales is still significant and to inquire into the existence of synchronization or functional relationships between scales with emphasis on eventual underlying non-local processes.

French Guiana, located in the Guiana Shield, is a natural reservoir for many zoonotic pathogens that are of considerable medical or veterinary importance. Until now, there are few data available on the description of parasites circulating in this area, especially on protozoan belonging to the phy-lum Apicomplexa; conversely, the neighbouring countries describe a high parasitic prevalence in animals and humans. Epidemiological surveillance is necessary, as new potentially virulent strains may emerge from these forest ecosystems, such as Amazonian toxoplasmosis. However, there is no standard tool for detecting protozoa in wildlife. In this study, we developed Meat-Borne-Parasite, a high-throughput meta-barcoding workflow for detecting Apicomplexa based on Oxford Nanopore Technologies sequencing platform using the 18S gene of fourteen Apicomplexa-positive samples collected in French Guiana. Sequencing reads were then analysed with MetONTIIME pipeline. Thanks to a scoring rule, we were able to classify as Apicom-plexa-positive 10 samples out of 14 and reveal the presence of co-carriages. The same samples were also sequenced with Illumina platform, for validation purposes. On samples identified as Apicomplexa-positive by both platforms, a strong positive correlation at up to genus level was reported. Overall, the presented workflow represents a reliable method for Apicomplexa detec-tion, which may pave the way for more comprehensive biomonitoring of zoonotic pathogens.

Nuclear Magnetic Resonance (NMR) and its various forms are extensively utilized in both research and clinical settings to analyse molecules. NMR data pre-processing, while essential to NMR data analysis, can be uniquely complex. Despite the availability of software tools, understanding these processes can be challenging, complicating the selection of appropriate pre-processing steps. In this review, we elucidate pre-processing steps in the time domain from a mathematical and statistical perspective, explaining direct current offset removal, eddy current correction, shift and linear prediction, weighting, zero filling, and domain transformation using plain language and fundamental mathematical formulas. Our objective is to clarify these processes in simple terms and provide general guidance.

Summary: PURPOSE. Development of a drainless energy-saving technology for the processing of high-moisture iron-containing sludge from metallurgical production. RESEARCH METHOD. The regularities of the combined process of chemical dehydration of high-moisture iron-containing sludge by powdered waste of lime and dolomite, hardening and agglomeration by pressing in a single technological cycle on the developed experimental plant were studied. In the course of laboratory studies, the temperature of the mass, the rate of dehydration, the chemical composition of the mixtures, the appearance of the resulting briquettes, the weight loss during agglomeration-humidity of the mixtures, and mechanical strength of the briquettes were measured. RESULTS. New patterns have been established that have made it possible to develop a nonfiring method for producing iron-containing materials and self-healing briquettes. The essence of his method, which is one of the main provisions of scientific novel provisions, is to combine dehydration and self-hardening of the mixture with the process of shaping by applying external pressure to the hardening mixture in molds to obtain agglomerated material in the form of briquettes in a single technological cycle. The proposed technology does not require drying or firing, and a set of strength properties occurs as the material is cooled in air during the day. A new energy-efficient and drainless method for the production of iron-containing briquettes has been developed, which combines the processes of chemical dehydration and agglomeration in one technological cycle. CONCLUSION. The proposed project and technology will allow organization of production for the processing of high-moisture iron-containing sludge and the production a complex iron-containing material as a secondary metal-containing raw material for metallurgical plants for the production of steel and rolled metal. and dolomite dust from dry gas cleaning as dehydrating and binding materials, as well as screening of coke and coal as a reducing agents. The proposed technology also solves the problems of environmental pollution and land acquisition for the storage of production waste.

Interhemispheric and frontoparietal functional connectivity have been reported to increase during explicit information processing. However, it is unclear how and when interhemispheric and frontoparietal functional connectivity interact during explicit semantic processing. Here, we tested the neural coupling hypothesis that explicit semantic processing promotes neural activity in the nondominant right hemispheric areas owing to synchronization with enhanced frontoparietal functional connectivity at later processing stages. We analyzed electroencephalogram data obtained using a semantic priming paradigm, which comprised visual priming and target words successively presented under direct or indirect attention to semantic association. Scalp potential analysis demonstrated that the explicit processing of congruent targets reduced negative event-related potentials, as previously reported. Current source density analysis showed that explicit semantic processing activated the right temporal area during later temporal intervals. Subsequent dynamic functional connectivity and neural coupling analyses revealed that explicit semantic processing increased the correlation between right temporal source activities and frontoparietal functional connectivity in later temporal intervals. These findings indicate that explicit semantic processing increases neural coupling between the interhemispheric and frontoparietal functional connectivity during later processing stages.

Magnetic Resonance Imaging (MRI) is widely used in clinics and research due to its accurate disease identification and non-invasive nature. MRI is based on Nuclear Magnetic Resonance (NMR), which is also extensively employed in various fields. NMR and its modern versions involve intricate pre-processing steps before data analysis. These steps are initially processed in the time domain and subsequently in the frequency domain. While our previous review focused on time domain pre-processing (https://www.preprints.org/manuscript/202310.2032/v1), this review delves into the mathematical and statistical aspects of frequency domain pre-processing. We discuss essential pre-processing steps like phase error correction, baseline correction, solvent filtering, calibration and alignment, reference deconvolution, binning/bucketing and peak picking, peak fitting/deconvolution and compound identification, integration and quantification, normalization and transformation. Furthermore, we offer practical recommendations for each step.

We describe a dataset that contains job description published on a popular online website in the information and technology sector. As the website focus mainly on United Kingdom based jobs, the data have a specific focus on this country. It contains 11.501 job vacancies and 13 related meta data information. The dataset is suitable for HR analysis using machine learning techniques such as natural language processing and neural networks.

Four decades of proteasome research have yielded extensive information on ubiquitin-dependent proteolysis. The archetype of proteasomes is a 20S barrel-shaped complex that does not rely on ubiquitin as a degradation signal but can degrade substrates with a considerable unstructured stretch. Since roughly half of all proteasomes in most eukaryotic cells are free 20S complexes, ubiquitin-independent protein degradation may coexist with ubiquitin-dependent degradation by the highly regulated 26S proteasome. This article reviews recent advances in our understanding of the biochemical and structural features that underlie the proteolytic mechanism of 20S proteasomes. The two outer α-rings of 20S proteasomes provide a number of potential docking sites for loosely folded polypeptides. The binding of a substrate can induce asymmetric conformational changes, trigger gate opening, and initiate its own degradation through a protease-driven translocation mechanism. Consequently, the substrate translocates through two additional narrow apertures augmented by the β-catalytic active sites. The overall pulling force through the two annuli results in a protease-like unfolding of the substrate and subsequent proteolysis in the catalytic chamber. Although both proteasomes contain identical β-catalytic active sites, the differential translocation mechanisms yield distinct peptide products. Non-overlapping substrate repertoires and product outcomes rationalize cohabitation of both proteasome complexes in cells.

Since the early 1980s, a large number of research works on enzymes from the red king crab hepatopancreas have been conducted. These studies have been relevant both from a fundamental point of view for studying the enzymes of marine organisms and in terms of the rational management of nature to obtain new and valuable products from the processing of crab fishing waste. Most of these works were performed by Russian scientists due to the area and amount of waste of red king crab processing in Russia (or the Soviet Union). However, the close phylogenetic kinship and the similar ecological niches of commercial crab species and the production scale of the catch provide the bases for the successful transfer of experience in the processing of red king crab hepatopancreas to other commercial crab species mined worldwide. This review describes the value of recycled commercial crab species, discusses processing problems, and suggests possible solutions to these problems. The main emphasis is placed on the enzymes of the hepatopancreas as the most highly salubrious product of waste processed from red king crab fishing.

Music and language are hypothesized to share neural resources, particularly at the level of syntax processing. Recent reports suggest that attention modulates this sharing of neural resources, but the time-course of the effects of attention, and the degree to which attention operates similarly on music and language, are yet unclear. In this EEG study we manipulate the syntactic structure of simultaneously presented musical chord progressions and garden-path sentences in a modified rapid serial visual presentation paradigm, while varying top-down attentional demands to the two modalities. The Early Right Anterior Negativity (ERAN) was observed in response to both attended and unattended musical syntax violations. In contrast, an N400 was only observed in response to attended linguistic syntax violations, and a P3 only in response to attended musical syntax violations. Results show that top-down allocation of attention indeed affects the processing of syntax in both music and language, with different neural resources acting upon the two modalities particularly at later stages of cognitive processing. However, the processing of musical syntax at an earlier stage of the perceptual-cognitive pathway, as indexed by the ERAN, is partially automatic, and is strongly indicative of separate neural resources for music and language.

The function of sleep in mammal and other vertebrates is one of the great mysteries of biology. Many hypotheses have been proposed, but few of these have made even the slightest attempt to explain the essence of sleep - the uncompromising need for reversible unconsciousness. During sleep, epiphenomena - often of a somatic character - occur, but these cannot explain the core function of sleep. One answer could be hidden in the observations made for long periods of time of the function of the central nervous system (CNS). The CNS is faced with conflicting requirements on stability and excitability. A high level of excitability is desirable, and is also a prerequisite for sensitivity and quick reaction times; however, it can also lead to instability and the risk of feedback, with life-threatening epileptic seizures. Activity-dependent negative feedback in neuronal excitability improves stability in the short term, but not to the degree that is required. A hypothesis is presented here demonstrating how calibration of individual neurons - an activity which occurs only during sleep - can establish the balanced and highest possible excitability while also preserving stability in the CNS. One example of a possible mechanism is the observation of slow oscillations in EEGs made on birds and mammals during slow wave sleep. Calibration to a genetically determined level of excitability could take place in individual neurons during the slow oscillation, so that action potentials are generated during the oscillations “up-phase”. This can only take place offline, which explains the need for sleep. The hypothesis can explain phenomena such as the need for unconsciousness during sleep, with the disconnection of sensory stimuli, slow EEG oscillations, the relationship of sleep and epilepsy, age, the effects of sleep on neuronal firing rate and the effects of sleep deprivation and sleep homeostasis. This is with regard primarily to mammals, including humans, but also all other vertebrates.

To improve carbon efficiency for urban logistics service system composed of a third-party logistics service provider (3PLs) and an e-business enterprise, low-carbon operation game between them was studied. Considering low carbon technology investment cost and sales expansion effect of low carbon level, profit functions for both players were constituted. Based on their different bargaining capabilities, totally 5 types of game scenarios were designed. Through analytical solution, Nash Equilibria under varied scenarios were obtained. By analyzing these equilibria, 4 major propositions were given, in which, some key variables and system performance index were compared. Results show that the best system yields could only be achieved under the fully cooperative situation; limited cooperation only for carbon emission reduction would not benefit the system performance improvement; E-business enterprise-leading game’s performance overtook 3PLs-leading ones.

Camelina (Camelina sativa L.) is an oilseed with potential for use as a raw material in second-generation biofuels. Camelina has a seed yield of up to 2380 kg ha^-1 and contains around 45% fatty acids. Selection of a suitable site is critical for production optimization. The objective of this study was to determine Chilean agro-climatic suitability for establishing camelina as a productive alternative. Climate and soil requirements and geographical restraints were evaluated for the species, considering the climatological characteristics of its regions of origin, as well as regions where camelina is successfully grown in the rest of the world. The variables considered include factors (maximum temperatures of the warmest month, water deficits, and degree days) and limitations (altitude, geomorphology, and current land use), which permitted the evaluation of the national territory for a certain level of suitability. It was determined that 1.3% of the national territory (960,664 ha) has some degree of suitability for camelina adoption. Between the Biobío and Los Lagos regions, 49.0% of land (471,203 ha) is in the category of without thermic restrictions, with mild water restrictions, and mild soil restrictions or without information, which can be used for camelina production. The Los Ríos region had 21.4% surface area (321,176 ha) with some level of suitability for camelina, the most suitable region to establish this crop in Chile.

An 18 month-old male was evaluated after presenting with disproportionate transaminitis in the setting of acute gastroenteritis. He had marked hepatomegaly on physical exam that was later confirmed with an abdominal ultrasound. Given this clinical picture, suspicion for a fatty acid oxidation disorder was raised. Further investigation revealed that his initial newborn screen was positive for carnitine palmitoyltransferase 1A (CPT1A) deficiency - a rare autosomal recessive disorder of long-chain fatty acid oxidation. Confirmatory biochemical testing in the newborn period showed carnitine levels to be unexpectedly low with a normal acylcarnitine profile. Thus, it was considered to be a false-positive newborn screen and metabolic follow up was not recommended. Repeat biochemical testing during this hospitalization revealed a normal acylcarnitine profile. The only abnormalities noted were a low proportion of acylcarnitine species from plasma, an elevated free-to-total carnitine ratio, and mild hypoketotic medium chain decarboxylic aciduria on urine organic acids. Gene sequencing of CPT1A revealed a novel homozygous splice site variant that confirmed his diagnosis. CPT1A deficiency has a population founder effect in the Inuit and other Arctic groups, but has not been previously reported in persons of Ashkenazi Jewish ancestry.

Spermatogenesis is critical for insect reproduction and is regulated by many different genes. In this study, we found that Forkhead transcription factor Fd59a functions as a key factor in spermatogenesis of Drosophila melanogaster. Fd59a contains a conversed Forkhead domain, and it is clustered to the FoxD subfamily with other FoxD members from some insect and vertebrate species. Mutation in Fd59a caused swelling in the apical region of the testis. More importantly, only a few mature sperm were present in the seminal vesicle of Fd59a mutant flies compared to the control flies, and the fertility of Fd59a2/2 mutant males was significantly lower than that of the control flies. Immunofluorescence staining showed that the homeostasis of testis stem cell niche in Fd59a2/2 mutant and Fd59a RNAi flies was disrupted, and apoptosis of sperm bundles was increased. Furthermore, results from RNA-sequencing and qRT-PCR suggest that Fd59a can regulate expression of genes related to reproductive process and cell death. Taken together, our results indicated that Fd59a plays a key role in the spermatogenesis of Drosophila.

Aim: The aim of this study was to investigate if tests used to assess core stability could be used to determine success in physiological tests applied to assess dynamic balance and agility for a young active population. Methods: Pearson's r correlation coefficient was used to assess the relationship between the core stability tests and the dynamic balance and agility tests. Evaluation of the tests was established using Cronbach's coefficient of variance as part of intra-rater reliability tests. An analysis of 18 active college aged students was conducted (males: n= 13, females: n= 5). The mean ± SD age for males was 19.2 years ± 3.22 years and for females was 19.4 years ± 1.14 years. Conclusion: The results indicate that there is no significant relationship between tests that assess core stability and tests conducted to assess dynamic balance in active young adults. With the exception of the abdominal flexion test, no significant relationship exists between the remaining core stability tests and agility T-Test. Core stability is not a determinant of balance and agility.

Recent years have seen a massive increase in research on mindfulness as both a therapeutic practice as well as a protective health factor. While many associations have been made between mindfulness and clinical outcomes, understanding of the underlying mechanistic processes is still in its infancy. In this review, associations between mindfulness and other established frameworks in cognitive control are explored—in particular the dual modes of control framework in cognitive control theory. Studies in mindfulness providing evidence towards a dual-mode mindfulness model are reviewed and compared with the proactive and reactive modes of cognitive control to identify common underlying principles. Studies involving cognitive testing, advanced neuroimaging, and neurotransmitter levels and their associations with proactive and reactive control are reviewed in the context of the dual modes of mindfulness. While evidence supporting relationships between proactive and reactive modes of mindfulness and cognitive control are preliminary, existing literature on mindfulness already supports the notion of two components of mindfulness and is in line with the hypothesis that cognitive control and mindfulness are closely related. Since preliminary evidence suggests that proactive forms of mindfulness are more associated with improvement in clinical outcomes, mindfulness programs could be modified to target proactive mindfulness practices to maximize outcomes. The implications of proactive and reactive mindfulness on therapy, limitations of the framework, and necessity of studies providing direct evidence are further discussed.

This study examines the Soil Organic Carbon (SOC) within Greek croplands, offering a comprehensive understanding of its dynamics. SOC, a cornerstone in soil health, nutrient cycling, and global carbon dynamics, assumes critical significance in sustainable agriculture and climate change mitigation. Drawing on diverse soil properties, including pH, soil texture as well as different drainage and slope categories, the research explores the nuanced relationships shaping SOC dynamics in the diverse agroecological landscape of Greece. The investigation transcends local boundaries, emphasizing SOC's global role in climate change mitigation by sequestering carbon dioxide. Two maps were used as data sources: 1) SOC stock baseline map (2010) by JRC, 2) SOC stock map (2021) by the Institute of Soil and Water Resources, Hellenic Agricultural Organization – DIMITRA in collaboration with FAO. Greek croplands emerge as a mosaic of agroecological diversity, where anthropogenic activities wield transformative influences on SOC stock, demanding a delicate balance between agricultural productivity and soil health. The study unveils the influence of Soil Order, weaving a tapestry of SOC variability. Microscopic factors, from soil texture to cation exchange capacity, further shape SOC dynamics, emphasizing the role of clayey soils and coarse materials in carbon retention. Although soil organic carbon decreased from 2010 to 2021 the degree of carbon loss varied. This scientific endeavor synthesizes existing knowledge and unveils novel insights, offering a narrative woven from diverse soil properties. The findings contribute valuable insights for sustainable land management practices and climate change mitigation strategies, underscoring the importance of region-specific approaches in addressing global challenges.

Asthma is a complex syndrome with multiple phenotypes and endotypes, and current diagnostic tools often fail to address this, therefore there is an increasing need for new predicting biomarkers for asthma which can enable personalized treatment strategies. Biomarkers can facilitate earlier diagnosis of asthma, potentially identifying the disease before clinical symptoms appear. This allows for early intervention, which can prevent asthma progression and complications and improve long-term outcomes. Reliable biomarkers can help in monitoring disease activity and treatment efficacy in real-time and can also lead to better disease management, reducing hospitalizations, emergency visits, and overall healthcare costs associated with poorly controlled asthma. Severe asthma patients often do not respond well to standard treatments. New biomarkers can help identify these patients and guide the development of novel therapeutic approaches specifically for severe asthma. In this review we summarize different candidate biomarkers on several levels of investigation: Genetic, subclinical inflammatory, allergen-specific immune response, and trained immunity, which could be promising for the early detection of asthma and better management of exacerbation and severity of symptoms in children.

Fused deposition modeling (FDM) is a rather new technology in the production of personalized dosage forms. Melting and printing of polymer–active pharmaceutical ingredient (API) – mixtures can be used to produce oral dosage forms with different dosage as well as release behavior. This process is utilized to increase the bioavailability of pharmaceutically relevant active ingredients that are poorly soluble in physiological medium by transforming them into solid amorphous dispersions (ASD). Release from such ASDs is expected to be faster and higher compared to the raw materials and thus enhance bioavailability. Printing directly from powder while forming ASDs was realized. Different techniques to change release patterns as well as a non-destructive way for determination of API distribution were shown. By measuring the melt viscosities of mixtures printed a rheological model for the printer used is proposed.

The main purpose of this work was to tests of form and size of fatigue wear of the top layer of oil gear pump wheel (3PW-BPF-24). The object of tests was operated during a determined number of hours at strictly controlled operational and environmental parameters. A microscope of focal differentiation by Alicona InfiniteFocus G4 (with Focus-Variation optical measuring technology) was used for an identification, measurement and description of noticed superficial defects, which resulted from the fatigue process of the top layer and an earlier thermochemical treatment. The work was finished with an analysis of obtained results (based on the analysis of 3D scans) and conclusions concerning about optimization of the thermal treatment. Taking into consideration the presented results (suggestions) in the technology of improving strength properties of gear wheels (of this type oil pumps) may render it possible to obtain an appropriate fatigue endurance of the top layer, i.e. a minimization of observed superficial defects.

This review examines additive manufacturing for refractory tungsten (W) and its alloys. It emphasizes the primary challenges and determining factors involved in additive manufacturing pure W, W alloys, and composites. Our focus in this regard extends to both process and alloying strategies designed to address critical issues such as densification, micro-cracking, and mechanical properties in tungsten-based components produced through additive techniques. Throughout the review, we organize existing knowledge and insights into convenient tables, serving as valuable resources for researchers embarking on a deeper exploration of these topics.

It is an utmost importance to characterize drug candidates as early in the development pipeline as possible. Appropriate application of the emerging high throughput analytical techniques unlocks the opportunities to obtain experimental data for in vitro absorption, distribution, metabolism and elimination (ADME) profiling of large compound libraries which is crucial for selection of lead compounds. In an usual scenario, only one drug candidate will be selected from thousands of early compounds based on several stability and screening results. A high to medium throughput screening approaches could significantly accelerates the subsequent research and production phases. Traditionally, the analysis of metabolic stability assays heavily relies on high throughput LC-MS/MS techniques to meet with the lead profiling demands. In this study presented an innovative experimental approach that can offer a refreshing alternative. Laser Assisted Rapid Evaporative Ionization Mass Spectrometry (LA-REIMS) is a quick and efficient method for characterizing complex biological samples without laborious sample preparation and chemical separation. Therefore, using an automated LA-REIMS well plate reader as fast as 8 seconds per sample, we assessed the oxidative metabolic stability of active agents using biomimetic metalloporphyrin-based oxidative model reactions at various pH for commercially available drug substances and compared stability measurements with traditional HPLC-DAD-MS results.

The processed tomato is one of the major food products of Italy and characterized today many Italian Regions, in northern and southern Italy, even though the firms in the industry have had difficulties, in recent years, due to an increase in the cost of raw materials. Furthermore, tomato processing firms are often characterized by significant investment in fixed assets and working capital, with an consequent increase in equity and/or debt financing, with consequent increasing of the risk of bankruptcy, as happens to many firms in the sector in recent years. The aim of the research is than to analyze the risk of bankruptcy of tomato processing companies applying financial ratios. To achieve this goal, the research consider the annual data of a sample of 17 tomato processing firms operating in the Inter Regional Interprofessional Organization “OI Pomodoro da Industria Nord Italia”; firms’ data are divided into still-active (not-distressed) and failed (distressed) firms, with the aim of analyzing the differences between financial data and management practices of the two groups of firms to evaluate the sustainability of the business cycle. The company data suggest that larger firms, with adequate financial structure, have been able to withstand the tomato market crisis in recent years, where distressed firms are on average smaller and suffer from a higher recourse to debt capital with lower profit margins than not-distressed firms. The research could then be applied by entrepreneurs, managers, bankers and public operator to define good practices of management to be achieved and measured with financial ratios even in order reduce the risk of distress for firms operating in tomato sector.

The polymerase chain reaction (PCR) is a widely used technique in the biosciences and has become increasingly popular in recent years. One of the key elements of this technique is the use of a DNA polymerase that is heat-stable and retains fidelity during the process. To this end, archaeal Fam-ily-B DNA polymerases are preferred due to their high thermostability and fidelity. In particular, the DNA polymerase from Thermus aquaticus (Taq DNApol) is widely utilized in PCR procedures. In this work, a novel in-silico structure-based methodology was employed to examine the most heat-tolerant DNA polymerase available. In spite of this, Thermococcus kodakarensis and Geobacillus stearothermophilus DNApol are more stable and heat-tolerant DNApols due to their high number of intra-protein interactions. Variations in the content of polar amino acids also played a significant role in the increase in heat stability. A further factor contributing to the stability of proteins is the stabilization of helix in secondary structure through the use of charged amino acids. DNApol from these organisms has been shown to be suitable for use in PCR, as well as in other biological processes able to withstand high temperatures. In this study, it has been demonstrated that im-provements in PCR performance can be easily obtained by blending elements from closely related archaeal polymerases, a strategy that may, in the future, be extended to other archaeal polymer-ases. This approach allowed for a comprehensive analysis of the enzyme's thermal stability and fidelity, leading to an improved understanding of the polymerase's properties and potential ap-plications

Это исследование сосредоточено на разработке системы искусственного зрения для гибкого дельта-робота-манипулятора и ее интеграции с межмашинной связью для оптимизации взаимодействия устройств в реальном времени. Эта интеграция направлена ​​на увеличение скорости роботизированной системы и улучшение ее общей производительности. Предлагаемое сочетание системы искусственного зрения с межмашинной связью позволяет обнаруживать и распознавать цели с высокой точностью в режиме реального времени в ограниченном пространстве, предназначенном для позиционирования, дальнейшей локализации и выполнения производственных процессов, таких как сборка или сортировка деталей. . В данном исследовании RGB-изображения используются в качестве входных данных для алгоритма MASK-R-CNN, а результаты обрабатываются в соответствии с особенностями прототипа дельта-манипулятора робота. Данные, полученные от MASK-R-CNN, адаптированы для использования в системе управления дельта-роботом с учетом ее уникальных характеристик и требований к позиционированию. Технология M2M позволяет манипулятору робота быстро реагировать на изменения, например, на перемещение объектов или изменение их положения, что имеет решающее значение для задач сортировки и упаковки. Система была протестирована в условиях, близких к реальным, чтобы оценить ее производительность и надежность.

Malnutrition and cognitive dysfunction are typical features of alcoholic liver disease (ALD) and are correlated with the development of complications. The aim of this study is to explore the effect of nutritional state and diet on cognitive function in ALD. A total of 43 patients with compensated alcoholic cirrhosis were enrolled, and neuropsychological test was assessed according to body mass index (BMI, <22 and ≥22). In the ALD animal study, mice were divided into 5 groups (n=9/group; normal liquid, 5% EtOH+regular liquid, 5% EtOH+high-carbohydrate liquid, 5% EtOH+high-fat liquid, and 5% EtOH+high-protein liquid diet) and fed the same calories for 8-week. To assess cognitive function, we performed T-maze studies weekly before/after alcohol binging. In cognitive function (BMI <22 /≥22), language score of Korea mini-mental state (7.4±1.4/7.9±0.4), Rey-complex figure (72.0±25.9/58.4±33.6), Boston naming (11.7±2.7/13.0±1.8), forward digit span (6.7±1.8/7.5±1.6), Korean Color Word Stroop (24.2±26.5/43.6±32.4), and interference score (33.9±31.9/52.3±33.9) revealed significant differences. In the T-maze test, alcohol significantly delayed the time to reach food, and binge drinking provided a temporary recovery in cognition. The alcohol-induced delay was significantly reduced in the high-carbohydrate and high-fat diet groups. Synaptic function exhibited no changes in all groups. Cognitive dysfunction is affected by nutritional status and diet in ALD.

In western societies about one in six employees works in shifts. Shiftwork is associated with a number of poor somatic and psychological health outcomes, especially sleep issues. Higher rates of absenteeism and accidents in the workplace are possible consequences. Still, prevention programs and treatment options that are specifically tailored to shift workers’ needs are rare. We devised a 4-week online sleep intervention (n = 21) and compared treatment outcomes to our outpatient treatment for shift workers (n = 12). Measures included the WHO-5, ISI, and ESS scales as well as sleep diaries. Shift workers reported worse symptoms of insomnia than other participants. Results show significant average increases in sleep efficiency (+ 7%) and total sleep time (+ 25 min.), as well as significant improvements regarding insomnia symptoms and wellbeing in the online sample. Rates of improvement did not differ between the online and outpatient samples. Sleep disorders affect a relevant part of the working population, especially shift workers. Online approaches to treatment of these issues seem feasible and effective. Randomized controlled trials are needed.

Particulate Organic Carbon (POC) plays a crucial role in the marine carbon cycle, influencing carbon flux and many important ocean biogeochemical processes in the Bay of Bengal. The purpose of this study was to examine the seasonal and annual variability of POC in the Bay of Bengal (BoB) region and its correlation with Sea Surface Temperature (SST) and Chlorophyll-a (Chlor_a). This study uses level-3 POC, Chlor_a, and SST data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua satellite. The surface POC concentration in the Bay of Bengal declined at a rate of -1.30 mgm^-3y^-1 over a ten-year period (2003-12) and ranged from 86.48 mgm^-3 to 110.93 mgm^-3 with an average of (93.74±7.50 mgm^-3). Seasonally, the mean POC was highest (97.40 mgm^-3) during the post-monsoon (October-November) and lowest (79.36 mgm^-3) in the pre-monsoon (March-May). Seasons were observed to have statistically significant differences in POC concentration in the study area (F_(3,2155) = 6.298; p <0.05). A strong positive correlation between POC and Chlorophyll was observed whereas a week positive correlation between POC and SST was observed. POC was observed to be very prominent in post-monsoon (97.40 mgm^-3) and in northeast-monsoon (97.14 mgm^-3) and moderate to low in southwest (81.12 mgm^-3) and pre-monsoon (79.36 mgm^-3). The Bay of Bengal’s annual mean POC:Chlor_a ratio ranged from 97.56 mgm^-3 to 332.89 mgm^-3, indicating a significant impact of biological processes on POC composition. To see how POC fluctuation affects biogeochemical processes in the Bay of Bengal, deeper investigation is required.

The abilities of quantitative description of noise are restricted due to its origin and only the statistical and spectral analysis methods can be applied while an exact time evolution cannot be defined or predicted. It emphasizes the challenges faced in many applications including communication systems where noise can play, on one hand, a vital role impacting signal-to-noise ratio but possessing, on the other hand, unique properties such as infinite entropy (infinite information capacity), exponentially decaying correlation function and so on. Despite the deterministic nature of chaotic systems, the predictability of chaotic signals is limited for a short time window putting them close to random noise. In this article, we propose and experimentally verify an approach to achieve Gaussian-distributed chaotic signals by processing the outputs of chaotic systems. The mathematical criteria, on which the main idea of the study is based on, is the Central Limit Theorem which states that the sum of a large number of independent random variables with similar variances approaches a Gaussian distribution. The study involves more than 40 mostly three-dimensional continuous-time chaotic systems (Chua’s, Lorenz’s, Sprott’s, memristor-based, etc.) whose output signals are analyzed according to criteria that encompass the probability density functions of a chaotic signal itself and its envelope and phase, statistical and entropy-based metrics such as skewness, kurtosis, and entropy power. We found that two chaotic signals of Chua’s and Lorenz’s systems exhibited superior performance across the chosen metrics. Furthermore, our focus extended to determining the minimum number of independent chaotic signals necessary to yield a Gaussian-distributed combined signal. The analytic and experimental results indicate that the sum of at least three non-Gaussian chaotic signals closely approximates Gaussian distribution. It allows generation of reproducible Gaussian-distributed deterministic chaos by modeling simple chaotic systems.

Patients with chronic (CM) and high frequency episodic migraine (HFEM) report cognitive complaints that contribute to migraine burden. Although, there is little evidence about the ef-fects of migraine treatments on these symptoms. Objective: To evaluate the effect of preventive treatment on the impact of cognitive symptoms, specifically the ability to concentrate, in individuals with CM and HFEM. Methods: Prospective cohort study in patients with CM and HFEM initiating treatment with botulinum toxin (BT) or anti-CGRP/R monoclonal antibodies (mAbs). Patients were evaluated at baseline and after 3 months of treatment, using a headache diary and a question about the im-pact of migraine attacks on concentration (never, rarely, sometimes, often, and always). Results: 108 patients (105 females, 44.3 years of age on average), 75% with CM and 27% with HFEM were included, 55 received a first session of BT and 53 received 3 injections of a monthly mAb. In both groups, there was a significant decline on the impact of migraine in concentration (related samples Wilcoxon signed rank test <0.001) compared to baseline. Reduction in monthly headache days was the main predictor of any concentration improvement, while treatment group, age, ongoing medication with topiramate or baseline medication overuse were not sig-nificant. Conclusion: Preventive treatment of HFEM or CM with drugs not acting on central nervous sys-tem is associated with a decrease of the impact of migraine attacks on concentration, predicted by the decline in headache days. These results indicate that migraine preventive treatment also improves non-pain symptoms, which is relevant given the contribution of cognitive complaints to the impact of headache attacks

Obsessive-compulsive disorder (OCD) remains, despite significant scientific progress, a pathological condition that is incomprehensible, due toits paradoxical psychological and behavioral symptoms. The presentwork aims at assessing current phenomenological understandings of this pathology. Among the cognitive models used today, post-rationalist cognitivism and cognitive neuropsychological psychotherapy, which both incorporate a phenomenological approach, seem most effective at grasping OCD patients’ experiences of the world, which are typically characterized by hyper-reflexivity, at the expense of "natural evidence." For OCD patients, the emotional sense of the world becomes a set of sterile rules that determine a suffering life.

This thesis presents findings from an online experiment aimed at evaluating individuals' attitudes towards their own cognitive biases that lead to objective mistakes. In a number of incentivized decision problems, a participant in the experiment might make a mistake and choose a dominated lottery (FOSD lottery), which is likely to result from a particular heuristic or cognitive bias. In this case, she is then confronted with her error under one of two treatments: a confrontation with an explanation only regarding the mistake or one with the addition of the supposed mechanism (bias). Following this explanation, her comfort level with her choice is measured. A bias that acted as a mechanism for the observed error could be considered advantageous (i.e., useful given alternative costs) or disadvantageous (i.e., mostly harmful) in other decision-making scenarios encountered during one's lifetime. Consequently, the perception of cognitive biases is subjective and may vary among decision makers, who may emphasize either the positive or negative aspects of such biases. The proposed methodology allows to determine whether these biases are perceived as useful rules of thumb, despite leading to a dominated lottery choice in that particular context, or unfavorable.Results revealed significant differences between the two treatments and across five different decision problems. Interestingly, the results showed that in only 60% of the cases, the participants felt uneasy about their mistakes. In the remaining instances, the participants demonstrated either indifference or a sense of comfort with their choice of a dominated lottery. The net effect of providing information on the cognitive biases that presumably lead to the mistakes, measured as the difference between treatments, revealed a negative perception of the mechanism behind the error in one problem, contentment with the mechanism for another, and no significant effect for the remaining biases.

This paper presents a case study analyzing which learning cognitive strategies are employed by students who play digital games in both in-school and out-of-school learning contexts. As a Case Study, it falls within the qualitative research category. The researcher integrated themselves to the subjects' learning facility for data-gathering, following all recommended ethical protocols for research involving underage subjects. The following data collection instruments were used: research diary, questionnaire, and interviews, conducted with teenagers who play digital games during formal learning activities. Analysis involved data-crossing and checking our data against existing studies and theories, as well as a cartographic production. Data indicates that digital games enhance student usage of cognitive strategies and favor competencies and abilities-based learning over formal learning contents.

The extensive utilization of drones has led to numerous scenarios that encompass both advantageous and perilous outcomes. By using deep learning techniques, it is aimed to reduce the dangerous effects of drone use through early detection of drones.The state of the art of the study is evaluation of deep learning approaches such as pre trained YOLOv8 drone detection for security issues. In this study, dataset collected by Mehdi Özel for a UAV competition are sourced from GitHub. These images are labeled using Roboflow, and the model is trained on Google Colab. To enhance model performance, dataset augmentation techniques including rotation and blurring are implemented. Following these steps, a significant precision value of 0.946, a notable recall value of 0.9605 and a considerable precision-recall curve value of 0.978 are achieved, surpassing many popular models such as Mask CNN, CNN, and YOLOv5.

The transition to sustainable mobility is a recognized socio-economic and environmental challenge, particularly among young adults in low- and middle-income countries (LMICs). This paper addresses the lack of comprehensive research on mobility patterns for LMICs by examining the travel patterns of students, staff, and lecturers at the National Advanced School of Public Works, Yaoundé (NASPW) to understand transport mode choices and barriers to the use of public and active transport modes. Data was collected through online questionnaires from 425 participants. Findings revealed that most students (27.5%) used multiple modes of transport, with moto-taxis being the most common (21%). Lecturers primarily used private cars (50%), while staff relied on taxis or multiple modes (33%). Accessibility, vehicle speed, and flexibility appeared as the most important reasons for the preferred modes of transport. Barriers included long waiting times and traffic congestion for public transport, and distance and inadequate infrastructure for active mobility. The usage of public transportation was encouraged by its affordability and reduced travel time, whilst active options were preferred due to their cost savings and health benefits. To promote sustainable mobility for campus travel, it is crucial to encourage active modes, develop mass transport systems, and raise awareness through symposiums and conferences among students and staff.

Background: Virtual Reality (VR) is transforming neuropsychological assessment by providing immersive environments that closely replicate real-world conditions. This study presents the Virtual Reality Trail Making Test (TMT-VR), a novel adaptation of the traditional Trail Making Test, aimed at evaluating cognitive functions in individuals with Attention Deficit Hyperactivity Disorder (ADHD). We examined the ecological validity, convergent validity, usability, and user experience of the TMT-VR compared to the traditional version. Methods: Fifty-three participants, including 25 with ADHD and 28 neurotypical controls, completed both the traditional and VR versions of the TMT. Participants also provided feedback on the VR experience via standardized questionnaires. Results: Statistical analyses demonstrated a significant positive correlation between TMT-VR scores and both the traditional TMT and ADHD symptomatology as measured by the Adult Self-Report Scale (ASRS), confirming the TMT-VR’s ecological and convergent validity. High usability and positive user experience ratings indicated strong acceptability of the VR platform. Notably, the ADHD group exhibited greater performance differences in the VR environment, suggesting that VR may more effectively capture real-world cognitive challenges. Conclusions: These findings suggest that TMT-VR is a valid, engaging, and ecologically valid tool for cognitive assessment in ADHD, and other clinical populations, offering enhanced insights over traditional methods.

Electricity consumption is often the hotspot of life cycle assessment (LCA) of products, industrial activities, or services. The objective of this paper is to provide a consistent, scientific, region-specific electricity-supply-based inventory of electricity generation technology for national and regional power grids. Marginal electricity generation technology is pivotal in assessing impacts related to additional consumption of electricity. China covers a large geographical area with regional supply grids; these are arguably equally or less integrated. Meanwhile, it is also a country with internal imbalances in regional energy supply and demand. Therefore, we suggest an approach to achieve a geographical subdivision of the Chinese electricity grid, corresponding to the interprovincial regional power grids, namely the North, the Northeast, the East, the Central, the Northwest, and the Southwest China Grids, and the China Southern Power Grid. The approach combines information from the Chinese national plans on for capacity changes in both production and distribution grids, and knowledge of resource availability. The results show that nationally, marginal technology is coal-fired electricity generation, which is the same scenario in the North and Northwest China Grid. In the Northeast, East, and Central China Grid, nuclear power gradually replaces coal-fired electricity and becomes the marginal technology. In the Southwest China Grid and the China Southern Power Grid, the marginal electricity is hydropower towards 2030.

The graphene based highly conductive and flexible devices has received momentous attraction and growth in recent years for potential multifunctional uses. The development of flexible and washable textile sensors, actuators, energy storage and energy harvesting systems. Textile fabrics coated with graphene and conductive polymers have been used for wearable self-powered e-textiles, such as tribo-electric, piezoelectric, and Thermoelectric. Herein this research, 100% woven cotton fabric is Fictionalized with graphene oxide (GO) using industrial-scale cold pad batch dyeing technique for the development of low-grade body heat into self-powered wearable e-textiles. The conductive polymer PEDOT: PSS was coated using layer by layer approach. The study demonstrates that the resultant textile electrodes showed reduced electrical sheet resistance from 2.5 MΩ to 15KΩ, with a reduced graphene oxide and further reduced to (2.5 -12.5 Ω) for conductive polymer. The sheet resistance was decreased with the number of dyeing cycles (10-15) and increased with 20 washing cycles vice versa. The study demonstrates that the sensor response and performance were improved with increasing the content percent of rGO and PEDOT: PSS. The tensile strength of as coated fabric electrodes was improved from (25.0-85.5 mPa) and fabric thickness was increased to (26-38µm) without compromising or influencing the air permeability. The resultant textile electrodes may potentially be used in smart textiles for self-powered flexible wearable generators, sensors, and energy storage. The study demonstrates that the cold pad batch dyeing method is a suitable and effective method for the fabrication of graphene-based textiles.

The purpose of this paper is to describe a feature selection algorithm and its application to enhance the accuracy of the reconstruction of phylogenetic trees by improving the efficiency of tree construction. Applying machine learning models for Arabic and Aramaic scripts such as Deep Neural Networks (DNN), Support Vector Machines (SVM) and Random Forests (RF) each model was used to compare the phylogenies. The methodology was utilized with a dataset containing Arabic and Aramaic scripts, demonstrating its relevance in a range of phylogenetic analyses. Results emphasize the essential role of feature selection by DNNs is outperforming other models in terms of Area Under the Curve (AUC) and Equal Error Rate (EER) across various datasets and fold sizes. Additionally, SVM and RF models show important understandings, advantages and limits of each approach within the context of phylogenetic analysis. This method not only simplifies the tree structures but also enhances their Consistency Index. Therefore offering a robust framework for evolutionary studies. The findings highlight the applications of machine learning in phylogenetics suggesting a path toward accurate and efficient evolutionary analyses and adding a deeper understanding of evolutionary relationships.

Geopolymers, made from aluminosilicate raw materials, offer significant environmental advantages over conventional concrete (CC) mixes. This study explores the compressive strength, setting time, and durability of geopolymer concrete (GPC) cylinders, some of which include an additional ordinary portland cement (OPC) mix. Consistent material composition and testing methods are emphasized to achieve reliable results. MATLAB was utilized to develop and assess a neural network model predicting the compressive strength of GPC based on key variables. The model was validated using 16 experimental samples and 1047 samples from various studies. The performance plot displayed the Mean Squared Error (MSE) over iterations, identifying the best training performance at epoch 658 with an MSE of 5.3408. The Error Histogram highlighted distribution errors between predicted and actual compressive strength values, with the highest range from -1.015 to 0.3587, meaning that the predicted values were mostly within a small margin of error from the actual values. This narrow error range indicates that the ANN model has high accuracy in its predictions, as most errors are small and clustered around zero, demonstrating reliable performance in estimating compressive strength. Regression analysis showed varying correlation coefficients (training set R=0.98373, test set R=0.88164, and whole set R=0.96982), indicating adequate predictive accuracy. The results demonstrate that properly trained neural networks can effectively predict the compressive strength of GPC, highlighting its potential as a sustainable construction material.

Cervical Cancer affects a large part of female population that makes the prediction of this disease based on Machine Learning (ML) by outmost importance. ML algorithms can be integrated in complex intelligent agent-based systems that can offer decision support to the resident medical doctors or even to experienced medical doctors. For instance can be mentioned the situation when an experienced medical doctor diagnose a case but he/she needs expertise support that is related to another medical specialty. Data imbalance is frequent in healthcare data and has a negative influencing effect in making predictions using ML algorithms. Cancer data generally and cervical cancer data particularly are frequently imbalanced. Based on this fact the study of data imbalance impact on diverse state-of-the-art ML prediction algorithms is important. This research subject is also motivated by the fact that in many research are presented experimental evaluations of algorithms without characterization of the data on that they have been applied. Such characterizations could give clear indication to other researchers regarding the applicability of the algorithms on their specific data. Specifically, if the data have the respective characteristics than are expectable the same performance evaluation results like those in the reported research. For the study we chosed a messy real-life Cervical Cancer dataset available in a recognized data repository included a large amount of missing and noisy values. To identify the best imbalanced technique for this medical dataset, it is compared the performance of eleven important resampling methods combined with the following state-of-the-art ML models: K-Nearest Neighbours (with k values of 2 and 3), binary Logistic Regression, and Random Forest, that are frequently applied in prediction types of researches in healthcare. The studied resampling methods includes seven undersampling methods namely Condensed Nearest Neighbour, Tomek Links, Edited Nearest Neighbours, Repeated Edited Nearest Neighbours, All K-Nearest Neighbours, NearMiss, Neighbourhood Cleaning Rule, and Instance Hardness Threshold, and four oversampling methods namely Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling Approach for Imbalanced Learning SMOTE, Support Vector Machine SMOTE and Borderline SMOTE. In the case of the dataset for the confidence interval with 95% confidence level was between 9.23 and 16.22, while the imbalance ratio is 12.73. The obtained results show that resampling methods help the learning models to improve the classification ability of cervical cancer. The applied oversampling techniques generally showed better results than undersampling methods. In the case of Logistic Regression classifier had the highest impact on balanced techniques, while Random Forest had promising performance, even before balancing techniques and KNN2 was generally better than KNN3.

Cloud Computing is a promising and emerging technology that is rapidly being adopted by many IT companies due to a number of benefits that it provides, such as large storage space, low investment cost, virtualization, resource sharing, etc. Users are able to store a vast amount of data and information in the cloud and access it from anywhere, anytime on a pay-per-use basis. Since many users are able to share the data and the resources stored in the cloud, there arises a need to provide access to the data to only those users who are authorized to access it. This can be done through access control schemes which allow the authenticated and authorized users to access the data and deny access to unauthorized users. In this paper, a comprehensive review of all the existing access control schemes has been discussed along with analysis. Keywords: role-based access control, attribute-based access control, attribute-based encryption

Post-acute COVID-19 vaccination syndrome (PACVS) is distinguished from the normal post-vaccination state by altered receptor antibodies (Semmler et al. Vaccines 2023, 11, 1642). Here, we explore the symptoms registry and standard serum markers acquired in the above study to delineate the disease phenotype of PACVS. Symptoms reported by the participants overlapped with established complex dysautonomia syndromes (ME/CFS, POTS, MCAS, SFN). However, many participants were qualified for several (131/191) or none (31/191) of these syndromes. Unbiased clustering (modified Jaccard index) revealed a prevalent cross-cohort symptomatology of malaise and chronic fatigue (&gt; 80% of cases). Overlapping clusters of (i) peripheral nerve dysfunction, dysesthesia, motor weakness, pain, vasomotor dysfunction, (ii) cardiovascular impairment, and (iii) cognitive impairment, headache, optic, oculo-motoric and acoustic dysfunctions were also frequently represented. Conspicuous serum markers encompassed increases in interleukins 6 and 8 and low fT3 levels (&gt; 80%), IgG-subclass imbalances and impaired iron-storage (&gt; 50%), and increases in sNFL (&gt; 30%). Serum markers were not correlated to specific symptoms or symptom clusters. A single and unique cause (i. e. SARS-CoV-2 vaccination) is improbable to trigger several distinct etiologies. Therefore, the symptoms and serological features observed in our study cohort probably represent facets of a single complex syndrome.

With increasing electronic medical data and the development of artificial intelligence, Clinical Decision Support Systems (CDSSs) assist clinicians in diagnosis and prescription. Traditional knowledge-based CDSSs follow an accumulated medical knowledgebase and a predefined rule system, which clarifies the decision-making process; however, maintenance cost issues exist in the medical data quality control and standardization process. Non-knowledge-based CDSSs utilize vast amounts of data and algorithms to effectively decide; however, the deep learning black-box problem causes unreliable results. EXplainable Artificial Intelligence (XAI)-based CDSS provides a valid rationale and explainable results. It ensures trustworthiness and transparency by showing the recommendation and prediction results process through explainable techniques. However, existing systems have limitations, such as the scope of data utilization and the lack of explanatory power of AI models. This study proposes a new XAI-based CDSS framework to address these issues; introduce resources, datasets, and models that can be utilized; and provides a foundation model to support decision-making in various disease domains. Finally, we propose future directions for CDSS technology and highlight societal issues that need to be addressed to emphasize the potential of CDSS in the future.

Already more than 140 countries consider or have pledged to reach net-zero emission targets by 2050 or earlier and the share of global emissions falling into an emission pricing scheme has steeply increased over the past three years. Even where there are no direct implications for industry (yet), there is a series of subtle pressure points driving an increasing number of companies across the globe to work towards climate neutrality and pledging ambitious emission reduction goals. This article sheds light on the pressure points, the subtle triggers, the underlying considerations as well as the hoped-for benefits for industrial companies from achieving net-zero emissions. The observations and ideas presented in this paper are derived from quantitative data obtained via the Energy Efficiency Index of German Industry (EEI) and qualitative data. Not only societal, work force, supply chain and investor expectations play a large role, but also many strategic considerations which have the potential to make the company more resilient and profitable, particularly in time of crisis. Those companies that do not move towards decarbonisation, on the other hand, may face a costly late-mover disadvantage. This piece uncovers subtle interconnections, helping stakeholders from industry and beyond to grasp opportunities and challenges ahead.

Based on the Graph Model of Conflict Resolution (GMCR), a two-stage decision framework is developed to reveal the essence of brownfield incidents and facilitate the resolution of brownfield conflicts caused by the incidents. More particularly, the forward GMCR is utilized in Stage I, the negotiation stage, to simulate the evolution of a brownfield conflict (BC) and predict its potential resolution via stability analysis. If no acceptable equilibrium can be obtained, the BC progresses into Stage II, the third-party-intervention stage, where the inverse GMCR is used to assist a third party in intervening the conflict to achieve a desirable outcome. To illustrate the practicality of this framework, a recent BC occurred in Changzhou, China, is taken as a case study. Invaluable insights are provided through the computation and investigation of the corresponding preference relationships.

The urbanization effects on land surface phenology (LSP) have been investigated by many studies, but few studies focused on the temporal variations of urbanization effects on LSP. In this study, we used the MODIS EVI, MODIS LST data and China’s Land Use/Cover Datasets (CLUDs) to investigate the temporal variations of urban heat island intensity and urbanization effects on LSP in Northeast China during 2001–2015. Land surface temperature (LST) and phenology differences between urban and rural areas represented the urban heat island intensity and urbanization effects on LSP, respectively. Mann-kendall nonparametric test and Sen's slope were used to evaluating the trends of urbanization effects on LSP and urban heat island intensity. The results indicated that the average land surface phenology (LSP) during 2001–2015 was characterized by high spatial heterogeneity. The start of the growing season (SOS) in old urban area had become earlier and earlier than rural area and the differences of SOS between urbanized area and the rural area changed greatly during 2001–2015 (−0.79 days/year, p < 0.01). Meanwhile, the length of the growing season (LOS) in urban and adjacent areas had become increasingly longer than rural area especially in urbanized area (0.92 days/year, p < 0.01), but the differences of the end of the growing season (EOS) between urban and adjacent areas did not change significantly. Next, the UHII increased in spring and autumn during the whole study period. Moreover, the correlation analysis indicated that the increasing urban heat island intensity in spring contributed greatly to the increases of urbanization effects on SOS, but the increasing urban heat island intensity in autumn did not lead to the increases of urbanization effects on EOS in Northeast China.

Cloud robotics is becoming an alternative to support advanced services of robots with low computing power as network technology advances. Recently, fog robotics has gained attention since the approach has merit relieving latency and security issues over the conventional cloud robotics. In this paper, a Function-as-a-Service based Fog Robotic (FaaS-FR) for cognitive robots is proposed. The model distributes the cognitive functions according to the computational power, latency and security with a public robot cloud and fog robot server. During the experiment with a Raspberry Pi as an edge, the proposed FaaS-FR model shows efficient and practical performance in the proper distribution of the computational work of the cognitive system.

A frequent co-morbidity of cerebrovascular pathology and Alzheimer's disease pathology has been observed over past decades. Accordingly, much evidence has been reported which indicates that microvascular endothelial dysfunction, due to cerebrovascular risk factors (e.g., atherosclerosis, obesity, diabetes, smoking, hypertension, aging), precedes cognitive decline in Alzheimer's disease and contributes to its pathogenesis. By incorporating appropriate drug(s) into biomimetic (lipid cubic phase) nanocarriers, one obtains a multitasking combination therapeutic which targets certain cell-surface scavenger receptors, mainly class B type I (i.e., SR-BI), and crosses the blood-brain barrier (BBB). Such targeting allows for various Alzheimer's-related cell types to be simultaneously searched out, in vivo, for localized drug treatment. This in vivo targeting advantage may be particularly important for repurposing an FDA-approved drug, especially one which has shown the added ability to restore some cognitive functions in certain animal models of Alzheimer's disease (e.g., the anticancer drug bexarotene); this (candidate repurposing) drug up to now, by itself (i.e, without nanocarrier), displayed poor CNS penetration in human subjects.

Background: This study investigates cognitive function changes in severe carotid stenosis patients undergoing carotid endarterectomy (CEA) and carotid stenting (CAS), across two decades. Methods: We compared cognitive function within 30 days post-procedure in 267 patients (first 100 each for CEA and CAS in two periods: 2008–2012 and 2018–2022) at a single institution. Assessments used Adenbrooke's Cognitive Examination–Revised (ACE-R), Mini-Mental State Examination (MMSE), Speech Fluency Test (SFT), and Clock Drawing Test (CDT), conducted before and 30 ± 2 days after surgery. Results: Patients (mean age 67.2 years, 70%+ carotid stenosis) exhibited different cognitive changes over the periods. In 2008–2012, significant declines in MMSE (CEA, p = 0.049) and CDT (CAS, p = 0.015) were noted among asymptomatic patients. Conversely, in 2018–2022, improvements were observed in ACE-R and MMSE for both symptomatic and asymptomatic patients undergoing CEA and CAS. Conclusion: Over a decade, advancements in interventional techniques and patient management have reduced cognitive decline risks in asymptomatic carotid stenosis patients, also improving cognitive functions in both symptomatic and asymptomatic individuals.

Numerous image processing techniques (IPTs) have been employed to detect crack defects, offering an alternative to human-conducted onsite inspections. These IPTs manipulate images to extract defect features, particularly cracks in surfaces produced through Additive Manufacturing (AM). This article presents a vision-based approach that utilizes deep convolutional neural networks (CNNs) for crack detection in AM surfaces. Traditional image processing techniques face challenges with diverse real-world scenarios and varying crack types. To overcome these challenges, our proposed method leverages CNNs, eliminating the need for extensive feature extraction. Annotation for CNN training is facilitated by LabelImg without the requirement for additional IPTs. The trained CNN, enhanced by OpenCV preprocessing techniques, achieves an outstanding 99.54% accuracy on a dataset of 14,982 annotated images with resolutions of 1536 × 1103 pixels. Evaluation metrics exceeding 96% precision, 98% recall, and a 97% F1-score highlight the precision and effectiveness of the entire process.

The availability of current land cover and land use (LCLU) information for monitoring the status of land resources has considerable value in ensuring sustainable land use planning. Similarly, the need to provide updated information on the extent of LCLU change in West Africa has become apparent, given the increasing demand for land resources driven by the rapid population growth in the sub-region. SERVIR West Africa, a regional consortium jointly supported by USAID and NASA to foster the use of geospatial and earth observation data and science for decision-making on critical environmental and food security challenges, in collaboration with the Food and Agriculture Organization of the United Nations (FAO), is building technical capacity in the sub-region to develop and implement harmonized regional land cover classification system for LULC change monitoring in West Africa, based on the Land Cover Meta Language (LCML-ISO 19144-2). This article focuses on the process, potential opportunities, outputs of this collaboration, exemplary use cases, and recommendations for the next steps.

This study addresses the problem of accurately predicting azimuth and elevation angles of signals impinging on an antenna array employing Machine Learning (ML). Using the information obtained at a receiving system when a transmitter’s signal hits it, a Decision Tree (DT) model is trained to estimate azimuth and elevation angles simultaneously. Simulation results demonstrate the robustness of the proposed DT-based method, showcasing its ability to predict the Direction of Arrival (DOA) in diverse conditions beyond the ones present in the training dataset, i.e., the results display the model’s generalization capability. Additionally, the comparative analysis reveals that DT-based DOA estimation outperforms the state-of-the-art MUltiple SIgnal Classification (MUSIC) algorithm, establishing DTs as competitive alternatives for DOA estimation in signal reception systems.

The abnormal accumulation of cells in the human brain, if left untreated, may cause brain damage. Management and treatment of these tumours require an early and accurate diagnosis, while their prognostic characterisation can also be beneficial in the choice of care planning for the patient. CDSSs are being continuously developed and integrated into routine clinical practice as they assist clinicians and radiologists to deal with an enormous amount of medical data, reduce clinical errors, and improve diagnostic capabilities. They assist detection, classification, and grading of brain tumours as well as alerting physicians of requirement of change in treatment plans. The aim of this systematic review is to identify various CDSSs used in brain tumour diagnosis and prognosis, that rely on data captured by any imaging modality. Based on the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, the literature search was conducted in PubMed and Engineering Village Compendex databases. This review examines various CDSS tool types, system features, techniques used, accuracy, and outcome, to provide the latest evidence available in the field of neuro-oncology. An overview of different types of CDSSs used to support clinical decision-making in the management and treatment of brain tumours, along with highlighting their benefits, challenges, and future perspectives has been provided.

This paper presents the behavior of decision makers, the possible choices and the strategies 1 resulting from the uncertainties related to the integration of renewable energies. Its uncertainties 2 are the risks associated with the volatility of renewable sources, the dynamics of energy production 3 as well as the planning and operation of the electricity grid. The goal is to model the risk-averse 4 decision-maker’s behavior and the choice of integrating renewable energies into the electrical system. 5 Following a bibliographic approach, we expose a methodology to model the decision-maker’s 6 behavior(risk aversion and predilection for risk) to risk taking. The risk-averse decision maker may 7 adopt nonlinear utility functions. Risk aversion is a behavior that reflects the desire to avoid risk 8 decisions and thus reduces the risk of adverse consequences. A decision support tool is provided to 9 the decision-maker to choose a best-fit strategy based on his preferences. The rational and risk-averse 10 decision-maker would seek to maximize a concave utility function instead of seeking to minimize its 11 cost. Taste or aversion to risk can be modeled by a thematic function of utility.

In this study, we introduce a novel framework for the estimation of residual renal function (RRF), based on the population compartmental kinetic behavior of Beta 2 Microglobulin (B2M) and its dialytic removal. Using this model, we simulated a large cohort of patients with various levels of RRF receiving either conventional high-flux hemodialysis or on-line hemodiafiltration. These simulations were used to estimate a novel population kinetic (PK) equation for RRF (PK-RRF) that was validated in an external public dataset of real patients. We assessed the performance of the resulting equation(s) against their ability to estimate urea clearance using cross-validation. Our equations derived entirely from computer simulations and advanced statistical modeling, and had extremely high discrimination (AUC 0.808 – 0.909) when applied to a human dataset of measurements of RRF. A clearance-based equation that utilized pre and post dialysis B2M measurements, patient weight, treatment duration and ultrafiltration had higher discrimination than an equation previously derived in humans. Furthermore, the derived equations appeared to have higher clinical usefulness as assessed by Decision Curve Analysis, potentially supporting decisions that for individualizing dialysis frequency in patients with preserved RRF.

Hazelnut is a widespread nut species, especially in Europe, that can be consumed raw or roasted, due to its pleasant taste and nutritional properties. Despite the renowned beneficial properties, hazelnuts contain several proteins capable of inducing food allergy in sensitized individuals such as Cor a 2 (profilin), Cor a 8 (lipid transfer protein), Cor a 9 (11S seed storage globulin, legumin-like) and Cor a 11 (7S seed storage globulin, vicilin-like). In the present paper we investigated the effectiveness of autoclave based treatments in decreasing the allergic potential of hazelnut as also assessed by submitting the treated material to in vivo skin prick test and to in vitro immunoblot analysis with sera of allergic individuals exposed to the treated food material. This preliminary analysis shows that autoclave treatment, preceded by hydration and/or followed by drying, seems to be a promising approach and appears to be effective in reducing the allergenicity of hazelnut in most patients, probably due to the denaturation of most major and minor allergenic proteins. This work will open to the opportunity to produce, in perspective, hypoallergenic hazelnut derivatives tolerated by the allergic subjects.