Accurate streamflow forecasting is crucial for effectively managing water resources, particularly in countries like Colombia, where hydroelectric power generation significantly contributes to the national energy grid. Although highly interpretable, traditional deterministic, physically-driven models often suffer from complexity and require extensive parameterization. Data-driven models like Linear Autoregressive (LAR) and Long Short-Term Memory (LSTM) networks offer simplicity and performance but cannot quantify uncertainty. This work introduces Sparse Variational Gaussian Processes (SVGPs) for forecasting streamflow contributions. The proposed SVGP model reduces computational complexity compared to traditional Gaussian Processes, making it highly scalable for large datasets. The methodology employs optimal hyperparameters and shared inducing points to capture short-term and long-term relationships among reservoirs. Training, validation, and analysis of the proposed approach consider the streamflow dataset from 23 geographically dispersed reservoirs recorded during twelve years in Colombia. Performance assessment reveals that the proposal outperforms baseline Linear Autoregressive (LAR) and Long Short-Term Memory (LSTM) models in three key aspects: adaptability to changing dynamics, provision of informative confidence intervals through Bayesian inference, and enhanced forecasting accuracy. Therefore, the SVGP-based forecasting methodology offers a scalable and interpretable solution for multi-output streamflow forecasting, thereby contributing to more effective water resource management and hydroelectric planning.

The effect of defects and microstructure on mechanical properties of Ti-6Al-4V welds produced by tungsten inert gas welding, plasma arc welding, electron beam welding and laser beam welding was studied in the present work. The mechanical properties of the different weld types have been evaluated with respect to micro hardness, yield strength, ultimate tensile strength, ductility and fatigue at room temperature and at elevated temperature. Metallographic investigation was carried out to characterize the microstructures of different weld types and fractographic investigation to relate the effect of defects on fatigue performance. Electron and laser beam welding produced welds with finer microstructure, higher tensile ductility and better fatigue performance than tungsten inert gas welding and plasma arc welding. Large pores and pores located close to the specimen surface were found to be most detrimental to fatigue life.

Plant reproduction is a very important process on Earth from the perspective of biodiversity, biomass gain and crop productivity. It is therefore important to understand sex determination process and many researchers are investigating the molecular basis of this phenomenon. However, information on the influence of transcription factors (TFs) on this process is limited, although cucumber is a model plant in this regard. In the present study, based on RNA-seq data for differential gene expression (DEG) analyses, we aimed to investigate the regulatory TFs that may influence the metabolomic processes in the shoot apex containing the forming flower buds. Therefore, a robust TF database was established for the B10v3 cucumber genome. Sex-specific interactome network maps were generated, indicating the regulatory TFs by their effects on DEGs and further on processes leading to the formation of different sex flowers. The network analysis identified major families of regulatory TFs. The most abundant families were: MYB, AP2/ERF, NAC and bZIP, and those with the greatest impact on developmental processes were identified, namely the AP/ERF family, followed by DOF, MYB, MADS and others. Thus, the central nodes and key regulators in the networks were identified with respect to male, female and hermaphrodite. Here, we proposed the first model of the regulatory network of TFs that influences the metabolism of sex development in cucumber. These findings may help to understand the molecular genetics and functional mechanisms underlying sex determination processes.

In Acropora, the complex canals in a coral colony connect all polyps into a holistic network to collaborate in performing biological processes, while axial canal is the largest canal amongst the network and distributes at the center of a coral branch. However, previous studies indicated that, in the non-radial symmetry transport system of Acropora, axial canal do not play a major role in the transport of hydroplasm, and the action of axial canal in coral growth is still obscure. In this study, we reconstructed six Acropora muricata samples by high resolution micro-computed tomography to investigate the growth patterns of axial canals during the processes of new branch forming and truncated branch rebuilding. We found that the axial canal of a new branch is transformed from a calice and the polyps in the new branch are budded from the polyp in the axial canal. Meanwhile, the axial canal can transport the calcareous skeletons to rebuild the tip of a truncated branch, which represents as the change in the diameter of axial canal and calcareous deposition/reduction in it. This work indicate the regulation of axial canal in the growth processes including budding, branching, and mineralising of an Acropora colony.

This paper presents Mathematical Formulation for Neutrino-Fission of a heavy nuclei in Neutrino-environments. It has been proposed that neutrino fission is a dynamic two-process reaction, where more than one neutrino can be captured by target nucleus. A quantity named Neutrino-Zeta with units of eV^-1 has been defined to represent Neutrino-environments. Neutrino-zeta with assumed excited energy states for heavy nuclei A∼230-270 is estimated to be 0.0212 MeV^-1, with the probability of fission Barrier energy state P(E_x,f)= 0.169064014 with E_x,f =6MeV. P(E_x,f)=0.222664 is highest at zeta=0.1MeV^-1 . It has been shown that probabilities of certain excitation energy states and their corresponding decay reactions at those excitation energies can be equal for two neutrino-zetas. Neutron induced fission for different neutrinos hypothesize that probability of symmetric fission channels is higher at low neutrino-zetas, which can be tested experimentally.

The AtomDB project provides models of X-ray and EUV emitting astrophysical spectra for optically thin, hot plasma. We present the new software package, PyAtomDB, which now underpins the entire project, providing access to the underlying database, collisional radiative model calculations, and spectrum generation for a range of models. PyAtomDB is easily extensible allowing users to build new tools and models for use in analysis packages such as XSPEC. We present two of these, the kappa and ACX models for non-Maxwellian and Charge-Exchange plasmas respectively. In addition, PyAtomDB allows full open access to the apec code which underlies all of the AtomDB spectra and has enabled development of a module for estimating the sensitivity of emission lines and diagnostic line ratios to uncertainties in the underlying atomic data. We present these publicly available tools and results for several X-ray diagnostics of Fe L-shell ions and He-like ions as examples.

Aim: the main aim of this study is to compare the onset of effectiveness of mental nerve block with lingual infiltration and inferior alveolar nerve block on patients visiting dental hospital for extraction of mandibular anterior teeth. Materials and methods: The study was set in a clinical setting. This was a randomized controlled double-blind cross study, where 25 subjects received two treatments at an interval of at least 1 week in a within a dental hospital.Inferior alveolar nerve block and lingual nerve block injection is given with 2mL lignocaine 2% with 1:2,00,000 epinephrine was administered for the right side. Mental nerve block along with lingual infiltration of the corresponding tooth was given with 2mL lignocaine 2% with 1:2,00,000 epinephrine was administered on the left side.Visual analogue scale (VAS) was used in this study, which consists of a scale with range of 0-10 pain severity on which 0 indicated absence of pain and 10 the worst pain possible. The patients were asked to give scores corresponding to his/her pain, after IANB, mental nerve block and lingual infiltration and these scores were recorde.Results: From the results we can see that patients gave scores between 3-4, stating that they had moderate pain during extraction after administration of IANB (54%) (graph 2) and VAS scores of 0-1 were given by patients, which is suggestive of no pain during extraction after administration of Mental nerve block and lingual nerve infiltration (62%)Conclusion: Within the limits of the study we can conclude that mental nerve block along with lingual infiltration is an effective way to completely and quickly anaesthetise the central incisor to the 1st premolar region.

Our prior studies have shown that B type Natriuretic Peptide (BNP) decreases absorption from the gastrointestinal (GI) tract. Since non-muscle myosin-type II (NMM-II) is known to have a role in epithelial tight junction regulation, we aimed to test whether BNP has an effect on the localization and expression of NMM-II in the intestinal villi. We treated wild type mice with a 10 ng/g intravenous bolus of BNP followed by infusion of BNP at 1ng/g/minute vs. vehicle control. The mice were then euthanized and intestinal tissue isolated and sectioned. The tissue was immune-stained for NMM-II and examined using fluorescence microscopy. The tissue section not treated with antibody to NMM-II was used as a methodological control to evaluate the non-specific binding. Scanning electron micrographic images were taken to compare structural differences between the two groups. Western blotting was performed to compare the regional protein expression of NMM-II and associated proteins including smooth muscle actin, kinesin, and E cadherin in small intestinal tissue between BNP vs. vehicle infused mice. Fluorescence microscopy revealed markedly increased localization of NMM-II at the crypts and core of intestinal villi of the jejunum, ileum and colon of BNP treated mice compared to vehicle. Transmission electron microscopy revealed that the microvilli of BNP treated mice assumed the distinctive appearance of ‘relaxed’ microvilli compared to vehicle. Such decrease in contractility was previously shown to decrease para-cellular permeability of epithelia. Our observation indicates that BNP alters the localization of NMM-II in intestinal villi and makes the intestinal villi structures assume a state of decreased permeability of the apical junctional complex. Further characterization of this process and understanding the specific mediators involved could lead to the identification of novel therapeutic targets for various disease states such as heart failure and malabsorption.

Subcellular targeting of opsins in optogenetics provides new possibilities for investigating the function of nerve cells. One of the widely used motifs for central targeting of opsins is the motif of potential-dependent potassium channel Kv2.1. We have expressed construct CHR2-Venus-Kv2.1 in the layer 2/3 pyramidal neurons of the murine cerebral cortex by means of in utero electroporation. It was found that, although the majority of neurons expressing CHR2-Venus-Kv2.1 demonstrated mainly central localization of fluorescence in the soma, proximal dendrites and axon, there was also significant population of neurons with disruption of the “correct” targeting resulting in the fluorescent protein distributed uniformly throughout the entire cell surface. We have suggested that observed mislocalization was caused by overexpression of the construct. Indeed, a decrease in the plasmid concentration during the in utero electroporation procedure resulted in almost complete absence of neurons with altered targeting. Thus, the possibility of “incorrect” targeting of CHR2 by the potassium channel motif Kv2.1 should be taken into account when using this construct in optogenetic experiments.

In practice, a post-extractivist vision for farmer communities in the Amazon demands the study and demonstration, in the field, of alternative economic activities that are not based on resource extraction but on adding value to currently generated residual biomass. Following principles of bioeconomy, this study presents an experimental analysis of a retort burner and a pilot-scale auger-type pyrolysis reactor used to convert coffee husks generated in a post-harvesting facility of a farmer´s cooperative into thermal energy and biochar, respectively. The study shows that use of coffee husks, whether for combustion or pyrolysis processes, can supply the thermal energy required by the post-harvesting processes. The combustion or pyrolysis of coffee husks avoids its accumulation and decomposition while replacing the fossil fuels used in post-harvesting operations, reducing costs and making the farmers independent of fossil fuel subsidies. Unlike combustion, the CO concentration in the flue gas during the pyrolysis process was below the eco-design standard of 500 mg/Nm3. According to the European Biochar Certificate, carbonized coffee husks can be regarded as biochar for soil amendment and carbon sequestration. A study is required to assess the ability of cooperatives to generate and trade carbon credits linked with the application of biochar in their cropping systems.

To better understand the genes with altered expression caused by infection with the novel coronavirus strain SARS-CoV-2 causing COVID-19 infectious disease, a tensor decomposition (TD)-based unsupervised feature extraction (FE) approach was applied to a gene expression profile dataset of the mouse liver and spleen with experimental infection of mouse hepatitis virus, which is regarded as a suitable model of human coronavirus infection. TD-based unsupervised FE selected 134 altered genes, which were enriched in protein-protein interactions with orf1ab, polyprotein, and 3C-like protease that are well known to play critical roles in coronavirus infection, suggesting that these 134 genes can represent the coronavirus infectious process. We then selected compounds targeting the expression of the 134 selected genes based on a public domain database. The identified drug compounds were mainly related to known antiviral drugs, several of which were also included in those previously screened with an in silico method to identify candidate drugs for treating COVID-19.

Introduction: In Traditional Chinese Medicine (TCM), tongue diagnosis plays an important role. Besides shape, colour and textural attributes of tongue regions have significance due to their association with different organs (viz. heart, lung, kidney-bladder, liver-gallbladder and stomach-spleen) in health and disease. Although numerous quantitative methods have been proposed to reduce inter and intra observer variability in tongue diagnosis, almost all of them considered only global colour/ textural features i.e. from entire body of tongue. In this work, regional colour and texture information of tongue have been analysed for healthy subjects (NOM) and patients suffering from chronic acidity-indigestion (ACD). Methods: Tongue boundaries are marked manually in each image and then tongue bodies are automatically partitioned into five regions linked with said internal organs. Rough assessments of regions’ boundaries are obtained from literature and then they are fine-tuned according to experts’ advice. As boundaries are not well defined, regions near the common boundary of two or more organs, are not assigned to any organ. Several first and second order statistical features based on grey scale intensities and a* values of LAB colour space (indicative of redness) have been extracted from five different regions of tongue images. Intragroup analysis using Wilcoxon signed rank test compared different regions within individual study group. Additionally, intergroup analysis using Wilcoxon rank sum test compared tongues of NOM with ACD for each region separately. Results: Intragroup analysis reveals a* values are statistically different (p<0.01) between stomach-spleen and liver-gallbladder regions for ACD while no significant change of a* are noted between these regions of normal tongue. It was found from intergroup analysis that correlation (one of the second order texture features extracted from grey scale images) of heart region has significantly decreased while correlation of kidney, liver-gallbladder and stomach-spleen regions significantly increased from NOM to ACD. Conclusion: This study sheds light on importance of inclusion of regional textural analysis of tongue in quantitative studies and can be applied to automated tongue diagnosis for other diseases.

Detecting and evaluating pain in people with cognitive impairment, especially dementia, is a challenge. Their ability to accurately report pain and comprehend related questions decreases the reliability of their self-report. This has contributed to the fact that pain is underdiagnosed and undertreated in this population, which can lead to involuntary ageism. The design and assessment of this training capsule under the context of the European COST-Action TD1005 responds to the need for education and training in "Pain Assessment In Cognitive Impairment, especially dementia" and the development of the internationally agreed observational PAIC15 scale. The 3-hour capsule, intended for healthcare workers and caregivers, consists of theoretical-practical levels: 1) basic concepts and challenges in pain in aging, cognitive impairment/dementia, 2) available scales and PAIC15, 3) video presentation of clinical cases illustrating a 3×2 matrix of situations (rest, transfer and experimental pain) and stages of disease (mild and moderate). The objectives focused on training the ability to observe, detect, and assess the signs of pain using PAIC15 and its feasibility. The pre-post analysis in a Master educational setting showed agreement with the results obtained in the Spanish national survey on 1) scarce knowledge of observational tools and their use, and 2) uncertainty in the identification of signs of pain and feasibility in moderate/severe scenarios. The capsule improved the ability to identify and evaluate the pain items, especially facial expressions, and their confidence on observational tools. Debriefings highlighted the relevance of observational scales in dementia and the gain obtained using the new observational instrument.

Background: No studies have examined whether interactions between the apolipoprotein E4 (ApoE4) allele and peripheral biomarkers, hypertension, and type 2 diabetes mellitus (T2DM) may impact the neurocognitive, behavioral and social dysfunctions in amnestic mild cognitive impairment (aMCI) and Alzheimer disease (AD). Aims: To clinically define and biologically validate a subgroup of aMCI subjects that take up an intermediate position between controls and AD patients. Methods: In 61 healthy controls, 60 subjects with aMCI, and 60 AD patients we measured the features of aMCI/AD using the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD). A composite BIORISK score was computed using the ApoE4 allele, serum folate, albumin, white blood cells, fasting blood glucose (FBG), atherogenic index of plasma (AIP), T2DM and hypertension. Results: Clustering and nearest neighbour analyses were unable to validate the aMCI subgroup. We constructed two z unit-based composite scores, the first indicating overall burden of cognitive, social, and behavioural deterioration (OBD), and a second reflecting the interactions between ApoE4, all other biomarkers, hypertension and T2DM (BIORISK). We found that 40.2% of the variance in the OBD score was explained by BIORISK, ApoE4, age and education. The OBD index was used to construct three subgroups (normal, medium, and high OBD) with the medium group (n=45) showing mild cognitive dysfunctions (MCD) in memory, language, orientation, and ADL. People with MCD show OBD and BIORISK scores that are significantly different from controls and AD.Conclusions: Petersen’s aMCI criteria cannot be validated and should be replaced by the more restrictive, biologically validated MCD class.

Increasingly, information technology facilitates the storage and management of data useful for risk analysis and event prediction. Studies on data extraction related to occupational health and safety are increasingly available; however, due to its variability, the construction sector warrants special attention. This review is conducted under the research programmes of the National Institute for Occupational Accident Insurance (Inail). Objectives: The research question focuses on identifying which data mining (DM) methods, among supervised, unsupervised, and others, are most appropriate to be applied to certain investigation objectives, types, and sources of data, as defined by the authors. Methods: Scopus and ProQuest were the main sources from which we extracted studies in the field of construction, published between 2014 and 2023. The eligibility criteria applied in the selection of studies, were based on the Preferred Reporting Items for Systematic Review and meta-analysis (PRISMA). For exploratory purposes, we applied hierarchical clustering, while for in-depth analysis, we use principal component analysis (PCA) and meta-analysis. Results: The search strategy based on the PRISMA eligibility criteria, provided us with 61 out of 2,234 potential articles, 202 observation, 91 methodologies, 4 survey purposes, 3 data sources, 7 data types, and 3 resource type. Cluster analysis and PCA organized the information included in the paper dataset into two dimensions and labels: "supervised methods, institutional dataset, and predictive and classificatory purposes" (correlation 0.97÷8.18E-01; p-value 7.67E-55÷1.28E-22) and the second, Dim2 "not-supervised methods; project, simulation, literature, text data; monitoring, decision-making processes; machinery and environment" (corr. 0.84÷0.47; p-value 5.79E-25÷3.59E-06). We answered the research question regarding which method, among supervised, unsupervised, or other, is most suitable for application to data in the construction industry. Conclusions: The meta-analysis provided an overall estimate of the better effectiveness of supervised methods (Odds Ratio = 0.71, Confidence Interval 0.53÷0.96) compared to not-supervised methods.

Considering the widespread issue of distracted eating, our study explores how cognitive dis-traction influences the sensory perception of food-related odors among individuals with varying weight statuses. We used the Tetris game to mimic the dynamic nature of real-life cognitive dis-traction. Participants, categorized into a lean and an overweight/obese group based on their body mass index (BMI), rated odor intensity and pleasantness under low and high distraction condi-tions. Respiratory movements were monitored to ensure accurate assessment of sensory percep-tion. No significant effects of distraction on odor intensity ratings were established. However, our findings reveal a significant reduction in odor pleasantness under high cognitive load, more pronounced in lean participants compared to those with overweight. Moreover, we observe an interaction between sex/gender and cognitive distraction effects in odor pleasantness perception. The differential effects of distraction across weight status groups and sex/gender are discussed in the context of hedonic motivations and compensatory mechanisms at play. This study sheds light into the mechanisms of distracted eating from the sensory perspective, that can pave the way for more personalized and effective strategies for promoting healthier eating habits in a world dominated by distractions.

The importance of cognitive health for academic success is well-recognized. However, longitudinal data regarding the associations of body fatness, physical fitness and cognition are relatively scarce and mixed. The purpose of this study was to investigate whether body fatness, cardiorespiratory fitness (CRF) and muscular fitness (MF) in preschool are associated with cognitive skills in first grade at school. 133 South Estonian children aged 6-7 years were recruited from 13 kindergartens and again at 7-8 years when they had entered school. Body fat percentage (BF%), CRF, MF as the mean of z-scores of relative handgrip strength and standing long jump tests, and cognitive skills (verbal, conceptual, perceptual) were studied. There were no associations between BF% and CRF in pre-school with perceptual, conceptual or verbal skills in school in boys and girls. In boys, higher MF in preschool predicted higher verbal skills (β = 0.293, p = 0.021) in school after adjustment for con-founders. Cognitive skills at baseline seemed predominantly predict later cognitive performance in school. In conclusion, higher muscular fitness in preschool predicted better verbal skills in first grade at school in boys but not in girls. Body fatness and cardiorespiratory fitness in preschool were not associated with cognitive skills in school.

Pure silver as well as silver compounds have been used as antiseptics for centuries. Nowadays, the most frequently applied form of silver is a nanoparticle form. Several studies demonstrate the ability of silver nanoparticles and silver salts to accumulate in the brain of laboratory ani-mals as well as the ability of silver nanoparticles to influence behavioral and cognitive functions of laboratory mammals. For the first time, the influence of a silver salt, in particular, silver citrate on the change dynamics of behavioral and cognitive functions of laboratory mice at the daily oral exposure during 30, 60, 120 and 180 days is investigated in the present research. We observed the anxiety increase after 30 days of the exposure as well as the ability of mice to adapt to the prolonged oral exposure of silver citrate manifested in the anxiety decrease, increase of locomotor activity and the tendency of long-term contextual memory improvement. Increase of locomotor activity and memory improvement might be associated with the sensitivity increase and regarded as compensatory mechanism to anxiety increase. The observed phenomenon is classified as a hormetic effect. A comparison with the analogous study implemented polyvi-nylpyrrolidone coated silver nanoparticles is carried out. It was concluded that the nanoform is more neurotoxic.

(1) Background. Cognitive impairment in people with Multiple Sclerosis (MS) has been studied in relation to some clinical variables (e.g., motor disability and disease duration) and lifestyle fac-tors such as Cognitive Reserve (CR). However, only very few studies have considered the interac-tion of clinical variables and cognitive reserve in preserving the integrity of the neuropsychologi-cal profile. In this paper, we hypothesised that a higher level of CR might predict good cognitive efficiency by modulating the clinical outcome of the disease. (2) Methods. A sample of 100 partic-ipants with MS (age range 30-74), was recruited and assessed remotely with a questionnaire to measure CR and a cognitive screening test. Data were analysed through Generalized Additive Models. (3) Results. We found that the model analysing the interaction between CR and disease duration, and between CR and motor disability was able to explain a significant percentage of cognitive performance. In particular, higher levels of CR predicted better cognitive performance despite long disease duration, unless the motor disability was severe. (4) Conclusion. This study highlights the crucial role of CR in modulating cognitive efficiency in people with MS.

In bilaterians organism, the signaling of pyramidal neurons (PyrNs) is linked to the relative (prevalent) molecular chirality and physiological, perceptual, cognitive, and psychological functions and dysfunctions, providing the coupling of the central nervous system downstream and upstream evolutionary and developmental processes. The most apparent and discriminating morphological specificity of PyrNs is the geometry of the cell body. However, the question "why/how PyrNs soma gains the shape of quasi-tetrahedral symmetry" has never been explicitly articulated. If the basic function of PyrNs is sensory space perception, supporting the orientation, and movement coordination, then the pyramidal shape of soma is the best evolutionary-selected geometry to perform sensory-motor coupling. In biology, the impact of chiral symmetry (handedness) is evident at all levels of biological organization, from the prevalent symmetry of biological molecules to the morphology and function of bilateral organisms. How the tetrahedral symmetry of biomolecules is linked to the morphology and functions of bilateral organisms remains a challenging question. Cell chirality represents an intermediate point connecting two poles of biochirality. In this holistic perspective, examining the PyrNs' morphology-circuitry-function link is crucial to understanding the complex interaction between genetic, epigenetic, and environmental factors in the evolution of the CNS. The predictive power of our hypothesis can be partially expressed by the statement that the most integral and reliable biomarker of the neurodegenerative (including aging) and mental (including all variants of psychiatric illness) disorders would be the detection of the hemispheric asymmetry of D-amino acids (D-AAs) level in pyramidal neurons (PyrNs).

RTP801/REDD1 is a stress-regulated protein whose levels are increased in several neurodegenerative diseases such as Parkinson’s, Alzheimer’s, and Huntington’s diseases (HD). RTP801 downregulation ameliorates behavioral abnormalities in several mouse models of these disorders. In HD, RTP801 mediates mutant huntingtin (mhtt) toxicity in in vitro models and its levels are increased in human iPSCs, human postmortem putamen samples and in striatal synaptosomes from mouse models of the disease. Here, we investigated the role of RTP801 in the hippocampal pathophysiology of HD. We found that RTP801 levels are increased in the hippocampus of HD patients in correlation with gliosis markers. Although RTP801 expression is not altered in the hippocampus of the R6/1 mouse model of HD, neuronal RTP801 silencing in the dorsal hippocampus with shRNA-containing AAV particles ameliorates cognitive alterations. This recovery is associated with a partial rescue of synaptic markers and with a reduction of inflammatory events, especially microgliosis. Altogether, our results indicate that RTP801 could be a marker of hippocampal neuroinflammation in HD patients and a promising therapeutic target of the disease.

Despite the clinical significance of partial posttraumatic stress disorder (PTSD), research on its associated cognitive function profiles is notably lacking. This study aimed to identify distinct cognitive function profiles in patients with partial PTSD. Medical records of Vietnamese War Korean Veterans undergoing psychiatric assessments, including the Clinician-Administered PTSD Scale (CAPS) and neurocognitive tests on the same day, were retrospectively reviewed. The participants were divided into partial PTSD (N = 10) and non-PTSD (N = 36) groups based on the CAPS interview. Neurocognitive test score profiles of the partial PTSD and non-PTSD groups were compared. Covariance analysis adjusted for potential confounders in cognitive function. In cognitive profiles, executive function, mental flexibility (measured by Trail Making Test B), and concentration (measured by the Mini-Mental Status Exam) were significantly poorer in the partial PTSD group. When adjusted for age, depressive symptoms, and education level, no significant differences were observed in the cognitive profiles between the partial PTSD and non-PTSD groups. In the current study, executive function and concentration showed deficits in patients with partial PTSD compared with those without PTSD. Partial PTSD cognitive profiles displayed no significant differences from non-PTSD profiles when adjusted for possible confounders such as depressive symptoms.

According to the cognitive radio (CR) paradigm, the efficient use of the radio spectrum is achieved by performing four main functions: spectral perception, spectral mobility, decision-making, and cooperation. In this work, a multivariable algorithm that considers geographic mobility (GM) is presented. The decision-making process includes a feedback mechanism for the dynamic selection of reservation channels in a wireless network with cognitive capabilities. Geographic mobility plays a crucial role in how radio takes advantage of its environment; however, it has been scarcely explored in the literature. In the present work, geographic mobility tests were carried out involving the interaction of CR and primary users (PU), where a measurement of the latency time of the proposed algorithm called: Algorithm for Decision-Making with Geographic Mobility (ATDeMoGeo) was evaluated and compared with other algorithms such as: Dijkstra's Algorithm, Analytic Hierarchical Process (AHP), Fuzzy Analytic Hierarchical Process (FAHP) and Decision-Making Algorithm with Modified Dijkstra (ATDDiM). It was found that the proposed algorithm is robust and that it considerably reduces the latency time to accurately determine the best communication channels, thanks to the decision-making function implemented to establish which channels the CRs can occupy. For the selection of the best reserve channels, a series of criteria were considered, such as: signal-to-interference plus noise ratio (SINR), bandwidth (BW), probability of channel availability (AP), estimated channel time of availability (ETA) using the random waypoint mobility model (RWPM). These criteria are evaluated for two types of service, i.e., real time (RT) and best effort (BE). For the evaluation and validation of the ATDeMoGeo algorithm, a network with cognitive characteristics in NS-3 was simulated. The simulation scenarios consisted of a base station (BS) with mobile CRs and PUs. Based on the previous criteria, i.e., BW, SINR, AP and ETA, the BS assesses the spectrum occupancy information obtained by the CRs. The results show that the proposed algorithm can be used without problem for the proposed scenarios with higher speed and accuracy for the selection of better communication channels.

Background: Cancer-related cognitive impairment (CRCI) is one of the most serious side effects of cancer that negatively impact the quality of life of cancer patients and survivors. There is evidence of CRCI in Hodgkin lymphoma patients (HL); however, there is a lack of studies examining the presence of cognitive deficits before starting any treatment in HL patients. Methods: Forty patients (N=40) newly diagnosed with HL (with no previous cancer diagnoses) and 40 healthy controls (N=40) matched for age, sex, education, and premorbid intellect completed the neuropsychological battery and subjective and objective measures of affective distress and quality of life. Results: The results showed impairment in three out of six cognitive domains: verbal memory and learning, speed of processing/ psychomotor speed, and abstraction/executive functions in the HL patients before the initiation of any treatment. Speed of processing/psychomotor speed domain negatively correlated with depression. Conclusion: Impairment in verbal memory and learning and abstraction/executive functions domains in HL patients seems to occur before the initiation of treatment independently of anxiety, depression, or physical symptoms. This suggests that HL itself may cause cognitive deficits in these cognitive domains. However, the underlying causes of CRCI still remain unclear.

The realization of the third-generation artificial intelligence (AI) requires the evolution from perceptual intelligence to cognitive intelligence, where knowledge graphs may not meet the practical needs anymore. Based on the dual channel theory, cognitive graphs are established and developed through coordinating the implicit extraction module and the explicit reasoning module as well as integrating knowledge graphs, cognitive reasoning and logical expressions, which have achieved successes in multi-hop question answering. It is desired for cognitive graphs to be widely used in advanced AI applications such as large-scale knowledge representations and intelligent responses, promoting the development of Al dramatically. This review discusses cognitive graphs systematically and elaborately, including basic concepts, generations, theories and technologies. Moreover, we try to predict the development of cognitive intelligence in the short-term future and further enlighten more researches and studies.

Pervasive and wide-ranging cognitive deficits are a core feature of schizophrenia and an important determinant of long-term functional outcome. The lack of sufficiently effective treatments for cognitive impairment associated with schizophrenia (CIAS) represents a major unmet need and a central roadblock towards recovery. This is partly due to the current therapeutic focus on clinical symptoms, and the relative neglect of cognitive impairments despite their functionally disabling effects. Furthermore, effective treatment is impeded by our limited knowledge of the complex pathophysiology, which gives rise to perturbed information processing. Here, we review mechanisms and effectiveness of available pharmacological and non-pharmacological treatments for CIAS. Current evidence indicates, that while techniques which broadly enhance neural plasticity show the greatest therapeutic potential, effect sizes are at best moderate. Among other reasons, this is due to a considerable heterogeneity of responses to individual interventions. Furthermore, we discuss how recent conceptual advances in operationalizing cognitive impairments based on cognitive neuroscience have the potential to address these issues and facilitate the development of novel treatment strategies for CIAS. This includes more clearly elucidating pathophysiological mechanisms in both humans and animal models, identifying new treatment targets as well as establishing biomarkers for a better prediction of treatment responses.

SerIoT is a Research and Innovation Project funded by the European Commission to demonstrate secure networks for the Internet of Things (IoT). This paper presents an overview of the design for smart and secure network infrastructures for the IoT, based on Software Defined Networks (SDN), named the SerCPN system, whose main component is a smart SDN-Controller with online cognitive security surveillance and reporting, which establishes and dynamically modifies paths that enhance security for IoT devices and end users, and offers a high quality of service (QoS) with required security constraints. This online cognitive surveillance and path management is based on the Cognitive Packet Network (CPN) principle and uses Random Neural Networks (RNN) for decision making.

Many industrial processes, several natural processes involving non-living matter, and all the processes occurring within living organisms take place in solution. This means that the molecules playing active roles in the processes are present within another medium, called solvent. The solute molecules are surrounded by solvent molecules and interact with them. Understanding the nature and strength of these interactions, and the way in which they modify the properties of the solute molecules, is important for a better understanding of the chemical processes occurring in solution, including possible roles of the solvent in those processes. Computational studies can provide a wealth of information on solute-solvent interactions and their effects. Two major models have been developed to this purpose: a model viewing the solvent as a polarizable continuum surrounding the solute molecule, and a model considering a certain number of explicit solvent molecules around a solute molecule. Each of them has its advantages and challenges, and one selects the model that is more suitable for the type of information desired for the specific system under consideration. These studies are important in many areas of chemistry research, from the investigation of the processes occurring within a living organism to drug design and to the design of environmentally benign solvents meant to replace less benign ones in the chemical industry, as envisaged by the green chemistry principles. The paper presents a quick overview of the modelling approaches and an overview of concrete studies, with reference to selected crucial investigation themes.

This article reevaluates the Woesean concept of crossing a ‘Darwinian threshold’ from pre-genomic communality, as prevailing in an ancestral ‘progenote’ state, to vertically stable lineages of autonomous and self-similar cells. This transition from collective trunk-line evolution to Darwinian speciation is dependent on the generation of modular organismal genomes. The same general principle should be valid at subcellular levels, allowing the emergence of semi-autonomous genomic agents, such as viruses and plasmid-carrying endogenous vesicles with organelle-like properties. As compartmentalized agents of endogenous nature could start with smaller genomes than those required for fully autonomous cells, it is conjectured that stable subcellular lineages emerged earlier than their cellular counterparts. Referring to the recent ‘pre-endosymbiont hypothesis’, it is proposed that free-living bacteria (the first ‘prokaryote’ cells) arose by ‘lineage escape’ from plasmid-bearing organelle-like compartments, evolving inside the internally complexifying ‘paracells’ of the progenote community. The double-membrane envelopes of diderm bacteria may have resulted from cell-biological processes facilitating cellular lineage escape. The later emergence of archaeal cells (resembling bacteria in ‘prokaryote’ appearance with unichromosomal genomes) and eukaryotic organisms (with compartmented cells and multichromosal genomes) can also be interpreted in terms of this modified progenote hypothesis. Conceivably, the multichromosomal genomes of eukaryotes were bundled in endogenous nuclear compartments to organize a ‘nuclear-cytoplasmic lineage’, which became vertically stable by perfecting mitosis/meiosis-like divisions and yet retained some intra-species population confluence by sexual division-fusion cycles.

Introductions- Liveable housing environments face the menace of global climate change. In-frastructure (including buildings and houses) continuously experiences significant impacts ex-acerbated by natural variability in climate. The study addressed how climate change impacts the resilience of residential buildings, increased maintenance frequency, and the wellbeing and comfort of residents in UK residential buildings. Design/Methodology/Approach: This study used deductive reasoning and an empirical epis-temological methodology as a data-collecting basis to evaluate primary information obtained via a questionnaire. Regression analysis was ultimately employed to analyse the collected da-ta. Result/Discussions: The findings show a significant relationship between climate change and the wellbeing of the occupants of UK residential buildings. Also, the results show that social wellbeing is more important to the occupants than mental and physical wellbeing. It reveals that residential buildings in the UK suffer the cost of maintenance due to continuous reduc-tion in the building fabric's resilience to the impacts of climate change; for instance, a recent increase in rainfall/storms results in unprecedented flooding, which continues to damage the UK residential building fabrics.

The study investigated roles of institutional types and ethnic/racial background on academic credit among the traditionally underrepresented population of the U.S. study abroad program. Using archival data, the study sampled the student’s enrollment and academic credit information spanning a period of 20 years (2003 - 2022). Data analysis Using One-Way Analysis of Variance (ANOVA) indicates significant main influence of institutional type (p<.001) and significant main influence of ethnic/racial identity (p<.001) on students’ academic credit. The result was discussed in terms of its relevance in educational policy re-evaluations, improvement of the study conditions of the underrepresented students, and enhancement of the enrollment opportunities of these minority population across all U.S. institutions of learning.

(1) Background: Mixed Reality is a technology that has gained attention due to its unique capabilities for accessing and visualizing information, becoming a valuable tool for medicine, particularly for the operating room and surgical learning; (2) Methods: A systematic review was conducted following the PRISMA guidelines to answer the research questions established using the PICO framework; (3) Results: Although implementation of Mixed Reality applications in the operations room presents some challenges, when used appropriately, it can yield remarkable results. It can make learning easier, flatten the learning curve for several procedures, and facilitate various aspects of the surgical processes; (4) Conclusion: Technical complexity, equipment costs, and steep learning curves present significant obstacles to the widespread adoption of Mixed Reality and computer-assisted evaluation. The possibility of integration of distinct medical imaging modalities and innovative functionalities holds promise for clinical applications. However, it is important to consider issues related to usability, bias, and statistical analyses need consideration. Mixed Reality offers significant benefits, but there are still open challenges such as ergonomic issues, limited field of view, and battery autonomy that must be addressed to ensure widespread acceptance.

Individuals with BPD grapple with unstable relationships, marked by extreme emotional swings and interpersonal conflicts. Emotional instability, a distorted self-image, and impulsivity further complicate matters, often leading to self-harm and suicide attempts as desperate coping mechanisms. Genetics and adverse childhood experiences contribute to BPD's development, shaping brain function and emotional regulation. Diagnosis involves meticulous assessments, paving the way for personalized treatments like dialectical behavior therapy and, in severe cases, hospitalization. Early intervention, professional support, and societal understanding are pivotal for positive outcomes, and challenging mental health stigma.

In order to the effect of hydrated phase change materials on suppressing coal spontaneous combustion, the thermogravimetric experiment and reaction activation energy analysis experiment were used to explore the changes of combustion characteristic, parameters characteristic temperature and activating energy of gas coal, long flame coal, meagre coal, and lean coal before and after adding hydrated phase change materials. The research results showed that hydrated phase change materials increased the coal samples’characteristic temperature point and have effective inhibitory effects on different stages of oxidation process.However, the effect was best at low temperatures, as hydrated phase change materials undergo phase change and absorb heat when heated at low temperatures, isolating coal from contact with oxygen. The activating energy increased by 1.138 KJmol-1～23.048 KJmol-1 and the mass loss reduced by 1.6%～9.3% after the inhibition of the coal sample,indicating that the oxidation rate of various coal samples can be slowed down and the spontaneous combustion can be suppressed by using hydrated phase change materials.At the same time, this material can also reduce the combustibility index of meagre coal and lean coal, as well as the comprehensive combustion index and maximum loss rate of long flame coal and gas coal.

This review explores the transformative impact of sensor technologies in early disease detection. It highlights recent advancements in nanotechnology-based sensors, particularly focusing on their applications in identifying disease biomarkers at early stages. The integration of these sensors with artificial intelligence for predictive analytics in healthcare is also discussed. Applications in cancer, infectious diseases, and chronic disease management underscore their diverse utility. Challenges in sensor technology adoption and future directions, including multiplexed sensors and wearable devices, are examined. This review underscores sensors' potential in enhancing disease management and patient outcomes.

Legumes develop root nodules that harbour endosymbiotic bacteria, rhizobia. These rhizobia convert nitrogen to ammonia by biological nitrogen fixation. A thorough understanding of the biological nitrogen fixation in legumes and its regulation is key to develop sustainable agriculture. It is well known that plant hormones affect nodule formation; however, most studies are limited to model legumes due to their suitability for in vitro, plate-based assays. Specifically, it is almost impossible to measure the effects of exogenous hormones or other additives during nodule development in crop legumes such as soybean as they have huge root system in soil. To circumvent this issue, the present research develops suitable media and growth conditions for efficient nodule development under in vitro, soil free conditions in an important legume crop, soybean. Moreover, we also evaluate the effects of all major phytohormones during soybean nodulation under identical conditions. This versatile, inexpensive, scalable and simple protocol provides several advantages over previously established methods. It is extremely time-and resource-efficient, does not require special training or equipment, and produces highly reproducible results. The approach is expandable to other large legumes as well as for other exogenous additives.

In the present study. At first, some properties of r-fuzzy soft β-closed sets are introduced on fuzzy soft topological spaces based on the paper Aygunoglu et al. (Hacet. J. Math. Stat. 2014, 43, 193-208). Also, we define the closure and interior operators with respect to the classes of r-fuzzy soft β-closed and r-fuzzy soft β-open sets, and investigate some of their properties. In addition, the concept of r-fuzzy soft β-connected sets is introduced and characterized with help of fuzzy soft β-closure operators. Thereafter, some properties of a fuzzy soft β-continuity are studied. Furthermore, we introduce and study the concepts of fuzzy soft almost (weakly) β-continuous functions, which are weaker forms of a fuzzy soft β-continuity. Also, the relationships between these classes of functions are specified with the help of some illustrative examples. Finally, we explore new types of fuzzy soft functions called, fuzzy soft β-irresolute (strongly β-irresolute, β-irresolute open, β-irresolute closed and β-irresolute homeomorphism) functions, and discuss some properties of them. Also, we show that fuzzy soft strongly β-irresolute → fuzzy soft β-irresolute → fuzzy soft β-continuity, but the converse may not be true.

Alzheimer’s disease (AD) and type 2 diabetes mellitus (T2DM) are comorbidities that result from the sharing of common genes. The molecular background of comorbidities can provide clues for the development of treatment and management strategies. Here, the common genes involved in the development of the two diseases and in memory and cognitive function are reviewed. Network clustering based on protein-protein interaction networks identified tightly connected gene clusters that have an impact on memory and cognition among the comorbidity genes of AD and T2DM. Genes with functional implications were intensively reviewed, and relevant evidence was summarized. Gene information will be useful in the discovery of biomarkers and the identification of therapeutic targets for AD.

This study aims to systematically review and meta-analyze the nitro-oxidative stress (O&NS)/antioxidant (ANTIOX) ratio in the peripheral blood of people with mild cognitive impairment (MCI). We searched PubMed, Scopus, Google Scholar, and Web of Science for articles published from inception until July 31, 2021. Forty-six studies on 3.798 MCI individuals and 6.063 healthy controls were included. The O&NS/ANTIOX ratio was significantly higher in MCI than in controls with a Standardized Mean Difference (SMD)=0.378 (95% CI: 0.250; 0.506). MCI individuals showed increased lipid peroxidation (SMD=0.774, 95%CI: 4.416; 1.132) and O&NS-associated toxicity (SMD=0.621, CI: 0.377; 0.865) and reduced glutathione (GSH) defenses (SMD=0.725, 95%CI: 0.269; 1.182) as compared with controls. MCI was also accompanied by significantly increased homocysteine (SMD=0.320, CI: 0.059; 0.581), but not protein oxidation, and lowered non-vitamin (SMD=0.347, CI: 0.168; 0.527) and vitamin (SMD=0.564, CI: 0.129; 0.999) antioxidant defenses. The results show that MCI is at least in part due to increased neuro-oxidative toxicity and suggest that treatments targeting lipid peroxidation and the GSH system may be used to treat or prevent MCI.

The holy grail of Artificial Intelligence (AI) has been to mimic human intelligence using computing machines. Autopoiesis which refers to a system with well-defined identity and is capable of re-producing and maintaining itself and cognition which is the ability to process information, apply knowledge, and change the circumstance are associated with resilience and intelligence. While classical computer science (CCS) with symbolic and sub-symbolic computing has given us tools to decipher the mysteries of physical, chemical and biological systems in nature and allowed us to model, analyze various observations and use information to optimize our interactions with each other and with our environment, it falls short in reproducing even the basic behaviors of living organisms. We present the foundational shortcomings of CCS and discuss the science of infor-mation processing structures (SIPS) that allows us to fill the gaps. SIPS allows us to model su-per-symbolic computations and infuse autopoietic and cognitive behaviors into digital machines. They use common knowledge representation from the information gained using both symbolic and sub-symbolic computations in the form of system-wide knowledge networks consisting of knowledge nodes and information sharing channels with other knowledge nodes. The knowledge nodes wired together fire together to exhibit autopoietic and cognitive behaviors.

Proper nutrition is crucial for normal brain and neurocognitive development. Failure to optimize neurodevelopment early in life can have profound long-term implications for both mental health and quality of life. Although the first 1000 days of life represent the most critical period of neurodevelopment, the central and peripheral nervous systems continue to develop and change throughout life. All this time, development and functioning depend on many factors, including adequate nutrition. In this review, we outline the role of nutrients in cognitive, emotional, and neural development in infants and young children with special attention to the emerging roles of polar lipids and high quality (available) protein. Furthermore, we discuss the dynamic nature of the gut-brain axis and the importance of microbial diversity in relation to a variety of outcomes, including brain maturation/function and behavior are discussed. Finally, the promising therapeutic potential of psychobiotics to modify gut microbial ecology in order to improve mental well-being is presented. Here we show that the individual contribution of nutrients, their interaction with other micro-and macronutrients, and the way in which they are organized in the food matrix are of crucial importance for normal neurocognitive development.

Purpose: Myelodysplastic syndromes (MDS) are caused by a stem cell failure, but the relationship between immune dysregulation and the course of disease has not yet been analyzed in detail. Experimental design: To get insights into the pathophysiologic and clinical relevance of the histotopography of immune cell subpopulations in this process, the immune cell infiltrate with focus on its spatial distribution was determined by multispectral imaging (MSI) in 147 bone marrow biopsies from MDS or secondary acute myeloid leukemia (sAML) patients and healthy controls (HC). In addition, the data were correlated to genetic alterations and clinical features of these patients including therapy response. Results: A high inter-tumoral heterogeneity in the frequency and spatial distribution of CD3⁺CD8⁺, CD3⁺CD8^-, CD3⁺FOXP3⁺ T cell subsets, MUM1p⁺CD3^- post-germinal B/plasma cells and CD34⁺ blasts was found in MDS and sAML samples. In HC only few B cells/plasma cells, but no T cell subpopulations were detected in the proximity to CD34⁺ blasts. In contrast, the frequency of these lymphocytes was increased in proximity to CD34⁺ blasts in both MDS and sAML independent of the karyotype, genetic alterations frequently detected in MDS, clinical risk stratification systems or treatment response to hypomethylating agents. Furthermore, an increased frequency of CD3⁺CD8⁺ T cells and MUM1p⁺ CD3^- B cells was found in responders to epigenetic drugs. Conclusions: Thus, we conclude that (i) T cell subsets do not belong to the normal stem cell niche, (ii) the presence of T and B cell subpopulations not directly affect the course of MDS, (iii) lymphocytes in the proximity to CD34⁺ blasts might indicate defective stem cell properties and (iv) the number of lymphocytes is a predictor of therapy response to hypomethylating agents.

The channels from the superfamily of transient receptor potential (TRP) activated by reactive oxygen species (ROS) are defined as redox channels. Those with the best exposure of the cysteine residues and hence the most sensitive to oxidative stress are TRPC4, TRPC5, TRPV1, TRPV4, and TRPA1, while others, such as TRPM2 and TRPM7 are indirectly activated by ROS. Activation by ROS has different effects on the tumorigenic process: some TRP channels may, upon activation, stimulate proliferation, apoptosis, or migration of cancer cells, while others inhibit these processes, depending on the cancer type, tumoral microenvironment and finally on the methods used for evaluation. Therefore, the use of these polymodal proteins as therapeutic targets is still an unmet need, despite their draggability and modulation by simple and mostly unharmful compounds. In this review, we intend to create some cellular models of the interaction between oxidative stress, TRP channels, and inflammation. Although the crosstalk between the three actors is rather theoretical, we intended to gather the recently published data and proposed pathways of cancer inhibition using modulators of TRP proteins, hoping that the experimental data corroborated with clinical information may finally bring the results from the bench to the bedside.

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

: On earth, plants play the most important part. Every organ of a plant plays a vital role in the ecological field as well as the medicinal field. But on the whole earth there are several species of plants are available. Different plants have different diseases. Therefore it is needed to identify the plants and their diseases to prevent loss. Now to identify the plants and their diseases manually is very time consuming. In this research an automatic plant and their disease detection system is proposed. For experimental purposes, high-quality leaf images are accepted for training and testing. For detecting the healthy and diseased area in a leaf, region-based and color-based region thresholding techniques were used. For feature selection Histogram Oriented Gradient (HOG) and Local Binary Pattern (LBP) method were applied. Finally for classification two-class and multi-class Support Vector Machine (SVM) was used. It is observed that both feature selection processes with SVM give 99% accuracy. Finally to understand the automated system a graphical user interface was created for all users.

Knowledge management gains space within the school organization and can contribute satisfactorily to the quality of teaching. In everyday life the school manager intuitively uses knowledge management practices without exploiting the potentials they offer or allow. In this context, the purpose of this work is to identify the level of implementation of knowledge management practices aimed at structuring the organizational processes used by the public school manager. The methodology adopted was exploratory, with a qualitative and quantitative approach. For data collection, an already validated instrument with twenty-seven questions was used. Respondents to the questionnaires, one hundred and eleven managers of the basic education schools that make up the public school system in a city in the northwestern region of Paraná, Southern Brazil. In analyzing the results, it was only at this point that we investigated only those practices that were related to the structuring of organizational processes. This decision is justified because of the responsibilities of the school manager in the execution of his work. The results indicated that the school in its daily life, makes use of practices of Knowledge Management related to the structuring of the organizational processes and that many are already applied by the managers.

This qualitative research study explores the multifaceted impacts of digital transformation on supply chain processes within contemporary business environments. Through in-depth interviews with industry professionals, the study investigates the implications of digitalization for visibility, collaboration, resilience, and innovation within supply chains. The findings highlight the opportunities presented by digital technologies for enhancing operational efficiency, mitigating risks, and creating value across supply chain networks. However, the study also underscores the challenges and barriers that organizations face in their digital transformation journey, including integration complexity, data security, and organizational culture. The research emphasizes the importance of strategic investments in digital capabilities, change management, and organizational culture to capitalize on the opportunities presented by digital transformation while addressing the associated challenges. By doing so, organizations can build agile, resilient, and competitive supply chains capable of thriving in an increasingly digitalized and interconnected world.

This dossier is aimed at individuals involved in the development, implementation, and application of AI-supported systems in the field of education, particularly those who are dealing with metadata in this context. The insights and recommendations described in this dossier are based on interviews with five experts from the USA and UK, research on metadata standards, and our personal experiences in the context of the INVITE competition. This dossier provides an overview of the current development of learning metadata and existing standards. It shows the efforts and obstacles to introducing those metadata standards from an international perspective based on interviews with five experts in the field. Furthermore, this dossier shows the international experiences in selecting and using metadata standards and how these findings can inspire discussions in the German-speaking discourse regarding the use of metadata in education.

The dynamics of the main photosynthetic structures is studied by mathematical modeling methods in this article. Chlorophyll portion variability in phytoplankton and the formation of energy-intensive substances in the process of photosynthesis underlie the models. These cellular components are considered in terms of their participation in the growth of specific biomass. Computational experiments are presented that simulate various degrees of environmental friendliness. The corresponding functions are built in accordance with seasonal fluctuations throughout the year in the Far East region of Russia. The stability of model solutions in long-term dynamics is also investigated. The models were tested for biological adequacy and their effectiveness was compared. For the model selected as a result of the comparison, the optimal control problem has been formulated and solved. It is shown that in this way it is possible to reduce the space of the initial components of the model system.

The paper presents design methodology for the production process of a new product from the point of view of the assembly operations technology criterion (Design for Assembly - DFA) in the conditions of high-volume production. Mentioned are DFA methods and techniques used in the implementation of a new product. Author presents a new method to assess design for manufacturability based on fuzzy variables based on fuzzy variables. An example was given to illustrate the proposed course of action

Using the unique combination of atomically resolved atom probe tomography (APT) and volume averaged neutron (resonance) spin echo (NRSE and NSE) experiments, the influence of nano-scaled clusters on the spin relaxation in spin glasses was studied. For this purpose, the phase transition from the paramagnetic phase to the spin glass phase in a Fe-Cr spin glass with a composition of Fe_17.8Cr_82.2 was studied in detail by means of NRSE. The microstructure was characterised by APT measurements, which show local concentration fluctuations of Fe and Cr on a length scale of 2 to 5 nm, which lead i) to the coexistence of ferro- and anti-ferromagnetic clusters and ii) a change of the magnetic properties of the whole sample, even in the spin glass phase, where spins are supposed to be randomly frozen. We show that a generalized spin glass relaxation function, which was successfully used to describe the phase transition in diluted spin glasses, can also be used for fitting the spin dynamics in spin glasses with significant concentration fluctuations.

This work aims to shed light on the differential diagnosis of Complex Post-Traumatic Stress Disorder (cPTSD), Post-Traumatic Stress Disorder (PTSD), and Borderline Personality Disorder (BPD) within the context of Intimate Partner Violence (IPV), which represents an extremely inno-vative field of clinical research. To this end, a critical review of the literature was conducted to identify and compare the clinical patterns and symptomatic overlaps among cPTSD, PTSD, and BPD, with an emphasis on their manifestation in both victims and offenders. The results show that despite some symptomatic similarities, cPTSD, PTSD and BPD have distinct clinical patterns in IPV. Disturbances in Self-Organization (DSO), are more commonly in offenders, while the di-agnosis of cPTSD is more aligned with the victims. In addition, cPTSD and characteristics of BPD, such as fear of rejection and instability of identity, constitute risk factors for IPV victimiza-tion. cPTSD is a predisposing factor not only for victims but also for offenders, while PTSD emerges as a consequential factor. The specific pathways linking PTSD, cPTSD, and BPD with IPV have significant implications for clinical practice. Further research is needed to understand these profiles and the mechanisms linking trauma-related features to IPV, which is crucial for imple-menting effective violence prevention programs.

Cognitive impairment affects 34-65% of People with Multiple Sclerosis (PwMS), significantly impacting their quality of life. Clinicians routinely address cognitive deficits with in-clinic neuro-behavioural interventions, but accessibility issues exist. Telerehabilitation is a promising solution that utilises digital tools for home-based cognitive remediation. This narrative review assessed the feasibility and efficacy of cognitive telerehabilitation in PwMS, identifying 13 relevant studies. Regarding the feasibility of cognitive telerehabilitation, evidence shows adherence rates are generally good, although, surprisingly, not all studies reported a measure of compliance with the cognitive training explored. Considering the efficacy of different rehabilitative techniques on cognitive performance of PwMS, findings are generally inconsistent, with only one study reporting consistently positive results. A range of methodological limitations are reported as potential factors contributing to the variability of results. Overcoming some of these issues will be key. Upcoming research is necessary to determine optimal intervention approaches, long-term effects of rehabilitation, and how to best integrate the use of telerehabilitation into routine clinical practice. By exploiting digital tools in the best way and addressing methodological concerns, home-based rehabilitation holds the potential for improving cognitive functions and enhancing the quality of life in PwMS.

Soil-borne Trichoderma spp. have been extensively studied for their biocontrol activities against pathogens and growth promotion ability with plants. However, the beneficial effect of Trichoderma on inducing resistance against insect herbivores has been considered only recently. Among diverse Trichoderma species, consistent with previous reports, we showed that root colonization by T. virens triggered induced systemic resistance (ISR) to leaf-infecting fungal pathogens. Whether T. virens induces ISR to insect pests has not been tested before. In this study, we investigated whether T. virens affects jasmonic acid (JA) biosynthesis and defense against fall armyworm (FAW) and western corn rootworm (WCR). Unexpectedly, the results showed that T. virens colonization of maize seedlings grown in autoclaved soil suppressed wound-induced production of JA, resulting in reduced resistance to FAW. Similarly, the bacterial endophyte Pseudomonas chlororaphis 30-84 suppressed systemic resistance to FAW due to reduced JA. Further comparative analyses of the systemic effects of these endophytes when applied into sterile or non-sterile field soil showed that both T. virens and P. chlororaphis 30-84 triggered ISR against hemibiotrophic fungal pathogen Colletotrichum graminicola in both soil conditions, but only suppressed resistance to FAW in sterile soil. In contrast to the effect on FAW defense, T. virens colonization of maize roots suppressed WCR larvae survival and weight gain. This is the first report suggesting the potential role of T. virens as a biocontrol agent against WCR.

As interest in renewable fuels grows, biomass has gained increasing attention for its potential applications. Understanding the kinetic and thermodynamic parameters of biomass pyrolysis and combustion are crucial for optimizing its valorization. In this study, TGA was employed to examine bamboo powder's pyrolysis and combustion behaviors under different temperature ramps in nitrogen and air environments. The TG and DTG curves reveal that bamboo pyrolysis occurs in three distinct stages: drying, devolatilization, and carbonization. Similarly, combustion also proceeds through three stages: drying, devolatilization, and char combustion. Notable differences in the temperature ranges of the key stages were observed between pyrolysis and combustion. Moreover, kinetic parameters were evaluated using the KAS, FWO, and STR model-free approaches. The findings indicate that, the activation energy during the oxidative devolatilization stage of combustion are notably lower compared to those during pyrolysis devolatilization. The disparity in activation energy is even more pronounced in the third stage. Thermodynamic analysis shows that the pyrolysis and combustion of bamboo are endothermic and non-spontaneous. It can be stably converted into value-added energy through pyrolysis or combustion process. This study provides essential data to aid in designing and scaling up the thermochemical conversion processes for bamboo.

Calcretes are indurated terrestrial carbonates that are widespread in arid and semi-arid settings and serve as important archives of present and past environments. Here we use geochemical tools to explore the nature and origin of calcretes documented from tropical Niue Island in the Southwest Pacific. The study recognizes two types of calcretes that differ in their mineral assemblage, microfabrics, elemental chemistry and carbon and oxygen isotopes. The calcretes common in the paleo-lagoon soils consist of 90% low-Mg calcite and ~10% highly weathered Mg-Al silicates. These pedogenic calcretes formed in the soil profiles within the vadose zone and bear the following distinctions: (i) Fe/Al ratio of 0.75, identical to the ratio in soils (Fe/Al=0.76 ±0.5), substantiates the link between the calcretes and soils; (ii) presence of rhizoliths, root voids, micritic nodules and clasts are consistent with a pedogenic calcrete fabric, and (iii) 13C and 18O depletions of -10.6‰ and -5.3‰, respectively, are compatible with carbon sources from microbial and roots respiration, and formation in oxygen isotope equilibrium with vadose waters. Unlike the pedogenic calcrete, a rare calcrete from the coastal terrace contains an exceptionally rare hydrotalcite mineral (65%) coated by microbial films. We contend that the hydrotalcite-rich calcrete was deposited through interaction of dolomite with seawater, similar to the method of producing hydrotalcite in the laboratory. 13C and 18O enrichments of 0.8 to 1.7‰ and -1.0 to -1.6‰, respectively, are in agreement with (i) mixed carbon sources consisting of microbial CO2 degassing, seawater HCO3 and dolomite dissolution, and (ii) oxygen isotope equilibration with a seawater-derived fluid.

In the current research work, electrical resistance tomography (ERT) was employed for crystallization process monitoring and visualization. A first-of-its-kind MATLAB-based interactive GUI application “ERT-Vis” is presented. Two case studies involving varied crystallization methods were undertaken. The experiments involving calcium carbonate reactive (precipitative) crystallization for the high conductivity solution-solute media and the cooling crystallization for sucrose crystallization representing the lower conductivity solution-solute combination were designed and performed. The software successfully provided key insights regarding the process progress in both crystallization systems. It could detect and separate the crystal agglomerations in the low as well as high conductivity solutions using visual analytics tools provided. The performance and utility of the software were studied using a software evaluation case study involving domain experts. Participant feedback indicated that ERT-Vis software helps in reconstructing images instantaneously, interactively visualizing, and evaluating the output of the crystallization process monitoring data.

Artificial intelligence methods and application have recently shown great contribution in modeling and prediction of the hydrological processes, climate change, and earth systems. Among them, deep learning and machine learning methods mainly have reported being essential for achieving higher accuracy, robustness, efficiency, computation cost, and overall model performance. This paper presents the state of the art of machine learning and deep learning methods and applications in this realm and the current state, and future trends are discussed. The survey of the advances in machine learning and deep learning are presented through a novel classification of methods. The paper concludes that deep learning is still in the first stages of development, and the research is still progressing. On the other hand, machine learning methods are already established in the fields, and novel methods with higher performance are emerging through ensemble techniques and hybridization.

Emerging evidence suggests that patients suffering from COVID-19 may experience neurocognitive symptoms. Besides, other studies indicate a probable association between leukocyte telomere length (LTL) and neurocognitive changes in subjects with post-COVID-19 condition. Our study was designed to determine the correlation between telomere length and cognitive changes in subjects post-COVID-19. The study included 256 subjects, categorized based on SARS-CoV-2 infection from 2020 to 2023. On the other hand, those subjects who have a psychiatric diagnosis are considered. Besides, the MoCA and MMSE scales were applied. Telomere length was determined using a Polymerase Chain Reaction, and statistical analysis was employed ANOVA and X2 tests. We identified a decrease in LTL in individuals with post-COVID-19 conditions compared to those without SARS-CoV-2 infection (p≤0.05). However, no association was found between LTL and cognitive impairment in subjects post-COVID-19. The findings suggest that LTL is affected by SARS-CoV-2 infection. Nonetheless, this important finding requires further research by monitoring neurological changes in subjects with post-COVID condition.

Objectives: To develop and validate the Communities Geriatric Mild Cognitive Impairment Risk Calculator (CGMCI-Risk), aimed at aiding community healthcare workers in the early identification of individuals at high risk of Mild Cognitive Impairment (MCI); Methods: Based on nationally representative community survey data, backward stepwise regression was employed to screen the variables, and logistic regression was utilized to construct the CGMCI-Risk. Internal validation was conducted using bootstrap resampling, while external validation was performed using temporal validation. The area under the receiver operating characteristic curve (AUROC), calibration curve, and decision curve analysis (DCA) were employed to evaluate the CGMCI-Risk in terms of discrimination, calibration, and net benefit, respectively; Results: The CGMCI-Risk model included variables such as age, educational level, sex, exercise, garden work, TV watching or radio listening, Instrumental Activity of Daily Living (IADL), hearing, and masticatory function. The AUROC was 0.781 (95% CI = 0.766 to 0.796). Calibration curve showed strong agreement, and the DCA suggested substantial clinical utility. In external validation, the CGMCI-Risk model maintained similar performance with an AUROC of 0.782 (95% CI = 0.763 to 0.801); Conclusions: CGMCI-Risk is an effective tool for assessing cognitive function risk within the community. It uses readily predictor variables, allowing community healthcare workers to identify the risk of MCI in older adults over a three-year span.

Introduction: Alzheimer's disease (AD) is the first cause of dementia worldwide without any current curative treatment. Facing an increasing prevalence and its associated costs, AD represents a public health challenge. Usual diagnostic methods still rely on extended interviews and paper tests provided by an exterior examiner. We aim to create a novel, quick cognitive-screening tool on a numerical tablet. Methods: This program, built and edited with Unity®, runs on Android® for the Samsung Galaxy Tab S7 FE®. Composed of seven tasks inspired by the Mini-Mental Status Examination and the Montréal Cognitive Assessment, it browses several cognitive functions. The architectural design of this tablet application is distinguished by its multifaceted capabilities, encompassing not only seamless offline functionality but also a mechanism to ensure the singularity of data amalgamated from diverse sites. Additionally, a paramount emphasis is placed on safeguarding the confidentiality of patient information in the healthcare domain. Furthermore, the application empowers individual site managers to access and peruse specific datasets, enhancing their operational efficacy and decision-making processes. We performed a preliminary usability assessment among healthy subjects. Results: 24 healthy patients were included with a final F-SUS score of "excellent". Participants perceived the tool as simple to use and achieved the test in a mean time of 142 seconds. No technical errors occurred. Conclusion: These preliminary results suggest that our new assessment on a numerical tablet allows a short cognitive screening.

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.

Epidemiological studies have shown that consuming olive oil rich in phenolic bioactive compounds is associated with a lower risk of neurodegenerative diseases and better cognitive performance in aged populations. Since oxidative stress is a common hallmark of age-related cognitive decline, incorporating exogenous antioxidants could have beneficial effects on brain aging. In this review, we firstly summarize and critically discuss the current preclinical evidence and the potential neuroprotective mechanisms. Existing studies indicates that olive oil phenolic compounds can modulate and counteract oxidative stress and neuroinflammation, two relevant pathways linked to the onset and progression of neurodegenerative processes. Secondly, we summarize the current clinical evidence. In contrast to preclinical studies, there is no direct evidence in humans of the bioactivity of olive oil phenolic compounds. Instead, we have summarized current findings regarding nutritional interventions supplemented with olive oil on cognition. A growing body of research indicates that high consumption of olive oil phenolic compounds is associated with better preservation of cognitive performance, conferring an additional benefit, independent of the dietary pattern. In conclusion, the consumption olive oil rich in phenolic bioactive compounds has potential neuroprotective effects. Further research is needed to understand the underlying mechanisms and potential clinical applications.

The first years of an infant’s life represent a sensitive period for neurodevelopment and see the emergence of nascent forms of executive function (EF), which are required to support complex cognition. Few tests exist for measuring EF during infancy, and the available tests require painstaking manual coding of infant behaviour. In modern clinical and research practice, human coders collect data on EF performance by manually labelling video recordings of infant behaviour during toy or social interaction. Besides being extremely time-consuming, video annotation is known to be rater-dependent and subjective. To address these issues, starting from existing cognitive flexibility research protocols, we developed instrumented toys as a new task instrumentation and data collection tool suitable for infant use. A commercially available device comprising a Barometer and Inertial Measurement Unit (IMU) embedded in a 3D-printed lattice structure was used to detect when and how the infant interacts with the toy. The data collected using the instrumented toys provides a rich dataset describing the sequence of toy interaction and individual toy interaction patterns, from which EF-relevant aspects of infant cognition may be inferred. Such a tool potentially provides an objective, reliable, and scalable method of collecting early developmental data in socially interactive contexts.

The ability to identify and rank risk is essential for efficient and effective supervision of financial service firms, such as banks and insurers. Risk ranking ensures limited resources are allocated where they are most needed. Today, automatic risk identification within insurance supervision primarily relies on quantitative metrics based on numerical data (e.g. returns). The purpose of this work is to assess whether Natural Language Processing (NLP) and cognitive networks can achieve similar automated risk ranking and identification by analysing textual data, i.e. NIDT=829 investor transcripts from Bloomberg. To this aim, this work explores and tunes 3 NLP techniques: (1) keyword extraction enhanced by cognitive network analysis; (2) valence/sentiment analysis; and (3) topic modelling. Results highlight that keyword analysis, enriched by term frequency-inverse document frequency scores and semantic framing through cognitive networks, could detect events of relevance for the insurance system like cyber-attacks or the COVID-19 pandemic. Cognitive networks were found to highlight events that related to specific financial transitions: The semantic frame of "climate" grew in size by +538% between 2018 and 2020 and outlined an increased awareness that agents and insurers expressed towards climate change. A lexicon-based sentiment analysis achieved a Pearson’s correlation of ρ=0.16 (p&lt;0.001,N=829) between sentiment levels and daily share prices. Although relatively weak, this finding indicates that insurance jargon is insightful to support risk supervision. Topic modelling is considered less amenable to support supervision, because of a lack of results’ stability and an intrinsic difficulty to interpret risk patterns. We discuss how these automatic methods could complement existing supervisory tools in automated risk ranking.

We aim to determine the sleep correlates of age-related brain loss in a sample of middle-aged to older males with obstructive sleep apnea. We evaluated consecutive treatment naïve male patients with OSA (AHI≥15 events/hr) without dementia, stroke or heart disease, from January to November of 2019. We collected demographic variables, vascular risk factors, and sleep questionnaires. We also obtained computerized neurocognitive testing with the Go-No-Go Response Inhibition Test, Stroop Interference Test, Catch Game Test, Staged Information Processing Speed Test, Verbal Memory Test and Non-Verbal Memory Test. We derived age and education adjusted domain-specific Z-scores for global cognition, memory, attention, processing speed and executive function. We used brain MRI T1-weighted images to derive total hippocampal and gray matter volumes. Partial correlations evaluated associations between the ISI, AHI, and oxygen level during sleep, with cognitive domains and brain volumes. Sixteen participants, age 40-76 years, 73% Hispanic/Latino, with mean AHI=48.9±25.5 and mean oxygen saturation of 91.4±6.9% during sleep. Hypertension was seen in 66% and diabetes in 27%. We observed that ISI and oxygen level during sleep had strong correlations with brain volumes and cognition. These preliminary findings may aid in developing future strategies to improve age-related brain loss in OSA.

Supply chain agility and resilience are key factors for the success of manufacturing companies in their attempt to respond to dynamic changes. Circular economy, the need for optimized material flows, ad-hoc responses and personalization are some of the trends that require supply chains to become “cognitive”, i.e. able to predict trends and flexible enough in dynamic environments, ensuring optimized operational performance. Digital Twins (DTs) is a promising technology, and a lot of work is done on the factory level. In this paper, the concept of Cognitive Digital Twins (CDTs) and how they can be deployed in connected and agile supply chains is elaborated. The need for CDTs in the supply chain as well as the main CDT enablers and how they can be deployed under an operational model in agile networks is described. Emphasis is given on the modelling, cognition and governance aspects as well as on how a supply chain can be configured as a network of connected CDTs. Finally, a deployment methodology of the developed model into an example of a circular supply chain is proposed.

Objective: A previous study showed that schizophrenia is accompanied by lowered levels of trace/metal elements including cesium. There are no data whether changes in cesium, rubidium and rhenium are associated with activated immune-inflammatory pathways, cognitive impairments, and the symptomatology of schizophrenia. Methods: This study measured cesium, rubidium, and rhenium, cognitive impairments (using the Brief Assessment of Cognition in Schizophrenia) and the cytokines/chemokines interleukin (IL)-1β, tumor necrosis factor (TNF)-α, and CCL11 (eotaxin) in 120 schizophrenia patients and 54 healthy controls. Severity of illness was assessed using the Brief Psychiatric Rating Scale (BPRS), the Scale for the Assessment of Negative Symptoms (SANS), the Fibromyalgia and Chronic Fatigue Syndrome Rating (FF) Scale and the Hamilton Depression Rating Scale (HAM-D). Results: Serum cesium was significantly lower in schizophrenia patients as compared with controls. Serum cesium was significantly and inversely associated with CCL11 and TNF-α, but not IL-1β. Moreover, there were significant inverse associations between serum cesium levels and the BPRS, FF, HAM-D and SANS scores and positive correlations between cesium and neurocognitive probe results including the Tower of London, Symbol Coding, Controlled Word Association, Category Instances, Digit Sequencing Task, and List Learning tests. Conclusion: The results suggest that lowered serum cesium levels may play a role in the pathophysiology of SCZ, specific symptom domains including negative, depressive and fatigue symptoms, neurocognitive impairments (spatial working, episodic and semantic memory and executive functions) and neuro-immune pathways as well.

Cognitive scientists have paid very little attention to magic as a distinctly human activity capable of creating situations or events that are considered impossible because they violate expectations and conclude with the apparent transgression of well-established cognitive and natural laws. And even though magic techniques appeal to all known cognitive processes from sensing, attention and perception to memory and decision making, the relation between science and magic has so far been mostly unidirectional, with the primary goal of unraveling how magic works. Building up from the deconstruction of a classic magic trick, we provide here a cognitive foundation for the use of magic as a unique and largely untapped research tool to dissect cognitive processes in tasks arguably more natural than those usually exploited in artificial laboratory settings. Magicians can submerge every spectator into the precise experimental protocol they have previously designed, accounting with ease for both circumstantial and social contexts. Magicians do not base the success of their experiments in statistical measures that smear out the individual in favor of an average spectator that we know never exists in the real world. They target each and everyone in the audience and, often, with a complete accomplishment. Magicians deliver their cognitive manipulations in real-time, in tight closed-loop with the audience, and in a single trial (they cannot afford to repeat the trick if it fails). Magic has also an inherent and strong social component, merging the private cognitive processes of each spectator with the group dynamics. Finally, when combined with the wide range of precise measuring and wearable technologies available today, magic paves the way for a road not taken towards real-world cognitive science. We dare to speculate that some of the mysteries of how the brain works may be trapped in the split realities present in each magic effect.

This study addresses the latent construct of attitudes towards environmental conservation based on study participant’s responses. We measured and evaluated the latent scale based on an 18-item scale instrument, over four experimental strata (N=945) in the US Virgin Islands and the Caribbean. We estimated the latent scale reliability and validity. We further fitted multiple alternative two-parameter logistic (2PL) and graded response models (GRM) from Item-Response Theory. We finally constructed and fitted equivalent structural and generalized structural equation models (SEM/GSEM) for the attitudinal latent scale. All scale measures (composite, alpha-based, IRT-based and SEM-based) were consistently and reliably valid measures of the study participants’ latent attitudes toward conservation. We found statistically significant differences among participant’s attributes relating to socio-demographic, physical and core environmental characteristics of participants. We assert that the nature of relationship between cognitive attitudes and individual as well as social behavior related to environmental conservation.

Abstract: In the Western of Mekong Delta belong to Vietnam a amount of alluvium sediments flows from upstream China to Vietnam, they are going from the river branches to in the Mekong Delta, then get out the Western Sea of Vietnam. This sediments accumulated gradually elevation the new land. The increase elevation of the land creates a fertile coastal plain where mangrove forests with a rich ecosystem of plants and animals. Over time, these forests change, with different plant species succeeding each other. This aims of this study to finding plant species in the forest, classification of forest types based on ecological regions, assessement the biodiversity of tree species, and compare the abundance communities, measuring the growth of the forest in these regions. In 2023, a comprehensive survey was conducted by using a systematic approach. The number of survey plots have done depend on the density of the forest, Base on the width of the forest range, the number of survey plots from 500m to 1,000m set up 1-2 plots, sub region set up from 10 to 15 plots. In total, 68 plots have done established in the erea, the area of plot is 100 square meters (10m x 10m). Using the statistical software in forestry to survey and analysis data. The result provided a complete picture of the tree species composition, distribution, and community structure characteristics in each ecological sub-region. The result of survey showed in the sub-region 1 have seven species as Sonneracia ovate, Rhzophora apiculata, Excoecaria agallochha, Avicennia officinalis, Avicennia alba, Agricerasvcoculatum, Bruguiera gymnorhiza. In the sub region 2 have eleven species as Sonneracia caseolaris, Annona reticulata, Lumnitzera littorea, Lumnizera racemosa, Rhizophora apiculata, Excoecaria agallocha, Avicennia officinalis, Avicennia alba, Hibiscus tillacerus, Bruguiera gymorhiza, Aericeras comiculatum. In the sub region 3 have eight as Sonratia caseolaris, Rhizophora mucronata, Rhizophora apiculata, Excoecaria agallocha, Avicennia marine, Avicennia alba, Bruguiera gymorhiza. In the region 4 have ten species as Sonneracia caseolaris, Sonneracia ovate, Lumnizera racemosa, Rhizophoa apiculata, Excoecaria agallocha, Avicennia marina, Avicennia officinalis, Aviecnnia alba, Bruguiera gymorhiza, Bruguiera cylindrica The total species of the mangrove forest in the Western Mekong Delta have 16 species from 11 plant families have been identified. Among these species have 6 dominant species include Avicennia oficinali),Avicennia alba, Rhizophora apiculata, Excoecaria agallocha, Someratia caseolaris, and Bruguiera yipamoriza. Two species easy grow on the new land are Avicennia officinalis Avicennia alba this findings show they can develope on the original new land for the shore of the Western Mekong Delta. The surveyed results also calculated the average of the height, diameter (D1.3), canopy, health of the nature mangrove tree for each sub region and total region. From these results showed the division of foresty structure, the structure of height, diameter (D1.3), canopy, vitality of the sub region and total region in the Western Mekong Delta.

We present contrast information, a novel application of some specific cases of relative entropy measures, designed to be useful for cognitive modelling of sequential perception of continuous signals. We explain the relevance of entropy in cognitive modelling of music and language. Then, as a first step to demonstrating the utility of constrast information for that purpose, we show empirically that its discrete case correlates well with existing successful cognitive models in the literature. We explain some interesting properties of constrast information. Finally we propose future work towards a cognitive architecture that uses it.

Cognitive reserve is the adaptability of cognitive processes in the face of brain aging and pathology. This study aimed to validate the Arabic version of the Cognitive Reserve Index Questionnaire (CRIq) in a healthy Lebanese sample. CRIq assesses cognitive reserve through three domains: education, working activity, and leisure time. Statistical measures, including descriptive and regression analysis along with structural equation modeling, were utilized to investigate the convergent and discriminant validity of CRIq, incorporating fluid intelligence (Gf) and measures of cognitive function, long-term memory encoding and retrieval (Glr), and processing speed (Gs). Results from 174 participants revealed that the activities assessed by CRIq-Arabic were comparable to the original CRIq study, with slight cultural differences. The internal consistency of CRIq-Arabic was good (Cronbach's α = 0.88), indicating reliability. Convergent validity was confirmed, with moderate to high loadings on the cognitive reserve latent construct. Discriminant validity was supported as correlations between cognitive reserve variables and non-target constructs (Gf, Glr, Gs) were less than 1. The findings provide an initial psychometric validation of the CRIq-Arabic. Further research in clinical samples is needed to enhance its utility in neuropsychological practice.

The vast rise in the number of Internet-connected devices necessitates a more accessible spectrum. As a result, Cognitive Radio was already proposed as a solution to the problem of restricted spectrum resources by utilizing available spectrum which is assigned to primary users. This method allows the secondary user to utilize the spectrum whenever the primary user is not using it, and it does so without intruding with the primary user. Whenever the secondary user detects the spectrum, it faced some issues, such as complexity in sensing leading to a lack of noise value, and the primary user is hidden to all secondary users. In order to tackle these challenges, an adaptive threshold matched filter detector and a cooperative matched filter detector are utilized in this paper to detect the spectrum. The probability of detection (Pd), probability of miss detection (Pm), and probability of false alarm (Pf) are the metrics used to assess sensing accuracy. To simulate suggested detectors, the MATLAB R2020a software was utilized. In comparison to earlier studies, the simulation conclusions reveal that the detection process starts with lower SNR values.

In this paper, we propose, implement, and test two novel downlink LTE scheduling algorithms. The implementation and testing of these algorithms were in Matlab, and they are based on the use of Reinforcement Learning, more specifically, the Qlearning technique for scheduling two types of users. The first algorithm is called a Collaborative scheduling algorithm, and the second algorithm is called a Competitive scheduling algorithm. The first type of the scheduled users is the Primary Users, and they are the licensed subscribers that pay for their service. The second type of the scheduled users is the Secondary Users, and they could be unlicensed subscribers that dont pay for their service, device to device communications, or sensors. Each user whether it is a primary or secondary is considered as an agent. In the Collaborative scheduling algorithm, the primary user agents will collaborate in order to make a joint scheduling decision about allocating the resource blocks to each one of them, then the secondary user agents will compete among themselves to use the remaining resource blocks. In the Competitive scheduling algorithm, the primary user agents will compete among themselves over the available resources, then the secondary user agents will compete among themselves over the remaining resources. Experimental results show that both scheduling algorithms converged to almost ninety percent utilization of the spectrum, and provided fair shares of the spectrum among users.

A variety of environmental factors contribute significantly to age-related cognitive decline and Alzheimer’s Disease (AD). Nutrition can alter epigenetics, improving health outcomes, which transmitted across generations; this process is called epigenetic inheritance. We investigate the beneficial effects of maternal resveratrol supplementation in offspring. We feed females SAMP8 with resveratrol-enriched diet during two months prior to mating. Direct exposed F1 generation and the transgenerational F2 generation were investigated. Object novel recognition and Morris water maze demonstrated improvements in cognition in the 6-month-old F1 and F2 generations from resveratrol fed mothers. A significant increase in global DNA methylation with a decrease in hydroxymethylation in F1 and F2 were found. Accordingly, Dnmt3a/b and Tet2 gene expression changed. Methylation levels of Nrf2 and NF-kβ genes promoters raised in offspring, inducing changes in target genes expression, as well as hydrogen peroxide levels. Offspring resulted from resveratrol fed mother showed increase AMPKα activation, mTOR inhibition and an increase in Pgc-1α gene expression and Beclin-1 protein levels. Endoplasmic reticulum stress sensors were found changed both in F1 and F2 generations. Overall, our results demonstrated that maternal resveratrol supplementation could prevent cognitive impairment in the SAMP8 mice offspring through epigenetic changes and cell signaling pathways.

The sulfur-containing amino acids methionine (Met), cysteine (Cys) and taurine (Tau) are common dietary constituents with important cellular roles. Met restriction is already known to exert in vivo anticancer activity. However, since Met is a precursor of Cys, and Cys produces Tau, the role of Cys and Tau in the anticancer activity of Met-restricted diets is poorly understood. In this work, we screened the in vivo anticancer activity of several Met-deficient artificial diets supplemented with Cys, Tau or both. Diet B1 (6% casein, 2.5% leucine, 0.2% Cys and 1% lipids) and diet B2B (6% casein, 5% glutamine, 2.5% leucine, 0.2% Tau and 1% lipids) showed the highest activity and were selected for further studies. Both diets induced a marked anticancer activity in two animal models of metastatic colon cancer, which were established by injecting CT26.WT murine colon cancer cells in the tail vein or peritoneum of immunocompetent BALB/cAnNRj mice. Diets B1 and B2B also increased the survival of mice with disseminated ovarian cancer (intraperitoneal ID8 Tp53 -/- cells in C57BL/6JRj mice) and renal cell carcinoma (intraperitoneal Renca cells in BALB/cAnNRj mice). The high activity of diet B1 in mice with metastatic colon cancer may be useful in colon cancer therapy.

This small article examines the pivotal roles of redundant and degenerate neural networks in the brain across three critical dimensions: evolutionary development, early childhood neural formation, and their implications in neurological diseases. Redundant networks, characterized by parallel neural pathways with similar functions, provide a foundation for cognitive resilience, ensuring continuity of brain functions despite damage or developmental anomalies. Degenerate networks, comprising structurally varied but functionally equivalent pathways, contribute to cognitive flexibility and adaptability, essential for innovative problem-solving and learning. From an evolutionary perspective, these networks have been instrumental in the brain's adaptation to diverse environmental challenges, facilitating cognitive maximization and the development of complex behaviors. The genetic underpinnings and environmental influences that shaped these networks are explored, highlighting their contribution to the evolutionary success of the human species. In the realm of early childhood development, the formation and reinforcement of these networks are crucial. The brain's plasticity during this period leads to the establishment of robust redundant networks and versatile degenerate networks, shaped significantly by environmental stimuli and learning experiences. This developmental phase lays the groundwork for cognitive abilities and resilience to neurological challenges later in life. Furthermore, the paper delves into the implications of these networks in the context of brain diseases. The disruption of the equilibrium between redundancy and degeneracy is linked to various neurological conditions, including Alzheimer's disease, Parkinson's disease, multiple sclerosis, stroke and Huntington's disease. Understanding the roles and balances of these networks offers insights into the pathophysiology of these diseases and potential therapeutic approaches. This review underscores the significance of redundant and degenerate neural networks in the evolutionary context, their critical formation during early childhood, and their profound impact on brain health and disease. It highlights the need for further research into these networks to enhance our understanding of brain function and to develop targeted interventions for neurological disorders.

In our country, the aging society is rapidly progressing, with an increase in patients with chronic kidney disease (CKD) and those undergoing dialysis, coupled with the aging of the population. As a result, the number of individuals with cognitive impairment (CI) is rising, and addressing this issue has become an urgent problem. One characteristic of dementia in CKD patients is the high frequency of vascular dementia, making the prevention through the management of classical risk factors such as hypertension, diabetes mellitus, dyslipidemia, smoking, etc., associated with atherosclerosis and arteriosclerosis. Other effective measures, including the use of renin-angiotensin system inhibitors, addressing anemia, exercise therapy, and lifestyle improvements, have been reported. Incidence and progression of CI may also be influenced by the type of dialysis or kidney replacement therapy, with reports suggesting that long-duration dialysis, low-temperature hemodialysis, peritoneal dialysis, and kidney transplantation (KTx) can have a possible preferable effect on preserving cognitive function.

Tests of selection based on population differentiation were performed on two highly polygenic traits important for success and quality of life: body height and educational attainment (EA). Polygenic scores (PGS) of EA and height, computed across three public genomic databases revealed differences between populations (1000 Genomes, HGDP, gnomAD) that matched the average IQ and height of ethnic groups (r ~0.9). A moderately strong correlation between latitude and EA PGS (r= 0.67) implies the effect of climate (seasonality or winter temperature) on selection for cognitive ability. The effect of latitude was reduced (β= 0.28) but remained significant after adding the sub-continental group variable to the regression model.The global Fst index revealed population differentiation at height and EA loci, significantly deviating from random SNPs. Substantial Linkage Equilibrium (LD) Decay between YRI and CEU was found (r= 0.6) but there was no correlation between LD decay and population differences in polygenic scores for EA (r= 0.015, p= 0.45), and slight inflation of height PGS difference due to LD decay (r= -0.04, p= 0.0315). Selecting the SNPs most robust to LD decay (r>0.8) resulted in larger PGS gaps for EA, but smaller for height. Finally, it is shown that PGS differences are more sensitive to the significance of GWAS loci than Fst, reflecting the major limitations of Fst as an index of selection.

Alzheimer’s disease (AD) is characterized by cognitive impairment and different non-cognitive deficits called “Behavioural and psychological symptoms of dementia” (BPSD) related to neurotrophin alterations, which differ from those presented in normal aging. Mouse models, both transgenics and inbreed mice models of AD, are a useful tool in understanding the underlying mechanisms of the disease. The SAMP8 (senescence-accelerated mouse prone 8) mice line was generated from AKR/J strain by Professor Toshio Takeda at the University of Kyoto. This strain exhibited a particular early-onset and accelerated aging phenotype. The present study characterizes and provides information regarding the non-cognitive, cognitive and neurotrophin alterations and their correlation, demonstrating the AD-like symptoms presented in older males SAMP8. The cognitive impairment presented was accompanied by a reduction in sociability and an increase in aggressive as well as anxiety behaviours. Furthermore, changes in two of the most important neurotrophins, such as brain-derived neurotrophic factor (BDNF) and neurotrophin-3 (NT3) were found. Thus, the present results reveal new insights in this useful inbred mouse model of neurodegeneration and AD, demonstrating the potential relationship between neurotrophin modifications, cognitive impairment and neuropsychiatric disorders (ND).

Pain is a multifaceted multisystem disorder that adversely affects neuro-psychological processes. This study compares the effectiveness of central-stimulation (tDCS) and peripheral-stimulation (TENS) in pain-inhibition during a cognitive-task in healthy volunteers and to observe potential neuro-cognitive improvements. Healthy eighty volunteers went through a complete experimental protocol comprising pain-induction (CPT), administration of tDCS/TENS, EEG-data acquisition, and cognitive assessments. The protocol for this study is categorized into 04-groups: G1(Control), G2(TENS), G3(Anodal-tDCS), and G4(Cathodal-tDCS). Paired t-tests (p

The mixing of terrestrial groundwater and seawater creates dynamic reaction zones in intertidal areas, where land-derived Fe(II) is oxidized to Fe(III) and then precipitates as Fe hydroxides at groundwater-seawater interface. These hydrogeochemical processes contribute to the formation of iron curtains beneath soil surface, which in turn influences the evolution of coastal aquifers. This paper provides a comprehensive review of physical and geochemical processes at field scale in coastal areas, explores the impact of mineral precipitation on pore structure at pore scale, and synthesizes reactive transport modeling (RTM) approaches for illustrating continuum-scale soil physio-chemical parameters during the evolution of porous media. Upon this review, knowledge gaps and research needs are identified. Additionally, challenges and opportunities are presented. Therefore, the incorporation of observational data into a comprehensive physico-mathematical model becomes imperative for capturing the pore-scale processes in porous media and their influence on groundwater flow and solute transport at large scales. Furthermore, a synergistic approach integrating pore-scale modeling and non-invasive imaging can provide detailed insights into intricate fluid-pore-solid interactions for future studies, as well as facilitate the development of regional engineering-scale models and physio-chemical coupled models with diverse applications in submarine science and engineering.

This study investigates the use of programming language Python to analyse and model chemical engineering processes. Using municipal sewage sludge (MSS) drying as an example, the study demonstrates how Python can support analyses and model drying data. The drying process was performed on a flat plate while maintaining a uniform, parallel drying air speed of 1.15 m s-1. The Python program helped to analyse the digitalised weight measurements from each sample. The program enables sorting input data, determining the drying critical point and evaluating the first and second drying periods.

Climate and land use changes are two factors affecting the hydrology of watersheds. The greatest effect of climate change is probably the change in the hydrological cycle, thus water availability for human consumption, industry, and power generation. While the change in land use can cause changes in the hydrological components of the basin, such as evapotranspiration, runoff, and groundwater recharge. From these projections, it is necessary to understand the processes involved in climate change and land use and its impact on water systems. In this context, the objective of this study was to simulate climate change scenarios combined with the land use change and to analyze the impacts on the hydrology in the Piracicaba basin. Two periods were selected based on the preliminary result of detection of the abrupt change point of the annual flow series, using Petitt's non-parameter test. The first period selected prior to the occurrence of the point of the change was from 1985 to 2000. The other period after the point of the change was 2001 to 2015. Land use maps of the years 1990 and 2010 were used to represent the two periods. The results showed a good performance of the SWAT model. The NS (Nash-Sutcliffe coefficient) was 0.88 in calibration for both periods. In the validation, the NS was 0.92 and 0.94 for the first and second periods respectively. We then simulate climate and land use changes scenarios and compare them to the current scenario (baseline). The simulation results showed that the hydrological processes in the Piracicaba basin in the period 1985-2015, had more intense responses to land use change than climate.

The contribution deals with the selection of progressive methods and technologies to improve the management of production processes in the industrial area. The first chapter of the contribution summarises the theoretical starting points for the management of production processes and their progressive concepts. Chapter 2 deals with the analysis of the current state of play in the use of methods and technologies in production management and summarise its problem areas. Chapter 3 presents the design of a system for selecting the appropriate method and technology to improve the management of the production process. The system presented is designed from data collection blocks, a benchmark for the performance of industrial enterprises' production processes, and data mining to gain knowledge of the impact of the methods and technologies used on the performance of production processes. The last block of the system allows the presentation of the obtained results, which sets out the recommendation for the choice of progressive method and technology. The system is partially verified in the last chapter of the contribution.

A major task in Functional Time Series Analysis is measuring the dependence within and between processes, for which lagged covariance and cross-covariance operators have proven to be a practical tool in well-established spaces. This article deduces estimators and asymptotic upper bounds of the estimation errors for lagged covariance and cross-covariance operators of processes in Cartesian products of abstract Hilbert spaces for fixed and increasing lag and Cartesian powers. We allow the processes to be non-centered, and to have values in different spaces when investigating the dependence between processes. Also, we discuss features of estimators for the principle components of our covariance operators.

The paper describes a method of solving some stochastic processes using generating functions. A general theorem of generating functions of a particular type is derived. A generating function of this type is applied to a stochastic process yielding polynomial time algorithms for certain partitions. The method is generalized to a stochastic process describing a rather general linear transform. Finally, the main idea of the method is used in deriving a theoretical polynomial time algorithm to the knapsack problem.

Sterilisation and preservation of vesicle formulations are an important consideration for their viable manufacture for industry applications, particular those intended for medicinal use. Here we undertake an initial investigation of the stability of hybrid lipid – block copolymer vesicles to common sterilisation and preservation processes, with particular interest in how the block copolymer component might tune vesicle stability. We investigate two sizes of polybutadiene-block-poly(ethylene oxide) polymers (PBd12-PEO11 and PBd22-PEO14) mixed with the phospholipid POPC considering the encapsulation stability of a fluorescent cargo and the colloidal stability of vesicle size distributions. We find that autoclaving and lyophilisation cause complete loss of encapsulation stability under the conditions studied here. Filtering through 200 nm pores appears to be viable for sterilisation for all vesicle compositions with comparatively low release of encapsulated cargo, even for vesicle size distributions which extend beyond the 200 nm filter pore size. Freeze-thaw of vesicles also shows promise for preservation of hybrid vesicles with high block copolymer content. We discuss the process stability of hybrid vesicles in terms of the complex mechanical interplay between bending resistance, stretching elasticity and lysis strain of these membranes and propose strategies for future work to further enhance the process stability of these vesicle formulations.

The agricultural sector, even though it has been greatly reduced and is in constant transformation, continues to be of strategic importance. Although it does not represent a quantitatively relevant employment sector, the dynamics are interesting because they reflect the structural, economic and social transformations that are affecting the sector in recent years; there is a growing need for external labour that corresponds to a massive recourse of foreigners to work. Innovative approaches are required to explore the capacity of social farming to create a sustainable and inclusive workplace for migrant. The overall methodological approach of the paper seeks to synthesize fieldwork research and qualitative interviewing to validate the Italian inclusive model. To do this, we have selected four experiences of Italian social agriculture in which migrants are included.

A numerical study for the effect of crest width, breaking parameter and trunk permeability on hydrodynamics and flow behavior in the vicinity of rubble-mound, permeable, zero-freeboard breakwaters (ZFBs) is presented. The modified two dimensional Navier-Stokes equations for two-phase flows in porous media with a Smagorinsky model for the subgrid scale stresses were solved numerically. An immersed-boundary/level-set method was used. The numerical model was validated for the cases of wave propagation over a submerged impermeable trapezoidal bar and over a low-crested permeable breakwater. Five cases of breakwaters were examined, and the main results are: (a) The size of the crest width, B, does not notably affect the wave reflection, vorticity and currents in the seaward region of ZFBs, while wave transmission, currents in the leeward side, and mean overtopping discharge, all decrease with increasing B. A non-monotonic behavior of the wave setup is also observed. (b) As the breaking parameter decreases, wave reflection, transmission, currents, mean overtopping discharge, and wave setup decrease. This observation is also verified by relevant empirical formulas. (c) As the ZFB trunk permeability decreases, an increase of the wave reflection, currents, wave setup, and a decrease of wave transmission and mean overtopping discharge is observed.

This paper questions the potential shift of reproduction towards assisted reproductive technologies due to benefits provided by genetic manipulation of embryos. In order to examine the viability of such a shift and its implications from a regulatory perspective, we relied on two panels of experts from Israel and Spain, using the Delphi method and a series of in-depth interviews. We anticipate, at a first stage, a continuous-steady growth in the use of IVF, supplemented by preimplantation genetic diagnosis and the introduction of CRISPR/Cas. At a second stage, attracting a growing share of fertile people would require developments in genomics. While it is unclear whether these developments will fully materialize, they could be replaced by technoscientific imaginaries generating perceived benefits. We conclude that the regulation of reproductive genetics is becoming more critical and complex. The aim should be to ensure good practices and equity, while providing more information to the public. A broad and inclusive societal debate may overcome the difficulty of drawing a clear line between medical uses and non-medical uses of genetic selection and engineering and may contribute to finding the right balance between allowing autonomous decisions of patients and protecting the public interest.

Research within the field of hydrology often focuses on comparing stochastic to machine learning (ML) forecasting methods. The comparisons performed are all based on case studies, while an extensive study aiming to provide generalized results on the subject is missing. Herein, we compare 11 stochastic and 9 ML methods regarding their multi-step ahead forecasting properties by conducting 12 large-scale computational experiments based on simulations. Each of these experiments uses 2 000 time series generated by linear stationary stochastic processes. We conduct each simulation experiment twice; the first time using time series of 100 values and the second time using time series of 300 values. Additionally, we conduct a real-world experiment using 405 mean annual river discharge time series of 100 values. We quantify the performance of the methods using 18 metrics. The results indicate that stochastic and ML methods perform equally well.

Dispersed silver nanoparticles (AgNPs) on the surface of titanium alloy (Ti6Al4V) and titanium alloy modified by titania nanotube layer (Ti6Al4V/TNT) substrates were produced by chemical vapor deposition method (CVD) using novel precursor of the formula [Ag₅(O₂CC₂F₅)₅(H₂O)₃]. The structure and volatile properties of this compound were determined using single crystal X-ray diffractometry, variable temperature IR spectrophotometry (VT IR), and electron inducted mass spectrometry (EI MS). The morphology and the structure of the produced Ti6Al4V/AgNPs, and Ti6Al4V/TNT/AgNPs composites were characterized by scanning electron microscopy (SEM) and atomic force microscopy (AFM). Moreover, measurements of hardness, Young’s modulus, adhesion, and surface free energy have been carried out. The ability to release silver ions from the surface of produced nanocomposite materials immersed in PBS solution has been estimated using inductively coupled plasma mass spectrometry (ICP MS). The wettability and the surface free energy of samples were estimated on the base of contact angle studies with the use of water and diiodomethane. Among the studied surface-modified titanium alloy implants, the best nano-mechanical properties were noticed for the Ti6Al4V/TNT15/AgNPs composite. The location of silver nanoparticles inside of titania nanotubes caused their lowest release rate, which may indicate on suitable properties above mentioned type of the composite for the construction of implants with a long term of antimicrobial activity.

In this study, a full-scale pilot testing was performed with side-by-side operation of a conventional enhanced biological phosphorus removal (EBPR) process and a side-stream EBPR (S2EBPR) process. A comparison of the performance, activities and population dynamics of key functionally relevant populations between the two configurations were carried out. The results demonstrated that, with the same influent wastewater characteristics, S2EBPR configuration showed more effective and stable orthophosphate (PO₄-P) removal performance (up to 94% with average effluent concentration down to 0.1 mg P/L) than conventional EBPR, especially when the mixers in side-stream reactor were operated intermittently. Mass balance analysis illustrated that both denitrification and EBPR performance have been enhanced in S2EBPR configuration through diverting primary effluent to anoxic zone and producing additional carbon (~40%) via fermentation in side-stream reactor. Microbial characterization showed that there was no significant difference in the relative abundances of Ca. Accumulibacter (~5.9%) and Tetrasphaera (~16%) putative polyphosphate-accumulating organisms (PAOs) between the two configurations. However, lower relative abundance of known GAOs was observed in S2EBPR configuration (1.1%) than the conventional one (2.7%). A relatively higher PAO activity and increased degree of dependence on glycolysis pathway than TCA cycle was observed in S2EBPR configuration using P release and uptake batch test. Adequate anaerobic solid retention time (SRT) and conditions that generate continuous and slow feeding/production of volatile fatty acid (VFA) with higher composition percentage of propionate in the side-stream reactor of S2EBPR process likely provide a competitive advantage for PAOs over GAOs.

Financial market data analysts are increasingly turning towards machine learning techniques for data analysis and making decisions. This paper explores how knowledge graphs, essentially a network of real entities and their relationships, can be used to improve collaborative financial market data analysis and make it more efficient and user friendly. This paper reviews the current state of financial market data analysis, the challenges in deploying machine learning models in industrial environments, and the concepts such as ML-Ops and knowledge graphs. A software architecture comprising various roles, layers and services such as Data Engineers, Data Analysts, public users, APIs, workflows, analytics libraries, UX layer, Business Layer, etc. is introduced and described in detail. Then, it discusses how knowledge graphs can be used to enhance collaborative financial market data analysis. Finally, a case study is presented to demonstrate the usage of the whole system, within the context of financial market data analytics of equities.

We report recent enhancements of the online atomic database at the Ohio State University, NORAD-Atomic-Data, that provides various parameters for radiative and collisional atomic processes dominant in astrophysical plasma. NORAD stands for Nahar Osu RADiative. The database belongs to the data sources, especially for the latest works, of the international collaborations of the Opacity Project and the Iron Project. The contents of the database are calculated values for energies, oscillator strengths, radiative decay rates, lifetimes, cross sections for photoionization, electron-ion recombination cross sections and recombination rate coefficients. We have recently expanded NORAD-Atomic-Data with several enhancements over those reported earlier. They are as follows. i) We continue to add energy levels, transition parameters, cross sections for atoms and ions with their publications. ii) Recently added radiative atomic data contains significant amount of transition data for photo-absorption spectral features corresponding to x-ray resonance fluorescence effect, prominent wavelength regions of bio-signature elements, such as phosphorus ions, and emission bumps of heavy elements, such as of lanthanides, that may be created in a kilonova event. We are including iii) collisional data for electron-impact-excitation, iv) experimental data for energies and oscillator strengths for line formation, v) experimental cross sections for photoionization that can be applied for benchmarking and other applications, and vi) introduction of a web-based interactive feature to calculate spectral line ratios at various plasma temperature and density diagnostics, starting with our recently published data for P II. We presented summary description of theoretical backgrounds for the computed data in the earlier paper. With introduction of experimental results, we present summary description of measurement of high resolution photoionization cross sections at Advanced Light Source of LBNL synchrotron set-up and briefly discuss other set-ups. These additions should make NORAD-Atomic-Data more versatile for various applications. For brevity, we provide information on the extensions and avoid repetition of data description of the original paper.

Pool boiling is extensively utilized in various fields, such as electronics cooling, battery cooling, nuclear power plants, and chemical processing. In the process of temperature control through boiling, the generation and transportation of bubbles play a crucial role in calculating the heat transfer capacity of the system. Therefore, it holds immense significance to gain a profound understanding of the mechanisms underlying bubble formation, growth, and detachment. Currently, numerous mechanistic explanations and empirical correlations have been proposed to elucidate various parameters of bubbles during the boiling process. These findings were considered valuable references in selecting appropriate boiling media and designing efficient heating surfaces. To comprehensively present the progress of bubble formation and heat transfer during the boiling process, the forces exerted on the bubbles was highlighted in this article. And a meticulous review on bubble force analysis and correlation formulas pertaining to various relevant parameters (e.g. nucleation sites density, bubble growth rate, detachment diameter, detachment frequency) were conducted. This review article also were expected to provide a novel foundation for further exploration of enhanced boiling heat transfer.

Dynamic neural networks (DNN) are types of artificial neural networks (ANN) that are designed to work with sequential data where context in time is important. In contrast to traditional static neural networks that process data in a fixed order, dynamic neural networks use information about past inputs, which is important if dynamic of a certain process is emphasized. They are widely used in natural language processing, speech recognition and time series prediction. In industrial processes, their use is interesting for the prediction of difficult-to-measure process variables. In an industrial process of isomerization, it is crucial to measure the quality attributes affecting the octane number of gasoline. Process analyzers that are commonly used for this purpose are expensive and subject to failures, therefore, in order to achieve continuous production in case of malfunction, mathematical models for estimating the product quality attributes are imposed as a solution. In this paper, mathematical models were developed using dynamic recurrent neural networks (RNN), i.e., their subtype of a long short-term memory (LSTM) architecture. The results of the developed models were compared with the results of several types of other data-driven models developed for an isomerization process, such as multilayer perceptron (MLP) artificial neural networks, support vector machines (SVM) and dynamic polynomial models. The obtained results are satisfactory, which suggests a good possibility of application.

Patterning, stability, and dispersion of the semiconductor quantum dots (scQDs) are the three issues strictly interconnected for successful device manufacturing. Recently several authors adopted direct optical patterning (DOP) as a step forward in photolithography to position the scQDs in a selected area. However, the chemistry behind the stability, dispersion, and patterning has to be carefully integrated to obtain a functional and therefore, commercial device. In this review are described different chemical strategies suitable to stabilize the scQDs both at a single level and as an ensemble with a special focus on the ones compatible with direct optical patterning (DOP). With the same purpose, the scQDs dispersion in a matrix was described in terms of the scQDs surface ligands interactions with the matrix itself. The chemical processes behind the DOP are illustrated and discussed for five different approaches that consider together stability, dispersion, and the patterning itself of the scQDs.