: The ichthyofauna of Lake Tanganyika consists of 12 families of fish of which five belong to Siluriformes (catfishes). Studies on siluriforms and their parasites in this lake are very fragmentary. The present study was carried out to help fill the knowledge gap on the monogeneans parasitizing the siluriform fishes of Lake Tanganyika in general and more particularly Clarias gariepinus. The gills of a specimen of Clarias gariepinus (Clariidae) were examined for ectoparasites. We identified the monogenean Gyrodactylus transvaalensis (Gyrodactylidae). This is the first time this parasite was found infecting gills. We are the first to observe a large spine in the male copulatory organ of this species, and to provide measurements of its genital spines; this completes the description of the male copulatory organ which is important in standard monogenean identification. This is the first monogenean species reported on C. gariepinus at Lake Tanganyika and the third known species on a representative of Siluriformes of this lake. It brings the total number of species of Gyrodactylus recorded in Lake Tanganyika to four. Knowing that other locations where this species has been reported are geographically remote from Lake Tanganyika, we propose a “failure to diverge” phenomenon for G. transvaalensis.

A number of research projects and a rich literature have dealt with the theme of abandoned medieval villages in Sardinia since the end of the 60s of the last century. Some more or less precise catalogues and reviews of villages in limited territories have been published. Only recently, however, this subject is being addressed in an interdisciplinary manner, combining traditional historical research with the results of archaeological surveys and excavation campaigns, geo-archaeology, toponymy, paleoclimatology. This allows us to have a picture of the landscape and human settlement evolution with its historical changes, conditioned not only by institutional superstructures but also by human and natural traumatic events. Particular attention will be given to the sudden changes that occurred between the thirteenth and fourteenth centuries. To carry out this survey, it is possible to use some very powerful IT tools which, through the aggregation, organization, correlation and management of information, allow the geo-localization of abandoned villages as proven by the documentary evidence. Thus, on this documentation, existing and acquired in the future, is founded the construction of the related information system. The most easy and suitable tools for this purpose are the CMSs (Content Management Systems) which, in association with GIS (Geographic Information Systems) engines, allow spatial and contextual analysis of the settlements, as they were inserted in their territory. This type of tools aggregates different peculiarities of the object of study, supporting a multidisciplinary reading on the argument. The computerized tools, integrated as a system, offer the possibility to implement it, feeding it and correcting it continuously, basing on new acquisitions. In this study, we will examine a historical areal, for which we have a sufficient number of sources available: Sarrabus, Colostrai and Quirra, adding to the geographical visual information, the temporal visual evolution.

Background: The thoracolumbar fascia (TLF) is thought to play a role in the development of LBP, but it is not yet clear which factor of TLF changes is a cause and which is an effect. Therefore, some studies used the cross-correlation function (CCR) to reveal time-dependent relationships between biomechanical and neuromotor factors. Methods: Ten patients with acute low back pain (aLBP) were matched to healthy controls. Simultaneous recording of surface electromyography (sEMG) of the erector spinae muscle (ES) and dynamic ultrasound (US) images of TLF deformation were performed during trunk extension. CCR functions and Granger causality were used to describe the relationship between the two measures. Results: CCR time lags were significant higher in the aLBP group (p = 0.04). Granger causality (GC) showed a direct effect of TLF deformation on ES activation only in the aLBP group (p < 0.03). Conclusions: The results suggest that in aLBP, ES activity is significantly affected by TLF, whereas this relation-ship is completely random in healthy subjects studied with CCR and GC comparisons of dynamic US imaging and sEMG data signals. Fascia-related disturbances in neuromotor control, particularly due to altered muscle spindle functions, are suspected as a possible mechanism behind this.

The recent global calamity of sea level rise and flooding is a major disaster. Some settlements on Java Island, Indonesia, have been reported to be sinking. Bedono Village is one village on the Central Java Coast that is vulnerable to sea level rise. Aside from the possibility of sea level rise, the calamity in this village is linked to prior mangrove degradation and fish pond expansion. This tragedy has displaced 78.63% of the population over a decade, and the remaining population is still surviving in the remaining lands. This study aims to investigate the effects of sea-level rise-related permanent inundation on the lives of the remaining residents in Bedono Village, with a focus on their adaptation and resilience methods. The results disclosed that remaining populations have taken active initiation by adopting multiple strategies that include structural prevention measures and nature based solution through mangrove reforestations. As a result based on mangrove assessment using Geographic Information Systems, mangrove cover increased from 1 ha in 2010 to 9.5 ha in 2012. This achievement has provided an alternative income through mangrove ecotourism. Women and multi stakeholder participation have also contributed to the implementation of adaptation strategy. Despite those significant achievements, the adaptation and resilience was challenged by lack of education and awareness towards sea level rise issues and their confounding disasters. The nature-based solution and local wisdom in Bedono Village is an example that is in line with GPDRR UNDRR 2022, the Sendai Framework, and The Bali Agenda for Resilience that ensures a whole-of-society approach to sea-level rise by ensuring that no one is left behind, creating an inclusive environment, and promoting gender.

Introduction. Self-sampling has the potential to increase cervical cancer screening (CCS) among women living with HIV (WLWH) in low and middle-income countries (LMICs). However, our understanding of how HPV self-collection studies have been conducted in WLWH is limited. The purpose of this scoping review was to examine the extent to which the HPV self-sampling has been applied among WLWH in LMICs. Method: We conducted multiple searches in several databases for articles published between 2000 and January 2022. With the combination of keywords relating to HPV self-sampling, LMICs, and WLWH, we retrieved over 9,000 articles. We used pre-defined inclusion and exclusion criteria to select relevant studies for this review. Once a study met the inclusion criteria, we created a table to extract each study’s characteristics and classified them under common themes. We used a qualitative descriptive approach to summarize the scoping results. Results: A total of 12 articles were included in the final review. Overall, 3,178 women were enrolled in those studies and 2,105 (66%) of them were WLWH. The self-sampling participation rate was 92.6%. The findings of our study show that 43% of the WLWH in 8 of the studies reviewed tested positive for high-risk HPV (hr-HPV) genotypes, indicating 4 out of 10 WLWH in the studies are at risk of cervical cancer. The prevalence of the hr-HPV in WLWH was 18% higher than that of HIV-negative women. Most women in the study found the self-sampling experience acceptable, easy to use, convenient, and comfortable. Self-sampling performance in detecting hr HPV genotypes is comparable to clinician-performed sampling. However, limited access (i.e., affordability, availability, transportation), limited knowledge about self-screening, doubts about the credibility of self-sampling results, and stigma remain barriers to wide acceptance and implementation of self-sampling. In conclusion, the findings of this review highlight that (a) cervical cancer is a threat to every sexually active woman but for WLWH the threat increases, (b) self-sampling laboratory performance is similar to clinician performed sampling, (c) self-sampling is associated with an increase in cervical cancer screening uptake and (d) WLWH reported a positive experience with self-sampling. However, personal, environmental, and structural barriers challenge the application of self-sampling in LMICs, and these need to be addressed. Keywords: keyword 1; keyword 2; keyword 3 (List three to ten pertinent keywords specific to the article yet reasonably common within the subject discipline.)

Although vaccinating the world is adopted by the WHO to limit COVID-19 transmission, people’s worries about vaccines may suppress their desire for vaccination despite vaccine availability. This study aimed to evaluate the levels of stress and anxiety among 250 Jordanians who received their first vaccine dose at a local community health center. The respondents completed the stress and anxiety subscales of the Depression Anxiety and Stress scale 21 (DASS-21) pre- and post-vaccination. The respondents expressed more moderate-severe levels of stress pre than post vaccination (20.8% and 13.2%, respectively). Meanwhile, 37.2% and 45.2% of the respondents expressed moderate-severe anxiety pre and post vaccination, respectively. Wilcoxon signed-rank test revealed that the drop in the level of stress from pre (median (IQR) = 5 (1-8)) to post vaccination (median (IQR) = 3 (1-7)) was statistically significant (z = -3.81, p = 0.001, r = 0.17) while the increase in anxiety was not. Anxiety median significantly dropped among individuals experiencing mild to severe anxiety pre vaccination. Similarly, stress and anxiety significantly increased among individuals expressing normal anxiety pre vaccination (z = -3.57 and -8.24, p values = 0.001, r = 0.16 and 0.37, respectively). Age positively correlated with post vaccination anxiety among respondents with mild pre vaccination anxiety, and it negatively correlated with pre vaccination level of stress in the normal anxiety group. Gender, marital status, respondents’ level of education, and history of COVID-19 infection had no significant correlation with anxiety or stress at either point of measurement. Overcoming their hesitancy to receive COVID-19 vaccine, individuals with normal levels of anxiety experienced a rise in their distress symptoms following immunization. On the contrary, vaccination seemed to desensitize anxious individuals. Policymakers need to formulate a population-specific plan to increase vaccine preparedness and promote psychological well-being over all during the pandemic.

The new climate law introduces a policy of sustainable construction, the assumption of which is the reduction of CO2 by the construction industry and the use of environmentally friendly materials, such as agricultural, mineral, and recycled waste, while limiting the consumption of natural resources. The article is a literature review that analyzes selected waste materials from various sectors of the economy that can be used as additives or partial substitutes for natural resources in the production of cement and in and cement building materials, the production of which reduces CO2 emissions, producing materials with high mechanical strength and environmentally friendly.

Background: The psychomotor agitation of the BPSD is one of the common issues in aged care facilities, leading to the poor functional and medical consequences. Psychotropic interventions are the preferable choice of treatment. But which medication should be the prescribers first preference? This review aims to compare pharmacological interventions for psychomotor agitation, judging them according to their effectuality and justifiability profiles. This is to be achieved by retrieving information from RCTs and systematic reviews. Objectives: This review evaluates evidence from RCTs, systematic reviews, and meta-analyses of BPSD patients who had taken agitation treatments. Assessing the efficacy of antidepressants and antipsychotic treatments when compared to each other for the purpose of improving agitation outcomes. Methods: This narrative review includes RCTs and retrospective studies that were comparing one or more active ingredient medications with another or with a placebo, along with sys-tematic reviews comparing antidepressants with antipsychotics such as quetiapine, olanzapine, and risperidone. Studies extracted by searching accessing databases, such as PubMed, OVID, and Cochrane with restrictions of date from 2000 to 2021 and English language. Quality of evidence: The quality of systematic reviews was judged against AMSTAR score, and RCTs were judged according to CONSORT checklist for RCT protocols. Conclusion: There are still few studies of serotonin targeting treatment of agitation in BPSD. The SSRIs such as citalopram were associated with a reduction in symptoms of agitation, and lower risk of adverse effects compared to antipsychotics. This review also illustrates brexpiprazole as a target of multimodal neurotransmitters such as dopamine, serotonin, and norepinephrine; and dextromethorphan, OR dextromethorphan associated with bupropion or quinidine as a blockade of NMDA receptors. The outcome of this review suggests that further studies involving more dementia/Alzheimer’s participants should be conducted. Future studies are required also to assess the long-term safety and efficacy of SSRI, brexpiprazole, dextromethorphan treatments for agitation in BPSD.

A simple underwater noise model suitable for use with explosives in the decommissioning of oil and gas subsea structures is introduced and evaluated against data from five projects in the US. The performance of the model is compared to four existing models for open water blasts, and for the severance of well conductors and piles. Simulated received underwater sound pressure levels were significantly correlated with measurements for all scenarios. The maximum total error achieved between simulations and measurements was 3.5%, suggesting that predictions are accurate to within 4% of the average measurement. A low relative bias was observed in the simulations when compared to measured values, suggesting only a small systematic underestimate (≤ 1% of average measurement) for most severance operations and a small overestimate (1.34%) for open water blasts.

A simple underwater noise model suitable for use with explosive severance of well conductors and piles during the decommissioning of oil and gas subsea structures is introduced and evaluated against data from five projects in the US. This study focuses on a novel model for the determination of sound exposure levels. The model has been developed to enable determination of impact areas for marine mammals and fish. Simulated received underwater sound exposure levels were significantly correlated with measurements for all scenarios. The maximum total error achieved between simulations and measurements was 2.6%, suggesting that predictions are accurate to within 3% of the average measurement. A low relative bias was observed in the simulations when compared to measured values, suggesting only a small systematic underestimate (≤ 0.5% of average measurement) for most severance operations and a small overestimate (0.14%) for open water blasts.

We reduplicate the Book “Dark Energy” by M. Li, X-D. Li, and Y. Wang, given zero-point energy calculation with an unexpected “length’ added to the ‘width’ of a graviton wave just prior to specifying the creation of ‘gravitons’, using the Rosen and Israelit model of a nonsingular universe. In doing so we are in addition to obtaining a wavelength 10^30 times greater than Planck’s length so we can calculate DE, may be able to with the help of the Rosen and Israelit model have a first approximation as to the arrow of time, and a universe with massive gravity. We have left the particulars of the nonsingular starting point undefined but state that the Rosen and Israelit model postulates initial temperatures of 10^-180 Kelvin and also a value of about Planck temperature, at 10^-3 centimeters radii value which may satisfy initial conditions asked by t’Hooft for describing an arrow of time. A key assumption is that the DE is formed at 10^-3 cm, after an expansion of 10^30 times in radii, from the Planck length radius nonsingular starting point. The given starting point for DE in this set of assumptions is where there is a change in the cosmic acceleration, to a zero value, according to Rosen and Israel, with time t = 1.31 times 10^-42 seconds. Which may be where we may specify a potential magnitude, V, which has ties into inflaton physics. The particulars of the model from Rosen and Israelit allow a solution to be found, without discussion of where that nonsingular starting point came from, a point the author found in need of drastic remedies and fixes.

Information and communication technologies (ICT) are increasingly permeating our daily life and we ever more commit our data to the cloud. Events like the COVID-19 pandemic put an exceptional burden upon ICT infrastructures. This involves increasing implementation and use of data centers, which increased energy use and environmental impact. The scope of this work is to take stock on data center impact, opportunities, and assessment. First, we estimate impact entity. Then, we review strategies for efficiency and energy conservation in data centers. Energy use pertain to power distribution, IT-equipment, and non-IT equipment (e.g. cooling): Existing and prospected strategies and initiatives in these sectors are identified. Among key elements are innovative cooling techniques, natural resources, automation, low-power electronics, and equipment with extended thermal limits. Research perspectives are identified and estimates of improvement opportunities are presented. Finally, we present an overview on existing metrics, regulatory framework, and bodies concerned.

There is a huge need to search for new treatment options and potential biomarkers of therapeutic response to antidepressant treatment. Depression and metabolic syndrome often coexist while pathophysiological overlap, including microbiota changes, may play a role. The aim of the study is to assess the effect of probiotic supplementation on symptoms of depression, anxiety and stress, metabolic parameters, inflammation and oxidative stress markers, and faecal microbiota in adult patients with depressive disorders depending on the co-occurance of MetS. The trial will be a four-arm, parallel group, prospective, randomized, double-blind, controlled design that will include 200 participants and will last 20 weeks. The probiotic preparation will contain Lactobacillus helveticus Rosell®-52, Bifidobacterium longum Rosell®-175. We will assess the level of depression, anxiety and stress, quality of life, blood pressure, body mass index and waist circumference, white blood cells count, serum levels of C-reactive protein, HDL cholesterol, triglicerides, fasting glucose, faecal microbiota composition and the level of some faecal microbiota metabolites, as well as inflammation markers and oxidative stress parameters in serum. The trial may establish a safe and easy-to-use treatment option as an adjunct in a subpopulation of depressive patients only partially responsive to pharmacologic treatment. (ClinicalTrials.gov identifier: ).

COVID-19 pandemic has the potential to adversely affect the mental health of healthcare workers (HCWs). The public healthcare system in Greece was already facing serious challenges at the outset of the outbreak following years of austerity and an escalating refugee crisis. The multi-center, cross-sectional study aims to assess the levels and associated risk factors of anxiety, depression, traumatic stress and burnout of frontline staff in Greece. A total of 464 HCWs in six reference hospitals completed a self-administered questionnaire comprising of sociodemographic and work-related information and psychometric scales. The proportion of HCWs with symptoms of moderate/severe depression, anxiety and traumatic stress were 30%, 25% and 33% respectively. Burnout levels were particularly high with 65% of respondents scoring moderate/severe in Emotional Exhaustion, 92% severe in Depersonalization and 51% low/moderate in Personal Accomplishment. Predictive factors of adverse psychological outcomes included fear, perceived stress, risk of infection, lack of protective equipment and low social support. The psychological burden associated with Covid-19 in healthcare professionals in Greece is considerable with more than half experiencing at least mild mental health difficulties. Findings signal the need for immediate organizational and individually tailored interventions to enhance resilience and support wellbeing under pandemic conditions.

Psychiatry remains in a permanent state of crisis, which fragmented psychiatry from the field of medicine. The crisis in psychiatry is evidenced by the many different competing approaches to psychiatric illness including psychodynamic, biological, molecular, pan-omics, precision, cognitive and phenomenological psychiatry, folk psychology, mind-brain dualism, descriptive psychopathology, and postpsychiatry. The current “gold standard” DSM/ICD taxonomies of mood disorders and schizophrenia are unreliable and preclude to employ a deductive reasoning approach. Therefore, it is not surprising that mood disorders and schizophrenia research was unable to revise the conventional classifications and did not provide more adequate therapeutic approaches. The aim of this paper is to explain the new nomothetic network psychiatry (NNP) approach, which uses machine learning methods to build data-driven causal models of mental illness by ensembling risk-resilience, adverse outcome pathways (AOP), cognitome, brainome, symptomatome, and phenomenome latent scores in a causal model. The latter may be trained, tested and validated with Partial Least Squares (PLS) analysis. This approach not only allows to compute pathway-phenotypes or biosignatures, but also to construct reliable and replicable nomothetic networks, which are, therefore, generalizable as disease models. After integrating the validated feature vectors into a well-fitting nomothetic network, clustering analysis may be applied on the latent variable scores of the R/R, AOP, cognitome, brainome, and phenome latent vectors. This pattern recognition method may expose new (transdiagnostic) classes of patients which if cross-validated in independent samples may constitute new (transdiagnostic) nosological categories.

Background SARS-CoV-2 has generated a life-treating pandemic and is the main challenge of this century. Some untranslated regions (UTRs) in SARS-CoV-2 genome, specifically leader sequence and transcription regulatory sequence (TRS) in 5’UTR, can be considered as Achilles' heel of virus. Leader sequence are found at the 5' ends of all encoded transcripts that highlights its importance. TRS can explain the host range and pathogenicity of coronavirus. However, our knowledge on the evolution and the role of UTRs in SARS-CoV-2 pathogenicity is very limited. This study is a pioneering attempt to unravel the evolution of key regions in 5' UTR of SARS-CoV-2 and discover the inhibitory microRNAs against 5' UTR of virus. Methods Evolution of TRS and leader sequence was compared between human pathogenic (SARS-CoV-2, SARS, and MERS) and non-pathogenic (bovine) coronaviruses. Profiling of microRNAs that can inactive the key UTR regions of coronaviruses, UTR-inhibitory microRNAs, was carried out. Findings We found a distinguished pattern of evolution in leader sequence and TRS of SARS-CoV-2, compared to the other coronaviruses. Mining all available microRNA families against leader sequences of coronaviruses resulted in discovery of 39 microRNAs with an acceptable thermodynamic binding energy against SARS-COV-2, SARS, MERS, Bat Coronavirus, or Bovine Coronavirus. Multivariate analysis demonstrated a distinguished pattern of binding of leader sequence of SARS-CoV-2 against microRNAs, with a lower binding stability. hsa-MIR-5004-3p was the only human microRNA that can target leader sequence of SARS and SARS-CoV-2. However, its binding stability remarkably decreased in SARS-COV-2 (-19.4 kcal/mol), compared to SARS-COV-2 (-25.9 kcal/mol). We found an insertion-type mutation in leader sequence of SARS-COV-2 that results in lower binding stability and escaping of viral leader sequence from hsa-MIR-5004-3p. Altogether, we suggest lack of innate human inhibitory microRNAs to bind to leader sequence and TRS of SARS-CoV-2 contributes to its high replication in infected human cells. On the other hand, mining of two hundred million deposited human genomic variants led us to discovery of 49 missense and splice-disrupt mutations in genomic structure of hsa-MIR-5004-3p. These mutations can negatively affect hsa-MIR-5004-3p function in preventing SARS-CoV-2 replication. Interpretation This study unravels the evolution of key regions in 5’UTR of SARS-CoV-2. Inducing microRNAs to bind to the leader sequence and TRS regions by drugs or food supplements can reduce virus replication. Enhancing the microRNA defence machinery against TRS and leader of virus has a potential to prevent SARS-CoV-2 infection at the first place. The mentioned strategy is rapidly achievable against COVID-19. Missense variation in genomic sequence of 5’UTR inhibitory microRNAs, such as hsa-MIR-5004-3p, can be considered as risk factor of COVID-19.

The current study assessed the influence of Anti Stripping Agents (ASA), Ground Tire Rubber (GTR) and waste polyethylene terephthalate (PET) on performance behavior of binder and Stone Matrix Asphalt (SMA) mixtures. Through this paper, the 85/100 penetration grade bitumen was utilized as original bitumen. Also, three liquid ASA’s (ASA (A), ASA (B), ASA (C)) were used as a mixture modifier. For this purpose, softening point, penetration, rotational viscosity, Dynamic Shear Rheometer, Multi Stress Creep Recovery (MSCR) and Linear Amplitude Sweep (LAS) tests were implemented to investigate the rheological properties of modified bitumen. For evaluating the behavior of modified mixtures several tests such as; Resilient Modulus, Tensile Strength, dynamic creep, wheel track and four-point beam fatigue tests were implemented. Based on MSCR test results, utilization of mentioned polymers enhanced the elasticity of bitumens and therefore the permanent deformation resistance of binders increases. Also by the addition of PET percentage, the rutting resistance improves. Results indicated that utilization of ASAs, PET and Crumb Rubber (CR) enhance the Resilient Modulus (Mr), Indirect Tensile Strength (ITS), rutting resistance, fatigue life and Fracture Energy (FE) of asphalt mixtures. Also based on results, modification of binder by PET/CR with a ratio of 50%/50% and ASA (B) have the highest fatigue life which indicates that this mixture has highest resistance against fatigue cracking.

The field of Cooperative Intelligent Transport Systems and more specifically Pedestrians to Vehicles could be characterized as quite challenging, since there is a broad research area to be studied, with direct positive results to society. Pedestrians to Vehicles is a type of Cooperative Intelligent Transport System, within the group of Early Warning Collision/Safety System. In this article, we examine the research and applications carried out so far within the field of Pedestrians to Vehicles Cooperative Transport Systems by leveraging the information coming from Vulnerable Road Users’, smartphones. Moreover, an extensive literature review has been carried out in the fields of Vulnerable Road Users Outdoor Localisation via smartphones and Vulnerable Road Users Next Step/Movement Prediction, which are closely related to Pedestrian to Vehicle applications and research. We identify gaps that exist in these fields that could be improved/extended/enhanced or newly developed, while we address future research objectives and methodologies that could support the improvement/development of those identified gaps.

Children’s beneficial relationships with animals are well known. Companion animals, particularly dogs have become an integral part of family life and children’s material culture. Aside the proven physiological benefits there is little research about what children say about their relationships with animals and how they describe them. In this paper we bring together both horse-human and dog-human interactions, finding common ground for understanding the complexity of human development, well-being and flourishing. Dogs in schools are fast becoming a trend in helping support and enhance children’s learning as well as their social and emotional well-being. Studies have shown that the very presence of a dog can increase children’s concentration, executive function and behavior. Also, equine therapy is gaining momentum and empirical studies are showing noteworthy benefits to children and young people. However, the lack of children’s voices means that the mechanisms for these benefits are somewhat unknown and unclear. In seeking to explore this, the authors utilize a visual, sensory and diffractive ethnographic approach to illuminate and illustrate, experiment and re-enact, how the children relate, share spaces and multiple subjectivities with their classroom canine, “Ted” and companion horse “Henry”. ”Henry” is part of a programme in which youngsters care for and engage in activities with horses.

Two important types of metal oxide nanoparticle catalysts Copper (II) oxide (CuO) and Cerium oxide (CeO₂) are prepared by a suitable method which was traditional precipitation (PT) method at calcination temperature of 400^oC for 5h and used for the synthesis of glycerol carbonate GC (C₄H₆O₄) from the direct reaction by the carbonylation of Glycerol GL (C₃H₈O₃) with Carbone Dioxide. The precipitation (PT) was an important route for the preparation of nanoparticles catalyst. The effects of performance of (CuO and CeO₂) nanoparticle catalysts on the conversion of glycerol GL, yield of glycerol carbonate GC, selectivity of glycerol carbonate are researched. XRD, XPS, BET, FT-IR, CO₂-TPD, H₂-TPR are used for the characterization of the prepared catalysts. Comparing the optimal performance between them under reaction conditions were 150 ^oC, 4MPa (40 bar.), 5h, and both CuO and CeO₂ catalyst amount 37.6 % (based on ratio of glycerol weight) by using 2-pyridinecarbonitrate (C₆H₄N₂) as dehydrating agent and dimethylformamide (DMF), (C₃H₇NO) as solvent. The glycerol conversion (X_GL), glycerol carbonate yield (Y_GC) and glycerol carbonate selectivity (S_GC) over 0.7g CuO are 57.151%, 47.524%, and 83.156%, respectively, and glycerol carbonate yield over 0.7 CeO₂ is 36.2185% or 35.076%, and the yield of GC could reach as high as 78.234% over 1.73g CeO₂, the both catalysts could be easily regenerated by washing with methanol and water after a reaction and then dried at 60 ^oC overnight after that calcination at 400 ^oC for 5h without loss of activity after five recycling times, In addition to, the (ICP- MS) results confirmed that the leaching of CuO and CeO₂ was below the detection limit.

In (Venetz et al., 2019), authors rebuilt the essential genome of Caulobacter crescentus through the process of chemical synthesis rewriting and studied the genetic information content at the level of its essential genes. Then, they reduced the native Caulobacter crescentus native Caulobacter NA1000 genome sequence real genome ( 4042929 bp ) to the 785,701-bp reduced synthetic genome. Here we demonstrate the existence of a palindromic-like mirror structure that exists in real genomes and disappears totally in the synthetic genome. This biomathematic meta-organization is based on characteristic proportions of Fibonacci numbers between DNA single strand nucleotides proportions TC / AG on the one hand and TG / AC on the other hand. In both cases, we suggest that this meta-structure enhances the three-dimensional cohesion of the two DNA strands of the genome. We then generalize this study to the different synthetic genomes and synthetic cells published by the Craig Venter Institute on Mycoplasma Mycoides JCVI-syn1.0 (in 2010), JCVI-syn3.0 (in 2016) and JCVI-syn3A (in 2019). Finally, in the discussion section, we extend this study to synthetic genomes of E-Coli and Yeast chromosome XII.

Over the past several years there has been much debate with regards to the prognostic and clinical significance of pancreatic ductal adenocarcinoma (PDAC) with lymph nodes metastasis. The PDAC gene-expression knowledge and the biologic alterations underlying the lymph node involvement convey a clinical implication in dealing with the theranostic window.To this end, we provide an original bioinformatic dissection of the gene-expression differences of PDAC according to the nodal involvement from a large public available dataset. Comprehensive transcriptomic analysis from 143 RNA-seq patient’s derived samples indicated that WNT increased activation and a peculiar immune-microenvironment identify subjects with nodal involvement.In frame of this thinking, we validated the WNT pathway role in increasing the likelihood of lymphatic dissemination in vitro. Moreover, we demonstrated for the first time in a PDAC model the potential therapeutic window that XAV-939, a specific WNT pathway inhibitor, has in re-educating a tumour permissive immune system. Finally, we outline the potential implication on bystander molecular drivers exerted by WNT molecular inhibition, providing a picture of the proteomic oncogenic landscape changes elicited by XAV-939 on PDAC cells and their clinical implication. Our findings hold the promise to identify novel immune-based therapeutic strategies targeting WNT to enhance PDAC cytotoxicity and restore anti-PDAC immunity in nodes-positive disease.

Background User fee exemption for maternal and child health care service policy was introduced with a focus on providing free caesarean sections (CS) in Nigeria from 2011 to 2015. This policy had a positive impact on access to facility-based delivery, but its effect on socioeconomic and geographical inequality remains unclear. This study’s main objective is to examine access to birth by CS in the context of free maternal health care. Specifically, the study examines socio-demographic and geographical inequality in access to birth by CS among women who gave birth between 2011 and 2015 under the free maternal health care policy using a population-based survey data obtained from two of the six main regions of the country. Methods Data were obtained from 1227 women who gave birth during the period the policy was in operation selected using cluster random sampling between May and August 2016. Adjusted and unadjusted binary logistic regression models were used to examine whether there is socio-demographic and geographical inequality in access to birth by caesarean section. Results The overall caesarean section rate of 6.1% was found but varies by income (14.1% in monthly income of over $150 versus 4.9% in income of $150 and below), education (11.8% in women with higher education versus 3.9% among women with secondary education and less) and place of residence (7.8% in urban areas versus 3.6% in rural areas). Women who earn a monthly income of $150 or less were 48% less likely to have a birth by CS compared to those who earn more. Compared to women who were educated to tertiary level, women who had secondary education or less were 54% less likely to have birth by caesarean section. Conclusion This study shows that inequality in access to CS persists despite the implementation of free maternal health care services. Given the poor access to facilities with capabilities to offer CS in most rural areas, free maternal healthcare policy is not enough to make birth by CS universally accessible to all pregnant women in Nigeria.

Structure-based design and synthesis of two biphenyl-N-acyl-β-D-glucopyranosylamine derivatives as well as their assessment as inhibitors of human liver glycogen phosphorylase (hlGPa, a pharmaceutical target for type 2 diabetes) is presented. X-ray crystallography revealed the importance of structural water molecules and that the inhibitory efficacy correlates with the degree of disturbance caused by the inhibitor binding to a loop crucial for the catalytic mechanism. The in silico derived models of the binding mode generated during the design process corresponded very well with the crystallographic data.

The effect of dentin pretreatment with desensitizing paste containing 8% arginine and calcium carbonate on the retention of laser-sintered cobalt-chromium (Co-Cr)-based crowns was examined. Forty molars were prepared using a standardized protocol. The Co-Cr crowns were produced using selective laser melting. The teeth were either pretreated with the desensitizing paste or not pretreated. After one week, each group was cemented with glass ionomer cement (GIC) or zinc phosphate cement (ZPC). Surface areas of the teeth were measured before cementation. After aging, the cemented crown-tooth assemblies were tested for retentive strength using a universal testing machine. The debonded surfaces of the teeth and crowns were examined at 2.7× magnification. Pretreating the dentin surfaces with the desensitizing paste before cementation with GIC or ZPC did not affect the retention of the Co-Cr crowns. The retention of the GIC group (6.04±1.10 MPa) was significantly higher than that of the ZPC group (2.75±1.25 MPa). The predominant failure mode for the ZPC and for the nontreated group of the GIC was adhesive cement-dentin; for the GIC-treated group, it was adhesive cement-crown. The desensitizing paste can be safely used to reduce postcementation sensitivity without reducing the retentive strength of Co-Cr crowns cemented with GIC or ZPC.

The paper presents an adaptation of numerical solution of first order linear differential equation in fuzzy environment. The numerical method is re-established and studied with fuzzy concept to estimate its uncertain parameters whose values are not precisely known. Demonstrations of fuzzy solutions of the governing methods are carried out by the approaches, namely Modified Runge Kutta method and Runge Kutta Merson method. The results are compared with the exact solution which is found using generalized Hukuhara derivative (gH-derivative) concepts. Additionally, different illustrative examples and an application in industry of the methods are also undertaken with the useful table and graph to show the usefulness for attained to the proposed approaches.

This study examines the effect of a hyaluronic acid (HyAc) containing tricalcium phosphate putty scaffold material (TCP-P) and of a particulate tricalcium phosphate (TCP-G) graft on bone formation, volume stability and osteogenic marker expression in biopsies sampled 6 months after bilateral sinus floor augmentation (SFA) in 7 patients applying a split-mouth design. Biopsies were processed for immunohistochemical analysis of resin embedded sections. Sections were stained for collagen type I (Col I), alkaline phosphatase (ALP), osteocalcin (OC) and bone sialoprotein (BSP). Furthermore, the bone area and particle area fraction were determined histomorphometrically. Cone-beam CT data recorded after SFA and 6 month later were used for calculating the graft volume at these two time points. TCP-P displayed more advantageous surgical handling properties and a significantly greater bone area fraction and smaller particle area fraction. This was accompanied by significantly greater expression of Col I and BSP and in osteoblasts and osteoid and a less pronounced reduction in grafting volume with TCP-P. SFA using both types of materials resulted in formation of sufficient bone volume for facilitating stable dental implant placement with all dental implants having been in function without any complications for 6 years. Since TCP-P displayed superior surgical handling properties and greater bone formation than TCP-G, without the Hyac hydrogel matrix having any adverse effect on bone formation or graft volume stability, TCP-P can be regarded as excellent grafting material for SFA in a clinical setting.

We summarize structural instabilities exhibited by the one dimensional (1D) (arene)2X family of organic conductors in relation with their electronic and magnetic properties. With a charge transfer of one electron to each anion X these salts exhibit a quarter-filled (hole) conduction band located on the donor stacks. Compounds built with donors such as fluorenthene and perylene derivatives and anions X such PF6 or AsF6 exhibit a high temperature (TP~170K) conventional Peierls transition which is announced by a sizeable regime of 1D 2kF charge density wave fluctuations (kF is the Fermi wave vector of the 1D electron gas located on Per stacks). Surprisingly, and probably because of the presence of a multi-sheet warped Fermi surface, the Peierls transition is considerably reduced in the perylene series α-(Per)2[M(mnt)2] where X is the dithiolate molecule with M=Au, Cu, Co and Fe. A special attention is devoted in this paper to physical properties of α-(Per)2[M(mnt)2] salts which with M=Pt, Pd and Ni incorporate segregated S=1/2 1D antiferromagnetic (AF) dithiolate stacks with 1D metallic Per stacks. We analyse conjointly the structural and magnetic properties of these salts in relation with the 1D spin-Peierls (SP) instability located on the dithiolate stacks. We show that the SP instability of the Pd and Ni derivatives occurs in the classical (adiabatic limit) while the SP instability of the Pt derivative occurs in the quantum (anti-adiabatic limit). Furthermore we show that in the Pd and Ni derivatives frustrated 1st neighbour direct and 2nd neighbour indirect (through a fine tuning with the mediated 2kF RKKY coupling interaction on Per stacks) AF interactions add their contribution to the SP instability to open a singlet-triplet gap. Our analysis of the data show unambiguously that magnetic α-(Per)2[M(mnt)2] salts are a typical realization of the physics predicted for two chain 1D Kondo lattices.

Objectives: Teachers’ intention understanding ability reflects their professional insight, which is the basis for effective classroom teaching activities. However, the cognitive process and brain mechanism of how teachers understand students' action intention in class are still unclear; Methods: This study used event-related potential (ERP) technology to explore the cognitive neural differences in intention understanding ability among teachers with different levels of knowledge and experience. The experiment used the comic strips paradigm to examine the ability of expert and novice teachers to understand students’ normative and non-normative classroom actions under different text prompts (“how” and “why”); Results: The results revealed that in the late time window, expert teachers induced larger P300 and LPC amplitudes when they understood students' classroom action intentions, while the N250 amplitudes induced by novice teachers in the early time window were significantly larger. In addition, for both types of teachers, when understanding the intentions behind students’ normative actions, the N250 amplitude was the most significant, while the P300 and LPC amplitudes were more significant for non-normative actions.; Conclusions: This study found that teachers at varying professional development stages had different time processing processes in intention understanding ability, which supported teachers' brain electrophysiological activities related to social ability.

The volatility and uncertainty associated with the high proportion of wind and PV output in the new power system significantly impact power and energy balance, making it challenging to accurately assess the risks related to renewable energy abandonment and supply guarante. Therefore, a probabilistic power and energy balance risk analysis method based on distributed robust optimization is proposed. Firstly, the affine factor and the flexible ramp reserve capacity of thermal power are combined to establish a probabilistic index, which serves to characterize the risk associated with power and energy balance. Drawing upon the principles of conditional value at risk theory, the risk indexes of load shedding power and curtailment power under a certain confidence probability are proposed. Secondly, the probability distribution fuzzy sets of uncertain variables are constructed using the distributionally robust method to measure the Wasserstein distance between different probability distributions. Finally, aiming at minimizing the operation cost of thermal power, the risk cost of power abandonment and the risk cost of load shedding, a distributed robust optimal scheduling model based on flexible ramp reserve of thermal power is established.

The sleeping and grazing behaviors of horses play a crucial role in their health and welfare. In the horse industry, with the prevalent trend of individual single-stall farming in stable, accurately identifying various behaviors of horses within stables is imperative. This study has established a spatio-temporal dataset for horse postures and behaviors, proposed an algorithm utilizing a SE-SlowFast network with an enhanced loss function. This algorithm achieves automatic recognition of multiple postures and behaviors of horse simultaneously. The model developed in this study demonstrates high accuracy in identifying three postures within the stable: Standing, Crouching, and Lying, achieving respective accuracies of 92.73%, 91.87%, and 92.58%. Additionally, it accurately recognizes two behaviors: Sleeping and Grazing, achieving best accuracies of 93.56% and 98.77%. The overall best accuracy of the model is 93.9%. Experiments on video recognition confirm that the suggested model recognizes horse video data accurately. This study marks the first time computer vision technology has been used in the horse farming industry, achieving non-intrusive recognition of multiple horse postures and behaviors. This study provides valuable data support for livestock managers to evaluate the health conditions of horses, enhances the welfare of horses in stables, and contributes to the advancement of modern horse husbandry practices.

This paper presents the Aether Physics Model (APM), a novel theoretical framework that offers a unified description of the fundamental forces and weak interaction. The APM proposes a quantum Aether composed of discrete units, recognizes two distinct charge manifestations, and treats all charges as distributed. Using dimensional analysis and simple equations, the APM derives force equations that unify gravity, electromagnetism, and the strong forces and the weak interaction. The model makes specific predictions about force strengths, maximum mass density, and fine structure constants for protons and neutrons. These predictions align with existing observations and offer new avenues for experimental testing. The APM addresses the limitations of the Standard Model while providing a more intuitive explanation for phenomena like the quantum Hall effect and Casimir effect. If validated, the APM could revolutionize our understanding of space, matter, and fundamental forces.

Background: Accurate sales forecasting in the mobile phone industry is critical to informed decision making by both the government and corporations. This study seeks to enhance mobile phone sales predictions by incorporating brand exposure as a predictive factor, hence addressing the gap left by the existing forecasting models that do not consider the impact of brand awareness. Method: This study thereby indicates a quantification method for brand exposure that merges word-of-mouth reviews with online search data. Word2Vec is used to extract initial keywords from a word-of-mouth corpus, whereas time-lag correlation analysis refines the core keywords. Later, PCA synthesizes brand exposure. The attention-LSTM framework hereby develops a forecasting model of mobile phone sales, effectively combining long short-term memory networks with the ability to capture time series dependencies and an attention mechanism to reduce data redundancy. Results: By considering brand exposure, the experiments demonstrate that the attention-LSTM model decreases the RMSE and MAPE indicators by 2.02% and 0.96%, respectively. In addition, compared with the ARIMA, SVR, BP neural network, and LSTM models, the attention-LSTM model decreases the average percentage errors by 6.52%, 3.42%, 2.56%, and 0.81%, respectively. Conclusion: Attention-LSTM captures dynamic sales trends very well and holds great significance over traditional models. Overall, including brand exposure, this model is very strong for the interpretation of mobile phone sales predictions, which has important implications in terms of policy as well as corporate strategies.

Cryo-electron microscopy (Cryo-EM) has revolutionized structural biology by enabling the determination of high-resolution 3- Dimensional (3D) structures of large biological macromolecules. Protein particle picking, the process of identifying individual protein particles in cryo-EM micrographs for building protein structures, has progressed from manual and template-based methods to sophisticated artificial intelligence (AI)-driven approaches in recent years. This review critically examines the evolution and current state of cryo-EM particle picking methods, with an emphasis on the impact of AI. We conducted a comparative evaluation of popular AI-based particle picking methods, using both general machine learning (ML) metrics and specific cryo-EM structure determination metrics. This analysis involved constructing the 3D density map from the picked protein particles and assessing the obtained resolution and particle orientation diversity, underscoring the significant impact of AI on cryo-EM particle picking. Despite the advancements, we also identified key obstacles, such as handling complex micrographs with small proteins. The analysis provides insights into the future development of more sophisticated and fully automated AI methods in cryo-EM particle recognition.

With the fast growth of smartphones and digital cameras, massive images are generated every day in the world. They are easily transmitted on the insecure channel-Internet. It has been a hot spot for protecting sensitive images during communication. A new cryptosystem is proposed using a six dimensional (6D) hyperchaotic system and DNA computing. The hash value is obtained with the function of SHA-512. It keeps the encrypted result closely connected with the original image. The initial values of the cryptosystem are produced with the hash value and the secret key. The pixel is divided into four parts, and a large matrix is formed. Scrambling is performed on the new image. DNA coding, modern DNA complementary rules, DNA computing, and DNA decoding are performed on the new image. Diffusion is also executed, and ciphered image is achieved finally. The experimental result reveals the outcome of the proposed algorithm. Security analysis shows that the designed algorithm has a huge secret key space, low correlation, and high sensitivity. It also signifies that the designed algorithm could protect against common attacks and is more secure than some existing methods.

Applying cold to a bone injury can aid healing, though its mechanisms are complex. This study investigates how cold therapy impacts bone repair to optimize healing. Cold was applied to a rodent bone model, with physiological responses analyzed. Vasoconstriction was mediated by an increase in TRP channels, TRPA1 (p=0.012) and TRPM8 (p<0.001), within cortical defects enhancing sensory response and blood flow regulation. Cold exposure also elevated hypoxia (p < 0.01) and VEGF expression (p < 0.001), promoting angiogenesis vital for bone regeneration. Increased expression of osteogenic proteins PGC-1α (p = 0.039) and RBM3 (p<0.008) suggests stimulated reparative processes. Enhanced osteoblast differentiation and ALP presence at days 5 (three-fold, p=0.014) and 10 (two-fold, p=0.010) were observed, along with increased osteocalcin (OCN) at day 10 (two-fold, p = 0.010) indicating presence of mature osteoblasts capable of mineralization. These findings highlight cold therapy's multifaceted effects on bone repair, offering insights for therapeutic strategies.

Patients with hematologic malignancies or non-malignant diseases may undergo allogeneic hematopoietic cell transplantation (HCT), which represents a potential curative treatment. However, they are still at risk of life-threatening complications, such as relapse, acute graft-versus-host disease (GvHD), and opportunistic infections. These complications are more likely if T cell reconstitution is delayed during the initial 3-4 months after HCT. Therefore, it is of clinical importance to advance early peripheral T cell expansion.The thymus is the cradle of T cell production, but it is extremely sensitive to conditioning drugs used in HCT. As a result, egress of T cells from the thymus is abrogated during the first 3-6 months after HCT. Instead, early T cell reconstitution depends on peripheral expansion of engrafted donor T cells. However, besides its established function to produce T cells, the thymus also produces thymic peptides with hormone-like activity. These molecules play an essential part in the development and nature of immune responses, and may have a role in modulating T cell expansion and function after HCT. In this review, we investigate the role of thymic peptides in shaping the dynamics of immune reconstitution early after HCT. Furthermore, we summarize current reports on clinical application of thymic peptides post-HCT and discuss their potential use in improving patient outcomes.

The current emphasis in the advancement of space-based synthetic aperture radar (SAR) is on lightweight payloads under 100kg with resolutions surpassing 1m. This focus is directed towards meeting the launch criteria for multiple satellites on a single rocket and cutting costs. This article discusses the creation and progress of a Ku-band SAR payload for the Taijing-4(03) Satellite, launched on January 23, 2024 accompanied by four other satellites. The SAR payload design was customized to meet the demands of a micro-nano satellite platform, resulting in a lightweight, flat design weighing less than 80kg, seamlessly integrated with the plate-shaped satellite platform. The article also introduces a beam optimization strategy for the phased array SAR antenna, significantly boosting the SAR system’s performance. The SAR payload provides various operating modes like slide-spot, strip, scan 1, scan 2, and others, with a maximum achievable resolution exceeding 1m. Extensive in-orbit testing of the payload produced numerous high-quality SAR images with potential uses in emergency disaster mitigation, safeguarding ecosystems, monitoring forests, managing crops, tracking sea ice, and more.

Objective: To investigate the causal relationships between intestinal microbiome biomarkers and colorectal cancer (CRC) and its subtypes. Method: The meta-analysis method was used to integrate the largest genome-wide association study data from the MiBioGen consortium (n=18,340). Single nucleotide polymorphisms (SNPs) significantly associated with intestinal microbial relative abundance were extracted as instrumental variables (IVs). Based on the Finnish database (R9 version) with 326,322 participants, the two-sample Mendelian randomization (MR) analysis was adopted to explore the causal relationships between intestinal microbes and CRC and its specific subtypes, such as colon cancer and rectal cancer. Result: A total of 3,310 SNPs were screened as IVs. The MR analysis revealed significant associations between various microbial traits and CRC, including its subtypes. Specific microbes, such as Erysipelotrichaceae, exhibited protective effects, while others, like Orato, were demonstrated as risk factors. Notably, specific biomarkers (e.g., Streptococcus thermophilus) for CRC were determined. This study provides new insights and potential targets for the pathogenesis, diagnosis, and treatment of CRC. Conclusion: Significant causal relationships exist between intestinal microbes and CRC and its pathological types. Specific microbes, such as Erysipelotrichaceae, play a protective role, whereas others, like Orato, serve as risk factors. These findings offer new insights into the pathogenesis of CRC and provide a foundation for future prevention and treatment strategies.

The thermomechanical and viscoelastic properties of a glass fiber polyethylene terephthalate glycol (GF/PETG) continuous unidirectional (UD) tape were investigated using differential scanning calorimetry (DSC), thermo-mechanical analysis (TMA), and dynamic mechanical analysis (DMA). The study identified five operational conditions based on Army Regulation 70-38 Standard. The DSC results revealed a glass transition temperature of 78.0 ± 0.3°C, guiding the selection of temperatures for TMA and DMA tests. TMA provided the coefficient of thermal expansion in three principal directions, consistent with known values for PETG and GF materials. DMA tests, including strain sweep, temperature ramp, frequency sweep, creep, and stress relaxation, defined the linear viscoelastic region and temperature-dependent properties of the material. The frequency sweep indicated an increase in modulus with rising frequency, identifying several natural frequency modes. Creep and stress relaxation tests showed time-dependent behavior, with strain increasing under higher loads and stress decreasing over time for all tested input values. Viscoelastic models fitted to the data yielded R² values of 0.99, demonstrating good agreement. The study successfully measured thermo-mechanical and viscoelastic properties across various conditions, providing insights into how temperature influences the material's mechanical response under extreme conditions.

Side-chain liquid crystal polymers have been mixed with ferromagnetic particles, and the for-mation of a monodomain in magnetic fields studied. At relatively low concentrations, the presence of ferroparticles substantially speeds up the rate of formation of a monodomain with the magnetic field and at a given concentration of ferrofluid that rate is independent of the magnetic field strength. In this way, rapid formation of a monodomain is possible at magnetic field strengths far lower than required for the liquid crystal polymer alone. This is anticipated to very helpful in the fabrication of devices based on monodomain liquid crystal elastomers. Wide angle x-ray scattering has been used to monitor the formation of the monodomain and small angle x-ray scattering can give some indication of the ferroparticle behaviour. A model is developed to explain the behaviour. The alignment properties of the ferroparticles is related to their ability to form chains under the influence of very low magnetic fields; these chains are of relatively low stability and may become disrupted after long times, high magnetic fields, or high concentrations. In general, the best results for alignment were at volume fractions below 1%, and under these con-ditions there is the potential for producing monodomain samples with improved properties; in particular shape changes with temperature are significantly larger as a result of improved backbone orientation. Experiments involving monodomain formation and director realignment suggest that the presence of ferroparticles results in a modification in the mechanism for alignment development driven by the organisation of the polymer backbone as a consequence of the con-straints offered by the morphology of the chains of ferroparticles.

Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality globally, posing significant challenges to public health. The rapid development of artificial intelligence, particularly Large Language Models like ChatGPT, has revolutionized various fields, including cardiovascular medicine. ChatGPT, recognized for its broad applicability, efficiency, and public acceptance, has been integrated into multiple facets of CVD management. This comprehensive review examines the current applications of ChatGPT in cardiovascular medicine, emphasizing its innovative and significant role in enhancing diagnosis and clinical decision-making, improving medical education, and facilitating research and academic communication. Furthermore, the review article addresses the significant challenges associated with ChatGPT and proposes directions for future development to overcome these obstacles and boost its effectiveness. By exploring the vast potential of ChatGPT to meet the needs of the large CVD patient population and the extensive community of cardiovascular professionals, this review aims to provide valuable insights for optimizing its use in cardiovascular medicine, ultimately improving patient outcomes and advancing the field.

Aim. The aim of this study is to compare data from two cohorts separated by a 17-year interval. We assessed the prevalence and severity of symptoms with the “dialysis symptom index” in these two groups recruited in 2007 and 2024 to determine how advancements in dialysis therapy have influenced symptom burden’s prevalence and severityMethods. We conducted a cross-sectional study of patients receiving thrice weekly, in-center hemodialysis. The population, recruited between February and March 2007, was comprised of patients receiving maintenance hemodialysis at the outpatient dialysis unit of our hospital. This dialysis facility is staffed by academic nephrologists. In May 2024, in the same unit, another population sample was recruited and studied as in 2007. Study coordinators administered the Dialysis Symptom Index (DSI) during the dialysis treatment. The DSI is made up of 30 questions, each of which addresses a specific physical or emotional symptom. Patients were asked to report symptoms that had been present at any time during the previous week by responding “yes” or “no”. For symptoms that were present, the patient was then asked to describe the symptom severity on a five-point Likert scale (1= not bothersome to 5=bothers very much). We generated a total symptom burden score that represents the total number of symptoms reported as being present, as well as a total symptom severity score that represents the sum of individual severity scores assigning a severity score of zero for symptoms that were not present. Results. We studied 71 patients in 2007 and 61 patients in 2024. The demographic, clinical and laboratory characteristics of the two study populations did not differ significantly. The total symptom burden score did not differ significantly between 2007 and 2024. The prevalence of most symptoms was similar in the two groups. The prevalence of constipation, decreased interest in sex and difficulty becoming sex aroused was higher in 2024 than in 2007. The total symptom severity was similar in the two periods. The severity of most symptoms was similar in the two groups. The severity of decreased interest in sex and difficulty becoming sex aroused was higher in 2024 than in 2007. Conclusion. The present study shows that, 17 years apart, the prevalence and severity of the symptom burden in patients on maintenance hemodialysis has not changed significantly. These results suggest that effort should be made to design adequate studies on the causes and pathogenesis of physical and emotional symptoms of patients on chronic hemodialysis and, once the underlying mechanisms are identified, it is amenable that high-quality studies on possible therapeutic pharmacological and non-pharmacological interventions will be performed.

Silver nanoparticles (AgNPs) have arisen as promising specialists in malignant growth treatment, especially in further developing radiotherapy results. In vitro examinations feature AgNPs' capacity to increment radiosensitivity in disease cell lines, prompting uplifted cell demise and decreased clonogenic endurance. Outstandingly, AgNPs display insignificant poisonousness to ordinary cells, recommending potential for specific focusing of disease cells during radiotherapy. The blend of AgNPs with cisplatin shows huge cytotoxicity and oxidative pressure, particularly in tumoral cells, underscoring the requirement for additional investigation of this therapeutic methodology. AgNPs likewise show guarantee in prompting oxidative pressure in pancreatic disease cells, recommending an expected application in pancreatic malignant growth treatment. In co-openness situations, AgNPs, when joined with cadmium, prompt metabolic variation, showing compromised cell safeguard systems. Moreover, AgNPs display potential in tweaking ABC carrier action, offering a procedure to defeat multidrug obstruction in disease cells. While featuring the significant capability of AgNPs in disease treatment, the rundown highlights the need for continuous exploration to clarify their components, well-being profiles, and clinical applications. Combining AgNPs with radiotherapy presents a promising road, tending to limits in ordinary disease therapies and underlining their multifunctionality in forming the eventual fate of malignant growth therapeutics.

In Mexico, 1.9% of child mortality among children aged 3 to 15 years is attributed to waterborne gastrointestinal diseases (WGD). This study employs a generalized bivariate logit econometric model to simulate the relationships between mortality risks and seven explanatory variables. Based on the model results and sensitivity analysis of the estimated parameters, a set of policies was designed to reduce the likelihood of child mortality. The proposed strategy involves im-plementing the following public policies, primarily targeting communities with extreme and high marginalization: increasing access to drinking water, improving housing conditions, expanding parental basic education coverage, and providing nutrition and healthcare to children from an early age. The findings reveal that children who speak an indigenous language face a mortality risk from WGD that is three times higher than those who do not, while children who receive medical services have a 29% lower risk of mortality compared to those who do not have access to them. It is recommended to offer free medical care in indigenous languages within high-marginalization communities. The combined impact of these policies is expected to signifi-cantly reduce child mortality due to WGD.

In the present work consideration is given to the primordial black holes ({\bf pbhs}) in the Schwarzschild-de Sitter Metric with small mass (ultralight) in the preinflationary epoch. Within the scope of natural assumptions, it has been shown that the quantum-gravitational corrections ({\bf qgcs}) to the characteristics of such black holes can contribute to all the cosmological parameters, shifting them compared with the semiclassical consideration. These contributions are determined by a series expansion in terms of a small parameter dependent on the hole mass (radius). For this pattern different cases have been considered (stationary, black hole evaporation...). It has been demonstrated that involvement of ({\bf qgcs}) leads to a higher probability for the occurrence of such {\bf pbhs}. Besides, high-energy deformations of Friedmann Equations created on the basis of these corrections have been derived for different patterns. In the last section of this work it is introduced a study into the contributions generated by the above-mentioned {\bf qgcs} in inflationary cosmological perturbations. Besides, it has been shown that non-Gaussianity of these perturbations is higher as compared to the semi-classical pattern.

This research investigates the economic resilience of small and medium enterprises (SMEs) during financial crises, focusing on the factors that enable these businesses to survive and recover from economic downturns. Employing a qualitative methodology, the study examines various aspects of SME operations, including financial management, innovation, leadership, government support, geographical location, social capital, and contingency planning. The findings reveal that effective financial management—encompassing cash flow management, debt restructuring, and diversified funding sources—is crucial for maintaining stability during financial crises. Innovation and adaptability also play significant roles, with SMEs that embraced new technologies and market strategies demonstrating greater resilience. Leadership emerged as a critical factor, with leaders who communicated effectively and managed stress contributing to their organization's ability to navigate crises. Government support provided essential relief, although its impact varied depending on accessibility and timeliness. Geographical location influenced resilience, with urban SMEs having better access to resources, while rural SMEs benefited from strong community networks. Social capital, including relationships with suppliers and customers, was vital for providing stability and support. Contingency planning and risk management were identified as essential for preparing for and responding to disruptions. The research highlights that resilience is a multifaceted attribute resulting from the interplay of financial prudence, adaptability, effective leadership, supportive policies, geographic factors, and strong social networks. These insights offer valuable lessons for SME owners, policymakers, and support organizations, emphasizing the need for a comprehensive approach to building and sustaining resilience in the face of economic challenges.

The production and consumption of plastic bottled water have several negative environmental impacts worldwide. The EU countries have adopted strategies and programs to reduce single-use plastics, including PET bottles. A key aspect of designing effective policies is understanding consumer behaviour. To identify barriers and motivations for drinking tap and bottled water, we conducted a nationally representative questionnaire survey among 3,411 respondents in the Czech Republic in 2022. The results reveal that people aged 18-34 appear to be moderate consumers of bottled water and very frequent consumers of tap water. Bottled water consumption tends to be less frequent among people with higher education, while tap water consumption is less frequent among people with lower incomes. The most important factors that explain the frequency of drinking bottled and tap water are taste perception, health concerns and habit. Health concerns about tap water and the unpleasant taste of tap water influence higher consumption of bottled water and vice versa. People with a strong habit of drinking tap water are less likely to consume bottled water. The constructs from the Theory of Planned Behaviour were statistically significant. The results can guide decision-makers in promoting tap water to consumers. To encourage tap water drinking, we suggest measures to increase the availability of tap water in public places in conjunction with campaigns targeting taste and health perception and the habit of drinking tap water.

The study examines the effects of transitioning to hydrogen production in the National Capital Region (NCR) and Palawan Province, Philippines, focusing on technology, environment, and stakeholder impact. The research, conducted through a July 2022 survey, aimed to assess public awareness, knowledge, risk perception, and acceptance of hydrogen and its environmentally friendly variant, green hydrogen, infrastructure. Disparities were found between urban NCR and rural Palawan, with lower awareness in Palawan. Safety concerns were highlighted, with NCR respondents generally considering hydrogen production safe, while Palawan respondents had mixed feelings, particularly regarding nuclear-based hydrogen generation. The report emphasizes the potential ecological advantages of hydrogen technology but highlights potential issues concerning water usage and land impacts. It suggests targeted public awareness campaigns, robust safety assurance programs, regional pilot projects, and integrated environmental plans to facilitate the seamless integration of hydrogen technology into the Philippines' energy portfolio. This collective effort aims to help the country meet climate action obligations, foster sustainable development, and enhance energy resilience.

This article addresses the complex challenge of simultaneously enhancing contrast and detail in an image, where improving one property often compromises the other. This trade-off is tackled using a multi-objective optimization approach. Specifically, the proposal's model integrates the sigmoid transformation function and unsharp masking highboost filtering with the NSGA-II algorithm. Additionally, a posterior preference articulation is introduced to select three key solutions from the Pareto front: the maximum contrast solution, the maximum detail solution, and the knee point solution. The proposed technique is evaluated on a range of image types, including medical and natural scenes. The final solutions demonstrated significant superiority in terms of contrast and detail compared to the original images. The three selected solutions, although all are optimal, captured distinct characteristics within the images, offering different solutions according to field preferences. This highlights the method's effectiveness across different types and enhancement requirements and emphasizes the importance of the proposed preferences in different contexts.

Analytical methodologies were developed based on the near infrared spectroscopy (NIR) associated with chemometric tools to predict the content of macronutrients (total carbohydrate, total protein, and total lipids) in medical food. We prepared the samples employed in this study according to a mixture design with percentual variations from 25% to +25% of macronutrients. Partial least squares (PLS) regression models were built with 70 and 30 samples as calibration and validation sets, respectively. We chose the best processing techniques and results based on the parameters of the ASTM 1655 standard. The models were used to determine these macronutrients in marketed enteral nutrition products, of three different batches, that have quality certification. The average errors in the determination of total carbohydrate, total protein, and total lipids were 1.8, 4.3, and 2.3 %, respectively. The percentages of errors found in this study suggest that the developed model may be useful for the quality control laboratory routine.

This study addresses the global issue of recycling used vehicle rubbish tires, typically burned out or trimmed to be reused in playground floors or road banks. In this study we explore a novel environmentally responsive approach or decomposing and recovering the carbon black particles contained in tires (25-30 wt.%) by vacuum pyrolysis. Given that carbon black is well known for its UV protection in plastics, the objective of this research is to provide an ecological alternative to commercial carbon black of fossil origin by recycling the carbon black (rCB) from used tires. In our research, we create a composite material using rCB and high density polyethylene (HDPE). In this article we present the environmental aging studies carried out to this composite material. The topographic evolution of the samples with aging and the oxidation kinetic of the surface and through the thickness were studied. A Beer-Lambert law is used to relate the oxidative index to the characteristic depth of the samples. This work helps to demonstrate the feasibility of using recycled carbon black particles from waste tires as a filler for UV protection.

Molecular scattering (Rayleigh scattering) has been extensively utilized from the ground with lidars and from space to observe the limb, thereby deriving vertical temperature profiles between 30 and 80 km. In this study, we explore how temperature can be measured using the new Ozone Mapping and Profiler Suite (OMPS) sensor, aboard the Suomi NPP and NOAA-21 satellites. OMPS comprises three instruments primarily tasked with studying the composition of the stratosphere. Among them, the Limb Profiler (LP) measures the radiance of the middle atmosphere's limb (stratosphere and mesosphere, 12 to 90 km altitude) across wavelengths from 290 to 1020 nm. This new dataset has been leveraged to derive temperature profiles with a vertical resolution of 1 km. To validate the method, the OMPS-derived temperature profiles were compared with data from four ground-based lidars, the ERA5 and MSIS models, and MLS at the lidar locations. The results indicate that OMPS and the lidars agree within a margin of approximately 5 K from 30 to 80 km. Comparisons with the models also show similar results, except for ERA5 beyond 50 km. Comparisons with MLS exhibit slightly higher deviations than those with the lidars. We investigated various bias sources, such as different attenuation sources that can generate errors up to 120 K in the UV range, while instrumental errors are around 0.8 K, and noise issues can cause errors up to 150 K in the visible range for OMPS. This study also highlighted the interest in developing a new miniaturized instrument that could meet real-time observation of atmospheric vertical temperature profiles using a constellation of CubeSats.

This paper investigates location-based adaptive beamforming techniques, including Maximum Ratio Transmission (MRT) and Zero Forcing (ZF), in complex wireless environments. The study leverages a digital twin simulation of the University of Glasgow campus to evaluate the proposed schemes under realistic conditions, such as user mobility and multipath propagation. The results demonstrate significant performance improvements with the location-based beam steering approaches. In the open space scenario, the location-based schemes achieved up to 40\% higher Signal-to-Interference-plus-Noise Ratio (SINR) and 30\% higher received power, along with reduced interference. The gains were even more pronounced in the digital twin environment, with up to 50\% improvement in SINR and 40\% increase in received power. Furthermore, the study evaluates the energy efficiency of the location-based adaptive beamforming techniques, showing up to 20\% reduction in energy consumption compared to conventional fixed-beam approaches.

Background: The relationship between the intake of artificial sweetener (AS) and adverse pregnancy outcomes is under-researched, and existing studies yield inconsistent conclusions. Methods/Objectives: A mendelian randomization (MR) approach was employed to investigate the causal relationship between the intake of AS and adverse pregnancy outcomes. Instrumental variables related to the exposure phenotype were selected for analysis. The analysis was conducted using genome-wide association studies summary data from public datasets. Inverse variance weighted, MR-Egger, weighted median, simple mode, and weighted mode were used to evaluate the causal relationship between exposure and outcomes. Sensitivity analysis and multivariable mendelian randomization enrolling body mass index, type 2 diabetes mellitus and fasting glucose was employed to further validate the consistency and robustness of results. Results: The intake of AS added to tea was associated with an increased risk of ectopic pregnancy [OR=1.821 (1.118 - 2.967), p=0.016] in univariable MR. The intake of AS added to cereal was linked to a reduced risk of ectopic pregnancy [OR=0.361 (0.145 - 0.895), p=0.028] and premature rupture of membranes [OR=0.116 (0.019 - 0.704), p=0.019], while the intake of artificial sweetener added to coffee was associated with an increased risk of placenta previa [OR=1.617 (1.042 - 2.510), p=0.032] adjusting for body mass index and type 2 diabetes mellitus in multivariable MR. No causal relationship was identified between the intake of artificial sweetener and the other adverse pregnancy outcomes. Conclusions: The consumption of artificial sweeteners during pregnancy warrants careful consideration.

Recent advancements in molecular diagnostics have highlighted the potential of circulating cell-free DNA (ccfDNA) as a biomarker in various diseases. This study aims to explore the role of ccfDNA in the early diagnosis, prognosis, and therapeutic monitoring of diseases, with a focus on oncology. We conducted a comprehensive review and analysis of current literature, combined with experimental approaches involving advanced genomic sequencing and bioinformatics techniques. Our research systematically characterizes the molecular composition of ccfDNA, including its origins, size distribution, and methylation patterns, across different disease states. Furthermore, we investigate the dynamics of ccfDNA release and clearance in the bloodstream, elucidating mechanisms that could influence its diagnostic accuracy. In the context of cancer, we analyze the correlation between tumor-derived ccfDNA and disease progression, response to therapy, and overall survival. Our findings indicate that ccfDNA holds promise as a non-invasive biomarker for early disease detection, real-time monitoring of treatment efficacy, and potentially, predicting relapse. This study contributes to the growing field of liquid biopsies and opens new avenues for personalized medicine, emphasizing the need for standardized protocols in ccfDNA analysis to maximize its clinical utility. The implications of our findings extend beyond oncology, suggesting potential applications of ccfDNA in other pathological conditions.

Information and Communication Technology (ICT) such as smartphones has been attracting attention to prevent elderly people from becoming isolated. For this reason, recent research has proposed training methods for acquiring smartphone functions. However, since the types of smartphone functions required vary from person to person, a one-size-fits-all method will not attract everyone's interest and the results will be limited. On the other hand, with a view to social implementation, it is necessary to clarify a method that is sufficiently effective in terms of cost and time. Previous research suggests that self-efficacy and social capital are the keys to acquiring smartphone skills among elderly people. Therefore, in this review, while looking back at previous research, we propose a study to demonstrate that by providing careful individual instruction by an experienced instructor to elderly people with little experience in smartphones, and then having them take turns teaching other participants after the instruction, their self-efficacy and social capital can be increased, and a positive spiral effect can be achieved to maximize the improvement of smartphone skills widely.

Background: Ovarian mesonephric-like adenocarcinoma (MLA) is a newly described histological type known for its aggressive behavior. This study aims to determine the frequency of ovarian MLA, review existing literature, and elucidate its clinicopathological characteristics, including potential therapeutic targets. Methods: We retrospectively reviewed the pathological diagnoses of 516 primary ovarian cancer surgical cases at our institution from 2010 to 2023. MLAs exhibiting typical morphological and immunohistochemical features were included. The frequency and clinicopathological characteristics of these cases were summarized. Additionally, we conducted a literature search using PubMed to collect and summarize previously reported cases of ovarian MLA. Results: Among the 516 primary ovarian cancer cases, we identified 3 cases (0.6%) of MLA. The patients were 52-76 years old, and the initial FIGO stages were IC1 (2 cases) and IIIB (1 case). All cases exhibited HRP, pMMR, PD-L1 negativity (CPS<1), and HER2-low expression. Two cases experienced metastatic recurrence. A literature review identified 97 cases of MLA. MLA frequently exhibited KRAS mutations (90%, 38/42), with a recurrence rate of 39% (26/67). Conclusion: MLA accounted for 0.6% of malignant ovarian tumors at our institution, all of which were advanced or recurrent cases. These cases showed HRP, pMMR, and PD-L1 negativity, indicating a lack of current therapeutic targets. Literature also reports a high incidence of advanced and recurrent cases, highlighting the need for accurate diagnosis and the development of new treatments. The frequent KRAS mutations suggest a potential therapeutic target for recurrent or metastatic MLA.

Recommender systems in education are becoming more widespread, typically focusing on recommending courses or study materials. This study proposes a machine learning approach to recommend a university degree based on high school and university standardised test results, incorporating students' socioeconomic information as input variables. The objective is to develop a tool for students’ decision-making, supporting the sustainable development goal of Quality Education by providing a data schema to maximise the likelihood of a successful match between the student's profile and the academic program. With its focus on equity in education, this study provides a data-driven approach to assist students in selecting suitable university degrees, aiming to improve educational outcomes and inspire a more equitable education system.

This review provides a comprehensive analysis of the pelvic plexus and its regulation across various mammalian species, including rats, cats, dogs, and pigs. The pelvic and hypogastric nerves play crucial roles in regulating pelvic functions such as micturition, defecation, and erection. The anatomical organization of these nerves varies, forming either well-defined ganglia or complex plexuses. Despite these variations, the neurons within these structures are consistently regulated by key neurotransmitters—norepinephrine and acetylcholine. These neurons also possess receptors for testosterone and prolactin, particularly in rats, indicating the significant role of these hormones in neuronal function and development. Moreover, neuropeptides such as vasoactive intestinal peptide (VIP), substance P, neuropeptide Y (NPY), somatostatin (SOM), galanin (GAL), and calcitonin gene-related peptide (CGRP) are co-released with neurotransmitters to modulate pelvic functions. This review highlights the complex interplay between neurotransmitters, neuropeptides, and hormones in regulating pelvic physiology and emphasizes the importance of hormonal regulation in maintaining the functionality and health of the pelvic plexus across different species.

This literature review presents a comparative analysis of cranial morphology in Allosaurus, focusing on its implications for feeding mechanics. Allosaurus, a large theropod dinosaur from the Late Jurassic period, exhibits distinct cranial features that are pivotal for understanding its predatory behaviors and ecological role. By examining fossilized skulls and employing advanced imaging techniques, this research identifies key morphological traits such as tooth structure, jaw articulation, and cranial robusticity. The analysis reveals that Allosaurus had a specialized cranial architecture that supported a powerful bite force and efficient processing of prey. Comparative metrics are applied to assess similarities and differences with other theropods, such as Tyrannosaurus rex, to elucidate variations in feeding strategies among theropods. The findings suggest that Allosaurus possessed adaptations that allowed it to exploit a diverse range of prey, indicating a versatile feeding strategy. These results enhance our understanding of theropod evolution and ecological interactions, providing insights into the functional significance of cranial morphology in dinosaurian predation. This review underscores the importance of cranial features in reconstructing the behavior and diet of extinct species and contributes to the broader field of vertebrate functional morphology.

This study presents a novel approach combining computer vision with topology optimization for additive manufacturing. Using specialized photogrammetry software, we capture high-resolution images of the design domain to create precise 3D models through detailed scanning. These models are converted into STL file format and remeshed using an adaptive algorithm within COMSOL 5.3 Multiphysics, managed by a custom MATLAB 2023. This integration allows optimal mesh resolution and accuracy in analyses. We applied this method to design a concrete pillar for 3D printing, aiming for a 75% volume reduction to enhance material efficiency and stability, crucial for extraterrestrial environments. The design, scanned with a 360-degree camera array, informed the MATLAB-based topology optimization process. Combining MATLAB’s optimization algorithms with COMSOL’s meshing and finite element solver solver, we explored material-efficient configurations. The results show significant volume reduction, particularly in the central design area, optimizing material use while maintaining structural stability. The optimization algorithm demonstrated rapid, stable convergence to near-optimal solutions within about 20 iterations, highlighting the method’s efficiency and robustness in complex design optimization.

This study explores the relationship between positive and negative habits and their rewards over time. Using a bibliographic and documentary approach, we analyze how the temporality of rewards influences the formation and maintenance of habits. We developed the "habit chart", which shows that positive habits, such as reading, healthy eating, physical exercise, meditation and personal relationships, offer incremental and lasting rewards. In contrast, negative habits such as sugar consumption, excessive use of social media and a sedentary lifestyle provide immediate gratification followed by a sharp decline. The analysis was based on studies such as those by Warburton et al. (2006), Lally et al. (2010), and Hu (2003) for positive habits, and Malik et al. (2010) and Kross et al. (2013) for negative habits. We conclude that practical strategies, such as financial incentives and creating enabling environments, can make long-term rewards more tangible, promoting the adoption of healthy behaviors. This study reinforces the importance of the temporality of rewards in habit formation and suggests evidence-based interventions to improve quality of life and general well-being.

While in the past, especially numerical approaches such as regression models and p-values were utilized to investigate whether the functional form between age and happiness is linear or U-shaped in a given country, recent advances have shown that such approaches can be misleading, and graphical analyses should amend the analyses. However, these have the downside that they rely to some extent on subjective interpretations and are hardly quantifiable. If applied carelessly, they can be misleading as well. We suggest two easily computed statistics to combine graphical and numerical approaches. We demonstrate their usage with ESS data (N > 440,000) and show how they enable a more nuanced investigation of functional forms. Furthermore, we discuss how statistical uncertainty can be handled in the age-happiness debate and emphasize practical significance, which needs to be kept in mind.

The interplay between the human gut microbiota and the peripheral immune system significantly affects the efficacy of immune checkpoint inhibitors (ICIs) in cancer therapy. Despite this profound connection, the practical implications of microbiota dysbiosis in cancer immunotherapy ICIs & and CAR-T remain intricate and poorly understood in practical terms. This paper offers a comprehensive review of current literature on the role of gut microbiota dysbiosis in tumor development, and emerging microbiota modulation interventions, with a focus on fecal microbiota transplantation (FMT). FMT is a cost-effective therapeutic avenue, particularly in addressing diseases rooted in antibiotic-resistant bacteria, such as Clostridium difficile. Here we provided insights into the applications of FMT and other gut microbiota therapies in the landscape of cancer treatment, emphasizing their influence on the tumor microenvironment and the efficacy of ICIs with suggestions for future research directions and their implications for clinical practice.

The review considers physics and some applications of a new approach that promises to be a breakthrough in efficiency and instrumentation of various methods for nondestructive testing (NDT) of composites. The approach is based on frequency-selective properties of defects which fall into resonance (Local Defect Resonance (LDR)) at certain frequencies. An activation of LDR enables to route the input acoustic energy directly to the defect that dramatically increases its vibration amplitude and thus facilitates defect-selective imaging, rises efficiencies of various derivative acoustic NDT methods (nonlinear, acousto-thermal) and enables to develop some new methodologies, like Resonant Air-Coupled Emission (RACE), machine and remote imaging.

Access to travel information is crucial for blind and partially sighted (BPS) people in travel planning. However, finding relevant information can be complex and challenging due to the unavailability of appropriate information and accessible information sources. To investigate this issue, a mixed-method study consisting of semi-structured interviews and co-design workshops was conducted to identify the needs of BPS people and the barriers BPS people experience due to the lack of access to such information. The findings provide insights into the information-seeking process and highlight the role of experience-sharing in cultivating a sense of agency, contribution, and interdependence. We contribute an accessible tourism ecosystem based on our findings to motivate research on technologies to support inclusive leisure travel for BPS people and to inform the design of inclusive tourism services.

This research explores the impact of 5G technology on supply chain efficiency and marketing innovations, highlighting its transformative potential and associated challenges. The study delves into how 5G enhances real-time tracking and monitoring within supply chains, significantly improving inventory management, reducing stockouts and overstocking, and ensuring optimal conditions during product transit. The advanced communication capabilities of 5G enable seamless collaboration among supply chain partners, facilitating quicker decision-making and improved risk management. Additionally, the integration of 5G technology with data analytics has optimized processes, improved forecasting accuracy, and led to cost reductions, showcasing the value of data-driven decision-making. In the marketing domain, 5G technology has driven the development of immersive augmented reality (AR) and virtual reality (VR) applications, enhancing customer engagement through interactive and experiential content. Personalized and targeted marketing strategies, supported by real-time data and deeper customer insights, have led to higher customer satisfaction and increased sales. The proliferation of connected devices, such as smart speakers and wearables, has further enabled personalized interactions and convenience, fostering brand loyalty and driving growth. Despite these advancements, the implementation of 5G presents challenges, including significant infrastructure investment, cybersecurity risks, and privacy concerns. Companies must navigate these issues by investing in robust security measures and developing comprehensive policies. The research concludes with an optimistic outlook on the future impact of 5G technology, emphasizing its potential to drive innovation, enhance operational efficiency, and transform customer experiences, ultimately shaping the future of business operations.

The modeling of psychiatric disorders poses significant challenges due to the complex nature of these conditions, which encompass a range of neuropsychiatric diseases such as autism spectrum disorder (ASD), schizophrenia (SCZ), bipolar disorder (BD), post-traumatic stress disorder (PTSD), anxiety and depression. The rising global prevalence of mental disorders and the urgency for more effective treatments have propelled the development of innovative in vitro models. This review presents a thorough examination of two-dimensional (2D) versus three-dimensional (3D) induced pluripotent stem cell (iPSC) models of neuropsychiatric diseases, offering insights into their respective capacities to mimic neurodevelopment and cellular phenotypes observed in these conditions. Our comparative analysis reveals that while traditional 2D cultures have been instrumental in elucidating disease pathways and high-throughput drug screening, they fall short in replicating the intricate cellular architecture and environment of the human brain. On the other hand, 3D organoid models, including brain organoids, better recapitulate the spatial organization, cell-type diversity, and functional connectivity of brain tissue, offering a more physiologically relevant context for studying disease mechanisms and testing therapeutic interventions. We assess the progress in modeling ASD, Schizophrenia, Bipolar Disorder, PTSD, and depression, highlighting the advanced understanding of disease etiology and potential treatment avenues offered by 3D iPSC technologies. Challenges remain, including the scalability, reproducibility, and maturation of organoids, but the potential for personalized medicine and the elucidation of disease ontogeny is unparalleled. The review concludes with a perspective on the future directions of psychiatric disease modeling, emphasizing the integration of 3D iPSC models with high-throughput technologies and computational approaches to enhance our understanding and treatment of these debilitating conditions.

This study examines how profitability moderates capital structure and dividend policy in selected Sub-Saharan African manufacturing enterprises. The study analyses 2012–2022 data from 131 listed firms in six countries using a quantitative and ex post facto research design. Debt-to-equity, asset structure, debt-to-asset, and return on assets are variable of interest. The debt-to-equity ratio negatively affects dividend policy, but asset structure positively affects it, according to pooled OLS and moderated regression models. Profitability considerably moderates the association between debt-to-equity and dividend policy, while debt-to-asset ratio has a positive but negligible influence. The findings emphasize the role of profitability in financial decision-making and suggest that firms should balance their capital structure and profitability to optimize dividend policies. The study fills gaps in emerging economy literature and offers managers and policymakers practical advice to improve financial performance and shareholder value in Sub-Saharan African-listed manufacturing enterprises.

Quantum dots are semiconductor Nano-structures with Optical or photoelectric properties and Quantum mechanical properties such as Quantum confinement due to the Nano-scale size of the molecular structure where Quantum mechanical effects are dominant. Because of its Optical properties Quantum dots have found applications in the development of future Solar cells and Photovoltaic detectors. The developments of these Solar cells and detectors are made possible through the use of Spray-coated layering of colloidal solutions containing the quantum dots forming what can be regard as thin quantum dot films, of which the compound thickness of all the layers is in nanometers. These quantum dots are capable of detecting a very wide range of electromagnetic frequencies and even single photon sources with very fast response time overall promising enhanced sensitivity. The features of quantum dots films gives them desirable advantages for detecting minute phase changes in Laser interferometric observatories such as LIGO which was designed and developed to detect gravitational waves coming from distant astrophysical sources. However the detection of low frequency gravitational waves with longer wavelength and those of much lower amplitudes have been rather elusive even with the high sensitivity of LIGO, as its usual Photodetector may not be sensitive to the interferometric phase changes due to gravitational waves of longer wavelengths and low amplitudes. As a potential means of improving phase change detection in LIGO, the use of quantum dots as photodetectors is conceptually proposed exploiting the fast reaction times and its sensitivity to wider range of frequencies. Whether it detects the interferometric phase changes induced by subtle amplitude or low frequency gravitational waves or not, it still promises an improvement to the detection capabilities of LIGO. Other interferometric gravitational experiments can also be performed, such as interferometric detection of Phase difference due to gravitational time dilation, using two laboratory interferometers one of which is positioned at sea level and the other at much higher altitude leading to a difference in gravitational potential. Together with the use of Quantum dot films as Photo-detectors one can detect the small interferometric phase difference between both interferometers due to Gravitational time dilation at different altitudes.

The present paper discusses the importance of economic externality and the economic phenomena involved. Section 1 draws on the framework of weak force theory in physics, we propose a new approach of dynamic analysis for economic externality. From a mesoscopic (intermediate scale) perspective, no distinction is made between the macro-demand and individual micro-buyers nor between the macro-supply and individual micro-sellers. Section 2 applies the isospin modeling method to construct two economic isospin models. One is between the achieving impulse and the fear-of-failure impulse, under the effect of economic externality. The other is between the market behavior and market residues, namely, market aftertaste. Section 3 discusses the microstructural properties of the market, including Dirac's equation and the market, Dirac -matrix notation, and the chirality and spin structure of market components. Section 4 discusses the helical structural characteristics of supply chains, including CPT theorems, Penrose zigzag helical processes, and several modeling directions of economic externality and buyer-seller interactions (weak interactions). Section 5 discusses the economic externality and the electroweak model, including extensive externality, externality mediations with Weinberg rotations, and economic analysis using lepton weak currents, including the analysis of lepton neutral weak currents. Finally, Section 6 provides a general discussion on the standard model of economic dynamics.

Related studies have shown that mitophagy plays a role in the progression of Atherosclerosis (AS). Utilizing GSE43292, GSE100927, and GSE159677 datasets, WGCNA and differential expression analysis identified mitophagy-related module genes and DEGs1, while single-cell analysis screened DEGs2. MR analysis revealed causal AS-linked genes in DEGs1. LASSO and expression validation identified biomarkers, evaluated via ROC curves. Further analyses included GSEA, immune infiltration, ANN, regulatory networks, and drug prediction. Four candidate genes were identified through WGCNA, differential expression analysis and MR analysis. The LASSO analysis identified CD36, IL1RN, and MME as biomarkers, while the MR analysis showed that IL1RN and MME are protective factors and CD36 is a risk factor in AS. The ROC curves disclosed that the ANN model had greater diagnostic power for AS than any other biomarker. The GSEA revealed that the biomarkers were enriched in lysosomes and hematopoietic cell lineage and were significantly positively correlated with monocytes. The regulatory network construct showed that small nucleolar RNA host gene 16 regulates MME via the micro RNAs. This study identified CD36, IL1RN, and MME as biomarkers of mitophagy in AS and indicated that they could be applied in the diagnosis and treatment of this condition.

This study investigated the effects of icon familiarity on visual search performance. An equal number of familiar and unfamiliar icons were selected as background icons. These background icons were randomly arranged, and a visual search experiment was conducted to locate the target icon. The results showed no significant difference in the time required to find the target icon, regardless of whether the target icon was familiar or unfamiliar. This finding contrasts with previous studies that have suggested a positive effect of target icon familiarity on visual search performance, indicating that the influence of target icon familiarity may not always be consistent and could depend on the characteristics of the background icons. However, when background icons were grouped based on their familiarity, the time taken to locate the target icon was significantly reduced compared to when the icons were randomly arranged. To understand the underlying mechanism of this effect, participants' gaze fixation times were observed. The results revealed that participants spent more time fixating on areas where the target icon was located, rather than on areas without the target icon, suggesting a more efficient visual search process. Moreover, the shorter the visual search time, the higher the proportion of time spent fixating on the area containing the target icon. Additionally, this study examined the contrast ratio between the color of the background space and the dominant color of the target icon, comparing it with the visual search time. The findings indicated that as the contrast ratio increased, the visual search time decreased.

The cavitating wake behind a wedge has been simulated employing both incompressible and compressible solvers with the purpose of comparing their performance. To develop the compressible solver, the ZGB cavitation model for incompressible flows has been modified to consider the compressibility effects. First, the equations of state for the vapor and liquid phases have been implemented. Then, a pressure limit and absorbing boundary conditions have been set to prevent a non-physical pressure field. To test the developed compressible solver, a series of verification tests have been conducted comprising a 1D sod cavitating tube and the cavitating vortex shedding behind a circular body at laminar flow conditions. After that, the incompressible and the validated compressible solvers have been applied to simulate the cavitating wake behind a wedge. The obtained results show similar predictions with the two solvers in terms of pressure, vortex shedding frequency and instantaneous and average vapor volume fraction profiles. Moreover, it is interesting to note that the components of the fluid force fluctuations on the body at the lowest frequencies, as predicted by both solvers, are indistinguishable. However, the higher frequency fluctuations of the forces tend to be better resolved and amplified when the compressibility is considered. Overall, both flow solvers have provided comparable cavitation phenomena, yielding results well aligned with experimental observations.

The search for natural product-based biopesticides from endophytic fungi is an effective tool to find new solutions. In this study, we studied a pre-selected fungal endophyte, isolate YCC4, from the paleoendemism Persea indica, along with compounds present in the extract and the identification of the insect antifeedant and nematicidal ones. The endophyte YCC4 was identified as Phyllosticta capitalensis by molecular analysis. The insect antifeedant activity was tested by choice bioassays against Spodoptera littoralis, Myzus persicae and Rhopalosiphum padi and the in vitro and in vivo mortality was tested against the root-knot nematode Meloiydogyne javanica. Since the extract resulted an effective insect antifeedant, a strong nematicidal and lacked phytotoxicity on tomato plants, a comprehensive phytochemical study was carried out. The chemical study resulted in the identification of two new metabolites, metguignardic acid (4) and (-)-epi-guignardone I (14), along the known dioxolanones guignardic acid (1), ethyl guignardate (3), guignardianones A (5), C (2), D (7) and E (6), phenguignardic acid methyl ester (8), and the meroterpenes guignardone A (9) and B (10) and guignarenone C (12), (-)-guignardone I (13) and the phyllomeroterpenoid B (15). Among these compounds, 1 and 4 were effective antifeedants against both insect species, while 2 was only active on the aphid M. persicae. The nematicidal compounds were 4, 7 and 8. This is the first report on the insect antifeedant or nematicidal effects of these dioxolanone-type compounds. Since the insect antifeedant and nematicidal activity of the P. capitalensis extract depends on the presence of dioxolanone components, future fermentation optimizations are needed to promote the biosynthesis of these compounds instead of the meroterpenes.

4D printing represents an innovative evolution of 3D printing technologies, incorporating time as a fourth dimension to create objects that can transform and adapt over time in response to environmental stimuli. This emerging field has shown significant advancements in materials science, design principles, and applications. Recent research focuses on developing responsive materials such as shape-memory polymers, hydrogels, and composites that can undergo controlled transformations in reaction to changes in temperature, humidity, or other external factors. The advancements in 4D printing have led to a wide array of applications, including self-healing structures, adaptive medical devices, and dynamic architectural components. This abstract examines the latest progress in 4D printing technologies, highlighting key advancements in material development and design strategies. It explores various case studies that demonstrate the practical implementation of 4D-printed objects across different sectors. Furthermore, the abstract discusses the challenges faced by researchers, including material limitations, scalability issues, and the need for standardized testing methods. The potential future directions of 4D printing are also considered, emphasizing the need for interdisciplinary collaboration to overcome current limitations and expand the range of applications. This research underscores the transformative potential of 4D printing in creating intelligent and adaptable systems, offering significant benefits across industries from healthcare to construction.

Gastric cancer (GC) is a major cause of cancer-related mortality worldwide, often associated with a bad prognosis because of its asymptomatic phenotype until advanced stages, highlighting the need for its prevention and early detection. GC development is preceded by gastric preneoplasia lesions (GPNL), namely atrophic gastritis (AG), intestinal metaplasia (IM), and dysplasia (DYS). GC is currently diagnosed by endoscopy, which is invasive and costly and has limited effectiveness for the detection of GPNL. Therefore, the discovery of non-invasive biomarkers in liquid biopsies, such as blood samples, to identify the presence of gastric preneoplasia and/or cancer lesions at an asymptomatic stage, is of paramount interest. This comprehensive review provides an overview of plasma/serum proteins recently identified and their diagnostic performance for the prediction of GPNL and early cancer lesions. Autoantibodies appear as promising biomarkers for AG, IM and early gastric cancer detection, along with inflammation and immunity-related proteins and antibodies against H. pylori virulence factors. There is a lack of specific protein biomarkers to detect DYS. Despite the need for further investigation and validation, some emerging candidates could pave the way to develop reliable, non-invasive diagnostic tests for the detection and prevention of GC.

Solar radiation corresponds to fundamental parameters for solar photovoltaic (PV) technology. Reliable solar radiation prediction became valuable to design solar PV systems, performance, operatively efficient planning, safety operation, grid dispatch and financial characteristics. However, high quality ground-based solar radiation measurements are scarce, especially for very short-term time horizon. Most of the existent studies trained the machine learning (ML) model used dataset with time horizon of 1-hour or day, very fewer studies have been reported using a dataset with a 1-mitute time horizon. In this study, a comprehensive evaluation of nine ensemble learning algorithms (ELA) is performed to estimate the solar radiation in Santo Domingo with a 1-minute time horizon dataset collected from a local weather station. The ensemble learning evaluated included seven homogeneous ensembles; Random Forest (RF), Extra Tree (ET), Adaptive Gradient Boosting (AGB), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), Light Gradient Boosting (LGBM), Histogram-based Gradient Boosting (HGB) and heterogeneous ensemble named as Voting and Stacking. RF, ET, GB, HGB were combined to develop Voting and Stacking and Linear Regression (LR) was adopted in the second layer of the Stacking. Five technical metrics, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Coefficient of Determination (R2) were used as criteria to determine the prediction quality of the developed ensemble algorithms. Comparison of the results indicates that the HGB algorithm offers superior prediction performance among the homogeneous ensemble learning and overall, the Stacking provides the best accuracy with metric values of MSE=3218.27, RMSE= 56.73, MAE=29.87, MAPE=10.60, R2=0.964.

The marathon sport events and those who participate in these have grown over the last years reflecting notably an augmentation of women and master runners participation. The aim of the present narrative review was to briefly present the results of studies on anthropometric, physiological and training characteristics, as well as predictors of performance, in marathon runners. It was observed that performance was better in runners with small body weight, body mass index, body fat percentage, and rate of endomorphy. With regards to physiology, an increased maximal oxygen uptake, anaerobic threshold, and improved running economy could result in faster race time. The training variables that could predict performance involved weekly training volume (distance) and intensity (running speed), as well as history of training (years). A combination of these three broad categories of characteristics may offer an approximate estimation of the race speed considering that other aspects (e.g., nutrition, biomechanic and motivation) influence race performance, too. In summary, the findings of the present study provided an overview of anthropometric, physiological and training characteristics associated with marathon race time; thus, an optimization of any of these characteristics would be expected to improve race time.

This paper addresses the study of synoptic-scale meteorological conditions that favor long-range pollen transport in southern South America combining airborne pollen counts, modeled three-dimensional backward trajectories, and synoptic and surface meteorological data. Alnus pollen transport trajectories indicate origins predominantly in Montane forests of the Yungas between 1500 and 2800 m altitude. The South American Low-Level Jet is the main meteorological feature that explains 64\% of the detected pollen arrival at the target site. Podocarpus and Nothofagus pollen instead are linked primarily to the widespread Subantartic forests in southern Patagonia. Their transport patterns are consistent with previous studies which show an association with synoptic patterns related to cold front passages carrying pollen in the free atmosphere (27% for Nothofagus and 25% for Podocarpus). These results show the significance of understanding long-distance pollen transport for disciplines such as climate change reconstruction and agriculture, emphasizing the need for further research to refine atmospheric circulation models and refine interpretations of past vegetation and climate dynamics.

In this paper, we introduce a comprehensive solution aimed at enhancing the understanding of three-dimensional atmospheric pollutant dispersion. This innovative solution develops a generalized model that extends previous research and is applicable to all parameterization schemes of the equation, including wind speed profiles and turbulent diffusion coefficients, while incorporating the dry deposition criterion. Our methodology involves subdividing the atmospheric boundary layer into distinct sublayers, which facilitates a detailed examination of pollutant dispersion dynamics. Extensive validation with data from the Hanford experiment has demonstrated the accuracy of this solution in simulating pollutant concentrations. The results exhibit a strong correlation between projected and observed concentrations, underscoring the statistical reliability of our approach. This validation situates the statistical indices of our solution within an acceptable range, confirming its accuracy in predicting atmospheric pollutant dispersion. These findings thus establish our solution as a valid and effective method for studying complex environmental phenomena.

Brazilian garimpeiros are a highly hard-to-reach and mobile population, with little access to basic hygiene and health services, and have been crossing the border to work irregularly in gold mines in French Guiana since the 90s. This study aimed to characterize this population and identify their main health problems. A cross-sectional study was carried out in the municipality of Oiapoque-AP, with two surveys: before (2018) and after (2019) the implementation of Malakit. Individuals were recruited from resting places and submitted to a questionnaire with demographic variables, history and knowledge of malaria, and health issues in the mines. Simultaneously, a clinical evaluation was performed. The garimpeiros were mainly men from Maranhão, Pará, and Amapá, with a low educational level and worked on average for 10 years in the mining sites. The study population mentioned numerous health problems: malaria, followed by leishmaniasis, flu, body aches, headaches, and digestive problems. Other diseases mentioned were skin diseases, bat bites, hepatitis, and HIV infection. This vulnerable population is constantly subjected to heavy routines and exposed to different diseases and infections that can spread across borders. Knowledge of this is essential for developing public health policies that can be integrated into specific epidemiological situations.

The human malaria parasite, Plasmodium falciparum, accounts for more than 500,000 deaths worldwide per year. From 2000 to 2019, the malaria global death toll fell from 864,000 to 576,000 deaths; however, progress on reducing this toll has since slowed, with the COVID-19 pandemic contributing to an increase in the mortality rate since 2020. The emergence of widespread resistance in P. falciparum to first-line treatment drugs has highlighted the need for new antimalarial drugs, and smarter approaches, including better combination therapies and mass drug administration. Nevertheless, recent successes of the first malaria vaccine (RTS,S/AS01) and the approval of a second (R21/MM), have renewed the promise of widely applicable vaccines in the future. Since 2015, the Eastern Mediterranean Region has experienced an overall increase in malaria cases and deaths. In the Arabian Peninsula, while most of the Gulf Cooperation Council (GCC) countries have been declared free of indigenous malaria (Bahrain, Kuwait, Qatar, and the United Arab Emirates), malaria is still endemic in two GCC countries (Saudi Arabia and Oman) and its neighbors (Yemen). Furthermore, a number of factors threaten malaria control in this region, at the level of the parasite (antimalarial drug resistance), the mosquito (the emergence of highly invasive species), the environment (climate change and increasing average temperatures), and society (imported malaria). In this review, we assess the current world-wide status of malaria in terms of public health, prevention and treatment, before providing an Arabian regional perspective, with a critical evaluation of the malaria situation in the GCC countries.

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.

Proactive deprescribing is the process of stopping a medicine before harm occurs. This study aimed to specify and validate with an international sample of healthcare professionals, a proactive deprescribing process of steps and constituent activities. We developed a proactive deprescribing process framework of steps which we populated with literature-derived activities required to be undertaken by healthcare professionals. We distributed a survey to healthcare professionals in-ternationally requesting for each activity; the frequency of its occurrence in practice and whether it was important. Extended response questions invited barriers and enablers to deprescribing. The 263 survey respondents were from 25 countries. A proactive deprescribing process was developed comprising four steps: (1) Identify a patient for potential stop of a medicine, (2) Evaluate a patient for potential stop of a medicine, (3) Stop a medicine(s), (4) After a medicine has been stopped, and 17 activities. All activities were considered important by ≥70% respondents. Nine activities re-quired healthcare professionals to undertake in direct partnership with the patient and/or care-giver, of which seven were only sometimes undertaken. Deprescribing interventions should focus on addressing the barriers and enablers of healthcare professionals undertaking the activities that require direct partnership with the patient and/or caregiver.

A comprehensive design framework is proposed for optimizing sparse MIMO (multiple-input, multiple-output) arrays to enhance multi-target detection. The framework emphasizes efficient utilization of antenna resources, including strategies for minimizing inter-element mutual coupling and exploring alternative grid-based sparse array (GSA) configurations by efficiently separating interacting elements. Alternative strategies are explored to enhance angular beamforming metrics, including beamwidth (BW), peak-to-sidelobe ratio (PSLR), and grating lobe limited field of view. Additionally, a set of performance metrics is introduced to evaluate virtual aperture effectiveness and beamwidth loss factors. The study employs the desirability function and explores optimization strategies for the partial sharing of antenna elements in multi-mode radar applications. A novel machine learning initialization approach is introduced for rapid convergence. Observations include the potential for peak-to-sidelobe ratio (PSLR) reduction in dense arrays and insights into GSA feasibility and performance compared to uniform arrays. The study validates the efficacy of the proposed framework through simulated and measured results. The study underscores the importance of effective sparse array processing in multi-target scenarios and highlights the advantages of the proposed design framework, offering clarity on sparse array efficiency and applicability in processing hardware providing clarity on its advantages over uniform and nonuniform arrays.

3.9 million individuals rely on kidney replacement therapy worldwide. They experience height-ened susceptibility to cardiovascular diseases and mortality, alongside an increased risk of in-fections and malignancies, with inflammation being key to explaining this intensified risk. This study utilized semi-targeted metabolomics to explore novel metabolic pathways related to in-flammation in this populations. We collected pre and post-session blood samples of patients who had already undergone one year of chronic hemodialysis. And used liquid chromatography and high resolution mass spectrometry to perform a metabolomic analysis. Afterwards, we employed both univariate (Mann-Whitney test) and multivariate (logistic regression with LASSO regulari-zation) to identify metabolites associated with inflammation. In the univariate analysis, in-dole-3-acetaldehyde, 2-ketobutyric acid, and urocanic acid showed statistically significant de-creases in median concentrations in the presence of inflammation. In the multivariate analysis, metabolites positively associated with inflammation include allantoin, taurodeoxycholic acid, norepinephrine, pyroglutamic acid, and L-hydroorotic acid. Conversely, metabolites showing negative associations with inflammation include benzoic acid, indole-3-acetaldehyde, methio-nine, citrulline, alphaketoglutarate, n-acetyl-ornithine and 3-4-dihydroxibenzeneacetic acid. Non inflamed patients exhibit preserved autophagy and reduced mitochondrial dysfunction. Under-standing inflammation in this group hinges on the metabolism of arginine and the urea cycle. Additionally, the microbiota, particularly uricase-producing bacteria and those metabolizing tryptophan, play critical roles.

This research explores the pivotal role of big data analytics in enhancing retail marketing and supply chain optimization. As the retail industry undergoes rapid digital transformation, big data analytics has emerged as a critical tool for gaining competitive advantage and improving operational efficiency. The study delves into the various applications of big data analytics, including customer segmentation, personalized marketing, demand forecasting, inventory management, dynamic pricing, and supply chain optimization. It highlights how retailers leverage data from diverse sources to gain insights into consumer behavior, market trends, and operational performance, leading to more informed decision-making. The findings underscore the significant benefits of big data analytics in enhancing customer experiences through targeted engagement and personalized service. Additionally, the research addresses the ethical considerations and challenges associated with data privacy, security, and potential biases in analytics models. It emphasizes the importance of maintaining high standards of data quality and ethical practices to ensure the responsible use of data. The conclusion reiterates the transformative impact of big data analytics in the retail sector, suggesting that while it offers substantial advantages, careful management of its challenges is crucial. The study provides valuable insights into how retailers can harness the power of big data analytics to drive growth, innovation, and long-term success in an increasingly competitive and data-driven market environment.

Bluetongue is not a contagious disease. It spreads very quickly and uncontrollably, considering that it is a vec-tor-borne disease transmitted by the genus Culicoides. Climate change has led to the fact that vectors and dis-eases have spread to the top of Northern Europe, causing great economic losses in livestock production. Even greater problem is controlling the disease, because numerous species of domestic and wild ruminants are sus-ceptible to bluetongue. The most effective tool against bluetongue disease is vaccination. There is no ideal vaccine against bluetongue virus (BTV) due to the serotype diversity of the strains. Thus, there is need for a vaccine for at least for 24 clinically significant serotypes. Sometimes it is difficult to obtain the desired amount of vaccine against a defined serotype on the market, and this led to the need for a new vaccine against blue-tongue disease. We tested an experimental inactivated novel vaccine against BTV4. Master seed BTV4 has been examined and characterized by sequencing. The novel BTV4 vaccine induce the onset of immunity at least at day 21 after the application of the first dose in more than 80% of vaccinated individuals, while specific antibodies against BTV are detected by the ELISA test for more than a year. Along with our laboratory results, the preliminary results of safety and efficacy trials are also presented. The side effects of novel vaccine are within the limits of permissible local reactions without generalized changes of health status, while the serology and challenge test leads to the conclusion that novel vaccine protects high percentage of vaccinated individuals against BTV4 or causes a significant reduction in the intensity and duration of the clinical signs in the vac-cinated sheep. Based on the trial results, new vaccine has given encouraging results in quality, safety and pre-liminary efficacy tests. Thus, we believe that a new vaccine against BTV is on the horizon. Keywords: bluetongue virus, inactivated vaccine, candidate, quality of vaccine, safety, efficacy We confirm that neither the manuscript nor any parts of its content are currently un-der consideration for publication with or published in another journal. All authors have approved the manuscript and agree with its submission to Vaccines.

Mycotoxins are toxic secondary metabolites secreted mainly by molds. The contamination of the raw materials used in the production of infant flours by these mycotoxins presents a risk to the health of infants and young children who are the main consumers of these flours. The objective of this study was to determine the prevalence of mycotoxins in raw materials and in infant flours from Burkina Faso. Analyses were carried out on 39 samples of raw materials and 26 samples of infant flour collected from artisanal and semi-industrial production units. The contents of AFB1, AFB2, AFG1, AFG2, FB1 and OTA were determined by LCMS/MS. The results indicated a variability in the contents of total aflatoxins (AFs), FB1 and OTA within the samples of raw materials. Also, 79.49% of the raw materials contaminated by total aflatoxins were above the maximum limit value and 61.54% of these raw materials had AFB1 levels above the recommended limit value. The highest levels of AFs were found in peanuts and maize. OTA was very present in soybean samples. The occurrence of mycotoxins was highly variable in infant flours within the same production unit (p < 0.001). and between different units (p < 0.001). Infant flours produced by artisanal production units had higher AFB1 contents than those produced by semi-industrial units. The need to control these mycotoxins is essential in order to considerably reduce their contamination and exposure in infants and young children and to preserve their health.

As environmental and health concerns increase, the trend of sustainable agriculture moves toward using biological agents. About 60% of all biological fungicides have Trichoderma species as the active ingredient, with T. harzianum as the most common species in these products. However, the name T. harzianum has often been incorrectly used in culture collections, databases, and the scientific literature due to the division of the Harzianum Complex Clade (HCC) into more than 95 cryptic species with only one being named T. harzianum. In this study, strains previously identified as T. harzianum in three surveys of Trichoderma species from soils in South and Central America were reanalyzed using phylogeny based on tef1α, rpb2, and ITS loci and combined with the corresponding species in two other studies, where the species were correctly identified. Based on the results of the five surveys, T. afroharzianum, T. lentiforme, T. endophyticum followed by T. azedevio and T. harzianum were found to be the dominant species of the HCC in South and Central America. This is the first report of identifying dominant Trichoderma species within the HCC in South and Central American soil based on multiple studies, which will be useful in selecting strains within the complex clade for formulation of biocontrol and biofertilizer products in the continent.

Temperature and pressure sensors currently encounter challenges such as slow response times, large sizes, and insufficient sensitivity. To address these issues, we developed tetraphenylethylene (TPE)-doped polyvinylidene fluoride (PVDF) nanofiber membranes using electrospinning, with process parameters optimized through a convolutional neural network (CNN). We systematically analyzed the effects of PVDF concentration, spinning voltage, tip-to-collector distance, and flow rate on fiber morphology and diameter. The CNN model achieved high predictive accuracy, resulting in uniform and smooth nanofibers under optimal conditions. Incorporating TPE enhanced the hydrophobicity and mechanical properties of the nanofibers. Additionally, the fluorescent properties of the TPE-doped nanofibers remained stable under UV exposure and exhibited significant linear responses to temperature and pressure variations. The nanofibers demonstrated a temperature sensitivity of -0.976 gray value/°C and pressure sensitivity with an increase in fluorescence intensity from 537 a.u. to 649 a.u. under 600 g pressure. These findings highlight the potential of TPE-doped PVDF nanofiber membranes for advanced temperature and pressure sensing applications.

Genetic factors influence medication response (pharmacogenetics), affecting the pharmacody-namics and pharmacokinetics of many medicaments used in clinical care. The ability of medica-tions to cross the blood-brain barrier (BBB) represents a critical putative factor in the effectiveness and tolerability of various central nervous system (CNS) medications at a pharmacokinetic (dosing) level. Pharmacogenetics has the potential to personalise medicine to a greater extent than has been possible, with the potential to help reduce heuristic delays to effective tolerable pharmacotherapy. Here, we critically examine and summarise the evidence, particularly for ABCB1 polymorphisms associated with various CNS medications. These transporters appear to have a role in BBB pharmacogenetics. We identify some of the most promising variants for clinical translation while spotlighting the complexities of the involved systems and limitations of the current empirical literature.

Arthrospira platensis, which is known as the most important food supplement of the future due to its high nutritional value, was the focus of this study. The main objective of this study was to develop a functional cracker enriched with proteins, essential amino acids, and polyunsaturated fatty acids with the incorporation of A. platensis. Therefore, this research aimed to improve the texture (including moisture and firmness) and sensory properties (such as color, taste, and smell) of crackers by adding A. platensis at three different levels (0%, 2.5%, and 5.0%). The sensory attributes of crackers were evaluated by twenty taste testers and thirty consumers, assessing color, odor, taste, crispness, and overall preference. The highest hardness value among the studied trials was observed in trial 14, which contained 5% A. platensis with 577 N. According to the results of the panel test, among the crackers with 5% A. platensis addition, trial 14 had the highest color value. To obtain the optimum formula, dough with 50 mg/g A. platensis, 625 mg/g flour, and 220 mg/g water was recommended. The addition of A. platensis has great potential to improve the nutritional, textural, and sensory properties of crackers.

Based on economic feasibility, renewable generators can use pumped hydro storage (PHS) to improve their profitability by performing energy arbitrage under real time pricing (RTP) schemes. In this paper, we present a new method to optimise the size and manage utility-scale wind-PV systems using PHS with energy arbitrage under RTP. The storage in the PHS is used to supply load consumption and/or energy arbitrage. Further, both load supply and power-generating systems are considered, and a genetic algorithm metaheuristic technique is used to perform the optimisation efficiently. Irradiance, wind speed, temperature, hourly electricity price, component characteristics, and financial data are used as data, and the system is simulated in 15 min time steps during the system lifetime for each combination of components and control variables. Uncertainty is considered for the meteorological data and electricity prices. The pump and turbine efficiencies and available head and penstock losses are considered as variables (not fixed values) to obtain accurate simulations. A sample application in Spain is demonstrated by performing a sensitivity analysis of different locations, electricity prices, and costs. The PHS is not worth considering with the present cost of components.

Understanding whether the long-term follow-up (LTFU) system is working for families is critical to measuring the success of newborn screening (NBS) and understanding why some families are lost to follow-up. Caregivers were recruited from six pediatric specialty care clinics. Data was gathered from caregivers via six focus groups (n=24) and open-ended responses on a survey (n=33). Caregiver participants represented a wide range of NBS-related conditions and children’s ages. While this is not the first study to gather caregivers’ input, it is unique in its breadth of perspectives (e.g., metabolic, endocrine, hemoglobinopathy, etc.) and its focus on LTFU. When asked about goals for their children, caregivers identified health-related goals (i.e., ability for children to care for themselves, not being hindered by diagnosis) and non-health related goals (i.e., defining themselves outside of disease, participating in sports, making friends). In qualifying the LTFU care they want and need for their child and the key factors that influence access and engagement, caregivers identified three themes: communication and relationships with providers; care team roles and factors; and care access and utilization factors. The themes identified are not disjoint; they are intertwined and collectively illustrate the lived experiences of families at the epicenter of LTFU care.

Depictions of robots as romantic partners for humans are frequent in popular culture. As robots are becoming part of human society, they gradually assume the role of partners for humans wherever necessary: as assistants, collaborators, or companions. Companion robots are sup-posed to provide social contact to those humans who could not have it otherwise. These com-panion robots are usually not designed to fulfill one of the most important human needs: the one for romantic and intimate contact. Human-robot intimacy remains a vastly unexplored ter-ritory. In this article, we review the state-of-the-art research in intimate robotics. We discuss major issues limiting acceptance of robots as intimate partners, public perception of robots in in-timate roles, and the possible influence of cross-cultural differences in these domains. We also discuss possible negative effects human-robot intimacy may have on human-human contact. Most importantly, we propose a new term “Intimate companion robots” to reduce the negative connotations of other terms used so far and improve the social perception of research in the domain. With this article, we provide an outlook on prospects for the development of intimate companion robots, considering the specific context of their use.

Complicated grief (CG) predicts decreased mental health over time. Furthermore, feeling connected to nature (CN) is positively associated with beneficial mental health outcomes, such as psychological wellbeing and perceived psychological resilience. Thus, we hypothesized that CN moderates the association between general grief or CG and negative mental health for bereaved people. Further, we hypothesized that one’s physical exposure to nature—that is, estimated time spent in nature and greenness (i.e., vegetation) surrounding one’s residential area—might moderate the association between general grief or CG and negative mental health for bereaved people. To test these hypotheses, we sampled 153 participants who experienced the death of a close other by COVID-19 infection. Participants reported CG, general grief, anxiety symptoms, depression symptoms, CN, estimated time spent in nature, and residential area postal code via a single online survey. We estimated greenness surrounding participants’ residential areas using their self-reported 5-digit U.S. postal code. Cross-sectional analyses indicated that, as predicted, CN attenuated the association between CG and depression, but only trended toward moderating the association between CG and anxiety and did not moderate the associations between general grief and depression or anxiety. Other variables related to the experience of nature—the estimated time an individual spends in nature and the greenness surrounding one’s residential area—did not moderate the association between general grief or CG and depression or anxiety. We thus conclude that a sense of feeling connected to nature—not simply spending more time in nature or being surrounded by nature—may serve an important role in the mental health status of people experiencing complicated grief, perhaps because CN replenishes general belonging when someone significant has passed away.

Due to the adverse effect of prolonged drought stress on plants, accurate detection is essential for water use efficiency and maintaining productivity. Hyperspectral imaging is frequently used for non-invasive plant phenotyping, allowing for the long-term monitoring of crop health due to its sensitivity to subtle changes in leaf constituents. The broad spectrum of hyperspectral data enables the development of multiple vegetation indices (Vis) derived from the different spectral regions to estimate plant biophysical and biochemical traits. However, the known VIs often do not generalize well and perform poorly for multiple plant stresses. This study proposes new VIs combined with machine learning models to identify drought stress in wheat species under different nitrogen (N) levels. A wheat experiment was set up in the glasshouse with four treatments: well-watered high-N (WWHN), well-watered low-N (WWLN), drought-stress high N and drought-stress low-N. In addition to ensuring that plants were watered according to the experiment design, photosynthetic rate (Pn) and stomatal conductance (gs) were taken regularly, serving as the ground truth data for this study. Sensitive spectral features were selected using a custom-designed ensemble modelling technique. New drought VIs are proposed using different combinations of the selected features. Three classification models (support vector machines, random forest and deep neural network) were developed and trained using four sets of data: known VIs, proposed VIs, combined VIs (from the known and proposed VIs) and PCA-transformed features (over the whole spectral data). From the results, the proposed VIs outperformed the known VIs, yielding > 0.94 accuracies for all three models, and the performance improved when they were trained with the combined VIs. The combined VIs were used to train three regression models to predict the stomatal conductances and photosynthetic rates of plants. The random forest regression model performed best, suggesting that it could be used as a stand-alone tool to forecast gs and Pn and track drought stress in wheat.