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.

Prepregs, which are resin-impregnated and expensive composites, are mainly limited to high-end applications within the aeronautical, defence, automotive, and energy sectors. Prepreg technology is mainly protected by under trade secrets resulting in limited studies on recent advancements. A Scopus search revealed 861 articles and 16 reviews for the keywords ‘epoxy’ and ‘prepreg’. However, this was narrowed down to 123 articles and 4 reviews using keywords ‘formula development’ and ‘B-stage’. In this review, the control strategies of B-staging, resin tack, and viscosity and their measuring techniques are discussed. Based on resin impregnation techniques and B-stage control, two approaches are proposed to develop prepreg resin formulations: conventional resin impregnation and viscosity-controlled resin impregnation. The approaches would be extremely useful especially for advancing beyond the existing prepreg applications and developing smart materials and functional composites through advanced resin modification strategies.

Amyotrophic lateral sclerosis (ALS) is a devastating disease whose etiology is currently unknown. This paper presents a novel theory in which ALS is caused by an impairment of the alpha2de;ta subunit of voltage-gated calcium channels (VGCCs), most likely due to autoimmune targeting. I show how the impairment mechanistically induces chronic calcium, which in turn explains the major clinical, epidemiological, and pathological evidence known about the disease. The same theory explains the major variant of frontotemporal dementia (FTD) that greatly overlaps with ALS.

The kidneys contribute to the overall health of the organism by maintaining systemic homeostasis. This process involves the regulation of various biological mechanisms, in which the Krüppel-like factor (KLF) transcription factors are essential for regulating development, differentiation, proliferation, and cellular apoptosis. They also play a role in the metabolic regulation of essential nutrients, such as glucose and lipids. The dysregulation of these transcription factors is associated with the development of various pathologies, which can ultimately lead to renal fibrosis, a process that severely compromises the function of these organs. In this context, the present article provides a comprehensive review of the existing literature, offering an enriching analysis of the findings related to the role of KLFs in the renal field, while also highlighting their potential therapeutic role in the treatment of renal diseases.

With the gradual development and integration of artificial intelligence into various industries, there is also a great range of integration in the financial industry. Therefore, this article focuses on the trend prediction model and financial risk management problems of deep reinforcement learning (DRL), one of the largest branches of artificial intelligence, in the cryptocurrency market. In addition, in the experimental part of this paper, the artificial intelligence machine learning Long short-term memory network (LSTM) model is used to make effective time series prediction and analysis on the relevant data of the cryptocurrency market, so as to make a large-scale analysis to improve the accuracy of market trend prediction and the effectiveness of risk management. In addition, in this experiment, technology-related indicators, emotional states of financial market customers and other content related to large language models are combined. While optimizing investment strategy by using deep reinforcement learning algorithm, machine learning prediction model is also used to capture the time dependence of financial market. The experimental results also show that the predicted results are consistent with the actual value. Therefore, the model has high practical application value in predicting the time series price trend of cryptocurrency in the financial market and indicates that the integrated DRL model framework can further optimize and manage the price and trading strategy of the financial market. Future research should focus on improving the LSTM model and incorporating more features to improve prediction accuracy and adapt to market changes.

Quantum computing operates using qubits, the basic units of quantum information, which enable quantum algorithms to solve complex problems that classical computers cannot efficiently handle. This review provides a comparative analysis of several promising qubit technologies, including superconducting qubits, topological qubits, spin qubits, photonic qubits, ion trap qubits, and nitrogen-vacancy (NV) centers in diamonds. We discuss the operational principles, key researchers and institutions in each field, current advancements, challenges, and future directions for these technologies. This paper aims to present a thorough overview of the different approaches in qubit technologies and their potential impacts on quantum computing’s future landscape.

This systematic review assessed the prevalence of lower back pain (LBP) in equestrian athletes (EA), using PRISMA guidelines. Observational studies published between 2004 and 2024 in English, Portuguese, Spanish, and German were included. The review identified relevant studies through EBSCO, MEDLINE, and SCOPUS (last search 30 August 2024), yielding 14 studies with a total of 4,527 participants. PEO of included studies: population comprised EA, exposure was equestrian sports (ES), and the outcome was LBP. The risk of bias was evaluated using the Observation Study Quality Evaluation tool, and six studies were deemed high-quality. LBP prevalence in EA was higher than in the general and athlete population, with point prevalence ranging from 27.9% to 87.9%. Sport-specific factors, including workload and stable duties, were significant risk factors. Methodological inconsistencies, such as varying definitions of LBP and a lack of standardized exposure assessment, and overall low quality of studies, limited the comparability of findings. This review underscores the need for more high-quality research and tailored interventions addressing both riding and off-horse activities in EA. The review was registered in PROSPERO (ID: CRD42024568577), and the study was funded by the Comprehensive Health Research Centre, Évora University.

Until now, Z-form RNAs were believed to only adopt a left-handed double helix structure. In this study, we describe the first observation of a right-handed Z-form RNA in NMR solution formed by L-nucleic acid RNA and present the first resolution structure of the complex between a right-handed Z-form RNA and a curaxin ligand. This newly described topology is of great interest as it shows that Z-form RNAs can also adopt a right-handed double helix structure and interact with ligand molecules. The structural information provides a platform for the design of topology specific Z-form-targeting compounds and is valuable for the development of new potent anticancer drugs.

This paper examines the role of identity politics in the exclusion and statelessness of the Rohingya in Myanmar. Using an anthropological lens, the study explores how the Rohingya resist their marginalization through cultural practices and informal leadership within refugee camps in Bangladesh. The analysis, based on ethnographic fieldwork, draws on theories of homo sacer and cultural resistance to demonstrate how Myanmar’s exclusionary national identity has stripped the Rohingya of citizenship, exposing them to violence and displacement. Despite this, the Rohingya actively preserve their identity and assert political agency, offering a critical perspective on statelessness and resilience.

Hydrogen combustion in internal combustion engines is a strongly awaited technology on the way to carbon neutrality, but still with major challenges to takle. There are plenty of combustion anomalies like preignition, backfire, knock and lube oil ignition that come along with the technology, hence not yet controllable. To gain more knowledge about their origin, optical engines and CFD simulations are well established methods to investigate and develop knew combustion processes. Hydrogen’s wide flammability range and its short quenching distance bring new challenges. However, optical engines give the best insight into the real combustion behavior. Therefore, their understanding and proper modeling in the simulation is of great importance to calibrate the simulation models accurately. For optical engines there are diverging opinions on how to deal with their blow-by losses. Due to their lack of oil different sealing mechanisms exist and those seem to be crucial for the question on how or wether the blow-by has to be modelled. proposed a numerical multi-zone blow-by model whereas the opinion at is to neglect those losses.

Sustainable development and climate change are related challenges for humanity because a good quality of life in the present and the future for human, natural, and economic systems must be achieved through the capacity to face climate change. In this context, this research aimed to identify the climate change adaptation measures contribution to the sustainable development aspects: environmental, social, and economic, applied to a case study, "The Coastal Watershed Conservation Project in the Context of Change Climate (C6)" through the Sustainable Development Goals (SDG) evaluation framework. The primary data collection was carried out directly from the project reports and the reports of 29 interventions in the communities. The results indicate that the adaptation actions implemented through the C6 project did contribute to sustainable development, to a greater extent, in the social aspect, followed by environmental aspects and later by economic ones; therefore, it can be concluded that one way to achieve sustainable development can be through the implementation of adaptation actions to face climate change.

This study proposes a simplified and fully renewable energy system, composed of two intermittent energy sources (wind and solar) and a long-duration energy storage technology using pumped hydro storage. The impact of intermittency on the medium- and long-term design of the energy matrix is evaluated using the OSeMOSYS model. The findings indicate that omitting intermittency results in a significant underestimation of costs and an inability to manage the variability of renewable energies effectively. Incorporating intermittency, although increasing the installed capacity and the amount of wasted energy, enhances the system's reliability. The inclusion of energy storage demonstrates the need to redistribute installed capacity in favor of solar energy to meet higher daytime demand. The study concludes that considering intermittency and storage is crucial for improving the accuracy of energy models, reducing losses, and optimizing operational costs in renewable energy-based systems.

Jararhagin-C (JarC) is a protein from the venom of Bothrops jararaca consisting of disintegrin-like and cysteine-rich domains. JarC shows a modulating effect on angiogenesis and remodeling of extracellular matrix constituents improving wound healing in mice experimental model. JarC is purified from crude venom and the yield is less than 1 %. The aim of this work was to obtain the recombinant form of JarC and to test its biological activity. For this purpose, the bicistronic vector pSUMOUlp1 was used. This vector allowed the expression of the recombinant toxin JarC (rJarC) in fusion with the small ubiquitin-related modifier (SUMO) as well as the SUMO protease Ulp1. After expression, this protease was able to efficiently remove SUMO from rJarC inside the bacteria. rJarC free from SUMO was purified at the expected molecular mass, and recognized by polyclonal anti-Jararhagin antibodies. In terms of biological activity, both the native and recombinant forms showed no toxicity to the HUVEC cell line CRL1730 and were effective in modulating cell migration activity in experimental in vitro model. These results demonstrate the successful production of rJarC and the preservation of its biological activity, which may facilitate further investigations into the therapeutic potential of this snake venom-derived protein.

This study aimed to determine the prevalence and demographic distribution of schistosomiasis in the Eastern Cape Province of South Africa through a cross-sectional retrospective analysis using secondary data from patients with microscopically confirmed schistosomiasis collected between 2019 and 2020. The study focused on both rural and semi-urban areas, including Bizana, Butterworth, Centane, Elliotdale, Flagstaff, Idutywa, Lusikisiki, Libode, Mqanduli, Port St. Johns, Willowvale, and Mthatha. Data were obtained from three districts: Alfred Nzo, Amatole, and OR Tambo, covering both rural and semi-urban regions. The study included patients of all ages who submitted urine samples for schistosomiasis testing in the specified districts. A simple random sampling method was used to select 337 clinical records from the National Health Laboratory Service (NHLS) of Mthatha. Hospital records from the NHLS Microbiology Department of Mthatha were analyzed using SPSS version 20. Statistical tests, including Fisher’s exact test, Pearson’s Chi-squared test, and Wilcoxon rank sum test, were employed to compare demographic variables. The Shapiro-Wilk test was used to assess the normality of quantitative data. St Barnabas Laboratory had the highest frequency of cases (34.1%), followed by Greenville Depot (17.8%) and Willowvale Laboratory (11.3%). Most cases were in the 10-19 age group (63.4%), followed by those under 10 years (24.9%). Males constituted 76.4% of the cases, while females accounted for 23.6%. Viable ova were observed in 98.2% of the samples. The study highlights a significant prevalence of schistosomiasis in the Eastern Cape Province, with a higher incidence in rural areas and among males aged 10-19. These findings underscore the need for targeted public health interventions and continuous monitoring to control and prevent schistosomiasis in the region.

This study examines the effects of tourism development on the well-being of impoverished residents in both urban (La Candelaria, Bogotá) and rural (La Macarena, Meta) regions of Colombia. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), the findings reveal that personal economic benefits from tourism are the most vital factor in enhancing residents' happiness and life satisfaction. The analysis outlines a pathway from economic gain to happiness, improving life satisfaction. Additionally, the study highlights tourism's role in exacerbating the urban-rural divide, offering insights into migration trends and regional inequality. Practical recommendations are provided for policymakers to ensure balanced tourism development across regions.

Background/Objectives: The increasing incidence and mortality rates of liver cancer have heightened the demand for the development of effective anticancer drugs with minimal side effects. In this study, the roles of exosomes derived from liver cancer stem cells (LCSCs) with PRELI (Protein of Relevant Evolutionary and Lymphoid Interest) modulation, and their miRNAs were investigated to explore the therapeutic for liver cancer. The purpose of this study is to identify a more effective and less toxic anticancer agent beyond the well-known Notch, p51, and Hippo signaling pathways, and to specifically propose the potential of PRELI-induced exosomes as an anticancer therapeutic candidate. Methods: Various techniques, such as miRNA profiling, flow cytometry, western blotting, and immunocytochemistry, were used to evaluate the effects of ex-osomes under PRELI up- and downregulation. Results: PRELI upregulation altered expression levels of miRNAs, including hsa-miR-378a-3p (involved in stem-like properties), hsa-miR-25-3p (contributing to cell proliferation), and hsa-miR-423-3p (driving invasiveness). Exosomes with downregulated PRELI inhibited AKT/mTORC1 signaling, while this signaling pathway was ac-tivated in LCSCs transfected with the candidate miRNAs. Under PRELI up-regulation, exosomes showed increased surface marker expression, promoting cancer progression. Conclusions: The modulation of PRELI in LCSCs affects miRNA expression significantly, revealing candidate miRNA targets for liver cancer treatment. Exosomes with PRELI down-regulation show potential as a novel therapeutic strategy.

Phakopsora pachyrhizi, the causative agent of the Asian Soybean Rust, is one of the most prominent causes of yield loss in soybean production worldwide. In this study, a combination of hyperspectral imaging with advanced data analysis methodology is shown to accurately detect soybean rust symptoms in early stages and differentiate them from other factors at leaf scale. Data analysis was performed with neural networks based on training data from a small subset of the respective experiments and compared with classical machine learning methods for its accuracy and early disease detection capabilities. Results show that even with a comparably small subset of training data the analysis through neural networks can outperform classical machine learning methods in a complex dataset with high variability in the designated classes. In two separate experiments it was possible to accurately detect disease in inoculated leaves before symptoms were visible to the human eye. These results do not only show the potential of this methodology for precision farming applications, but also allow accurate and objective assessment of symptom severity and development for scientific studies and resistance breeding.

In this work, we present a real-time heart rate estimation method from a video using face detection, color-based signal extraction, and signal processing tech- niques. The proposed approach computes the heart rate by analyzing the color fluctuations in the facial region of interest (ROI). This report outlines the math- ematical formulation and results of the implementation.

Digital transformation in government services is seen as a progression towards offering more efficient and convenient services. In the wake of the COVID-19 pandemic, many governments worldwide have expedited the digitisation of their services to facilitate social distancing. Digital transformation encompasses the shift from manual, face-to-face services to automated, online-based systems. This process occurs in three stages: digitisation, digitalisation, and digital transformation. However, the varying stages of digital government implementation across countries impact service outcomes differently. Little research has been devoted to focusing on the outcome metrics crucial for the success of digital services, particularly in Kuwait. Moreover, although the existing literature has explored some metrics, specific metrics have been neglected. This study aimed to investigate current trends and preferences in digital government services usage using empirical quantitative data gathered from users of digital services in Kuwait. Specifically, the aim is fourfold: (i) to identify crucial outcome metrics for the success of digital services, (ii) to assess how users evaluate these services according to these metrics, (iii) to examine significant differences between digital transformation and digitalisation services, and (iv) to develop and validate a model for evaluating the success of digital transformation services. To achieve the study's objectives, 378 users of digital government services were surveyed quantitatively. This study found that users primarily access services through smartphones and applications, underscoring the need for mobile optimisation. There was a marked preference for real-time, automated services, with dissatisfaction for those requiring lengthy approval processes. Six key metrics were identified and grouped into three domains: service-related (customer satisfaction and service quality), convenience-related (accessibility and ease of use), and security-related (perceived security and perceived trust). Digital transformation services outperformed digitalisation services in five key areas: customer satisfaction, service quality, accessibility, ease of use, and perceived security, except perceived trust, which remained consistent across both service types. Several recommendations were proposed to advance government digital transformation services, boost efficiency, and enhance the user experience.

The diploid and autotetraploid of the eight tartary buckwheat cultivars were used to make a karyotype study by the conventional squash method to establish a reference for polyploid breeding. In this study, the eight cultivars exhibited similar karyotypic features between diploids and autotetraploids. The chromosome number in diploids was found to be 2n=2x=16, while 2n=4x=32 in autotetraploids. T6, T9, T18, T22, T27 and T36 possessed one pair of SAT chromosomes, whereas T25 and T39 had two pairs of SAT chromosomes. Compared with diploids, the total number of chromosomes and SAT chromosomes doubled in autotetraploids. However, the karyotype of autotetraploids remained unchanged in all cases which belonged to the 2A type. The karyotype of the same crop with different ploidy in diploid and autotetraploid buckwheat is the same. These eight cultivars' karyotype and chromosome number of autotetraploids are reported for the first time.

Geography has been a fundamental determinant of economic development throughout history, influencing resource distribution, trade routes, climate conditions, and the spatial organization of economic activity. This essay delves into the multifaceted role of geography in shaping economic outcomes, drawing upon historical analyses and contemporary economic theories. By examining the historical origins of economic growth, the impact of geographical barriers and advantages, the influence of climate and natural resources, and the spatial distribution of industries, we explore how geographical factors contribute to international inequality and the divergent development trajectories of nations and regions. The discussion incorporates insights from seminal works, including Koyama and Rubin's "How the World Became Rich," Davis and Weinstein's analysis of the geography of economic activity, and Redding and Venables' exploration of economic geography and international inequality, among others. We also consider the roles of institutions, governance, and technology as mediating factors that can amplify or mitigate the influence of geography on economic development. By highlighting the interplay between geography and these mediating factors, the essay underscores the importance of tailored policy interventions that consider geographical realities. Understanding this complex relationship is essential for addressing global and regional disparities and formulating effective development strategies that leverage geographical advantages while mitigating disadvantages.

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.

Background: Dengue virus (DENV) infection poses a serious yet underaddressed public health challenge in many tropical and subtropical regions worldwide. The epidemiology of DENV in Ghana is not well understood despite heightened risks due to suitable climatic conditions and the presence of competent vectors. Aim: We aimed to systematically review studies reporting dengue virus seroprevalence in Ghana and pool estimates through meta-analysis. Methods: PRISMA guidelines were followed to search the literature using PubMed, Web of Science, and Scopus databases from inception to September 24, 2024. Studies presenting primary DENV seroprevalence data among Ghanaian populations were included. Quality was assessed using JBI tools. Meta-analyses estimated pooled prevalence with 95% CIs using random-effects models. Heterogeneity was evaluated using I2 statistic. Results: A total of 9 studies met the selection criteria, with 8 studies involving febrile patients and 1 involving blood donors. The pooled seroprevalence rates were 33% (95% CI: 16–50%) for IgG, 6% (95% CI: 0–12%) for IgM, 2% (95% CI: 0–3%) for DENV RNA, and 32% (95% CI: 0–68%) for IgG/IgM combined. Notably, the IgG seroprevalence exhibited substantial heterogeneity (I2=99%). DENV-2 and DENV-3 were the predominant serotypes identified. Conclusion: This review established the prevalence and circulation of DENV in serogroups in Ghana. Higher seropositivity and heterogeneity underscore the need for standardized large-scale surveillance to optimize disease characterization and guide control. Integrating dengue preven-tion into existing vector control programs could help reduce risks in Ghana.

The stems of Rubus idaeus L., a by-product of the fruit-food industry, are known sources of bioactive compounds. The main objective of this study was to investigate the composition of the polyphenolic compounds in R. idaeus stems. 7 cultivated raspberry varieties, 13 garden samples, including five well-known raspberry varieties and 13 wild raspberry samples from different locations in Estonia were analyzed. The HPLC-MS method detected 62 substances, of which 42 were identified, 12 were tentatively identified, and 8 compounds remained unknown. Protocatechuic acid pentoside was dominant in most varieties and in all garden and wild raspberry samples. Dihydroxybenzoic acid hexoside 1, p-coumaroyl quinic acid 1, quercetin 4’-glucuronide and p-coumaric acid glycoside were found in significant quantities. Correlations between the contents of individual compounds have been established. When studying the dynamics of polyphenolic compounds accumulation on the example of the GR1 sample during the year, it was found that the largest amount of them in raspberry stems accumulates in April, slightly less in January and October. Investigating the dependence of the accumulation of polyphenols on the parts of the stem, it was found that the upper parts have the highest phenolics content. Therefore, it is recommended to harvest approximately the upper third of the stem.

This article describes a new application of the global optimization method HQF ASA to the investigation of the Riemann hypothesis, in the sense of searching for zeros of the Riemann zeta function ζ outside the critical line but inside the (open) subset of the critical strip ((0,1/2)∪(1/2,1))×(−∞,∞). The underlying idea is very simple: a complex root r of a given complex function f : C → C must satisfy the condition ||f(r)|| = 0, that is, f(r) must have 2-dimensional Eu- clidean norm equal to 0, considering r as a point of R2. In the opposite direction, for any r ∈ C, ||f(r)|| = 0 implies f(r) = 0 and r is a root of f. Focusing again on the Riemann zeta function ζ, the open and simply connected region Rc = (1/2,1)×(0,∞) is used along the text, taking into account the symmetries of zeros of ζ in the critical strip. In this fashion, finding a global optimizer for ||ζ|| is equivalent to finding a root of ζ. Therefore, and taking into account the well-known efficacy of HQF ASA, a good sign of the trueness of RH would be not finding global minima in several optimization sessions with expanding subsets of Rc. In practical terms, several optimization sessions are executed using QMF ASA with increasing subsets of Rc, searching for global minima of ||ζ||. After presenting numerical results and figures, the adequacy of the proposed approach as a tool for preliminary empirical analysis for this type of problem is discussed.

Assessing the suitability of older adults with acute myeloid leukemia (AML) to intensive chemotherapy or stem cell transplantation remains a long-standing challenge. Geriatric assessment, which evaluates multiple dimensions of health, may influence a patient's ability to tolerate intensive or mild intensity approach, including treatment related mortality. Prospective studies are needed to validate different fitness criteria as well as to compare the effectiveness of geriatric assessment–based fitness against other criteria, in order to identify which aspects of the geriatric assessment are linked to treatment tolerance. Hopefully, validation studies should include different group of patients receiving either intensive or lower-intensity chemotherapy. At a minimum, the geriatric assessment should measure comorbidity burden, cognition, physical function, and emotional health—factors previously associated with mortality in AML. These assessments should be conducted before starting chemotherapy in order to minimize the treatment's impact on the results. While treatment tolerance has traditionally been evaluated through toxicity rates in solid tumor patients, AML treatment often results in high toxicity rates regardless of intensity. Therefore, early mortality should be the primary endpoint for assessing treatment tolerance, given its significant and clear implications. Other important endpoints might include declines in functional status, quality of life, and treatment adjustments or discontinuation due to toxicities. Validating these fitness criteria is essential for guiding treatment choices, improving supportive care, determining trial eligibility, interpreting study outcomes, and informing drug labeling.

Background: The COVID infection became the source of serious disagreements between scientists, doctors, politicians and private citizens, directly after it’s detection in the population in January 2020. The pandemic had some very unusual properties as well as the people’s reaction to it. It was feared, that the virus might be used, intentionally or accidentally, to interfere with the usual, democratic way of the upcoming election. Objective: Re-search the original COVID statistic and metadata to obtain better understanding of the pandemic including the biological (viral) as well as the social (human) aspects of it. Provide a professional, independent, non-biased view of the pandemic, that is not polluted by political and media suggestions. Methods: Publicly available data – covering the first 5 months of the COVID pandemic, 2020 Jan-May - were collected and analyzed with the usual, common statistical methods, like student’s t-test and regression analyses.The political attitude of the states were characterized by a ratio (D/R) of the number of left, democrat (D) and right, republican (R) oriented citizens in the respective states, as determined by opinion research. Results: De novo statistical analyzes of publicly available data (covering the initial five months of the pandemic) suggests that the negative medical consequences of COVID pandemic were significantly more expressed in “democrat-dominated” (D) states than in “republican-dominated”, (R) states. The proportion of COVID test positive persons was higher in D-states (6.831 +/- 1605, n=14 and 1,165 +/- 323, n=13 respectively, p<0.001). The difference in mortality was even more striking, 8-times more person/million population died in COVID related conditions in solid D states than in solid R states (438 +/- 128, and 59 +/- 13; p<0.001, respectively). The COVID associated mortality increased two fold from right to left on the political scale of the states (2.7% +/- 0.3 vs. 5.3% +/- 0.5, p<0.001). The correlation between COVID deaths (mortality) and the age/pre-existing medical condition(s) of the diseased is very strong (R2= 0.98) and the overwhelming majority of COVID “victims” are persons who were close to death even independently of the epidemic. Conclusions: Alarming signals regarding the dangerousness of COVID infection seems to have originated in a few states with predominantly left oriented citizens (high D/R ratio). The COVID mortality statistic seems to be seen and analyzed with little or no respect for the context of mortality caused by other well-known diseases or even by the natural, age related deaths. We conclude that it is absolutely necessary to pay serious attention to the correct analyses and interpretation of the epidemic data to avoid being misled or manipulated by false circumstances.

Road safety is a critical public health concern in Nepal, especially in urban areas where inadequate infrastructure exacerbates traffic incidents. This study reviews and synthesizes 21 peer-reviewed papers to investigate how road infrastructure contributes to traffic accidents. Key factors include poor road conditions, reckless driving, and inadequate traffic management. Findings indicate that road traffic accidents (RTAs) significantly increase morbidity, mortality, and economic burdens in Nepal. To address these issues, the study recommends infrastructure improvements, stricter enforcement of traffic laws, and enhanced public education on road safety.

Although large language models demonstrate impressive capabilities, they sometimes generate irrelevant or nonsensical text, or produce outputs that deviate from the provided source input—an occurrence commonly referred to as hallucination. To mitigate this issue, we introduce a novel decoding method that incorporates both factual and hallucination prompts(DFHP), utilizing a contrastive output distribution to highlight the disparity in output probabilities between model predictions influenced by factual prompts and those affected by hallucination prompts. Experiments on both multiple-choice and text generation tasks show that our approach significantly enhances the factual accuracy of large language models without requiring additional training.

Bioactive like resveratrol, epigallocatechin gallate, curcumin, and other polyphenols often target various signalling pathways, including NFκB, TGFβ, and Wnt/β-catenin in a degenerative joint disease like osteoarthritis (OA), by executing epigenetic-modifying activities. Epigenetic modu-lation can target genes of disease pathophysiology by histone modifications, promoter DNA methylation, and non-coding RNA expression, some of which are directly involved but less ex-plored in OA. OA patients often explore options that can improve the quality of their life in ad-dition to existing treatment with nonsteroidal anti-inflammatory drugs (NSAIDs). Although bi-oactive and natural compounds exhibited therapeutic potential against OA, several disadvantages loom, like insolubility and poor bioavailability. Nanoformulated bioactives promise a better way to alleviate OA since they also control systemic events, including metabolic, immunological, and inflammatory responses, by modulating host gut microbiota that can regulate OA pathogenesis. Recent data suggest gut dysbiosis in OA. However, limited evidence is available on the role of bioactive as epigenetic and gut modulators in ameliorating OA. Moreover, the question of whether polyphenolic bioactive modulates gut microbial response that results in epigenetic-controlling activities of OA pathogenesis is not known. This narrative review explored the interplay of nanoformulated bioactive and host gut microbiota on chondrocyte growth, metabolism, and epigenetic alteration linked with OA progression and pathogenesis.

The construction industry, characterized by high energy use and carbon emissions, necessitates a thorough and accurate life cycle assessment (LCA). This review investigates how building information modeling (BIM) software can streamline the LCA process to improve both efficiency and precision. Although BIM has considerable potential, challenges remain, such as issues with software interoperability and a lack of standardized options for BIM-integrated LCA tools. The review also evaluates the strengths and weaknesses of various BIM software, LCA tools, and energy consumption tools, and highlights case studies of BIM-LCA integration. It provides a critical analysis of methods and techniques for BIM-LCA integration and data exchange, including the import of bills of quantities, Industry Foundation Classes (IFC), the use of BIM viewers, direct LCA calculations with BIM plugins, and calculations using LCA plugins. The study concludes with future outlooks, aiming to direct the development of improved LCA tools that offer better integration with BIM software, which is crucial for advancing sustainable construction practices.

Background/Objectives: Interest in diversity is growing worldwide. Today, understanding and social acceptance of diverse people is becoming increasingly important. Therefore, in this study, we aimed to clarify the relationship between gray matter volume (GMV), which is thought to reflect brain health, and understanding of diversity (gender, LGBTQ, and race). Methods: GMV was the value of the Gray Matter Brain Healthcare Quotient (GM-BHQ) based on MRI image analysis. Meanwhile, diversity was calculated from the answers to the psychological questions included in the World Values Survey Wave 7 (WVS7). Results: As a result of the analysis, in the group of participants with the highest understanding of diversity (PHUD. n = 11), not only the GMV at the whole brain level but also the GMV of the central executive network (CEN) and saliency network (SN) were shown to be significantly higher than the theoretical value estimated from sex, age, and BMI at the 5% level. In addition, the GMV of the default mode network (DMN) was also shown to be higher than the theoretical value at the 10% level. Meanwhile, in the group of others (n = 10), there was no significant difference from the theoretical value. These differences between PHUD and others were also observed when comparing the two. Conclusions: These results suggest that understanding diversity requires a healthy brain, centered on three networks that govern rational judgment, emotion regulation, other-awareness, self-awareness, and the valuing of actions. This is the first study to show that brain structure is related to understanding and acceptance of diverse people.

The emergence of Artificial Intelligence (AI) has transformed the way historical data is interpreted, uncovering previously unseen trends and fundamentally altering modern management practices. This shift has significantly impacted the analysis of business history and leadership studies. However, there is still a notable gap in research regarding the effects of AI-driven reinterpretations of historical events on contemporary business strategies, leadership frameworks, and ethical considerations. This study aims to address this gap by exploring the methodologies and implications of AI in business history analysis, focusing on case studies of Walmart and JPMorgan Chase. Integrating foundational theories such as evolutionary theory, path dependency, transformational, and authentic leadership reveals how AI-driven insights can refine strategic planning, advance leadership development, and promote equitable management practices. It also highlights the ethical challenges in using AI for historical analysis, particularly the risks of perpetuating existing biases. Findings indicate that AI's capacity to analyze large volumes of historical data significantly reshapes our understanding of business evolution and leadership effectiveness. The study concludes with actionable recommendations for adopting ethical AI practices, outlining future research directions, including developing AI models for contextual analysis and exploring AI’s role in promoting diverse leadership models. This research advances academic discourse by providing a theory-driven framework for leveraging AI in business history and leadership studies. It underscores AI's transformative potential and complex implications for future business practices.

The high performance of liquid chromatography(HPLC) has long been an indispensable tool for vigilance and analysis across range disciplines such as pharmaceuticals, environmental monitoring, food safety attributes etc. We will discuss about HPLC method development and validation, types of liquid chromatography methods focusing on proper use of a mobile phase like an eluent selection, how to optimize the conditions as well take it through its various procedures required for approval from regulatory bodies. Strategies to improve method robustness, sensitivity and reproducibility are highlighted. The future breakthroughs in HPLC technology and some innovative trends have also been speculated to limit this fundamental methodology.

In this paper we have investigated the driven quintic Duffing equa- tion. Using some analysis technique, we are able to predict the number of limit cycles around the equilibrium and to develop a theoretical ap- proach to chaos transition in damped driven systems.

Bioactive like resveratrol, epigallocatechin gallate, curcumin, and other polyphenols often target various signalling pathways, including NFκB, TGFβ, and Wnt/β-catenin in osteoarthritis (OA) pathogenesis, by executing epigenetic-modifying activities. Epigenetic modulation by bioactive is gaining traction with the onset of nutrigenomics. Such modulations can target genes by histone modifications, promoter DNA methylation, and non-coding RNA expression, some of which are directly involved in disease pathophysiology but less explored in OA. OA patients often explore options that can improve the quality of their life in addition to existing treatment with nonsteroidal anti-inflammatory drugs (NSAIDs). Although bioactive and natural compounds exhibited therapeutic potential against OA, several disadvantages loom over them, like insolubility and poor bioavailability. Nanoformulated bioactives promise a better way to alleviate OA since they also control systemic events, including metabolic, immunological, and inflammatory responses, by modulating host gut microbiota that can regulate OA pathogenesis. Recent data suggest gut dysbiosis in OA. However, limited evidence is available on the role of bioactive as epigenetic and gut modulators in ameliorating OA. Moreover, the question of whether polyphenolic bioactive modulates gut microbial response that results in epigenetic-controlling activities of OA pathogenesis is not known. This narrative review explored the interplay of nanoformulated bioactive and host gut microbiota on chondrocyte growth, metabolism, and epigenetic alteration linked with OA progression and pathogenesis.

The United Nations Global Sustainability Agenda 2030 introduces a transformative framework through its Sustainable Development Goals (SDGs), which are designed to achieve equitable, prosperous, and sustainable development globally. Unlike the incremental Millennium Development Goals (MDGs), the SDGs aim for systemic changes across economic, social, and environmental dimensions, ensuring that no one is left behind. This research paper provides a comprehensive examination of Pakistan's progress towards achieving the SDGs under the UN Global Sustainability Agenda 2030. Specifically, it includes an analysis of the prioritization of SDGs in Pakistan, highlights federal and institutional initiatives aimed at promoting sustainable development, and explores the role of the China-Pakistan Economic Corridor (CPEC) in advancing these goals. Additionally, the paper critically analyzes the SDGs in Pakistan’s annual status report, identifies the key challenges faced by the nation in implementing these goals, and offers recommendations for enhancing the effectiveness of SDG implementation. Overall, the focus is primarily on the detailed analysis of the annual status report, the challenges encountered, and strategic recommendations for achieving excellence in SDGs in Pakistan.

Urbanization growth has intensified the challenge of managing and treating increasing amounts of municipal solid waste (MSW). Landfills are commonly utilized for MSW disposal because of their low construction and operation costs. However, this practice produces huge volumes of landfill leachate, a highly polluting liquid rich in ammoniacal nitrogen (NH3-N), organic compounds and various heavy metals, making it difficult to treat in conventional municipal wastewater treatment plants (WWTPs). In recent years, research has shown that microbial biofilms, developed on carriers of different materials and called “Moving Bed Biofilm Reactors” (MBBRs), may offer promising solutions for bioremediation. This study explored the biofilm development and the nitrification process of moving bed biofilms (MBBs) obtained from high am-monia-selected microbial communities. Using Crystal Violet Staining and Confocal Laser-Scanning Microscopy, we followed the biofilm formation stages correlating nitrogen removal to metagenomic analyses. Our results indicate that MBBs unveiled a 10-fold more enhanced nitrification rate than the dispersed microbial community pre-sent in the native sludge of the Porto Sant’Elpidio (Italy) WWTP. Four bacterial families, Chitinophagaceae, Comamonadaceae, Sphingomonadaceae, and Nitrosomona-daceae accumulate in structured biofilms and significantly contribute to the high ammonium removal rate of 80% in 24 hours as estimated in leachate-containing wastewaters.

Procrastination among students in higher education is a widely recognized problem, with significant effects on academic performance and personal well-being. This descriptive study aims to assess the level of procrastination among nursing students in Bengaluru and determine the association between procrastination and selected demographic variables, such as age, gender, and parental education. A total of 100 nursing students from Sneha College of Nursing participated in the study. Data were collected using a structured questionnaire and a 16-item procrastination scale. The results revealed that 55% of the students had low levels of procrastination, while 45% exhibited high levels. However, no significant association was found between procrastination and demographic variables like age, gender, religion, year of study, or parental education. The findings suggest that while procrastination is a common issue among nursing students, demographic variables may not be reliable predictors of procrastination behavior. These results call for interventions to improve time management and reduce procrastination among nursing students to enhance their academic outcomes.

This research explores the role of transparency and traceability in the agricultural food supply chain. By utilizing blockchain's decentralized and secure features, stakeholders can improve information sharing from farm to consumer, transparency and traceability issues. Real-world examples illustrate blockchain's successful application in agriculture, boosting transparency, efficiency, and trust and create immutable records, enhance visibility, and provide reliable data, while combating fraud and promoting sustainability. It also examines blockchain's impact on food safety through IoT and smart contracts, highlighting successful implementations in supply chain management, the research underscores blockchain's potential to build consumer trust in food safety, quality, and origin.

This paper is intended to correlate the impact of leadership skills on organizations' performance, all organizations grow differently, and their growth depends mostly on positive performance from one year to another which has direct correlations to the leadership dreams, visions, foresight and aspirations for the organization. There is one thing for the leader to have vision, but the leader needs to have followership to assist in implementing their vision to make it goals which translate to performance in the organization and growth. The leadership style or management skills greatly influence how they command and motivate followership to actualize their dreams or the organizational visions and mission which results in the organization's performance. A positive leadership influence produces or actualizes a positive organisational performance that will move the organization towards the vision of the organization. Knowing the difference between a manager and a leader is pertinent in explaining the correlation between leadership and organizational performance. The understanding of these two roles provides credence to the correlation between effective leadership and organisational performance, and the understanding of the term, the use and the importance of strategic management to leadership and organizational performance. There is a wide difference between managers and leaders, which is based on personality and their approach to the resolution of challenges they are confronted with.

Corn is both the most important and nitrogen (N)-fertilized crop, but N losses from agricultural systems impact the environment. There is a lack of long-term data comparing effects of tilled systems receiving organic N fertilizer (TONF; e.g., manure) versus inorganic N fertilizer (TINF; e.g., urea) on N balance and losses. A long-term study assessing effects of TONF and TINF and comparing them to no till (NT) and strip till (ST) systems receiving inorganic nitrogen fertilizer (INF) was conducted with an irrigated continuous corn (CC) system with minimal erosion in Fort Collins, Colorado, USA. An N balance found a system nitrogen use efficiency (NUE_Sys) for TONF (manure) of 86.3%, which was higher than the NUE_Sys of TINF (enhanced efficiency fertilizer, EEF) of 60.2%. TONF (manure) contributed to higher HGY (2 of 11 yr.), higher C sequestration (P<0.05), and higher soil organic N content (P<0.05) than TINF. Soil phosphorus (P) and nitrate were higher with TONF than TINF (P<0.05). Both TONF and TINF had higher HGY and HGY N content (HGYN) than NT and ST (P<0.05). From 2012 to 2023, the HGY of the TONF did not decrease, while the HGY of TINF decreased with time (P<0.05). TONF is a best management practice to increase yields and N and C sequestration while reducing N losses compared to TINF and achieving higher yields than NT and ST. A combination of manure (30% of N input) with EFF (70% of N input) contributed to a synergistic effect that increased agronomic productivity.

Crypto-ransomware has emerged as one of the most significant cybersecurity threats in recent years. Ransomware attacks, particularly those involving the encryption of data and demanding payment in cryptocurrency, have caused severe disruptions across various critical industries, including healthcare, energy, and finance. The proliferation of these attacks is largely attributed to the anonymity provided by cryptocurrency transactions and the increasing sophistication of cybercriminal tactics. The impact of ransomware extends beyond immediate financial losses, often leading to prolonged operational downtime, reputational damage, and even risks to public safety. In this research paper, we explore the growing threat of crypto-ransomware by examining real-life case studies in key industries, analyzing the countermeasures taken, and proposing additional solutions to mitigate the risk. The goal is to raise awareness and provide actionable insights for organizations to better protect themselves against this pervasive threat.

Solanum indicum Linn, is commonly known as Indian Nightshade or Poison Berry belongs to the family Solanaceae. This review studied the botanical description, traditional uses of its seeds, its chemical composition, pharmacological properties and modern applications in medicinal system. The plant is rich in alkaloids, steroidal saponins, flavonoids and phenolic acids having diverse pharmacological activities and phytochemical profile. It is used in treatment of various respiratory disorders, inflammation, skin problems and gastrointestinal disease since ancient times. Scientific research in modern times also tested many of the traditional uses revealing anti-inflammatory, antioxidant, antimicrobial activities as well as some cytotoxic and even antidiabetic effects. These pharmacological activities show potential future as uses for organic drugs, natural health products, skincare formulations and agricultural advancements.

3D printing has significantly transformed the design and manufacture of robotic prostheses, making these devices more accessible, customized, and functional. This paper examines the historical evolution of prosthetic technology, tracing its development from rudimentary mechanical devices to the integration of advanced technologies, such as 3D printing. This innovation has enabled the production of prostheses at lower costs while enhancing their adaptability and performance. The review highlights how 3D printing has driven a disruptive shift in prosthetic customization, and how emerging technologies—including smart materials and artificial intelligence—have expanded the capabilities of prosthetic devices, offering more adaptive and natural movement. However, challenges persist, particularly regarding the need for standardization and infrastructure expansion to ensure equitable access to these technologies. Future research into novel materials and manufacturing techniques holds the potential to improve further the functionality, affordability, and accessibility of prosthetic devices. In conclusion, while 3D printing has marked a significant milestone in the evolution of robotic prosthetics, overcoming existing challenges is essential to realize its global impact and benefits fully.

In metropolitan regions, which are particularly vulnerable to seismic damage, numerous reinforced concrete (RC) buildings are being constructed. Public safety has been an important consideration to safeguard and protect people, buildings, and infrastructure from the potential effects of earthquakes. The assessment of seismic vulnerability within urban areas encompassed an analysis of both building vulnerability and the scale of seismic hazards prevalent in the locality. This assessment aimed to ascertain the likelihood of building damage resulting from ground motion induced by an earthquake of a specific magnitude. In recent decades, considerable efforts have been made to develop and improve methods for assessing earthquake damage to reinforced concrete (RC) buildings. These efforts included the creation of seismic hazard and seismic risk indices that were used to quantify the potential destruction of individual building components or the entire building. This scholarly review article presents an in-depth analysis and concise summary of the primary techniques (including qualitative or empirical, quantitative, analytical, and experimental test methods) devised for appraising the seismic vulnerability of reinforced concrete frame buildings, pre- and post-earthquake occurrences. It is a valuable reference for policymakers, engineers, researchers, and specialists engaged in earthquake risk mitigation efforts.

This literature review focusses on diagnostics of Equine Asthma (EA), possible influencing factors on diagnostic techniques and latest developments in diagnosing horses during EA remission or in case of subclinical disease. Routine EA diagnostics include clinical examination of the respiratory system including percussion and auscultation, clinical pathology including white blood cells and arterial blood gas analysis, bronchoscopy to evaluate the amount and viscosity of respiratory secretion, bronchoalveolar lavage and cytology of tracheal aspirates (TA) and bronchoalveolar lavage fluid (BALF). Grading of EA severity and inflammatory subtype is based on BALF cy-tology, while TA cytology helps to rule out previous bacterial infections. Different factors have an impact on the airways regarding structure of the epithelium, cytology and inflammatory markers possibly influencing the diagnosis of EA. Short-term exercise increases the total cell count and inflammatory mediators in BALF of men, asymptomatic horses and other species. Further factors involve cold or chlorinated air, long-term training effects and the co-incidence of further respir-atory disease, in particular exercise-induced pulmonary hemorrhage. As BALF cytology may be unremarkable during EA remission and low-grade disease, exercise and other factors stressing the bronchial epithelium may help to diagnose these patients.

Arabidopsis and other model plants can help improve crops by providing a reference framework for assembling omics data from plants. Arabidopsis is extensively studied and has a well-curated and annotated genome, making it an excellent reference organism for comparative analysis. When there is no reference genome of the organism being investigated, using the genome of Arabidopsis may be an option thanks to the extensive knowledge we have about it. Crop improvement can help to increase crop yields, improve crop quality, and enhance resistance to pests, diseases, and environmental stresses. It can also help to reduce the use of pesticides and fertilizers, which can have negative impacts on the environment and human health. Therefore, crop improvement is essential to ensure food security and sustainability in the face of global challenges such as climate change, land degradation, and water scarcity. The assembly process of genomic, transcriptomic, and metabolomic data from plants using carefully selected information on Arabidopsis can lead to a deeper understanding of organisms and their potential for genetic improvement for crops. However, the successful integration of data requires expertise and experience to design more effective and precise crop improvement strategies.

Background Hepatic resection offers promising outcomes for patients with hepatocellular carcinoma (HCC) but can be constrained by factors like patient suitability. Continuous advancements in laparoscopic and robotic technologies have made minimally invasive hepatectomies (MIH) viable alternatives to open hepatectomies with benefits in recovery and complications. Materials and Methods We completed a retrospective review on 138 HCC patients at the Hume-Lee Transplant Center that underwent OH or MIH between 2010 and 2020. Univariate and multivariate analyses were completed on demographic, clinical, and tumor-specific data were collected to assess the impact of these variables on overall and disease-free survival at 1-, 3-, and 5-years. Preoperative metrics like length of hospital stay (LOS) and operation duration were also evaluated. Results Of the 109 OH and 29 MIH patients, MIH patients demonstrated shorter LOS and operative times. However, overall survival (OS) and disease-free survival (DFS) were similar between groups, with no significant variations in 1-, 3-, and 5-year survival rates. Ages > 60 years and lack of preoperative transcatheter arterial chemoembolization (TACE) were significant predictors of inferior OS and DFS in multivariate analyses. Conclusions MIH is an efficient substitute to OH with comparable survival even in older patients. The reduced LOS and operation time enhances its feasibility, and older patients previously denied for curative resection may qualify for MIH. Preoperative TACE also enhances survival outcomes, emphasizing its general role in managing resectable HCCs. Both robotic and laparoscopic hepatectomies offer acceptable short- and long-term clinical outcomes, highlighting MIH as the standard choice for HCC patients.

This paper presents a comprehensive exploration of Data Quality terminology, revealing a significant lack of standardisation in the field. We propose a novel approach to aggregating disparate Data Quality terms used to describe the multiple facets of Data Quality, under common umbrella terms, with a focus on the ISO 25012 standard. Our aim is to design a Data Quality Data Model that serves as a universally applicable framework for Data Quality assessment. We introduce four additional Data Quality dimensions: Governance, Usefulness, Quantity, and Semantics, enhancing specificity, complementing the framework established by the ISO 25012 standard, and understanding of Data Quality aspects. The ISO 25012 standard, while tailored for software development, offers a foundation for the development of our proposed Data Quality Data Model. This is due to the prevalent nature of software development across a multitude of domains. In contrast, frameworks like ALCOA+ that are specific to certain domains lack the ability to be generalised. The model we propose can be seen as a "Rosetta Stone" for Data Quality terminology, facilitating a seamless communication of Data Quality between different domains when collaboration is required to tackle cross-domain projects or challenges.

The growing emphasis on sustainability and environmental stewardship in the pharmaceutical industry necessitates innovative solutions to manage pharmaceutical waste, particularly medication bottles. This research paper explores the potential of integrating Radio Frequency Identification (RFID) and blockchain technology to enhance the traceability, accountability, and efficiency of recycling empty medication bottles. By utilizing RFID for real-time tracking and blockchain for secure, immutable data storage, this proposed system improves transparency across the supply chain and incentivizes consumer participation in recycling programs. The solution provides benefits to pharmaceutical manufacturers, regulators, and consumers, contributing to sustainability, regulatory compliance, and environmental protection.

This research centres on advancing the utilisation of a novel Deep Ensemble model called RicEns-Net for predicting crop yields through sophisticated multi-modal data engineering. Initially, data for the study were acquired from Ernst \& Young (EY), a prominent global technology and management consulting firm, as part of their annual data science challenge (EY Open Science Challenge 2023). The predictive models leverage multi-modal data by integrating radar and optical-type remote sensing data alongside meteorological data to create supplementary features essential for predicting crop yield. The primary data features are derived from spectral indices extracted from Sentinel 2 satellites, while more advanced features are generated through combinations and interactions of these fundamental features. Furthermore, data from Sentinel 1 and 3, along with meteorological measurements such as surface temperature and rainfall, account for the intricate natural factors influencing crop yield, creating a comprehensive set of data features based on existing research. To tackle the complexity introduced by numerous data features, extensive data engineering was conducted to identify the most predictive features among 100+ and mitigate the "curse of dimensionality," resulting in 15 features from 5 different modalities. The proposed RicEns-Net comprises multiple machine learning algorithms in a deep ensemble approach to leverage the best features of each of these techniques, achieving optimal performance. The proposed model achieves an MAE value of 341 kg/Ha for prediction, surpassing the performance of the EY Open Science Challenge 2023 winner models and state-of-the-art deep/machine learning approaches.

Millet is a staple food in Northern Nigeria, widely used for the preparation of various millet‐based dishes. However, traditional processing methods, such as de‐husking, can significantly affect the grainʹs nutritional and antioxidant properties. This study evaluates the antioxidant composition and nutritional value of four millet varieties cultivated in Northern Nigeria, with a particular focus on the impact of de‐husking on these parameters. Our analysis encompasses the proximate composition, phytochemical content, antioxidant vitamins, mineral elements, amino acid profile, and antioxidant activities of the millet samples. The findings reveal that all millet varieties contain significant levels of antioxidant compounds, including phenolics, flavonoids, and vitamins, although their concentrations varied due to genetic differences and environmental factors. Notably, in the mineral composition, higher (p<0.05) levels of selenium (Se) and magnesium (Mg) were observed, while trace levels of iron (Fe), copper (Cu), and zinc (Zn) were present. The study demonstrated that de‐husking significantly (p<0.05) reduced the levels of most nutritional and antioxidant parameters, underscoring the superior nutritional value of whole grains compared to their de‐husked counterparts. This highlights the importance of consuming millets in their unprocessed form to maximise health benefits. Moreover, the presence of essential phytochemicals, amino acids, and dietary fiber in these grains, suggests their potential role in reducing the risk of chronic diseases such as cancer, cardiovascular diseases, obesity, and diabetes. These findings underscore the nutritional and functional potential of millet grains as valuable sources of essential nutrients, which can be utilised in nutraceutical formulations or as functional foods for promoting human health. This study emphasises the need to preserve the integrity of millet grains during processing to fully capitalise on their health benefits.

Objectives: the aim of this study was the assessment and monitoring of the safety and quality of the existing water supplies in Ibb and Taiz governorates from the microbial side and testing the quality of drinking water in the Ibb and Taiz governments and comparing it with the World Health Organization. Methods: the water samples (99 samples) taken from some water sources, distribution reservoirs, and different regions were studied for bacteriological analysis, in which they were inoculated into culture media using two methods: the presumptive test for the most probable number and the confirmatory test for bacterial identification. Results: the results indicated the non-existence of indicator bacteria that were used to indicate the possible presence of fecal bacteria among 24 (24.24%) water samples taken from Taiz governorate, while the presence of an unacceptable number of Escherichia coli in house tanks (30%) was indicated among 75 (74.25%) water samples taken from Ibb governorate. Conclusions: the results of the study in Taiz governorate show that the water is fit for human consumption, as well as the samples taken from water sources and distribution networks in Ibb governorate, but the results of the samples taken from house tanks showed that the water is not fit for human consumption and needs to be treated.

The optimization of ligand-receptor binding affinities is essential for enhancing therapeutic efficacy and specificity, particularly in the design of peptide-based drugs. ziconotide, a potent blocker of the N-type calcium channel CaV2.2, has demonstrated therapeutic potential in the treatment of moderate to severe chronic pain. However, there is limited structural and binding affinity data available for scaling the design of ziconotide analogues with improved efficacy and specificity. In this study, I present a structural biophysics-based approach to develop a scalable in silico framework for generating and analyzing ligand-receptor binding affinity landscapes, focusing on ziconotide and its receptor, CaV2.2. This framework integrates advanced computational structural tools and binding affinity calculations to produce high-accuracy structural and intermolecular binding data. The insights gained from this scalable approach will guide the design of ziconotide analogues with enhanced efficacy, offering a powerful computational workflow for high-throughput ligand optimization. Additionally, when combined with artificial intelligence (AI) algorithms, this computational workflow generates high-accuracy structural and biophysical data, towards an AI- and structural biophysics-driven paradigm shift in peptide discovery and design.

Olivine-like NaFePO4 glasses and nanocomposites are promising materials for cathodes in sodium batteries. Our previous study concerned the influence of high pressure on DC electric conductivity of NaFePO4 glasses. This work presents the results of AC measurements of glasses and nanocomposites from pressure treatment. Depending on the temperature range, the electronic conduction mechanism changed from variable range hopping (between Fe2+/Fe3+centers) to phonon-assisted hopping. For DC electric conductivity the portrayal via the classic Mott’s Variable Range Hopping is limited to low temperatures. Spectra of AC conductivity for glasses and pressure-formed nanocomposites fulfill Jonscher’s scaling law with power exponents ranging from 0.3 to 0.9 depending on temperature and the electron hopping mechanism. Due to the decrease in distance between hopping centers, an increase of AC conductivity around 2-3 orders of magnitude was observed after high pressure-high temperature treatment. Up to 200 K temperature dependencies of AC conductivity follow the pattern suggested by Mott's theory. Above that temperature corresponds to phonon-assisted hopping with activation energies EAC≈0.40 eV (for glass: amorphous solid) and EAC≈0.18 eV (for glass + nanocrystallites nanocomposite). Notable that they are considerably lower than values of EDC for DC conductivity. Results obtained show that high-temperature compressing, just below the glass temperature, significantly leads to a stable decrease in distance between hopping centers related to nanocrystallites in a glassy matrix. Consequently, it leads to an essential rise in electric conductivity and volumetric capacity. AC spectra of electronic conductivity are disturbed by the conduction of Na+ mobile ions, which impacts polaron conductivity and can be a limiting factor for conductivity.

Streamflow forecasting is of great importance in water resources management and flood warnings. Machine learning techniques can be utilized to assist with river flow forecasting. By analyzing historical time series data on river flows, weather patterns, and other relevant factors, machine-learning models can learn patterns and relationships to present predictions about future river flows. In this study, an Autoregressive Integrated Moving Average (ARIMA) model is constructed to predict the monthly flows of the Athabasca River at three monitoring stations: Hinton, Athabasca, and Fort MacMurray, in Alberta, Canada. The three monitoring stations upstream, midstream, and downstream were selected to represent the different climatological regimes of the Athabasca River. Time series data were used for the model training to identify patterns and correlations using moving averages, exponential smoothing, and Holt-Winters' method. The model's forecasting was compared against the observed data. The results show that the determination coefficients were 0.99 at all three stations, indicating strong correlations. The root mean square errors (RMSEs) were 26.19 at Hinton, 61.1 at Athabasca, and 15.703 at Fort MacMurray, respectively, and the mean absolute percentage errors (MAPEs) were 0.34%, 0.44%, and 0.14%, respectively. Therefore, the ARIMA model captured the seasonality patterns and trends in the stream flows at all three stations and demonstrated a robust performance for hydrological forecasting. This provides insights and predictions for water resources management and flood warnings.

This review examines eutrophication in freshwater ecosystems, a widespread environmental issue stemming from excessive nutrient input, largely due to human activities. The study explores the origins, consequences, evaluation techniques, and control methods for eutrophication. Primary causes include agricultural discharge, urban development, and climate shifts. The effects are far-reaching, impacting biodiversity, water quality, and socio-economic factors. The assessment utilizes a combination of chemical, physical, and biological markers, with cutting-edge technologies like remote sensing improving monitoring efforts. Mitigation strategies are diverse, incorporating preventative actions (such as nutrient limitation and land management), corrective measures (including bio-manipulation and chemical applications), and regulatory initiatives (like water quality guidelines and nutrient exchange schemes). While case studies demonstrate successful interventions, they also reveal ongoing challenges, particularly ecosystem unpredictability and climate change influences. The research highlights the promise of emerging technologies, notably machine learning, in enhancing management practices. Knowledge gaps persist, especially regarding eutrophication-biodiversity dynamics in tropical environments and the socio-economic ramifications of interventions. The study emphasizes the importance of flexible, comprehensive approaches that blend scientific insights with practical solutions and strong policy structures to tackle this intricate environmental challenge in an era of climate change.

Pedagogical translanguaging (PTL) refers to the use of educational techniques which incorporate learners’ entire linguistic repertoire. Recent studies indicate that PTL is efficient in the teaching of morphological awareness to bilingual children. The question remains whether it can successfully be applied in the highly specific context of adult forced migrants' classrooms. This study describes a new protocol developed within the framework of PTL to teach derivational morphology to L2 French and Greek adults. We used questionnaires to assess quantitatively and qualitatively the feasibility of the protocol, teachers' and learners' attitudes, and learners' emotions in the PTL lesson. A total of 141 migrant learners (79 forced migrants) and 13 teachers were involved in 23 2-hour lessons. Teachers and learners gave overall high evaluations of the feasibility of the PTL protocol and of their pleasure to teach and learn using PTL tools. Learners’ ease to learn and learning benefits were positively influenced by their perception of the proximity between their L1 and L2. Learners reported higher positive emotions and lower negative emotions during the PTL lesson, while forced migrants showed globally higher hope and shame than voluntary migrants, as well as higher benefits from PTL on enjoyment. These findings suggest that the use of pedagogical translanguaging in migrants’ classrooms is feasible and develops positive attitudes and emotions, which are more pronounced in forced migrants.

Background: States adopt different tax bases on smokeless tobacco (SLT), making tax incidence on SLT not directly comparable across states. In addition, how taxes are passed through to SLT prices among states that impose specific taxes, and whether the pass-through rates for SLT are affected by the uptake and evolution of e-cigarettes is unknown. Objective: This study will calculate the tax incidence on SLT and investigate how SLT taxes are passed to prices at the 25th, 50th, and 75th percentile levels, as well as whether these pass-through rates vary by e-cigarette uptake and evolution. Methods: We regressed SLT prices on specific taxes using Ordinary Least Square regressions while controlling for state-, year-, and quarter-fixed effects. We then tested the difference in tax pass-through rates by different periods. Findings: The average tax incidence on chewing tobacco, moist snuff, dry snuff, and snus is 22%, 22%, 23%, and 20%, respectively. For moist snuff, taxes were fully passed to prices at the 25th and 50th percentiles and overly passed to prices at the 75th percentile. The e-cigarette uptake and evolution significantly raised taxes by 13 cents and 14 cents per ounce, respectively, for moist snuff at the 75th percentile prices. Conclusions: There is room to additionally raise SLT taxes. Given lower tax pass-through rates for lower-priced SLT, price promotion restrictions and minimum pricing laws may be needed to increase the cost of low-priced products. Tobacco companies increase tax pass-through to premium SLT as e-cigarettes grow popular, like the pricing strategy adopted for cigarettes.

Litter decomposition is a crucial biochemical process regulated by microbial activities in forest ecosystem. However, the dynamic response of fungal community during litter decomposition to vegetation changes is not well understood. Here, we investigated the litter decomposition rate, extracellular enzyme activities, fungal community, and nutrient cycling-related genes in leaf and twig litters over a three-year decomposition period in a pure Liquidamabar formosana forest and a mixed L. formosana / Pinus thunbergii forest. The result showed that during the three-year decomposition, twig litter decomposed faster in the mixed forest than that in the pure forest. In both leaf litter and twig litter, β-cellobiosidase, N-acetyl-glucosamidase exhibited higher activities in the mixed forest, whereas phosphatase, β-glucosidase and β-xylosidase were higher in the pure forest. The fungal α-diversity were higher in both litters in the pure forest compared to mixed forest, with leaf litter showing higher α-diversity than twig litter. Fungal species richness and α-diversity within leaf litter increased as decomposition progressed. Within leaf litter, Basidiomycota dominated in the mixed forest while Ascomycota dominated in the pure forest. Funguild analysis revealed that symbiotroph and ectomycorrhizal fungi were more abundant in the mixed forest compared to the pure forest. In the third-year decomposition, genes related to phosphorus cycling was most abundant in both forests, with the pure forest having a higher abundance of cex and gcd genes. Fungal community structure, predicted functional structure, and gene composition differed between the two forest types and between the two litter types. Notably, the fungal functional community structure during the first-year decomposition was distinct from that in the subsequent two-year. These findings suggest that both dominant tree species, litter quality and decomposition time significantly influence the litter decomposition by recruiting different fungal community, thereby affecting the entire decomposition process.

In this research, we investigate the integration of an Intrusion Detection System (IDS) with a Digital Twin (DT) to enhance the cybersecurity of physical devices in cyber-physical systems. Using Eclipse Ditto as the DT platform and Snort as the IDS, we developed a near-realistic test environment that included a Raspberry Pi as the physical device and a Kali Linux virtual machine to perform common cyberattacks such as Hping3 flood attacks and NMAP reconnaissance scans. The results demonstrated that the IDS effectively detected Hping3-based flood attacks but showed limitations in identifying NMAP scans, suggesting areas for IDS configuration improvements. Furthermore, the study uncovered significant system resource impacts, including high CPU usage during SYN and ACK flood attacks and persistent memory usage after NMAP scans, highlighting the need for enhanced recovery mechanisms. This research presents a novel approach by coupling a Digital Twin with an IDS, enabling real-time monitoring and providing a dual perspective on both system performance and security. The integration offers a holistic method for identifying vulnerabilities and understanding resource impacts during cyberattacks. The work contributes new insights into the use of Digital Twins for cybersecurity and paves the way for further research into automated defense mechanisms, real-world validation of the proposed model, and the incorporation of additional attack scenarios. The results suggest that this combined approach holds significant promise for enhancing the security and resilience of IoT devices and other cyber-physical systems.

The research aims to study the economic struggles faced by Muslims residing in non-Islamic countries, where they encounter challenging circumstances in abiding by national tax laws and Islamic Sharia principles. The economic conditions not only make it arduous to adhere to their religious obligations but also put the Muslim community in disadvantageous position. The study primarily focuses on exploring the treatment of the third pillar of Islam, known as "Zakah,” and how it loses its relevance due to various reasons that exist in the non-Islamic societies. The research delves deeply probes into the reasons and examines why an investment in precious metals is comparatively less fruitful for a Muslim than for a non-Muslim due to Zakah obligations. It also investigates how Zakah fund, in the absence of a monitoring authority is unable to make a difference in the poverty conditions of Muslims in a non-Islamic societies non-Islamic societies.

Infection-related cardiovascular diseases (CVDs) represent a significant health challenge, necessitating novel therapeutic strategies targeting key receptors involved in inflammation and infection. Antimicrobial peptides (AMPs) offer a promising approach, potentially disrupting pathogenic processes. This study aimed to investigate the efficacy of AMPs as therapeutic agents by examining their interactions with critical CVD-related receptors. A comprehensive computational approach was utilized to assess the interactions between AMPs and receptors associated with CVDs. Molecular docking studies were conducted to evaluate AMP binding to target receptors: ACE2, CRP, MMP9, NLRP3, and TLR4. The top-performing AMPs were further analyzed through 100 ns molecular dynamics (MD) simulations, and their binding affinities were quantified using MM/PBSA calculations. The analysis revealed that Tachystatin, Pleurocidin, and Subtilisin A exhibit strong binding affinities to key CVD-related receptors, including ACE2, CRP, and MMP9. These AMPs demonstrated the potential for disrupting receptor-peptide interactions critical to infection and inflammation. MD simulations confirmed the stability of AMP-receptor complexes, with MM/PBSA calculations showing significant binding energies. Future directions include conducting in vitro and in vivo studies to validate the therapeutic efficacy and safety of these AMPs in clinical settings.

The global prevalence of cancer is on the rise, and while targeted therapies, early diagnosis, im-munotherapies, and conventional personalized chemotherapy have significantly upgraded out-comes for cancer patients, there is still a significant likelihood that cancer cannot be fully eradi-cated. Cancer stem-like cells (CSCs) and Epithelial-mesenchymal transition (EMT) exhibit pivotal roles in cancer cell metastasis, tumor recurrence, chemotherapy resistance, and cancer cell death, among other processes. This review aims to elucidate the molecular mechanisms underlying EMT and CSCs, their interplay, associated signaling pathways, and regulation of the EMT process and the initiation of CSCs. EMT induction confers mesenchymal properties and the potential of cancer cells to adopt the CSC state. These processes are intricately associated with the regulation of Notch and Wnt signaling pathways, transforming growth factor-β (TGF-β), as well as the expres-sion of miRNAs. Further exploring the relationship between EMT and CSCs is essential for the development of new chemotherapeutic strategies and the identification of novel therapeutic tar-gets, and hence, the development of therapeutic strategies targeting EMT, or CSCs holds promise for improving cancer therapy.

Cytokines play a substantial role in the pathophysiology of infectious diseases. Consequently, genetic polymorphisms in cytokine genes can have profound effect on the susceptibility to or protection from infections and also influence the clinical manifestations, disease severity as well as outcome. The objective of this brief narrative review is to provide an update on the recent research findings on the role of genetic polymorphisms of IL-18, a recently discovered cytokine on certain infectious diseases of public health relevance. The current review suggests there is a significant impact of IL-18 polymorphisms on different infectious diseases. The presence of G and T alleles at position -656 is suggested to have a probable protective effect against VL. The −137G/−607C (GC) and −137C/−607C (CC) haplotypes in the promoter region are associated with increased susceptibility to severe malarial anemia. SNPs rs544354 and rs574429 (GC) are linked to a higher risk of P. falciparum infection and increased parasitemia. The -137 C/C genotype and -137 G allele (with G/G genotype) are associated with faster progression to AIDS. In contrast, the IL-18 genotype does not seem to affect overall HIV susceptibility but influences disease progression. The G and GG genotypes at position -137 and allele A at position -607 are likely associated with protection against HBV infection. Conversely, the C allele at position -137 is linked to increased susceptibility to HBV infections. The GG genotype of IL-18 -137 G > C variant is associated with increased susceptibility to pulmonary tuberculosis (PTB). The AC genotype of IL-18 -607A>C has been linked to PTB susceptibility, particularly in patients with co-morbid diabetes mellitus. SNPs −137G/C, 113T/G, and +127C/T are associated with susceptibility to H. pylori infection, indicating that these genetic variants may influence the risk of H. pylori-related diseases. There are conflicting results in studies conducted in different populations (e.g., genetic polymorphism, studies conducted in India and China against HBV infection) highlighting the need for further research with larger sample sizes to resolve discrepancies. Due to the variability in findings, extensive studies in diverse populations are needed to better understand the impact of IL-18 polymorphisms.

The paper considers two models of queuing with a varying structure based on the introduction of additional transient intensities into known models or their combinations, which create stationary distributions convenient for calculation. In the first model, it is a probabilistic mixture of known stationary distributions with given weights. In the second model, this uniform distribution is repeatedly used in physical statistics. Both models are based on the selection of states, between which additional transient intensities are introduced. The algorithms used in the paper for introducing new transient intensities are closely related to the concept of flow in a deterministic transport network. The introduced controls are selected so that the marginal distribution of the combined system is a mixture of the marginal distributions of the combined systems with different weights determined by the introduced transient intensities. As a result, the process of functioning of the combined system is obtained by switching processes corresponding to different combined systems at certain points in time.

Introduction: Arterial hypertension and obesity are two major risk factors for cardiovascular morbidity and mortality closely linked. The objective of this study was to describe the epidemiological and clinical characteristics of hypertensive patients according to their level of obesity as proposed by the classification of the World Health Organization (WHO). Patients and Methods: Retrospective study (January 2022-April 2023) involving 230 hypertensive and obese adult patients whose mean age was 52.5 ± 12.05 years [Extremes: 24-85 years] followed on an outpatient basis and classified into 3 Classes according to their body mass index (BMI). Epidemiological, clinical parameters and cardiovascular risk stratification were analyzed. The relevance of the risk stratification method was analyzed by area under the curve (AUC). Statistical analysis was performed using SPPS version 26 software (SPSS Inc., Chicago, IL, USA). Results: Classes 1 (n=170; 73.9%), 2 (n=43; 18.7%) and 3 (n=17; 7.4%) were identified. There were significantly more women than men in Class 3 (9.7% versus 2.7%; p=0.001). The risk of being diabetic (OR=0.135;95%CI=0.034–0.541;p=0.005), of having angina pain (OR=0.084;95%CI=0.009–0.790;p=0.030) or developing chronic renal failure (OR=2.416;95%CI=1.471–85.268;p=0.020) was significantly higher in Class 3 than in Class 1. According to cardiovascular risk stratification, 152 patients (66.1%) were at high and very high risk. The level of cardiovascular risk of our patients was not related to the degree of obesity based on body mass index (p=0.32). The risk stratification method was relevant (AUC˃0.70;p˂0.001). Conclusion: Morbid obesity exposes hypertensives more to cardiovascular, renal and metabolic complications.

Recent advancements in Artificial Intelligence (AI) and generative models have significantly accelerated the evolution of internet services. In data centers, where data is often distributed across multiple machines, leveraging these machines to expedite the training of machine learning applications is common. However, this approach incurs substantial communication overhead. To mitigate this, data compression techniques such as quantization, sparsification, and dimension reduction are gaining attention, especially with iterative training algorithms that frequently exchange gradients. With the advancement of FPGA technology, FPGA-based bump-in-the-wire network interface controllers (smartNICs) have demonstrated notable benefits for data centers, matching ASIC switches in port-to-port latency and capacity. However, FPGA-based switches often have spare hardware resources beyond their switching functions, which are typically wasted. we propose a novel architecture that integrates FPGA-based switches with ensemble learning methods, utilizing these spare resources on FPGA-based network switches. This architecture employs random projection as a compression method to accelerate machine learning tasks while maintaining high accuracy. Our system accelerates the training process for Multilayer Perceptron (MLP) models with a speedup of 2.1-6.7 times across four high-dimensional datasets. In addition, it leverages the Hamming distance metric for k-Nearest Neighbors (kNN) classification, combining random projection with Hamming encoding, to offload large-scale classification tasks from downstream servers. This achieves a worst-case speedup of 2.3-8.7 times and a memory reduction of 3.9-27.7 times compared to CPU-based kNN classifiers using cosine distance. Our architecture addresses the challenges of high-dimensional data processing and exemplifies the potential of AI-driven solutions in accelerating and optimizing internet services. Moreover, the programmability of FPGAs allows the system to adopt various compression methods, extending its applicability. Offloading tasks to FPGAs can significantly reduce query response times, showcasing a crucial step towards achieving greater levels of autonomic computing and the rapid evolution of internet systems.

Artificial Intelligence (AI) is increasingly becoming integral to medical practice, potentially enhancing outcomes in thoracic surgery. AI-driven models have shown significant accuracy in diagnosing non-small cell lung cancer (NSCLC), predicting lymph node metastasis, and aiding in the efficient extraction of electronic medical records (EMR) data. Moreover, AI applications in robotic-assisted thoracic surgery (RATS) and perioperative management reveal potential in improving surgical precision, patient safety, and overall care efficiency. Despite these advancements, challenges such as data privacy, biases, and ethical concerns remain. This manuscript explores AI applications, particularly machine learning (ML) and natural language processing (NLP), in thoracic surgery, emphasizing their role in diagnosis and peri-operative management. It also provides a comprehensive overview of the current state, benefits, and limitations of AI in thoracic surgery, highlighting future directions in the field.

Installation of offshore wind farms is rising, driven by the goal of changing the global energy matrix. However, many of its possible impacts are still unknown. Increased noise levels, disruptions to food chains, pollution due to traffic, and impacts on fishing communities and tourism are all potential effects to consider. Marine habitats are essential carbon dioxide sinks. Therefore, losing marine biodiversity due to offshore wind farms can be counterproductive in mitigating climate change. Balancing biodiversity conservation, wind potential, and political interests is challenging. Today, Uruguay has practically decarbonized its electrical matrix, incorporating wind, solar, and biomass energy alongside hydroelectricity. In line with this, the country's Hydrogen Roadmap highlights green hydrogen as relevant, potentially serving as a transportation fuel for both domestic consumption and export. Combining the country's strong base of wind experience with its sustainable policy, it plans to implement offshore wind farms to produce green hydrogen, making studies of its impacts crucial. This paper reviews the current social and environmental information on the Uruguayan coastal habitat, analyzes onshore wind farms' ecological studies, and examines offshore wind farms' global environmental and social impacts. Finally, it proposes suggested studies for environmental approval for offshore wind farms.

Harms of combustible cigarette (CC) use in pregnancy for fetal development are well-studied. Less understood is potential impacts of newer non-combustible cigarette alternatives, including electronic cigarettes (EC). Our goal was to examine whether EC use during pregnancy predicts increased risk of adverse birth outcomes. This retrospective cohort study used data from the Obstetrics Initiative (OBI), a statewide collaborative of 70 maternity hospitals. OBI’s clinical registry of data on nulliparous, term, singleton, vertex fetal presentation pregnancies were from medical records. Three groups of pregnancy cigarette users (Controls (n=26,394), CC (n=2,216), EC (n=493) were compared on birth outcomes, controlling for background differences. Compared to controls, the EC group had significantly lower birth weight, while CC had reduced birthweight and greater rates of arterial cord pH < 7.1. Compared to EC, CC users had higher rates of neonates requiring antibiotics and NICU admission. Growing evidence suggests EC are not safer alternatives to CC, and use during pregnancy should be discouraged. Additional research is needed, as non-significant trends for increased risk of several adverse neonatal outcomes following EC use were found, potentially significant in larger studies with average-risk for adverse pregnancy outcomes and when frequency and timing of EC exposure are considered.

Nurses continue to experience intense psychological repercussions despite the official conclusion of the COVID-19 pandemic. In this cross-sectional study conducted after the end of the pandemic crisis, in nurses, we evaluated the levels and explored the associations between anger, insomnia and resilience. A total of 442 nurses participated in an online survey and were invited to state their work experience, gender and age and complete the self-report measures of the Dimensions of Anger Reactions-5 (DAR-5), the Athens Insomnia Scale (AIS) and the Brief Resilience Scale (BRS). 62.1% of participants presented with positive scores on the AIS, 41.5% displayed positive values on the DAR-5 scale, whereas 24.9% of nurses demonstrated scores indicative of low resilience on the BRS scale. Regression analysis revealed that 23.5% of the variance in the AIS scores can be attributed to the DAR-5 scores and 3% to the BRS scores. Mediation analysis confirmed the protective role of resilience contributing as a negative mediator in the DAR-5 and the AIS relationship. Screening for insomnia symptoms and anger issues among nurses after the end of the pandemic and implementing appropriate interventions is considered imperative to avoid long-term health consequences.

From the evening of 1st May 2023 to 3rd May 2023, severe hydro-meteorological events occurred in the Italian Emilia-Romagna region, particularly in the provinces of Forlì-Cesena, Ravenna, Bologna, Modena, and Reggio Emilia. Additionally, on 16th-17th May, another intense meteorological event occurred, impacting the Romagna provinces. Such events caused extensive flooding, landslides, isolation of many areas, evacuation of numerous families, and significant damages to infrastructure, agriculture, buildings, and essential services. Many hilly municipalities were cut off, and thousands of civilians had to be evacuated, losses of life occurred. The consequences beyond the recorded immediate impacts on infrastructure and life were impressive, and extended to the regional economy, specifically in the Fruit Valley, where, in addition to immediate yield losses, long-term damage to orchard production is expected due to persistent flooding. The civil and cultural building heritage has also been heavily affected, both in the countryside and in inhabited centers. Some of the damage, direct and indirect, caused by flooding on buildings will also see an evolution in the medium to long term that will have to be addressed. This paper analyzes the reasons that led to the atmospheric phenomenon to understand the increasing potential occurrence of similar events from the perspective of climate change. This paper document analyzes the causes and effects of these events, additionally considering the future impacts of similar hazards potentially occurring in the context of climate change. It is crucial indeed to remark how such events, when occurring in high density agricultural areas, have the potential to threat the entire regional production system for extended periods. For example, the alteration of direct physiological processes due to root drowning may compromise production of entire productive agricultural extensions and many years will be required to their replacement.

This study addresses the gap in research on the application of thermochromic polymers (TPs) in food packaging and their potential for real-time temperature monitoring, aiding in the assessment of food quality and shelf-life. TPs exhibit a visible color change in response to temperature varia-tions. A comprehensive systematic review (SR) across multiple engineering peer-review databases using predefined terms was conducted. Additionally, international patent databases were inves-tigated using the same predefined terms. Independent experts reviewed the methodology to identify and address potential biases. A total of 288 eligible articles and 922 patents were identified. After a duplicate selection and extraction process according to the inclusion criteria, four related full-text publications were selected from the initial 288 articles, and five relevant patents were selected from the 922 patents. The qualitative review suggests that TPs hold significant promise as food packaging materials due to their unique physical properties. The study concludes that TPs offer valuable properties for the food packaging industry, meriting further investigation to exploit their benefits fully.

The study assessed the impact of different diets on gastrointestinal pH, intestinal microbiota, digestive tract morphometry, and productive performance in guinea pigs. A total of 160 improved genotype guinea pigs were used, distributed into three groups with different diets: alfalfa (T1), alfalfa + concentrate (T2), and concentrate (T3), to which a mixture of probiotics combined with vitamins, minerals was added and amino acids in T2 and T3. Gastrointestinal pH showed significant differences between treatments in the various segments of the digestive tract. The intestinal microbiota varied according to the diet, with Escherichia coli being the predominant bacterium in weaned guinea pigs. Intestinal morphometry was significantly altered with the addition of a reinforced probiotic mixture, improving the length and density of intestinal villi. Productive parameters such as weight gain, feed consumption, and feed conversion were better in guinea pigs fed a mixed diet with 0.5% probiotic mixture. These results indicate that intestinal pH may influence microbial composition, which impacts productive efficiency and immune health, highlighting the importance of balanced nutrition and supplementation to optimize the health and performance of guinea pigs.

Robotic systems are crucial in modern manufacturing. Complex assembly tasks require the collaboration of multiple robots. Their orchestration is challenging due to tight tolerances and precision requirements. In this work we set up two Franka Panda robots performing a peg-in-hole insertion task. We structure the control system hierarchically, planning the robots’s trajectories feedback-based with a central policy trained with reinforcement learning. These trajectories are executed by a low-level impedance controller on each robot. To enhance training convergence, we use reverse curriculum learning incorporating domain randomization, varying initial configurations of the task. After training, we test the system in a simulation, studying the impact of curriculum parameters on emerging process characteristics like process time and variance. Finally, we transfer the trained model to a real-world setup, comparing results with simulation as well as classical path planning and control approaches.

Background/Objectives: Cardiovascular diseases are one of the leading causes of morbidity and mortality worldwide. Health-related quality of life is crucial to assess the impact of cardiovascular diseases and to guide therapeutic strategies. The Short Form 36 Health Survey and the RAND 36-Item Health Survey questionnaires are common tools for measuring health-related quality of life in patients with cardiovascular disease, but their reliability may vary according to the population studied. The aim of this study is to compare the reliability of the SF-36 and the RAND-36 in a population with cardiac pathology, addressing the question of which of these instruments offers a more consistent and useful measurement in this specific group. Methods: Cross-sectional observational study carried out at the University Hospital of Burgos (Spain). 413 patients with cardiovascular pathology referred to the Cardiac Rehabilitation Unit were included. Patients with incomplete data or who did not participate in the program were excluded. Internal consistency (Cronbach’s alpha), item-total correlation and reliability and a half-half analysis were performed. Results: Both questionnaires showed similar and adequate reliability for patients with cardiovascular pathology. Internal consistency, as measured by Cronbach’s alpha, was above 0.80 for most dimensions, supporting its robustness. Significant inter-item and inter-dimension correlations were found in both scales, except in some specific cases in the dimension ‘Physical Functioning’. The half-half analysis confirmed the good reliability of both scales. Conclusions: Both the SF-36 and the RAND-36 are highly reliable tools for assessing health-related quality of life in patients with cardiovascular disease. The results may have significant implications for clinical practice, helping in the selection of health-related quality of life monitoring instruments and in the evaluation of the efficacy of therapeutic interventions.

This study evaluated the impact of rough, smooth, and very smooth Titanium (Ti) and zirconia (Zr) surfaces on the neutrophil expression of elastase (NE), matrix metalloproteinase (MMP)-8, MMP-9 and extracellular traps (NETs). Ti and Zr disks with a diameter of 10mm and a thickness of 1.5mm were processed using diamond-impregnated polishing burs and paste to generate rough (R; Ra= > 3µm), smooth (S; Ra= ≥1 to 1.5µm) and very smooth (VS; Ra= <0.1µm) surfaces. Peripheral neutrophils from Wistar rats were cultured on these surfaces and the culture supernatants subsequently analyzed for the presence of NE, MMP-8, and MMP-9 by ELISA, while NETs formation was demonstrated immunohistochemically by staining neutrophils with CD16b and DNA with DAPI. Overall, the expression of NE and MMP-8 were significantly higher from neutrophils cultured on both on Ti and Zr rough surfaces compared to the very smooth surface (R>S>VS) after 2h and 4h of culture. The expression of MMP-9 also increased with culture time however no significant surface effects on expression were observed. Similarly, rough Ti and Zr surfaces (R & S) also showed significantly larger NETs formation compared to the very smooth surface (VS) after 4h and 8h culture. Our findings suggest that increasing surface roughness on Ti and Zr triggers higher secretion of NE, MMP-8 and NETs formation.

When dealing with complex models in real situations, many optimization problems require using more than one objective function to represent the relevant characteristics of the system under consideration adequately. This is why multiobjective optimization algorithms that can deal with several objective functions are required in order to obtain reasonable results in an adequate processing time. This paper presents the multiobjective version of a recent metaheuristic algorithm that optimizes a single objective function, known as the Majority-minority Cellular Automata Algorithm (MmCAA), inspired by cellular automata operation. The algorithm presented here will be known as the Multiobjective Majority-minority Cellular Automata Algorithm (MOMmCAA). The MOMmCAA adds repository management and multiobjective search space density control to complement the performance of the MmCAA and make it capable of optimizing multiobjective problems. To evaluate the performance of the MOMmCAA, benchmark test sets are taken (DTLZ, quadratic, and CEC-2020), along with real-world engineering design problems, compared against other multiobjective algorithms recognized for their performance (MOLAPO, GS, MOPSO, NSGA-II, and MNMA). The results obtained in this work show that MOMmCA produces comparable performance with the other metaheuristic methods, which shows it as a competitive algorithm to be used for multiobjective problems. MOMmCAA was implemented in MATLAB, and its source code can be consulted in GitHub. https://github.com/juanseck/MOMmCAA

The role of trees in watershed hydrology is governed by many environmental factors along with their inherent characteristics and not surprisingly has generated into diverse debates in the literature. Herein, this state-of-the-art review provides an opportunity to propose a conceptual model for understanding the role of trees in watershed hydrology and examine the conditions under which they can be an element that increases or decreases water supply in a watershed hydrology. To achieve this goal, this review addressed the interaction of forest cover with climatic conditions, soil types, infiltration, siltation and erosion, water availability, and the diversity of their ecological features. The novelty of the proposed conceptual model highlights that tree species and densities, climate, precipitation, type of aquifer, and topography are important factors affecting the relationships between trees and water availability. This suggests that forests can be used as a nature-based solution for conserving and managing natural resources, including water, soil and air. To sum up, forests can reduce people’s imprint, thanks to their role in improving water and air quality, conserving soil, and other ecosystem services. The outcomes of this study should be valuable for decision-makers when investing in reforestation in a watershed hydrology.

Since the 2005 discovery of the first enzyme capable of depolymerizing polyethylene terephthalate (PET), an aromatic polyester once thought to be non-biodegradable, extensive research has been undertaken to identify and engineer new biocatalysts for plastic degradation. This effort was directed towards developing efficient enzymatic recycling technologies that could overcome the limitations of mechanical and chemical methods. These enzymes are versatile molecules obtained from microorganisms living in various environments, including soil, compost, surface seawater, and extreme habitats such as hot springs, hydrothermal vents, deep-sea regions, and Antarctic seawater. Among various plastics, PET and polylactic acid (PLA) have been the primary focus of enzymatic depolymerization research, greatly enhancing our knowledge of enzymes that degrade these specific polymers. They often display unique catalytic properties that reflect their particular ecological niches. This review explores recent advancements in marine-derived enzymes that can depolymerize synthetic plastic polymers, emphasizing their structural and functional features that influence the efficiency of these catalysts in biorecycling processes. Current status and future perspectives of enzymatic plastic depolymerization are also discussed, with a focus on the underexplored marine enzymatic resources.

INTRODUCTION: This study focusses on diagnosis of stages of AD (Alzheimer’s disease) including MCI (Mild Cognitive Impairment) from two data modalities - gene expression and clinical data of ADNI (Alzheimer’s Disease Neuroimaging Initiative ) participants using multiclassification. The gene expression dataset is highly imbalanced and of HDLSS (high-dimensional and low-sample-size) characteristics. This is the only study where multiclassification based AD stage diagnosis is done to identify multiple stages of Alzheimer. We are able to achieve the best multiclassification result in both the modalities and identify new genetic biomarkers. METHODS: Combination of XGBoost and SFBS (“Sequential Floating Backward Selection”) methods is used to select features. We are able to select the most effective 95 gene probsets out of 49,386. For clinical study data, 8 most effective biomarkers could be selected using SFBS. For both genomic and clinical data, DL (‘Deep Learning’) classifier is used to identify stages - CN (Cognitive Normal), MCI (Mild Cognitive Impairment), AD (Alzheimer’s Disease / Dementia). Because of high data imbalance in genomic data, border line oversampling is used for model training and original data for validation. RESULT & DISCUSSION: With clinical data, we achieved ‘ROC AUC’ scores 0.97, 0.95, 0.94 for CN, MCI, Dementia stage respectively . We achieve ‘ROC AUC’ scores 0.75, 0.74, 0.70 for CN, MCI, Dementia stage respectively and 0.67 for both micro average F1 scores and micro weighted F1 score. This is the best result so far for AD stage diagnosis from gene expression profile data through multiclassification with ADNI data. Results reflect that our multiclassification model can efficiently handle the imbalanced data of HDLSS nature to identify samples of minority class. MAPK14, ZNF835, MID1, HLA-DQA1, TEP1 are some of the new genes found to be associated with AD risk. DRAXIN, HSPA12B, USP47 etc. are found to be AD preventive or suppressor.

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.

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&lt;0.001), within cortical defects enhancing sensory response and blood flow regulation. Cold exposure also elevated hypoxia (p &lt; 0.01) and VEGF expression (p &lt; 0.001), promoting angiogenesis vital for bone regeneration. Increased expression of osteogenic proteins PGC-1α (p = 0.039) and RBM3 (p&lt;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.

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.