This research aims to propose a novel approach to evaluate and minimize the scrap rate in the industrial production of premium food cans with distortion printing. Beyond cost considerations, a critical aspect of modern food can manufacturing is the aesthetic quality of the graphical display. In addition to traditional formability requirements, a waving requirement is defined. Detailed real production conditions are provided and discussed. The material of interest is a double cold-reduced (DR) low-carbon steel sheet and chromium-coated tin-free steel with a thickness of 0.16 mm. These sheets are laminated on both sides with PET film before distortion printing on the exterior. A material parameter identification method is proposed and illustrated to address the challenges in characterizing such a thin sheet. The thickness profile and flange length are key criteria for this identification. Digital image correlation (DIC) and a microscope are used to measure the thickness distribution and flange length. Within the manufacturing system, uncertainties arising from material properties and forming processes can lead to scraps or defects. Finite element analysis (FEA) is adopted for process analysis and validated with experiment. Uncertainty propagation via metamodeling, employing radial basis function (RBF) neural networks, is adopted for the scrap rate evaluation. The study concludes with process optimization recommendations to reduce scrap.

Background: Breast cancer incidence and mortality exhibit a rising trend globally among both premenopausal and postmenopausal women suggesting that there are serious errors in our preventive and therapeutic measures.Purpose: Providing a series of valuable, but misunderstood inventions highlighting the role of increasing estrogen signaling in prevention and therapy of breast cancer instead of its inhibition. Results: 1. Breast cells and breast cancer cells with germline BRCA1/2 mutations similarly show defect in liganded estrogen receptor (ER) signaling demonstrating its role in genomic instability and cancer initiation. 2. In breast cancers, the increased expression of certain receptor family is an effort for self directed improvement of genomic defects, while the weakness or loss of receptors indicates a defect requiring medical repair. 3. ER overexpression in breast cancer cells is capable of strengthening estrogen signaling and DNA repair, while in ER negative tumors, HER2 overexpression tries to upregulate unliganded ER activation and genome stabilization. 4. ER positive breast cancers responsive to endocrine therapy may show a compensatory ER overexpression resulting in a transient tumor response. Breast tumors, non responsive to endocrine therapy, show HER2 overexpression for compensation the complete blockade of liganded ER activation. 5. In breast tumors, somatic mutations serve upregulation of liganded or unliganded activation of ERs helping genome stabilization and apoptotic death. 6. The dynamic communication between breast cancer and its microenvironment is a wonderful collaboration among molecular players fighting for genomic repair and apoptosis in the tumor. 7. In breast cancers, there is no resistance to genotoxic or immune blocker therapies, but rather the non responsive tumor cells exhausted all compensatory possibilities against therapeutic damages. Conclusion: Understanding the behavior and ambition of breast cancer cells may achieve a turn in therapy via applying a supportive care instead of genotoxic measures.

Safety signal detection, an integral component of Pharmacovigilance (PhV), aims to identify new or known adverse events (AEs) resulting from the use of pharmacotherapeutic products. Post-marketing spontaneous reports from different sources are commonly utilized as a data source for detecting these signals but there are underlying challenges arising from data complexity. This paper investigates the implementation of the Apriori algorithm, a popular method in association rule mining, to identify frequently co-occurring drugs and AEs within safety data. We discuss previous applications of the Apriori algorithm for safety signal detection and conduct a detailed study of an improved method specifically tailored for this purpose. This enhanced approach refines the classical Apriori method to effectively reveal potential associations between drugs/vaccines and AEs from post-marketing safety monitoring datasets, especially when AEs are rare. Detailed comparative simulation studies across varied settings, coupled with the application of the method to vaccine safety data from the Vaccine Adverse Event Reporting System (VAERS), demonstrate the efficacy of the improved approach. In conclusion, the improved Apriori algorithm is shown to be a useful screening tool for detecting rarely occurring potential safety signals from the use of drugs/vaccines using post-marketing safety data.

Cancer, notably breast cancer, is a significant health concern globally, with a substantial burden in Asia, particularly in Pakistan. Late diagnosis exacerbates mortality rates, emphasizing the importance of robust screening programs. This article examines the effectiveness of breast cancer screening initiatives and intervention four years after initial pilot study that spanned 2015-2019, using data from the Islamabad Federal Breast Screening Centre, to assess readiness for national guideline implementation in Pakistan and beyond. Methods: This retrospective cross-sectional study, conducted at the Federal Breast Screening Center (FBSC), involved 5,580 patients undergoing mammography screening over an four-year period from 2015 to 2023. The study examined patients' data, including Breast Imaging Reporting and Data System (BIRADS) categories through double reading mammography and complementary ultrasound (USG) findings. Demographic information, including age, ethnicity, education status, socio-economic factors, breastfeeding history, breast density, and family history, was recorded to analyze its correlation with breast cancer diagnosis and subsequent management. Data analysis was performed using SPSS 2021. Results: The study included a total of 5580 patients who visited the Federal Breast Screening Centre from 2020 to 2023. The mean age of patients attending screenings from 2020 to 2023 was 48.50 ± 9.55 years, with the most common age group being 41-45 years 1,364 (24.4%), followed by 46-50 years 1,008 (18.1%). The ethnicity with the highest frequency was Punjabi 4,647 (83.3%). The distribution of patients across BI-RADS categories was as follows: BI-RADS 0 (n=175, 3.10%), BI-RADS I (n=1,933, 34.60%), BI-RADS II (n=1,691, 30.30%), BI-RADS III (n=642, 11.50%), BI-RADS IV (n=612, 11.00%), BI-RADS V (n=389, 7.00%), and BI-RADS VI (n=138, 2.50%). The prevalence rate of invasive breast cancer during these four years was 10.4%. Conclusion:In conclusion, breast cancer poses an escalating challenge associated with increasing mortality rates. Pakistan, alongside its South Asian counterparts, is witnessing a surge in cases, emphasizing the urgency for comprehensive interventions. Research endeavors such as this provide valuable insights into demographic profiles, aiding in the formulation of tailored guidelines. Moreover, the establishment of additional federal centers for screening is imperative to enhance patient access and mitigate the issue of delayed diagnoses, aligning Pakistan's healthcare infrastructure with that of developed nations.

Stagnating low renovation rates, vacancies and underused town centers, unprecedentedly high levels of land sealing and excessive consumption of resources through the construction of new buildings and infrastructure as well as the effects of climate change were some of the reasons for carrying out the monumentum ad usum research project.Thermodynamic simulations were used to carry out in-depth analyses of specific building parameters such as energy balances and how they are influenced by the effects of climate change. Life cycle analyses under ecological and economic aspects examined the aspects of sustainability and the individual behaviour of predominantly listed existing buildings under standardized conditions.The project results show that sustainable refurbishment can significantly increase the attractiveness of using historic and listed buildings for non-profit building owners. The results and conclusions of the project are as varied as the recommendations derived from them. In practice, old buildings consume significantly less energy than predicted in the structural calculations. Correction factors are to be developed and thermodynamic simulation calculations are to be used instead of energy performance certificates for more realistic forecasts of energy requirements and emission savings potential. The aspects of sustainability in refurbishment are essential for the protection of our architectural heritage.

Interaction with social agents employing natural language have been discussed especially since Weizenbaum (1966) observed the social effects of his chatbot named 'Eliza', which imitated a cli-ent-centered conversation. With Nass and Reeves’ media equation theory and computers as social actors paradigm, a well-tested framework has been introduced to the study of human-computer interaction. In the 1990s, there was a renewed focus on natural language analysis, which has be-come a crucial area of research in machine learning. Recent advancements in data processing and storage capacities have led to the development of AI-generated utterances that closely resemble human utterances. As a result, AI-based systems can now mimic social relationships to a signifi-cant extent. In light of these technological developments, the article explores whether quasi-social relationships should be considered within the field of social psychology and examines the poten-tial advantages and challenges of this approach.

In the rapidly evolving landscape of AI-supported programming education, there's a growing interest in leveraging such tools to support students helping about what good code makes out. This study investigates the impact of ChatGPT on code quality among part-time undergraduate students enrolled in introductory Java programming courses. With no prior Java experience, students from two separate groups completed identical programming exercises emphasizing coding conventions and code quality. Utilizing static code analysis tools, we assessed adherence to a common coding convention ruleset and calculated cyclomatic and cognitive complexity metrics for submitted code. Our comparative analysis highlights significant improvements in code quality for the ChatGPT-assisted group (treatment group), with marked reductions in rule violations and both cyclomatic and cognitive complexities. Specifically, the treatment group demonstrated greater adherence to coding standards, with fewer violations across several rules, and produced code with lower complexity. These results suggest that ChatGPT can be a valuable tool in programming education, aiding students in writing cleaner, less complex code and better adhering to coding conventions. However, the study's limitations, including the small sample size and the novice status of the participants, necessitate further research with larger, more diverse populations and in different educational contexts.

Traffic accidents are among the most common causes of death worldwide. According to statistics from the World Health Organization (WHO), 50 million people are involved in traffic accidents every year. Canada, particularly Montreal, is not immune to this problem. Data from the Société de l’Assurance Automobile du Québec (SAAQ) shows that there were 392 deaths on Québec roads in 2022, 38 of them related to the city of Montreal. This value represents an increase of 29.3% for the city of Montreal compared to the average for the years 2017 to 2021. In this context, it is important to take concrete measures to improve traffic safety in the city of Montreal. In this article, we present a web-based solution based on machine learning that predicts the severity of traffic accidents in the city of Montreal. This solution uses dataset of traffic accidents that occurred in Montreal between 2012 and 2021. Classification algorithms such as eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Random Forest (RF) and Gradient Boosting (GB) were used to develop the prediction model. When evaluating the prediction model, performance metrics such as precision, recall, F1 score, and accuracy are taken into account. The performance analysis shows an excellent accuracy of 96% for the prediction model based on the XGBoost classifier. The other models (CatBoost, RF, GB) achieved 95%, 93% and 89% accuracy, respectively. The prediction model based on the XGBoost classifier was deployed using a client-server web application managed by Swagger-UI and the Flask Python framework.

With the increase in technological advancements, architects and engineers over the past couple of decades have been trying to achieve high efficiency within construction projects. Their goals also include reducing global warming effects from construction and building new construction faster for the growing population in urban cities. This requires using a construction method that is less harmful to the environment and faster than other traditional methods. Modular construction, also known as modular construction, is the method that has the potential to make this happen. This review paper discusses what modular construction is and how it is different from the traditional on-site construction. Environmental impacts of using modular construction and what leads to it being more efficient and faster than on-site construction. The study doesn’t suggest that modular construction is the solution to all the problems being faced in the construction industry today, but it is a potential solution that if used in the right situation and correct manner, can have a huge positive impact on the construction industry.

Objective: To analyze prevalence of tuberculosis among the people who presented PIMS with complaints of fever, cough, difficulty in breathing during COVID pandemic.Study Design: Retrospective/ Cross-sectional study.Place and Duration: Department of Radiology, Pakistan Institute of Medical Sciences, Islamabad. 2020-2023. Methods: Total 17121 patients who presented PIMS during COVID pandemic with complaints of high grade fever, cough, difficulty in breathing ,were included in this study. Following the acquisition, the HRCT scans were conducted by a skilled and experienced technician and researcher, utilizing GE Healthcare equipment for HRCT Chest procedures. The scans covered the lung apices to the bases and were executed with maximal inspiration, employing a CT scanner from Toshiba Aquilion. Frequency of TB among all cases was recorded. SPSS 21.0 was used to analyze all data.Results: This study incorporated a total of 17,121 patients, comprising 9,961 (58.1%) males and 7,160 (41.8%) females. The mean age of the enrolled cases was 49.11±7.67 years, demonstrating consistency with the findings of a prior study [33]. This study also examined the prevalence of Tuberculosis in different years based on imaging findings of HRCT chest as follows: in 2020, 582 cases out of 2,450, equivalent to 23.7%; in 2021, 2,377 cases out of 9,584, equivalent to 24.8%; in 2022, 1,099 cases out of 3,913, equivalent to 28.1% and in 2023, 295 cases out of 1,174, equivalent to 25.1%. The average prevalence of TB cases from 2020 to 2023 was determined to be 4,353 cases out of 17,121, which is equal to 25.42%.Conclusion: As per the National Tuberculosis Program (NTP) in Pakistan, the prevalence rate of tuberculosis stands at 340 cases per 100,000 population, equivalent to 0.34%. However, our study reveals an exceptionally high frequency of TB cases. Specifically, the prevalence of Tuberculosis in 2020 was 23.7% (582 out of 2,450), in 2021 it was 24.8% (2,377 out of 9,584), in 2022 it surged to 28.1% (1,099 out of 3,913), and in 2023 it was 25.1% (295 out of 1,174). The average prevalence of TB cases over the period from 2020 to 2023 was calculated at 25.4% (4,353 out of 17,121). Consequently, our findings suggest the presence of a substantial tuberculosis burden in Pakistan, equating to a TB Tsunami in Pakistan.

Isokinetic testing of the knee joint is the “gold standard” in strength assessment in sport especially in soccer, where it is part of pre-season screening to establish norms in case of injuries. There are many devices on the market that make it difficult to use of normative data. Isokinetics literature in females’ soccer players is scarce in comparison to males. Furthermore, evaluation protocols strongly differ from each other, using different ranges of speeds, movement patterns and output data. Thus, it is difficult for the researcher and the clinician to choose a correct protocol for isokinetic evaluation in female soccer players. The indexes of isokinetic performance that are widely accepted are the hamstring/quadriceps (H/Q) ratio and dominant/non-dominant (D/ND) ratio as reference. In this review are highlighted some common features of isokinetic testing in female soccer players of different levels of qualification. From this study, it emerges that, for a reliable test, 3 reps at the speed of 60°/sec in knee flexion/extension is preferable due to the high reliability and that an optimal H/Q ratio in healthy female soccer players of different level of qualification and age is 50%, while the interlimb differences (D/ND) should be below 10%.

Studies on evolution have made significant progress in multiple disciplines, but evolutionary theories remain scattered and controversial. Here we deduce that, thermodynamically, many carbon-based entities (CBEs) on the Earth tend to absorb energy from widespread relatively temperate heat streams on the Earth flowing from the solar, geothermal, and other energy sources, to form higher-hierarchy CBEs (HHCBEs). This has been the driving force of evolution leading to accumulation of HHCBEs for billions of years. We further deduce three progressive mechanisms of evolution including natural selection from the driving force. We hence establish the CBE evolutionary theory (CBEET) which reinterprets the major aspects of evolution in a comprehensive and comprehensible way. The CBEET provides novel explanations for natural selection, origin of life (abiogenesis), macroevolution, sympatric speciation, and evolutionary tempos. It suggests that evolution is driven hierarchy-wise by thermodynamics and favors fitness and diversity. It elucidates that altruism, collaboration, and obeying rules with balanced freedom are important throughout the CBE evolution which includes chemical evolution, biological evolution, and animal group evolution. The CBEET refutes several erroneous views including negative entropy and survival of the fittest. It integrates with research advances in multiple disciplines and links up laws of physics, evolution in biology, and harmonious development of human society.

In the present study we examine the relationship among sovereign yields, temperature and precipitation using a large monthly panel data set which consists of 20 eurozone members, over the period 1980M1 – 2023M4. To account for possible asymmetries along the distribution of the climate variables we assume a quadratic modelling specification and apply various mean panel estimation techniques of heterogeneous coefficients. At a next step, to consider possible nonlinearities in the distribution of the dependent variable (sovereign yields), we apply the quantile via moments methodology of Machado and Santos Silva (2019), which accounts for possible cross-sectional dependence and slope heterogeneity. We contribute to the existing literature in two main ways. First, by applying a quantile methodology that provides a more in-depth analysis of the climate effects along the distribution of the sovereign yields, especially in the presence of non-normally distributed data. Second, we find that climate change, as proxied by higher temperatures or lower precipitation (drought), will increase the sovereign risk of all countries, but the magnitude of the impact will be higher for countries that are already characterized by higher sovereign risk levels and/or face extreme weather conditions (hotter countries and/or countries with low levels of precipitation).

Concrete, the most-used construction material by weight, presents a significant environmental challenge due to its high production environmental footprint and huge quantities of demolition waste.Iran, with a thriving construction sector, faces escalating waste management issues.This paper provides a comprehensive review of strategies to increase concrete recycling in Iran. It explores the current state of concrete recycling practices, the environmental and economic benefits, and the existing barriers hindering wider adoption.The paper then analyzes various strategies across the concrete life cycle, including: demolition practices, collection and transportation systems, processing technologies, and market development for recycled concrete aggregates (RCAs).Several case studies are examined in order to identify best practices and strategies used by countries with successful concrete recycling programs that provide valuable insights about the factors contributing to their success which will be useful for implementation in Iran.Furthermore, the review concludes with recommendations tailored to the Iranian context, emphasizing the crucial role of collaboration between different stakeholders in Iran.

The construction industry plays a crucial role in shaping the built environment and contributes to the global economy. However, the construction process is associated with negative impacts on the environment and communities. This paper examines the environmental impacts of construction and presents strategies for mitigating them, focusing on sustainable construction practices. The research draws on a comprehensive literature review, case studies, and expert interviews to fully understand the construction industry and its environmental effects. The findings indicate that the implementation of greater supervision of every construction project phase, using alternative design practices, and the use of sustainable construction practices can reduce the negative impacts of construction on the environment and society. The study concludes that sustainable construction practices can contribute to a better environment for a better quality of life and should be embraced by the construction industry. The recommendations for future work include the adoption of green building standards, the use of renewable energy sources, the reduction of waste, and the promotion of stakeholder engagement. Future research should focus on the evaluation of the effectiveness of these strategies and their scalability.

This paper presents a compartmental model aimed at investigating the pharmacokinetics of medical THC after oral administration. The model was developed to be multi-compartmental, describing the kinetics of THC and its metabolites in bodily tissues. Utilizing the law of mass action, the model was converted into ordinary differential equations (ODEs), illustrating the rate of concentration changes of THC and its metabolites in each compartment. Subsequently, we applied the nonstandard finite difference (NSFD) method to construct numerical solution schemes. These schemes were implemented in the MATLAB program, along with estimated pharmacokinetic rate constants. The results of the investigation demonstrate that the simulation curves depicting the plasma concentration-time profiles of THC and THC-OH closely resemble the actual data samples, indicating that this model accurately describes the pharmacokinetics of medical THC and its metabolites. Moreover, the model exhibits predictive capabilities regarding the pharmacokinetics of THC and its metabolites in various tissues. Consequently, the model serves as a valuable tool for enhancing our understanding of the pharmacokinetics of THC and its metabolites and for guiding adjustments to dosage and administration durations for oral medical THC products.

The paradigm shift has revealed a need among industrial designers to have entrepreneurial capabilities. Being the most effective modality to exercise discipline today. The implementation of this approach requires reviewing the discursive scaffolding that the design has been using to set immovable high standards of complexity and quality, reducing the field of professional action and limiting the intervention alternatives. This article considers the dynamics and trends in people's lifestyles, explores possible strategies and specific content for the entrepreneurial training required by design.

Animals are distinguished by their complex developmental programs, in which transcription factors play essential roles in coordinating the regulation of the genome. Thus, it has been hypothesized that animal origins involved the evolution of increasingly complex transcriptional regulatory mechanisms that permit greater spatiotemporal control over gene expression. Here, we revisit this hypothesis in light of new genomic and functional data from diverse phylogenetically-relevant taxa, including early branching animals (sponges, ctenophores) and close relatives of animals (choanoflagellates, filastereans, ichthyosporeans). We argue that many of the mechanisms posited to explain animal transcriptional complexity, such as large increases in transcription factor numbers, new transcription factor families, and distal enhancers, did not factor significantly into animal origins. Instead, we propose that the re-purposing of pre-existing transcriptional regulatory modules through cis-regulatory evolution and gene duplication events, combined with new protein-protein interactions among TFs, may have been fundamental to the transcriptional regulatory architecture of the first animals.

Newcastle disease (ND) is a viral infection that affects domestic and wild type birds especially poultry and establishing serious economic costs for poultry industry. Newcastle disease virus’ (NDV) HN membrane receptor is attractive therapeutic candidate, contributes to pathogenicity with various functions such as fusion and prevent viral self-agglutination to permit viral spread. No validated HN inhibitor has been reported yet. However, the purpose of current study was to determine potential compounds via pharmacophore-based screening of natural molecules to inhibit HN receptor of NDV. Physicochemical characteristics and phylogenetic analysis were determined to elucidate structural information and phylogeny of target protein across different species as well as members of gene family. Fill missing residues of HN target protein and evaluated via PROCHECK and VERIFY 3D. We employed shape and feature-based pharmacophore model and screen natural compound’s library through numerous scoring schemes. Top 48 hits with 0.8860 pharmacophore fit score were subjected towards structure-based molecular docking. Top 9 compounds was observed witihin the range of -8.9 to -7.5 kcal/mol binding score. Five best-fitting compounds in complex with HN receptor were subjected to predict biological activity and ADMET analysis. Top two hits were selected for MD simulations to validate binding modes and structural stability. Upon scrutinization, A1 (ZINC05223166) emerges as potential HN inhibitor to treat ND, necessitating further validation via clinical trials.

The BRAFV600E mutation, found in approximately 50% of melanoma cases, is associated with aggressive tumor behavior and poor prognosis. This study aimed to assess its impact on immunogenic cell death (ICD), a pivotal cytotoxic process triggering anti-tumor immune responses. Through comprehensive in silico analysis of The Cancer Genome Atlas data, we explored the association between the BRAFV600E mutation, immune subtype dynamics, and tumor mutation burden (TMB). Our findings revealed that the mutation correlated with a lower TMB, indicating a reduced generation of immunogenic neoantigens. Investigation into immune subtypes reveals an exacerbation of immunosuppression mechanisms in BRAFV600E-mutated tumors. To assess the response to ICD inducers, including doxorubicin and Me-ALA-based photodynamic therapy (PDT), compared to the non-ICD inducer cisplatin, we used distinct melanoma cell lines with wild-type BRAF (SK-MEL-2) and BRAFV600E mutation (SK-MEL-28, A375). We demonstrated a differential response to PDT between the WT and BRAFV600E cell lines. Further transcriptomic analysis revealed upregulation of IFNAR1, IFNAR2, and CXCL10 genes associated with the BRAFV600E mutation, suggesting their involvement in ICD. Using a gene reporter assay, we showed that PDT robustly activated the IFN-1 pathway through cGAS-STING signaling. Collectively, our results underscore the complex interplay between the BRAFV600E mutation and immune responses, emphasizing a putative correlation between tumors carrying the mutation and their responsiveness to therapies inducing the IFN-1 pathway, such as the ICD inducer PDT, possibly mediated by the elevated expression of IFNAR1/2 receptors.

Abstract: Background: epileptic encephalopathies (EE) are characterized by severe drug-resistant seizures, early-onset, and unfavorable developmental outcome. We describe a cohort of pediatric patients with EE who underwent intravenous methylprednisolone (IVMP) pulse therapy to examine its efficacy/tolerability. Methods: This is a retrospective study from 2020 to 2023. Inclusion criteria were: ≤18 years at the time of IVMP pulse therapy and at least 6 months of follow-up. Efficacy and outcome, defined as seizure reduction >50% (responder rate), were evaluated at 6 and 9 months of therapy, and 6 months after therapy suspension; quality of Life (QoL) was also assessed. Variables predicting positive post-IVMP outcomes were identified using statistical analysis. Results: Twenty-one patients were included. The responding rate was 85.7% at 6 and 9 months of therapy, and 80.9% at 6 months after therapy suspension. Genetic etiology and encephalopathy related to status epilepticus during NREM sleep (ESES) were predictive of efficacy (p=0.0475). The fourth month of therapy was the minimum time point to establish the effectiveness of the treatment. All patients showed improvements in QoL, EEG tracing and postural-motor development. No relevant adverse events were observed. Conclusions: Our study confirms the efficacy and tolerability of pulse IVMP treatment in pediatric patients with EE, especially with genetic etiology and ESES. The fourth month of therapy was the minimum time point to establish its effectiveness. QoL, EEG and postural-motor development also showed improvement.

An approach to estimate the dynamic characteristic of multilayered three-dimensional cubic quasicrystal cylindrical shells, annular plates, and truncated conical shells with different boundary conditions is presented. These investigated structures can be in a vacuum, totally filled with quiescent fluid, and subjected to internal flowing fluid, in which the fluid is incompressible and inviscid. The velocity potential, Bernoulli’s equation, and impermeability condition have been applied to the shell-fluid interface to obtain an explicit expression, from which the fluid pressure can be converted into the coupled differential equations in terms of displacement functions. The state-space method is formulated to quasicrystal linear elastic theory to derive the state equations for three structures along the radial direction. The mixed supported boundary conditions are represented by means of the differential quadrature technique and Fourier series expansions, respectively. A global propagator matrix, which connects the field variables at the internal interface to those at the external interface for the whole structure, is further completed by joint coupling matrices to overcome the numerical instabilities. Numerical examples show the correctness of the proposed method and the influence of the semi-vertical angle, different boundary conditions, and the fluid debit on the natural frequencies and mode shapes for various geometries and boundary conditions.

The present work consisted of an exploratory study aiming to evaluate in vitro the potential of AuNPs during Radiation Therapy (RT) in human pancreatic adenocarcinoma cells. AuNPs coated with hyaluronic and oleic acids (HAOA-AuNPs) or with bombesin peptides (BBN-AuNPs) were used. AuNPs were characterized by Atomic Force Microscopy (AFM) and Dynamic Light Scattering. BxPC-3 tumour cells were irradiated with a 6 MV X-rays beam, in the absence or presence of AuNPs. AFM showed that HAOA-AuNPs and BBN-AuNPs are spherical with a mean size of 83+-20 nm and 49+-12 nm, respectively. For RT alone, a reduction in cell viability of up to 33+-12% was obtained compared to the control (p ≤ 0.0001). HAOA-AuNPs alone at 200 and 400 μM showed a reduction in cell viability of 20+-4% and 35+-4%, respectively, while for BBN-AuNPs, at 50 and 200 μM, a reduction in cell viability of 25+-3% and 37+-3% was obtained, respectively, compared to the control (p < 0.0001). At 72 h post-irradiation, a decrease in cell viability of 26+-3% and 22+-2% between RT+HAOA-AuNPs at 400 μM and RT+BBN-AuNPs at 50 μM, compared to RT alone, was obtained (p < 0.004). The combination of RT with AuNPs led to a significant decrease in cell viability compared to the control, or RT alone, thus representing an improved effect.

Bangladesh's railway system mostly uses typical manual railway crossing technique or boom gates through its 2,955.53 km rail route all over the country. The accidents are frequently happening in the railway crossings due to not having obstacle detectable and quickly operating gate systems, and also for fewer safety measures in the railway crossing. Currently, there are very few automatic railway crossing systems (without obstacle detectors) available, however, all of them are dependent on the national power grid without a backup plan for any emergency cases. Bangladesh is still running a bit behind in the power generation of its consumption, hence it is not possible to have a continuous power supply at all times all over the countryside. We aim to design and develop a smart railway crossing system with an obstacle detector to prevent common types of accidents in the railway crossing points. We design to use two infrared (IR) sensors to operate the railway crossing systems which will be controlled by the Arduino Uno. This newly designed level crossing system will be run with the help of sustainable renewable energy which is cost-effective, eco-friendly, and apply under the national green energy policy towards achieving sustainable development in Bangladesh as a part of the global sustainable goal to face climate change challenges. We have summarized the simulated results of several renewable energy sources including a hybrid system and optimized the Levelized Cost of Energy (LCOE), and the payback periods.

Emerging applications of optical technologies are driving the development of miniaturised light sources, which in turn require the fabrication of matching micro-optical elements with sub-1 mm cross sections and high optical quality. This is particularly challenging for spatially-constrained biomedical applications where reduced dimensionality is required, such as endoscopy, optogenetics, or optical implants. Planarisation of a lens by the Fresnel lens approach was adapted for a conical lens (axicon) and was made by direct femtosecond 780 nm/100 fs laser writing in SZ2080™polymer with photo-initiator. Optical characterisation of the positive and negative fraxicons is presented. Numerical modeling of fraxicon optical performance under illumination by incoherent and spatially extended light sources is compared with the ideal case of plane wave illumination. Considering the potential for rapid replication in soft polymers and resists, this approach holds great promise for the most demanding technological applications.

Visual Patient Avatar ICU is an innovative approach to patient monitoring enhancing the user's situation awareness in intensive care settings. It dynamically displays the patient's current vital signs through changes in color, shape and animation. The technology can also indicate patient-inserted devices, such as arterial lines, central lines and urinary catheters, along with their insertion locations. We conducted an international, multi-center study using a sequential qualitative-quantitative design to evaluate users' perception of Visual Patient Avatar ICU among physicians and nurses. Twenty-five nurses and twenty-five physicians from the ICU participated in the structured interviews. Forty of them completed the online survey. Overall, ICU professionals expressed a positive outlook on Visual Patient Avatar ICU. They described Visual Patient Avatar ICU as a simple and intuitive tool that improved information retention and facilitated problem identification. However, a subset of participants expressed concerns about potential information overload and a sense of incompleteness due to missing exact numerical values. These findings provide valuable insights into user perceptions of Visual Patient Avatar ICU and encourage further technology development before clinical implementation.

Sleep quality has been directly linked to cognitive function, quality of life, and a variety of serious diseases across many clinical domains such as psychiatry and cardiology. Standard methods for assessing sleep involve overnight studies in hospital settings, which are uncomfortable, expensive, not representative of real sleep, and difficult to conduct on a large scale. Recently, a number of commercial digital devices have been developed that record physiological data which can act as a proxy for sleep quality in lieu of standard electroencephalogram recording equipment. Each device company makes different claims of accuracy and measures different features of sleep quality, and it is still unknown how well these devices correlate with one another and perform in a research setting. In this pilot study of 21 participants, we investigated whether outputs from four sensors, specifically FitBit, Withings Aura, Hexoskin, and Oura Ring, were related to known cognitive and psychological metrics, including the PSQI and N-back test. We found that sleep metrics extracted from these devices did not predict cognitive and psychological metrics well in our pilot data. However, we did identify certain signification associations, specifically the Oura Ring’s total sleep duration and efficiency in relation to the PSQI measure with p=0.004 and p=0.033, respectively. Additionally, correlation of various sleep features among the devices across the sleep cycle was almost uniformly low. These findings can hopefully be used to guide future sensor-based sleep research.

With the rapid progress of genetic engineering and gene therapy, World Anti-Doping Agency has alerted to gene doping and prohibited its use in sports. However, there is no standard method available yet for detection of transgenes delivered by recombinant adenoviral (rAdV) vectors. Here we aimed to develop a detection method for transgenes delivered by rAdV vectors in a mouse model that mimics gene doping. rAdV vectors containing mCherry gene was delivered in mice through intravenous injection or local muscular injection. After five days, stool and whole blood samples were collected, and total DNA was extracted. As additional experiments, whole blood was also collected from mouse tail tip until 15 days from injection of the rAdv vector. Transgene fragments from different DNA samples were analyzed using semi-quantitative PCR (sqPCR), quantitative PCR (qPCR), and droplet digital PCR (ddPCR). In the results, transgene fragments could directly be detected from blood cell fraction-DNA, plasma-cell free DNA and stool-DNA by qPCR and ddPCR, depending on specimen type and injection methods. We observed that a combination of blood cell fraction-DNA and ddPCR was more sensitive than other combinations used in this model. These results could accelerate the development of detection methods for gene doping.

ABSTRACT：Background: Diagnosing rhabdomyolysis has usually defined at least five or ten times the upper limit creatine kinase level; but it is conservative for Exertional rhabdomyolysis. The aim was to analysis the biomarkers during a military operation in an extreme environment with high physical demand. Material and Methods: Seventeen male military personnel (age: 27.83 ± 2.96 years; weight: 76.31 ± 7.67 kg; and height 174.92 ± 5.91 cm) participated for this investigation. The creatine kinase (CK); blood urea nitrogen (BUN); creatinine (Cr); sodium (Na); potassium (K); aspartate amino transferase (AST) and alanine amino transferase (ALT) were analyzed. Urine specific gravity (USG) the hydration status was monitored. The Generalized Linear Models for repeated measures and the Friedman’s two-way test were used to comparative moments. Results: The BUN; Cr; Na; and K mean values showed a nonsignificant. The statistical significance of the test was found in CK and AST. There was considerable variability of CK between subjects during the operation. The moderate correlation between CK and Cr was found during study. Conclusion: Elevated CK levels after the operation were characterized as physiological pattern. These results reinforce that CK values are not predictors of renal function in activities with intense and continuous physical demand

The accurate detection of clouds is an important first-step in the processing of remotely sensed satellite data analyses and subsequent cloud model predictions. While initial cloud retrieval technology began with the exploitation of one or two bands of satellite imagery, it has accelerated rapidly in recent years as sensor and retrieval technology have exploded, creating a new era in space exploration. Additionally, initial emphasis in satellite retrieval technology focused on cloud detection for cloud forecast models, more recently cloud screening in satellite acquired data is playing an increasingly critical role in the investigation of cloud free data for the retrieval of soil moisture, vegetation cover, ocean colour concentration and sea surface temperatures, as well as environmental monitoring of a host of products, e.g. atmospheric aerosol data, to study the Earth’s atmospheric and climatic systems. With about 60% of the Earth covered by clouds, on average, it is necessary to both accurately detect clouds in remote sensing data to screen cloud contaminate data in remote sensing analyses. In this paper, the evolution of cloud detection technology is highlighted with advancement in sensor hardware technology and possible AI algorithmic advances. Additionally, a discussion is presented on methods to obtain the cloud truth data needed to determine the accuracy of these cloud detection approaches.

This work proposes a conceptual design for a process for recovering Polystyrene (PS) using solvents of agro-industrial origin. The literature describes the dilution of Expanded Polyestyrene (EPS) in limonene, followed by its insolubilization with alcohols for recovery. However, there is no information on the solubility limit for the PS + limonene + alcohol system, which is critical for the process design. To determine the solubility limit, we diluted the waste EPS in D-limonene, mixed it with ethanol to form a precipitate, and used a gravimetric method to measure the mass of the compounds. These results allowed for the conceptual design of an EPS recycling process using a chemical process simulator, which includes a separator, a distillation column, and auxiliary equipment such as heaters, coolers, and pumps. An empirical correlation was obtained for the solubility limit, which enabled the design of a process for the treatment of 52 kg/s of PS using 0.75 kg /s of ethanol and 2.4 kg / s of D-limonene once the stationary state had been reached. The distillation column is six-stage, with a reflux ratio of 1.5 and duties of 30 000 kW and -25 847 kW for the condenser and reboiler, respectively.

Human Papillomavirus (HPV) self-sampling has the potential to increase cervical cancer screening (CCS) and reduce the cervical cancer burden in Medically Underserved Women (MUW). However, interventions promoting self-sampling are limited. We examined the effectiveness of an intervention study in increasing CCS among MUW. We conducted a quasi-experimental intervention study. Face-to-face verbal approach was to recruit MUW (n=85, mean age 48.57±11.02) living in a small city in the US. Behavioral intervention based on reframing, reprioritizing, and reforming (3R model) was used to educate the women about CCS in a group format. The women completed pre-and post-intervention assessments and followed-up interviews. The primary outcome was CCS uptake. Mixed methods analyses were conducted using a t-test for the primary outcome, PROCESS for mediation analysis, and NVivo for interview data. Majority of women (75%) completed self-testing. High-risk HPV prevalent among the women was 11%, and of those, 57% followed-up with physicians for care. We found that the significant increase in the women’s post-intervention screening behaviors was mediated by the increase in knowledge (Indirect Effect [IE] = .1314; 95% CI, .0104, .4079) and attitude (IE = .2167; 95% CI, .0291, .6050) scores, (p&lt;0.001). Interview analyses offered further explanations (see the explanations in parenthesis) why MUW found the intervention messages acceptable (encourages proactive behavior), feasible (simple and easy to understand), and appropriate (helpful and informative). Barriers including lack of trust and fear of results were identified. The findings suggest that an intervention that combines the 3R model and self-sampling may increase CCS among MUW.

The advent of social commerce (SC) has transformed the landscape of online consumer behavior, emphasizing the significance of customer-to-customer relations in shaping sustainable relation-ships with customers. This research investigated the intricate relationships between social com-merce dimensions, particularly suggestions and recommendations, forums and communities, and ratings and reviews, and their influence on customer-to-customer value co-creation (C2CVCC) and sustainable customer relationships (SCRs). A questionnaire was designed and administered to 635 respondents. We examined the psychometric properties of the measurements and subse-quently applied Partial Least Squares as a Structural Equation Modeling method (PLS-SEM) for hypothesis testing. The findings reveal that the dimensions of SC significantly impact C2CVCC, with ratings and reviews playing a pivotal role. Furthermore, C2CVCC emerges as a substantial mediator in the path between SC and SCRs. The empirical analysis showcases strong support for the proposed model, with robust path coefficients (β) and p-values confirming direct and indirect effects. These findings offer valuable insights for businesses seeking to leverage SC and customer interactions to enhance SCRs in the digital era. Understanding the dynamics of C2CVCC within the context of SC has become essential for marketers and businesses aiming to thrive in today's competitive online marketplace.

Geopolitical situations, such as the Ukraine–Russia war, the Israel–Hamas war, and pandemics, can disrupt supply chains, such as the rerouting of oil tankers via South Africa to avoid the impact of the Israel–Hamas conflict. These events will probably occur again, but they are black swan events and difficult to predict. How can firms manage these events to minimise disruption to their supply chains? This paper proposes that a blockchain architecture has the potential to build resilience in supply chains and give them the flexibility to withstand the challenges of black swan events. The decentralisation ability of blockchain technology enables separation of the discrete elements of a sustainable supply chain so contagion does not impact the overall sustainable supply chain. To achieve flexibility in a sustainable supply chain ecosystem requires governance, leadership, and management. Also, artificial intelligence (AI), in conjunction with blockchain, is a technology that can enhance the flexibility and resilience of the supply chain. Thus, AI can act as a controller, co-ordinator, and an integrator in sustainable supply chain systems.

The lack of application of management models that allow strategies to be converted into action and then measure their results in order to apply improvement actions to meet the objectives in small and medium-sized companies (SMEs); they generate problems in their management causing the closure of their operations unexpectedly; To mitigate the risks of closure and bankruptcy with the support of ICT, an alternative was presented considering that risk management is important in decision making at a strategic, tactical and operational level. The objective is to design a prototype to mitigate risks and prevent threats to avoid the closure of this type of company. Through the analysis of related articles, applying sequences of methodological steps; It was determined that the proposed results are aimed at how to identify and mitigate the risks and threats that cause their bankruptcy and closure. Prototypes resulted that allow mitigating risks, preventing threats and minimizing vulnerabilities in the management of SMEs. It was concluded that the proposed prototype to detect threats and vulnerabilities provides an optimal security model for small and medium-sized companies; As a result of the simulations, a security percentage of 77.92% was established, which means that the greater the number of risks and threats, the greater the probability that a small and medium-sized company will close.

OBJECTIVES: To assess the predictive value of serum trypsin and Trypsinogen Activation Peptide (TAP) for the severity of AP by means of a single center cohort study as well as a systematic review of the current literature. METHODS: The literature search was conducted using Medline (PubMed), EMBASE and the Cochrane Central Register. A total of 142 patients with acute pancreatitis (AP) were included in the cohort study and parameters of the revised Atlanta criteria of 2012 and the APACHE II were assessed. RESULTS: The review showed promising results for the predictive value of serum trypsinogen-2, but conflicting results for serum TAP and trypsin. In the cohort study, patients were observed for 4 days after diagnosis of AP: 9 patients had severe AP, 35 patients had moderate AP and 81 patients had mild AP. The ratio of the geometric mean of severe vs mild AP for trypsin was 0.72 (95% CI: 0.51-1.00), p=0.053 and for TAP 0.74 (95% CI: 0.54-1.01), p=0.055 respectively. CONCLUSION: The cohort study showed an inverse correlation of serum levels of TAP and trypsin with severity of AP. Serum TAP and trypsin have an inferior predictive value of severity of AP compared to the clinical APACHE II score.

The problem addressed in this article consists of the motion control of a quadrotor, comprising model disturbances and uncertainties. In order to tackle model uncertainty, adaptive control based on reinforcement learning is used. The distinctive feature of this article, in comparison with other works on quadrotor control using reinforcement learning is the exploration of the underlying optimal control problem in which a quadratic cost and a linear dynamics allow an algorithm that runs in real time. Instead of identifying a plant model, adaptation is obtained by approximating the performance index given by the Q-function using directional forgetting recursive least squares that rely on a linear regressor build from quadratic functions of input/output data. The adaptive algorithm proposed is tested in simulation in a cascade control structure that drives a quadrotor. Simulations show the improvement in performance that results when the proposed algorithm is turned on.

In this study, we examined the effects of the organizational power resources used by school principals on teachers' work alienation and trait anxiety levels. Then, we examined the mediating role of trait anxiety in the relationship between organizational power resources and work alienation. We used the correlational scanning model and mediation model in the research. The research sample consists of 400 teachers working in Turkish public schools. In our research, "Organizational Power Scale in Schools", "Work Alienation Scale" and "Trait Anxiety Scale" were used. As a result of the research, we found significant relationships between the variables. Accordingly, there is a positive relationship between trait anxiety and work alienation, a positive relationship between trait anxiety and coercive power and legal power, a negative relationship between trait anxiety and expert, charismatic and reward power, a positive relationship between work alienation and coercive power, and a positive relationship between work alienation and reward power. Negative relationships were observed between expertise and charismatic power. Finally, it was determined that trait anxiety had a mediating role in the relationship between the power resources used by school principals and the level of work alienation of teachers.

Pancreatic cancer is a refractory cancer with limited treatment options. Various cancer types are resistant to the tumor necrosis factor-related apoptosis-inducing ligand (TRAIL). Eugenol, the main component of clove oil, exhibits anticancer, anti-inflammatory, and antioxidant effects. However, no studies have reported that eugenol increases TRAIL sensitivity by upregulating death receptor (DR) expression. Here, we aimed to investigate eugenol as a potent TRAIL sensitizer. Increased apoptosis and inhibition of cell proliferation was observed in pancreatic cancer cells treated with eugenol and TRAIL compared with those treated with eugenol alone. Eugenol upregulated the expression of DR5, inhibited FLICE-inhibitory protein (FLIP), an anti-apoptotic protein, and increased p53, a tumor suppressor protein. In addition, eugenol induced the generation of reactive oxygen species (ROS) and caused endoplasmic reticulum (ER) stress. C/EBP-homologous protein (CHOP) knockdown using siRNA decreased the expression of DR5 and reduced the combined effects of eugenol and TRAIL. These results demonstrate that eugenol enhances TRAIL-induced apoptosis by upregulating DR5 through the ROS-mediated ER stress-CHOP pathway, enhancing ER stress by inducing p53 and downregulating FLIP expression. This suggests that eugenol has the potential to treat pancreatic cancer by increasing cell sensitivity to TRAIL.

The introduction provides an overview of beginning the development of a plastic design of metal thin-walled cross-sections. This large study investigates in detail together 14 steel and four extruded aluminium cross-sections. Six groups of various shaped cross-sections that consist of: four I-shaped doubly symmetric sections (two I-sections, one I-section with lips, one H-section); three monosymmetric sections with axis of symmetry z (monosymmetric I-section, T-section, diamond section); four monosymmetric sections with axis of symmetry y (two channels, channel with inside lips, channel with outside lips); two point symmetric sections (Z-section with and without lips); four asymmetric sections (L-profile, sigma section, two closed sections: oblique and irregular sections; non-warping sections along midline, but with negligible warping along the element's thicknesses: Tsection and L-profile are included in the above groups. Four extruded aluminium cross-sections are: I 200a section, diamond section and closed oblique and irregular sections. For all 18 cross-sections, the plastic section moduli of three kinds were calculated: Wpl,y,nB, Wpl,z,nB for bimoment not considered to be constraint; Wpl,y, Wpl,z, Wpl,wfor bimoment considered to be restraint; maximum values Wpl,y,max, Wpl,z,max, Wpl,w,max. The values of cross-section plastic resistances Npl, Mpl,y,Rd, Mpl,z,Rd and Bpl are also calculated in numerical examples. The values of cross-section properties are calculated in different ways to verify the correctness of the results. The following ways of calculations are used: the rules in Eurocode EN 1993-1-1: 2022; own MathCad programmes; own software. Recommendations for educational institutes and designers in practice are provided, including simple formulae for all the cross-sectional properties for the doubly and monosymmetric I-shaped sections, channels and Z-sections. Formulae are presented in six tables containing formulae in the dimensionless form, which are useful for parametrical studies and formulae for direct designs. The background of the Eurocode rules in EN 1993-1-1: 2022 is explained together with recommendations about how to avoid problems with using them.

Over 2.2 billion people across the globe face significant barriers to accessing essential ophthalmic care, with elderly, rural, and refugee populations being disproportionately affected. These obsta-cles deepen existing disparities in eye care. 3D printing is a novel technology that has the potential to transform the field and improve access through alleviating many patient-specific barriers. This article delves into the evolution of 3D printing within ophthalmology, highlighting its current ap-plications and future potential. It explores various 3D printing techniques and biomaterials, dis-cussing their effectiveness in creating advanced solutions such as bioengineered corneas, ocular prosthetics, and innovative treatments for dry eye syndrome. Additionally, 3D printing has rev-olutionized drug delivery systems for conditions like glaucoma, retinal diseases, and for ocular brachytherapy. Despite its promise, the integration of 3D printing into clinical practice presents challenges, which the article addresses alongside strategies for overcoming them. By mapping out the technological advancements and challenges, this review offers a roadmap for enhancing global eye care accessibility and improving patient outcomes on a global scale.

Peanut is one of the important legumes grown in Kenya, ranking second after beans. Its production is most common in Western and Nyanza regions of the country with higher volumes being produced in Nyanza Region compared to the Western Region. The value chain for peanuts in Kenya consist of many actors including input dealers, producers, aggregators, traders, processors, exporters and consumers. The general objective of this study was to map the value chain for peanuts, indicating the opportunities that value chain actors can benefit from and strategies that can be used to upgrade the chain. The specific objectives were to characterize the key actors along the peanut value chain; determine the costs and value added along the groundnut value chain; and determine the constraints and opportunities in the value chain and suggest ways to upgrade the chain. A cross-sectional survey was conducted among the key actors in the value chain in 2 purposively selected sub-counties in Kenya where peanuts are predominantly produced marketed and/or consumed. The sub-counties are Karachuonyo and Nyakach. The data collected included socio-economic characteristics of the value chain actors, sales volumes and prices, trends in demand and supply, constraints and opportunities in peanuts and trade policy related issues. Secondary information mainly on value chain aspects was drawn from past research reports and internet. The data were analyzed using descriptive statistics and parameters such as means and percentages were determined and used to interpret the results. These enabled the drawing of peanut value chain map illustrating interrelations among chain actors, and activities they conduct. Results show that more females (68%) compared to 32% male participate in peanut production. Results further show that the female farmers were order compared to the male farmers. Few farmers were aware of improved technologies, for example only about 26% were aware of existence of improved peanuts varieties, and only 1.5% used the improved seed. No peanut farmer was using inorganic fertilizers, citing high price, nature of the soil and lack of knowledge as the major hindrance. At wholesale level, the chain was dominated by male traders partly attributed to the very versatile nature of the business that requires intensive travels, and also being capital intensive afforded by few females. On the other hand, retail traders were dominated by females. Retailers do deal in small quantities and therefore use less capital compared to other categories of traders. The majority of the processors were males also attributed the capital-intensive nature of the processing business. Most of the businesses were located in urban areas because of easier access to the raw materials and markets for the processed groundnuts. On multi-stakeholder platforms with various parties in the chain, an analysis of strengths, weaknesses, opportunities, and threats (SWOT) was undertaken. Cost variables were used to calculate gross margin profit and benefit-cost ratios. Overall, the sector was mostly a fresh food market, with a focus on peanut producers, processors, and direct sales to wholesalers, retailers, and end users. The SWOT analysis indicated numerous prospects, such as advantageous production ecologies, processing alternatives, and insatiable local and international markets. To operate synergistically with a collective profit motivation, institutional players must network with primary actors. Priority production restrictions, such as seed availability, chemical fertilizer costs, field pests and diseases, and declining soil fertility, should be addressed.

The study delves into the Zaalklapspruit valley-bottom wetland in South Africa, a site designed through ecological engineering and currently impacted by acid mine drainage due to an upstream defunct coal mine. The research conducted in 2022, aimed to uncover the dynamics of contaminant dispersal within such a wetland setting, focusing on the origins, routes, and ultimate recipients of various metals and sulfur compounds. Analysis revealed that the bottom sediment is rich in organic material, with pH levels observed between 6.05 and 6.59, and exhibited notably low oxidation-reduction potentials, reaching down to −219.67 mV at sample point S3. Notably, Manganese displayed the highest adsorption rates, whereas Iron was predominantly absorbed by the roots of Typha capensis and the algae Klebsormidium acidophilum. The wetland's macrophyte rhizospheres hosted a rich array of microbiota, including families like Helicobacteraceae and Hydrogenophilaceae, which are crucial in metal and sulfur processing. This investigation sheds light on the intricate biogeochemical processes taking place involving the wetland's sediment, macrophyte root systems, periphyton, and microbial populations, underscoring the potential of ecologically engineered wetlands in the remediation of waters affected by acid mine drainage.

Seeds of Arazá and Acai, in addition to the byproduct of the Sacha inchi seed, after the extraction of the oil known as Sacha Inchi oil press-cake (SIOPC), are considered wastes and often discarded; however, they have valuable potential as sources of nutrients and bioactive compounds. We aimed to analyze, the bromatological, and chemical composition of these three byproducts of the fruit industry. Further cytotoxic, antioxidant, and antihypertensive activities of extracts ethanol: water were evaluated through in vitro tests. The Bromatological test showed that SIOPC had a low protein content (8.1%), while AS had a high fiber content (48.9%), however, were poorest in nutrients Ethanol: water extracts from all seeds exhibited diverse profiles of secondary metabolites, with significant concentrations of polyphenols (155.88 µg gallic acid/100 mg dry weight), flavonoids (411.69 µg quercetin/100 mg dry weight), and carotenoids in AS (43.66 µg ß-carotene/100 mg dry weight). AS and ACS extracts excelled in ABTS and DPPH radical scavenging assays, and AS showed high α-glucosidase inhibition, previously unreported in the literature. The results suggest that both seeds and extracts are sources of nutritional and bioactive metabolites with high antioxidant and hypoglycemic activity. that could be used in the food industry and the health sector. These by-products underline the importance of sustainable harnessing these sources of the native Amazonian Region

The fixed offshore platforms in Mexico produce approximately 1.90 million barrels of oil daily, valued at about 1.5 million dollars, positioning Mexico as a significant oil-producing nation globally. Ensuring the structural integrity of these platforms is highly complex due to various factors, including operational loads, meteorological conditions, extreme storms, seismic activity, and fatigue in tubular joints. Consequently, in situ studies have been conducted, focusing on operational analysis, static storm conditions, dynamic seismic strength, ductility, ultimate wave resistance, and spectral fatigue analysis. These six analyses (three linear, two nonlinear, and one spectral fatigue) provide insights into the structural behavior under different loading conditions and allow evaluation against current standards to conclude their structural integrity. The results obtained confirm that the structural integrity is adequate, as they meet current regulations and standards.

In this paper, we introduce a control method for the linear quadratic tracking (LQT) problem with zero steady-state error. It is achieved by augmenting the original system with an additional state representing the integrated error between the reference and actual outputs. In its essence, it is a linear quadratic integral (LQI) control embedded in a general LQT control framework, with the reference trajectory generated by a linear exogenous system. During the simulative implementation for the specific real-world system Car-in-the-Loop (CiL) test bench, we assume that the 'real' system is completely known. Therefore, for the model-based control, we can have a perfect model identical to the 'real' system. It becomes clear that stable solutions can scarcely be achieved with controller designed with the perfect model of the 'real' system. Contrary, we show that a model learnt via Bayesian Optimization (BO) can facilitate a much bigger set of stable controllers. It exhibits an improved control performance. To the best of the authors' knowledge, this discovery is the first in the LQT related literature.

Additive Manufacturing (AM) Direct Laser Fabrication (DLF) of Ti-5Al-5V-5Mo-3Cr (Ti5553) is being developed as a method for producing aircraft components. The additive manufacturing process can produce flaws near the surface, such as porosity and material voids, which act as stress raisers, leading to potential component failure. Eddy current testing was investigated to detect flaws on or near the surface of DLF Ti5553 bar samples. For this application, the objective was to develop an eddy current probe capable of detecting flaws 500 µm in diameter, located 1 mm below the component's surface. Two initial sets of coil parameters were chosen: The first, based on successful experiments that demonstrated detection of a near surface flaw in Ti5553 using a transmit-receive array probe, and the second, derived from simulation by Finite Element Method (FEM). An optimized transmit receive coil design, based on the FEM simulations, was constructed. The probe was evaluated on Ti5553 samples containing sub-surface voids of the target size, as well as samples with side-drilled holes and samples with holes drilled from the opposing inspection surface. The probe was able to effectively detect 80% of the sub-surface voids. Limitations included the probe’s inability to detect sub-surface voids near sample edges and a sensitivity to surface roughness, which produces local changes in lift-off. Multifrequency mixing improved signal-to-noise ratio when surface roughness was present on average by 22%. A probe based on that described in this paper could benefit quality assurance of additively manufactured aircraft components.

This paper is a study of a generalization of the quantum Riemannian Hamiltonian evolution, previously analyzed by us in our work [6], in the geometrization of quantum mechanical evolution in a Finsler geometry. We find results with dynamical equations governing the evolution of the trajectories defined by the expectation values of position. The analysis appears to provide an underlying geometry described by a geodesic equation, with connection form with a second term which is an essentially quantum effect. These dynamical equations provide a new geometric approach to the quantum evolution where we suggest a definition for "local instability" in the quantum theory.

: Climate change necessitates urgent actions to mitigate carbon dioxide (CO2) emissions from fossil fuel-based energy generation. Among various strategies, the deployment of carbon capture and storage (CCS) solutions are critical for reducing emissions from point sources such as power plants and heavy industries. In this context, cryogenic carbon capture (CCC) via desublimation has emerged as a promising technology. While CCC offers high separation efficiency, minimal downstream compression work, and integration potential with existing industrial processes, challenges such as low operating temperatures and equipment costs persist. Ongoing research aims to address these hurdles in order to optimize the desublimation processes for widespread implementation. This review consolidates diverse literature works, providing insights into the strengths and limitations of CCC technology, including the latest pilot plant scale demonstrations. The transformative potential of CCC is first assessed on a theoretical basis, such as thermodynamic aspects and mass transfer phenomena. Then, recent advancements in the proposed process configurations are critically assessed and compared through key performance indicators. Furthermore, future research directions for this technology are clearly highlighted.

In order to address the issue of nutrient deficiency in coconut coir-based nursery substrates, three materials with nutrient supplementation effects, namely pig manure, composted rice husks, and agricultural humic acid, were added in equal volumes to the coir-based substrate. The newly formulated substrates were then tested for their seedling growth effects on cucumber and rice. Results indicate that the substrate formulation consisting of "10% pig manure + 30% peat + 50% coconut coir + 10% vermiculite" was capable of satisfying the growth requirements of both cucumber and rice seedlings, with the highest acceptance among farmers.

In this paper, we propose a system of nonlinear mixed variational inequality problems, which consists of two elliptic mixed variational inequality problems on Banach spaces. Under suitable assumptions, using the Kakutani-Ky Fan fixed point theorem and Minty techniques, we prove the solution set to the system of nonlinear mixed variational inequality problem is nonempty, weakly compact and unique. Additionally, we suggest a stability result for the system of nonlinear mixed variational inequality problem by perturbing the duality mappings. Furthermore, we present an optimal control problem governed by the system of nonlinear mixed variational inequality problems and establish a solvability result.

Facial recognition systems are widely used systems around the globe. Their applications in real world scenarios like law enforcement, surveillance, ID card identification, information security, access control and many more has made them very important in today’s world. These systems have shown promising results and excellent performance; however, these systems need tremendous number of facial images for training before they are implemented in the real-world scenarios. The problem arises when we face lack of training images due to which the performance of such systems decreases exponentially. The problem becomes even worse when there is only one image per subject for training our facial recognition system, the probe image might contain variation like illumination, expression and disguise which are hard to be accurately recognized. Generative models have achieved great success for handling the lack of training data by generating synthetic data that resembles the original data. We have created a new method by fine tuning the basic Conditional Generative Adversarial Network (CGAN) to generate synthetic facial images provided only single image per subject. These synthetic facial images contain six variations that are smile, anger, stare, illumination and disguise. We increased the size of the dataset and then applied a convolutional neural network for facial recognition, which improved the accuracy of our system from 0.76 to 0.99.

The ocean’s mixed layer depth (MLD) plays an important role in understanding climate dynamics, especially during extreme weather occurrences like hurricanes. This study investigates the effects of Hurricane Katrina (2005) on the MLD in the Gulf of Mexico, using the Delft3D modeling system. By integrating hydrodynamics and wave dynamics modules, we simulate the ocean’s response to extreme weather, focusing on temperature, salinity and MLD variations. Our analysis reveals significant cooling and mixing induced by Katrina, resulting in spatial and temporal fluctuations in temperature (~± 4°C) and salinity (~± 2.5 ppt). The MLD is estimated using a simple threshold method, revealing a substantial deepening to ~120 m on August 29th-30th during Hurricane Katrina in the middle of northern Gulf of Mexico, compared to an average MLD of ~20-40 m during pre-storm conditions. It took about 18-days to recover to ~84% of the pre-storm level after Katrina. Compared to the stand-alone FLOW model, the coupled FLOW+WAVE model yields a deeper MLD of ~5%. The MLD recovery and wave effect on the MLD provide insights from various scientific, environmental and operational perspectives, offering a valuable basis for ocean management, planning and applications, particularly during extreme weather events.

The aim of the research was to determine the most productive tree cutting methods and the factors influencing them in flat and sloping terrain in tree plantations in Brazil. The study utilized drone-captured video material from eucalyptus and pine plantations harvesting operations. In both terrains, two cutting methods were compared, differing in the felling to the side method used: either at the edge of the harvesting front or inside. In addition, on flat terrain, the efficiency of forward felling was studied in relation to the aforementioned methods. In slope terrain, the machines were also equipped with a winch assistance system. Statistical tests were used to determine the most productive cutting methods by analyzing differences in productivity. With an average tree size of 0.3 m3, cutting productivity was 45 m3/E0h in pine cuttings and 55 m3/E0h in eucalyptus cuttings. The average cutting productivity on flat terrain was about 11 m3/E0h higher than on sloping terrain, mainly due to the time spent attaching the winch assistance system, which was a necessary phase of the work in sloping terrain. The research results suggest that it would be the most productive to use sideways felling inside the harvesting front method. However, the need for further research is evident if we want to precisely identify the harvesting conditions and work phases in the tree cutting cycle affecting the differences in the productivity of the harvesting chain in tree plantations.

With the rapid development of intelligent transportation, Vehicle Ad hoc Networks (VANETs) is becoming increasingly important.As a critical component of intelligent transportation systems, the Conditional Privacy-Preserving Authentication (CPPA) scheme plays a significant role in ensuring drivers' privacy and security.However, current CPPA schemes still face limitations in terms of security and efficiency, with these schemes either imposing higher computational overhead for enhanced security or compromising some level of security to reduce overhead. Motivated by this challenge, we propose a CPPA scheme that strikes a better balance between efficiency and security.By leveraging the token mechanism to achieve multiple authentication and minimizing information leakage. Furthermore, our performance analysis demonstrates that our ECC-based algorithm can significantly reduce overhead, with pseudonymous generation requiring only 0.0039ms.

Wireless Sensor Networks (WSNs) are gaining traction in the realm of network communication, renowned for their adaptability, configuration, and flexibility. The forthcoming network traffic within WSNs can be forecasted through temporal sequence models. In this correspondence, We present a method (TSENet) that can accurately predict the traffic in the cellular network. TSENet is composed of transformers and self-attention network. We have designed a temporal transformer module specifically for extracting temporal features. This module accomplishes this by modeling the traffic flow within each grid of the communication network at both near-term and periodical intervals. Simultaneously, we amalgamate the spacial features of each grid with information from its correlated grids, generating spacial predictions within the spacial transformer. Furthermore, we employ self-attention aggregation to capture dependencies between external factor features and celluar data features. Empirical assessments performed on a genuine cellular traffic dataset offer compelling evidence substantiating the efficacy of TSENet.

Tourism is the main economic sector in the Balearic Islands (Spain) and hotel housekeepers (HHs) are a large occupational group, in which stress is becoming a major issue. This study aims at exploring in-depth factors perceived as stressors by HHs and key-informants, and their effects on work-life balance (WLB). A qualitative design with phenomenological approach was used, conducting six focus groups with 34 HHs and 10 individual interviews with key-informants. Results were analyzed adopting the job demands-resources model and a gender perspective. High demands –e.g work overload, time pressure, physical burden...-, lack of enough resources and little control –derived from role conflict, unexpected events...- were the most important factors explaining HHs’ stress. Additionally, this imbalance was perceived as leading to health problems –mainly musculoskeletal disorders-. Working schedule was mentioned as a facilitator to WLB, whereas an imbalance between job demands and resources led to work-home conflict –preventing them from enjoying leisure time-. Multiple roles at work and at home increased their stress. HHs experienced their job as invisible and unrecognised. Regarding practical implications, our recommendations for hotel organization include reducing workload and increasing resources, which would improve the job demands-resource balance, diminish negative mental and physical outcomes and improve WLB.

The aim of this research is to investigate the influence of model height employed in the deep drawing of orthodontic aligner sheets on force transmission and aligner thickness. Forty sheets (Zendura FLX) were thermoformed over four models of varying heights (15, 20, 25, and 30 mm). Normal contact force generated on the facial surface of the upper right central incisor (Tooth 11) was measured using pressure-sensitive films. Aligner thickness around Tooth 11 was measured at five points. A digital caliper and a micro-computed tomography (µ-CT) were employed for thickness measurements. The normal contact force exhibited an uneven distribution across the facial surface of Tooth 11. Model 15 displayed the highest force (88.9±23.2 N), while Model 30 exhibited the lowest (45.7±15.8 N). The force distribution was more favorable for bodily movement with Model 15. Thickness measurements, conducted using a digital caliper and µ-CT, revealed substantial thinning of the aligner after thermoforming. This thinning was most pronounced at the incisal edge (50% of the original thickness) and least at the gingivo-facial part (85%). Additionally, there was a progressive reduction in aligner thickness with increasing model height, which was most significant on the facial tooth surfaces. We conclude that the thermoplastic aligner sheets undergo substantial thinning during the thermoforming process, which becomes more pronounced as the height of the model increases. As a result, there is a decrease in both overall and localized force transmission, which could lead to increased tipping by the aligner and a diminished ability to achieve bodily movement.

Sleep is a crucial aspect of geriatric assessment for hospitalized older adults, and implementing AI-driven technology for sleep monitoring can significantly enhance the rehabilitation process. Sleepsense, an AI-driven sleep-tracking device, provides real-time data and insights, enabling healthcare professionals to tailor interventions and improve sleep quality. This study explores the perspectives of an interdisciplinary hospital team on implementing Sleepsense in geriatric hospital care. Using the Interpretive Descriptive approach, we conducted focus groups with physicians, nurses, care aides, and an activity worker. The Consolidated Framework for Implementation Research (CFIR) informed our thematic analysis to identify barriers and facilitators to implementation. Among 27 healthcare staff, predominantly female (88.89%) and Asian (74.1%) and mostly aged 30-50 years, themes emerged that Sleepsense is perceived as a timesaving and data-driven tool that enhances patient monitoring and assessment. However, barriers such as resistance to change and concerns about trusting the device for patient comfort and safety were noted, while facilitators included training and staff engagement. The CFIR framework proved useful for analyzing implementation barriers and facilitators, suggesting future research should prioritize effective strategies for interdisciplinary team support to enhance innovation adoption and patient outcomes in rehabilitation settings.

Individuals diagnosed with systemic arterial hypertension (SAH) are considered risk groups for COVID-19 severity. This study assessed differences in cardiac autonomic function (CAF) and functional capacity (CF) in SAH individuals without COVID-19 infection compared to SAH individuals post-COVID-19. Participants comprised 40 SAH individuals aged 31 to 80 years old, grouped as SAH with COVID-19 (G1; n=21) and SAH without COVID-19 (G2; n=19). CAF was assessed via heart rate variability (HRV), measuring R-R intervals during a 10-minute supine period. Four HRV indices were analyzed through symbolic analysis: 0V%, 1V%, 2LV%, and 2UV%. FC assessment was performed by a 6-minute walk test (6MWT). G1 and G2 showed no difference between significant age, anthropometric, clinical and medication. G2 exhibited superior 6MWT performance, covering more distance (522±78 vs. 465±59 meters, p&lt;0.05). Specifically, G2 demonstrated a moderate, positive correlation between 6MWT and the 2LV% index (r=0.58; p&lt;0.05). Shorter walking distances were observed during 6MWT in SAH individuals post-COVID-19. However, the study did not find impaired cardiac autonomic function in SAH individuals post-COVID-19 compared to those without. This suggests that while COVID-19 impacted FC, CAF remained relatively stable in this population.

Background: Patients with allergic rhinitis (AR) have bothersome symptoms that may assess using the visual analog scale (VAS). Asthma frequently is associated with AR. The present study investigated the role of VAS in children with newly-diagnosed AR. Methods: Parameters were VAS for nasal and asthma symptoms, and doctor’s perception of these diseases, medication use, and asthma comorbidity. Results: 67% of AR children had asthma. Severity of perceived nasal symptoms was associated with more intense asthma symptoms and doctor’s perception of disease severity (p<0.001 and p=0.02, re-spectively. Conclusions: Allergic rhinitis frequently is associated with asthma in child-hood. Symptom perception assessment using VAS is a simple tool for assessing their intensity. Moreover, the severity of nasal symptoms is associated with asthma symptom severity. Therefore, asthma should be carefully considered in AR children and require appropriate symptom perception assessment.

The primary objective of this study is to examine the extent of financial integration between Latin American and US financial markets, particularly in light of recent efforts to foster integration through trade agreements. Spanning from January 1, 1990, to December 31, 2019, the sample focuses on major market indices and key sectors. Financial integration is quantified using a DCC multivariate GARCH model, incorporating a smooth transition model, structural breaks, and regression-based approaches. Results indicate increased comovement with the US for main market indices in Argentina, Chile, Colombia, Mexico, and Peru, while Brazil shows a decrease. Similar trends are observed in sectoral analyses. The study also reveals heightened correlation post-trade agreements. Structural break analysis highlights significant shifts in dynamic correlations for countries with US free trade agreements. These findings support the argument of increased financial integration, bearing significance for portfolio diversification and international policy formulation.

Monkeypox virus infection is a recognized public health emergency. Little research has been done on treatment options for this disease. Until recently, there was not a single published work describing the usage of specific drugs in human patients with monkeypox virus infection. This paper gives the first bibliometric analysis of monkeypox treatment options based on data available on PubMed and Scopus. It also reviews the specific drugs used in the treatment of monkeypox. That includes data on Tecovirimat, Cidofovir, Brincidofovir, and Vaccinia Immune Globulin. Tecovirimat is a promising option in progressive disease in terms of efficacy and safety. However, Brincidofovir has been associated with discontinuation of treatment. Cidofovir is also not the preferred drug among physicians. Currently, Tecovirimat can be further used for the management of aggravating cases. More studies should be conducted on Tecovirimat to treat this condition, mainly through controlled trials.

Phytonutrients are plant foods that contain many natural bioactive compounds, called phytochemicals, which expose specific biological activities. These phytonutrients and their phytochemicals may play an important role in health care maintaining normal organism functions (as preventives) and fighting against diseases (as therapeutics). Phytonutrient’s components are the primary metabolites (i.e., proteins, carbohydrates, and lipids) and phytochemicals or secondary metabolites (i.e., phenolics, alkaloids, organosulfides, and terpenes). For years, several phytonutrients and their phytochemicals have demonstrated specific pharmacological and therapeutic effects in human health such as anticancer, antioxidant, antiviral, anti-inflammatory, antibacterial, antifungal, and immune response. This review summarizes the effects of the most studied or the most popular phytonutrients (i.e., turmeric, garlic, cinnamon, graviola, and oregano), and any contraindication found. This article also calculated the physicochemical properties of the main phytochemicals in the selected phytonutrients using Lipinski’s, Veber’s and Ghose’s rules. Based on our revisions for this article, all these phytonutrients have consistently shown several in vitro, in vivo, and clinical studies with great potential as preventives and therapeutics on many diseases.

Sleep disturbances and chronic pain are prevalent and interrelated conditions that significantly impact individuals' quality of life. Understanding the intricate dynamics between sleep and pain is crucial for developing effective treatments that enhance the well-being of affected individuals and reduce the economic burden of these debilitating conditions. This narrative review examines the complex relationship between sleep disturbances and chronic pain. We describe the prevalence and types of sleep disturbances and sleep disorders in chronic pain patients. Posteriorly we critically review the clinical and experimental evidence investigating the relationship between sleep disturbances and chronic pain aiming to clarify the impact of chronic pain on sleep and, conversely, the impact of sleep disturbances on pain perception. We conclude that a bidirectional relationship between chronic pain and sleep disturbances is almost a consensus in the literature, however, the strength of each direction of the association is less clear. As of now, the literature suggests that sleep impairment is a stronger predictor of pain than pain is a predictor of sleep impairment. Additionally, addressing sleep disturbances in chronic pain patients is crucial, as poor sleep has been linked to higher levels of disability, depression, and pain-related catastrophizing.

Nitrogen oxides (NOx), pivotal atmospheric pollutants, significantly imperil the environment and human health. The CH4-SCR process, leveraging the abundant and accessible methane (CH4), emerges as a promising avenue for NOx abatement. Previous studies have demonstrated that zeolite support with twelve-membered ring (12-MR) and five-membered ring (5-MR) structures are susceptible to framework collapse in the presence of water, leading to catalyst deactivation. Consequently, there is a necessity to explore novel zeolites with enhanced hydrothermal stability for application in CH4-SCR processes. This research introduced for the first time an investigation into a novel In-H-SSZ-39 catalyst, synthesized via ion exchange and meticulously optimized for preparation conditions, including calcination temperature, In ions concentraiton, and reaction conditions, including CH4/NO ratio, O2 concentration, water vapor content, and Gas hourly space velocity (GHSV). Notably, the In/H-SSZ-39 catalyst achieved superior performance of 42.1% at 605 °C, with a calcination temperature of 500 °C and an In ion concentration of 0.066 M, under CH4/NO ratio of 1.5:1, 10% O2, and GHSV of 21000 h-1. In addition, the catalytic performance was influenced by the concentration of water vapor and the GHSV value.

Throughout the evolution of machine learning, the size of models has steadily increased as researchers strive for higher accuracy by adding more layers. This escalation in model complexity necessitates enhanced hardware capabilities. Today, state-of-the-art machine learning models have become so large that effectively training them requires substantial hardware resources, which may be readily available to large companies but not to students or independent researchers. To make the research on machine learning models more accessible, this study introduces a size reduction technique that leverages stages in Pyramid Training and Similarity Comparison. Our results demonstrate that pyramid training can reduce model complexity while maintaining accuracy of conventional full-sized models, offering a scalable and resource-efficient solution for researchers and practitioners in hardware-constrained environments.

The timely and accurate detection of HIV infection is pivotal in stemming the tide of this global pandemic and facilitating early treatment initiation. This review offers a comprehensive examination of the diverse array of methods employed for HIV detection, spanning traditional serological assays to cutting-edge technologies such as nucleic acid amplification, biosensor innovations, and artificial intelligence-driven diagnostics. Each method is scrutinized in terms of its principles, applications, strengths, and limitations. By integrating these complementary approaches and fostering collaboration across disciplines, researchers and clinicians gain a multifaceted understanding of HIV infection, enabling them to navigate the complexities of the virus with precision and insight. Through a multidisciplinary lens, encompassing immunology, molecular biology, engineering, and public health, this review aims to inform future directions in HIV detection, ultimately guiding efforts towards personalized, precise, and accessible diagnostics for all individuals affected by the HIV/AIDS epidemic.

Objectives: Minimally invasive direct coronary artery bypass (MIDCAB) is an alternative for revascularisation of isolated left anterior descending (LAD) or as multi-vessel (MV) procedure for diagonal branch (RD) or circumflex artery (RCX) region. Methods: From 2021 to 2022 91 patients underwent MIDCAB procedure in our heartcenter. 80% were male. Eighty percent of the patients had two or three vessel coronary artery disease. Mean age was 65±10 years. The left internal mammary artery (LIMA) was anastomosed to the left anterior descending via left minithoracotomy approach in all patients. Fourteen patients received multi-vessel MIDCAB (15%) with two or three anastomoses a saphenous vein graft (as T-graft to LIMA) was anastomosed to RD or RCX. Results: Mean operation time was 2.6±0.8 hours. The 30-day mortality was 0. Mean required packed red blood cells (pRBC) was 0.4±1.2 unit. The intensive care unit stay (ICU) was 1.5±1.6 days mean. We observed two patients (2%) with wound infections postoperatively. Four patients underwent rethoracotomy because of bleeding. Mean follow-up time was 1.5±0.5 years. Seventy-eight patients (86%) were in New York Heart Association (NYHA) class 1, 11 patients (12%) were in NYHA class 2. One patient received percutaneous coronary intervention due to de novo stenosis of the RCA. Late mortality was 2%. The Kaplan-Meier survival rate was 98.8% at 1 and 2 years. Conclusion: MIDCAB is technically demanding. Our postoperative and short-term clinical results demonstrate that this procedure is safe and feasible. Optimal patient selection and an experienced surgical team are mandatory.

Plasminogen activator inhibitor-1 (PAI-1) has a significant role in fibrinolysis, atherogenesis, cellular senescence, and chronic inflammation. OSA (obstructive sleep apnea) leads to increased PAI-1 levels and the development of cardiovascular disease (CVD). The aim of this study was to determine the effects of CPAP therapy in patients with severe OSA on coagulation parameters and PAI-1. This prospective, controlled study enrolled 57 patients who were newly diagnosed with severe OSA, 37 of whom had had a good CPAP adherence after 6 months of therapy (usage of the device for at least 4 h per night), and their data were analyzed. The analysis showed a statistically significant increase in D-dimer values (415 (316.5-537.5) before CPAP therapy vs. 499 (327-652) after therapy, p = 0.0282) and a decrease in fibrinogen values (3.665 ± 0.752 before CPAP therapy vs. 3.365 ± 0.771) after therapy, p = 0.0075)). The PAI-1 concentration values before and after CPAP therapy did not differ significantly (17.35±7.01 ng/ml before CPAP therapy vs. 17.42±6.99 ng/ml after therapy, p = 0.9367). This study shows a tendency to improve the fibrinolytic capacity in patients with OSA after CPAP therapy although PAI-1 levels did not differ significantly.

This study aimed to investigate the venom sac extracts (VSE) of the European hornet (EH) Vespa crabro (Linnaeus, 1758) (Hymenoptera: Vespidae), focusing on the differences between stinging females, gynes (G) and workers (W), at the protein level. Using a quantitative “Sequential Window Acquisition of all Theoretical Fragment Ion Mass Spectra” (SWATH-MS) analysis, we identified and quantified a total of 240 proteins. Notably, within the group, 45.8 % (n = 110) showed significant differential expression between VSE-G and VSE-W. In this set, 57.3 % (n = 63) were upregulated and 42.7 % (n = 47) downregulated in the G. Additionally, the 200 quantified proteins from the class Insecta belong to 16 different species, six of them to the Hymenoptera/Apidae lineage, comprising seven proteins with known potential allergenicity. Thus, phospholipase A1 (Vesp v 1), phospholipase A1 verutoxin 2b (VT-2b), hyaluronidase A (Vesp v 2A), hyaluronidase B (Vesp v 2B), and venom allergen 5 (Vesp v 5) were significantly downregulated in the G, and vitellogenin (Vesp v 6) was upregulated. Overall, 46 % of the VSE proteins showed differential expression, with a majority being upregulated in G. Data are available via ProteomeXchange with identifier PXD047955. These findings shed light on the proteomic differences in VSE between EH castes, potentially contributing to our understanding of their behavior and offering insights for allergy research.

Autism spectrum disorder (ASD) is a neurodevelopmental disease characterized by persistent deficits in social interaction and communication, repetitive movements, abnormal focusing on objects, or activity that can significantly affect the quality of life of the afflicted. Brain neuronal and glial cells have been implicated. It has a genetic component but can also be triggered by environmental factors or drugs. For example, prenatal exposure to valproic acid (VPA) or acetaminophen (APAP) or ingestion of propionic acid (PPA) can increase the risk of ASD. Recently, epigenetic influences on ASD have been brought to the forefront of investigations on the etiology, prevention, and treatment of this disorder. Epigenetics refers to DNA modifications that alter gene expression without making any changes to the DNA sequence. Although an increasing number of pharmaceuticals and environmental chemicals are being implicated in the etiology of ASD, here we specifically focus on the molecular influence of the above-mentioned drugs on epigenetic alterations in neuronal and glial cells and their potential connection to ASD. It is concluded that a better understanding of these phenomena can lead to more effective interventions in ASD.

Ophthalmological services face global inadequacies, especially in low- and middle-income countries, which are marked by a shortage of both practitioners and equipment. This study employed a portable slit-lamp microscope with video capabilities and cloud storage for a more equitable global diagnostic resource distribution. To enhance accessibility and quality of care, this study targeted corneal opacity, a global cause of blindness. To boost the online diagnosis efficiency, an AI pipeline was developed using anterior segment videos to detect corneal opacity. First, we extracted image frames from videos and learned them using a Convolutional Neural Network(CNN) model. Second, we manually annotated to extract only the corneal margins, adjusted the contrast with CLAHE, and learned using the CNN model. Finally, we performed semantic segmentation of the cornea using the annotated data. The results showed an accuracy of 0.8 for image frames, and 0.96 for corneal margins. Dice and IoU were 0.94 for semantic segmentation of corneal margins. While corneal opacity detection from video frames seemed challenging in the early stages of this study, manual annotation, corneal extraction, and CLAHE contrast adjustment significantly improved accuracy. Integrating manual annotation into the AI pipeline through semantic segmentation achieved a high accuracy in detecting corneal opacity.

Layered halide perovskites have emerged as a promising contender in solid-state lighting thanks to their tunable bandgap, high luminescence quantum yield, and high color purity. However, the fabrication of perovskite light-emitting devices in laboratories usually experiences low de-vice-to-device reproducibility since perovskite crystallization is highly sensitive to ambient condi-tions, such as humidity and temperature. Although device processing inside gloveboxes is primarily used to reduce the influence of oxygen and moisture, several extraneous variables, including gas-eous pollution traces and thermal fluctuations in the inert atmosphere or contaminations from re-sidual solvents confined in the enclosed environment, can destabilize the crystallization process and alter the properties of the emissive layers. Here, we examine different experimental set-ups for spin-coating deposition of layered perovskites in inert atmospheres and their influences on the formation of polycrystalline thin films. Our results demonstrate that fluctuations in the glovebox properties (concentrations of residual O2 and H2O or solvent traces), even in very short timescales, can negatively impact the consistency of the perovskite film formation, while thermal variation plays a relatively minor role in this phenomenon. Furthermore, careful storage of chemical species inside the workstation is critical for reproducing high-quality perovskite layers. Consequently, when applying our most controlled environment for perovskite deposition, the photoluminescence life-time of perovskite thin films shows a standard deviation of only 3%, whereas the reference set-up yields a 15% standard deviation. Regarding complete perovskite light-emitting diodes, the uncer-tainties in statistical luminance and EQE data are significantly reduced from 230% and 140% to 38% and 42%, respectively.

Multiple sensor nodes known as detection stations make the sensor networks; each node is light and portable. Every sensor node contains power source, microcomputer, transducer and transceiver. Power source provides power to each node. Micro-computer is used for storing and processing the output coming from the sensors. The transducer is used to generate the signals and the transceiver is used to receive and transmit data to the central computer. Eavesdropping gets facilitated with wireless communication, and it has many useful applications in military, homeland, hostile and uncontrolled environments. So it is prone to the high level of security. The process in which information is gathered to form a summarized type for analysis is known as data aggregation, as it is used to reduce the energy consumption in wireless sensor networks. The security issues have become crucial in data aggregation, especially when gets deployed in hostile and remote environment. In wireless sensor networks many secure aggregations have been proposed. It still faces some resource constrained that’s why new techniques are needed. In our survey we will discuss those approaches and their pros and cons.

In 1944 the architect Antonio Gómez Davó designed and built a new house for Mr. Ferrer at Rocafort in the suburbs of Valencia (Spain). In this same year, Europe, America, Russia and even Japan were still at war and Spain was only recovering from its own intestine conflict. Therefore, architectural innovations and influences were scarce as was the circulation of specialized journals on the matter. But in many creations, like the ceramic vaults and the brise-soleil, Le Corbusier himself had declared his profound nostalgia from the Mediterranean, a Sea that he had come to appreciate in his travels to the “orient”. In the case of Gómez Davó, having been born and raised in a prominent family of Valencia he could not remain indifferent to the design features that appeared in the vernacular architecture of the area, specially the type of inclined louvers of Arabic descent, that covered bow-windows and balconies and which have come to be known in Spain as the Majorcan louvers; nowadays even employed by Rafael Moneo for instance at the extension of the Painter Miro Foundation. However, with so many difficult circumstances surrounding him, Gómez Davó could not get to the point of producing a ground breaking design based on solar assumptions for the whole façade of the building, instead when providing an entrance porch apt for spending life, meals and well deserved rest hours outdoors in the pure Mediterranean tradition, he ventured to construct a surprising perforated wall oriented to the South in order to control radiation in winter and provide shade in the summer while affording excellent light and superb conditions of ventilation. By means of our own devised simulation tools we have analyzed the conditions of the house and especially of such innovative brise-soleil at times reminiscent of Alvar Aalto solutions for day-lit roofs, which he was aware intuitively and by his own incipient study of solar geometry, that needed adaptation from so distant latitudes as Finland and Valencia. By outlining such unknown and bold precedent we contribute to revitalize the early and daring pioneers of solar architecture in peripheral Spain and Europe, at the birth of critic regionalism, a fact often disregarded in conventional history of Modern Architecture.

This paper presents a mathematically rigorous investigation into the incompleteness of Einstein-de Sitter spacetime. The incompleteness of the spacetime is proved using results regarding isometries between connected Semi-Riemannian manifolds following the computation of the Einstein tensor of the Einstein-de Sitter spacetime. Various properties of the Einstein-de Sitter spacetime apart from its incompleteness is also discussed based on its incompleteness with the help of various results of Semi-Riemannian manifolds. Also, few properties of spacetime which cannot be concluded from the incompleteness of the spacetime is also discussed.

Low dropout (LDO) regulators are widely used in portable electronic devices because they 1 occupy small chip and printed circuit board (PCB) areas. This work presents the design of a low 2 noise low dropout (LDO) linear voltage regulator using folded cascode operational amplifier and 3 source follower buffer for frequency compensation. The proposed design achieves low output noise 4 with feedback loops, which makes it possible to improve load and line regulations as well as the 5 transient response for voltage applications in comparison with the literature. The designed LDO 6 linear regulator works under the input voltage of 2.8-4.8 V and provides up to 100 mA load current 7 for an output voltage of 2.6 V. 8 9 Moreover, the operational amplifier design contributes to the major reduction in the frequency 10 noise, and make sure the stability of LDO under different conditions. Furthermore, the novel LDO 11 design topology has very less dropout voltage, fast transient response and minimized power dissi- 12 pation under maximum load current, worst process corner, and maximum temperature to achieve 13 high power efficiency, maintain output voltage in the presence of fast load changes, and to lower the 14 power loss across the pass element especially at high load. Design criteria are addressed in detail. 15 16 This LDO topology is able to achieve ultra-low noise i.e. 2 /muv/√Hz for frequency >1kHz. The 17 proposed LDO is implemented in the cadence virtuoso H9A with CMOS 130 μm technology.

Any material object vibrating or rotating excites in spacetime around gravitational waves. Atomic nucleus being highly dynamical object also excites continuously micro gravitational waves which might form around the nucleus specific structure resembling optical diffraction grating and which gives huge effect to nuclear elastic scattering cross sections. The close similarity between the nuclear diffractive scattering and the optical Debye-Sears Effect is shown.

Elevated blood glucose levels, known as glycemia, play a significant role in sudden cardiac arrest, often resulting in sudden cardiac death, particularly among those with diabetes. Understanding the internal mechanisms has been a challenge for healthcare professionals, leading many research groups to investigate the relationship between blood glucose levels and cardiac electrical activity. Our hypothesis suggests that glucose-sensing biophysics mechanisms in cardiac tissue could clarify this connection. To explore this, we adapted a single-compartment, computational model of the human atrial node's action potential. We incorporated glucose-sensing mechanisms with voltage-gated sodium ion channels using ordinary differential equations. Parameters for the model were based on existing experimental studies to mimic the impact of glucose levels on atrial node action potential firing. Simulations using voltage clamp and current clamp techniques showed that elevated glucose levels decreased sodium ion channel currents, leading to a reduction in the sinoatrial node action potential frequency. In summary, our mathematical model provides a cellular-level understanding of how high glucose levels can lead to bradycardia and sudden cardiac death.

China’s leading development of a complete battery value chain for Electric Vehicles (EV) is restructuring the global automotive sector. The competitive advantage of China's EV battery industry lies on its firms’ core competency and political economic geography. Based on first-hand empirical material and data obtained from years of fieldwork in an EV battery cluster in South China, this paper identifies what the Chinese EV battery industry’s core competency is and details how it is built up from below. The current core competency of Chinese battery firms is their mass manufacturing capability to supply lithium-ion batteries vehicle manufacturers (OEMs) with stable and consistent quality as well as competitive prices. This competency is acquired by firms through technological learning at workshop level while taking use of their experiences accumulated in mass production of batteries for consumer electronics sectors. Furthermore, the rapid learning and accumulation of knowledge on battery manufacturing at large scale is also facilitated by the industrial cluster environment where firms are embedded. Supported and promoted by local government policies, Chinese EV battery clusters are composed of firms from different segments of a complete battery value chain. The findings illuminate the likely strategic strengths, weaknesses, and futures of the Chinese EV battery industry in global competition.

As society becomes more reliant on Lithium-ion (Li-ion) batteries, state-of-health (SOH) estimation will need to become more accurate and reliable. Therefore, SOH modelling is in the process of shifting from using simple and continuous charge/discharge profiles, to more dynamic profiles constructed to mimic real operation, when ageing the Li-ion batteries. However, in most cases, when ageing the batteries, the same exact profile is just repeated until the battery reaches its end-of-life. Using data from batteries aged in this fashion to build a model, there is a very real possibility that the model will rely on the built-in repetitiveness of the profile. Therefore, this work will examine the dependence of the performance of a multiple linear regression on the number of charges used to train the model, and their location within the profile used to age the batteries. The investigation shows that it is possible to build models using randomly selected partial charges while still reaching errors as low as 0.5%. Furthermore, it shows that only two randomly sampled partial charges are needed to achieve errors of less than 1%. Lastly, as the number of randomly sampled partial charges used to create the model increases, then the dependence on particular partial charges tends to decrease.

As an important support for power system with high penetration of renewable energy, energy storage system (ESS) has changed the traditional model of simultaneous implementation of electricity production and consumption, and its installed capacity under the source-grid-load scenario is rising year by year, but it faces the problems of insufficient utilization and benefits. This study analyzes the functional combination of ESS under the source-grid-load scenarios, and proposes a comprehensive benefit evaluation method of energy storage projects based on fuzzy decision-making trial and evaluation laboratory (DEMATEL) and super efficiency data envelopment analysis (DEA) model, which considers the perspective of economic and social environment. Firstly, the functional requirements of energy storage on the source-grid-load scenarios are explored respectively, and the characteristics of various functions are analyzed to form functional combination schemes. Secondly, index modeling is carried out from three aspects: the whole life cycle cost, functional combination benefit and social environmental benefit, and the comprehensive benefit evaluation index system of energy storage project is proposed, which includes both economic and social environment. Then, the intuitionistic trapezoidal fuzzy number is combined with DEMATEL to form an effective analysis method for the input-output relationship of the indexes, and the comprehensive evaluation is realized based on the super efficiency DEA model. Finally, the effectiveness of the proposed evaluation method and the rationality of the functional combination are verified under source-grid-load scenarios respectively.

In order to deal with imprecision, ambiguity, and uncertainty in data analysis, Pawlak introduced rough set theory in 1982. This paper aims to expand the scope of basic set theory developed by presenting the notions of δP-upper and δP-lower approximations, that are based on the notion of δP-open sets, we additionally examine a few of their fundamental characteristics.

Oil floating on the sea surface can be detected by both passive and active methods using the ultraviolet-to-microwave spectrum, whereas oil immersed below the sea surface can signal its presence only in visible light. This paper presents an optical model of a sea area deeply polluted by an oil suspension (10 ppm) located in a layer (thickness 5 m) separated from the sea surface by a clear layer (thickness 1 m). The impact of wavelength and state of the sea surface on reflectance changes is shown based on the results of Monte Carlo ray tracing. A two-wavelength index of reflectance is proposed to detect oil suspended in the water column (645-469 nm).

The deposition of dust particles on solar panel modules constitutes a significant challenge, leading to a potential reduction in solar cell performance. This work tackles this issue by investigating the surface modification of a layer of transparent indium tin oxide (ITO), with a detailed analysis of the influence of its characteristics via different annealing conditions/processing. The ITO layer has been studied to enhance its characteristics for dust removal applications. Of particular note is that the developed ITO layer can be applied to dust removal using electrostatic or magnetic-based approaches. Thus, the correlation between annealing gas (using Argon, Oxygen, and Nitrogen), annealing temperature, and annealing ramping rate is explored with the obtained surface topography, as well as electrical and optical properties. The developed ITO layers annealed in the Argon medium at 600°C with an annealing ramping rate of 50°C/s showed smooth surface roughness, the highest electrical conductivity, and a relatively low refractive index. Such findings can play a crucial role in the future implementation of using the enhanced ITO layer for dust mitigation in solar panels, thus improving their long-term performance.

Developing a dependable gas and condensate value chain is vital for the ongoing prosperity of the oil and gas industries. To achieve this, NNPC is enhancing its production facilities by employing process modeling and simulation with the assistance of Symmetry iCON®. As a part of this initiative, a simulation model was created using the Symmetry iCON® pipe plexus solver to simulate the dynamic environment of the gas and condensate pipeline network in the Nigerian Southern region. To validate the model, data from a single month, specifically December 2022, was utilized. Furthermore, the algorithm was utilized to generate projections for the month of January 2023, providing valuable insights into its effectiveness. The study also highlighted the significance of accurately estimating the characteristics and properties of a pseudo component of C6+ in relation to thermodynamics and material properties, which was a particularly insightful finding. he assessment of this property has transitioned from being relatively unimportant to becoming critically important, primarily due to the lack of condensate data monitoring at specific terminals. However, it's worth noting that the model currently has an error margin of 4-6% when predicting the data. As more information becomes available in the future, the model can be easily fine-tuned to better align with the actual conditions, allowing for more accurate predictions.

The field of personalized medicine is undergoing a transformative shift through the integration of multi-omics data, which encompasses genomics, transcriptomics, proteomics, and metabolomics. This synergy allows for a comprehensive understanding of individual health by analyzing genetic, molecular, and biochemical profiles. The generation and integration of multi-omics data enable more precise and tailored therapeutic strategies, improving the efficacy of treatments and reducing adverse effects. However, several challenges hinder the full realization of personalized medicine. Key hurdles include the complexity of data integration across different omics layers, the need for advanced computational tools, and the high cost of comprehensive data generation. Additionally, issues related to data privacy, standardization, and the need for robust validation in diverse populations remain significant obstacles. Looking ahead, the future of personalized medicine promises advancements in technology and methodologies that will address these challenges. Emerging innovations in data analytics, machine learning, and high-throughput sequencing are expected to enhance the integration of multi-omics data, making personalized medicine more accessible and effective. Collaborative efforts among researchers, clinicians, and industry stakeholders are crucial to overcoming these hurdles and fully harnessing the potential of multi-omics for individualized healthcare.

It is challenging to precisely measure the slow interseismic crustal deformation rate from SAR data. The long-wavelength orbital errors, owing to the uncertainties in satellite orbit vectors, commonly exist in SAR interferograms, which degrade the precision of the InSAR products and become the main barrier of extracting interseismic tectonic deformation. In this study, we propose a novel temporal network orbital correction method that is able to isolate the far-fault tectonic deformation from the mixed long-wavelength signals based on its spatio-temporal characteristic. The proposed approach is straightforward in methodology but could effectively separate the subtle tectonic deformation from glaring orbital errors without ancillary data. Both synthetic data and real Sentinel-1 SAR images are used to validate the reliability and effectiveness of this method. The derived InSAR velocity fields clearly present the predominant left-lateral strike-slip motions of the Tuosuo Lake segment of the Kunlun fault in western China. The fault-parallel velocity differences of 5-6 mm/yr across the fault between areas ~50 km away from the fault trace are addressed. The proposed method presents significantly different performance from traditional quadratic approximate method in the far-field. Through the implementation of the proposed method, the RMSE between the LOSGPS and our derived descending InSAR LOS is reduced to less than one-third of the previous study, suggesting its potential to enhance the availability of InSAR technology for interseismic crustal deformation measurement.

This comprehensive review explores the multifaceted landscape of supercapacitors, highlighting their pivotal role in meeting the global demand for efficient energy storage solutions. Supercapacitors, with their high power density, rapid charge-discharge cycles, and extended cycle life, offer a compelling alternative to conventional batteries and capacitors. The review delves into the fundamental mechanisms of energy storage in supercapacitors, categorizing them into Electrochemical Double-Layer Capacitors (EDLCs), pseudocapacitors, and hybrid capacitors. It discusses significant advancements in supercapacitor technology, including nanostructured materials, flexible and wearable supercapacitors, solid-state supercapacitors, and hybrid supercapacitors, highlighting their potential applications across industries such as transportation, renewable energy, consumer electronics, and industrial power management. Through continued research and development, supercapacitors are poised to play a pivotal role in driving the transition towards a greener and more efficient future.

The escalating global demand for clean water has propelled research into sustainable and cost-effective purification methods. Activated carbon (AC) derived from biowaste materials presents an environmentally friendly and economically viable solution for water treatment. This review article provides an extensive overview of the synthesis techniques, characterization methods, and applications of biowaste activated carbon (BAC) for water purification. It discusses the diverse sources of biowaste, such as agricultural residues, food waste, and biomass, and explores various activation methods including physical, chemical, and biological processes. Furthermore, the article examines the adsorption mechanisms, performance factors, regeneration strategies, and emerging trends in BAC synthesis and application. Insights from this review can guide future research endeavors and facilitate the development of sustainable water purification technologies.

Blisks subjected to rough machining for channel creation must undergo finishing processes, and such processes must achieve the required tolerance limits. A high-quality surface finish and predictable long tool life are critical for the finish milling of blisks. Accordingly, the aim of this study was to optimize parameters for the finish machining of an Inconel 718 alloy monolithic blisk. Ball-cone mills were used to machine the blade surface at a constant depth. A sensory tool holder was used to collect cutting force signals during machining, and a digital microscope was used to examine tool wear. The surface texture measuring instrument was used to measure blisk blade surface roughness to evaluate processing quality. This study manipulated two cutting parameters, namely cutting speed and feed per tooth, and investigated their effects. The relationship between cutting conditions and machining efficiency was analyzed. According to the experimental results, we identified a set of optimal parameters for marginal tool wear and fast cutting speeds and then estimated the corresponding tool life by using the derived parameters.

Evolution remains an incessant process in viruses, allowing them to elude host immune response and induce severe diseases, impacting the diagnostic and vaccine effectiveness. Predicting emerging viral genomes is crucial, particularly in diseases like dengue, where viruses disrupt host cells, leading to fatal outcomes. Deep learning has been applied to predict dengue fever cases; there has been relatively less emphasis on its significance in forecasting emerging Dengue Virus (DENV) serotype. While Recurrent Neural Networks (RNN) were originally designed for modeling temporal sequences, our proposed DL-DVE generative and classification model, trained on complete genome data of DENV, transcends traditional approaches by learning semantic relationships between nucleotides in a continuous vector space instead of representing contextual meaning of nucleotide characters. Leveraging 2000 publicly available DENV complete genome sequences, our Long Short-Term Memory (LSTM) based generative and Feedforward Neural Network (FNN) based classification DL-DVE model showcases proficiency in learning intricate patterns and generating sequences for emerging serotype of DENV. The generative model showed accuracy of 93% and the classification model provided insight into the specific serotype label, corroborated by BLAST search verification. Evaluation metrics such as ROC-AUC value 0.818, accuracy, precision, recall and F1 score all to be around 99.00%, demonstrated the classification model’s reliability.  Our model classified the generated sequences as DENV-4, exhibiting 65.99% similarity to DENV-4 and around 63-65% similarity with other serotypes, indicating notable distinction from other serotypes. Moreover, the intra-serotype divergence of sequences with a minimum 90% similarity underscored their uniqueness. We analyzed the conserved motifs in the genome through MEME Suite (version 5.5.5). Our research strives to contribute to the ongoing fight against the Dengue virus by offering predictive insights into its genomic evolution. Looking ahead, proactive predictive modeling before mutations occur holds potential for guiding vaccine design and diagnostic kit development.

The practice of teaching and scientific research on cadaveric material remains crucial for medical education, especially in surgical disciplines. However, in Italy, this practice has been neglected due to legislative insufficiency and financial constraints. Although innovative methods and tools like simulators and e-learning have been adopted, direct hands-on experience with human cadavers remains irreplaceable for medical and surgical education. The absence of clear legislation governing cadaveric dissection has limited availability for teaching and research, resulting in economic burdens for universities and individuals seeking proper surgical training. To address this issue, the Law No. 10/2020 and the recent implementing decree were introduced in Italy, providing detailed legislation on the donation of bodies for educational and research purposes. The law emphasizes the importance of respecting the donor's specific choices and aligns with constitutional principles promoting culture, research, and health protection. However, some critical issues related to consent procedures, duration of body availability, and preservation of anatomical parts remain. Additionally, the law's dissemination among the population needs improvement. Future optimization could include allowing donors to choose the timing of body donation and considering different timeframes for body availability. Furthermore, the implementation of consent procedures could be simplified to increase donations. The law should also address the need for appropriate reception centers and allocate resources for effective dissemination. Despite these challenges, Law No. 10/2020 represents a significant step forward in enhancing medical-surgical training, scientific research, and the overall quality of patient care in Italy.

Global warming leads in general to a reduction in precipitations and non-rainfall water contributions such as dew. The present study, carried out in Gujarat (India) for 17 years between 2005 and 2021, shows however a rare increase of the rain falls and dew condensation, the latter related to an increase in relative humidity and a decrease in wind amplitudes. Rain was obtained from meteo stations. Dew was calculated from an energy model and meteo data from 15 meteo stations (14 in India and 1 in Pakistan) and compared to early and dedicated measurements in 13 sites. Maps of dew, rain and their relative contributions during the period are provided. They show that, while rain mainly occurs during the monsoon (June to September), dew forms during the dry season (October to April). This water resource was mapped over the Gujarat state of India by using a kriging process. According to the site, the dew/rain ratios can represent between 4.6% (Ahmedabad) to 37.2% (Jamnagar) on the whole period of 17 years. This positive evolution, which is observed since 2015-2017, is likely to continue in the future.

Septic arthritis (SA) represents an orthopedics urgency and mainly affects the knee joint. Due to its devastating effects on cartilage, immediate management is crucial. SA is characterized by an an-nual incidence of 2 to 10 cases per 100,000 individuals, with mortality rates fluctuating between 0.5% and 15%, with a substantially higher mortality rate observed in older people (15%) in con-trast to younger cohorts (4%). The etiology of septic arthritis is multifactorial: a spectrum of Gram-positive and Gram-negative bacteria can contribute to the development of this condition, especially Staphylococcus aureus. The treatment involves urgent (arthroscopic or arthrotomic) debridement associated with adequate antibiotic therapy. Intra-articular antibiotic carriers can also be used to increase their local concentration and effectiveness. The case of a 67-years-old woman affected by knee SA from methicillin-susceptible S. aureus is presented. She was treated with an arthroscopic debridement enhanced by intra-articular antibiotic-loaded calcium sulphate beads, together with antibiotic therapy. At 2-year follow-up, the infection has been eradicated and the patient fully recovered. This is the first description, to our knowledge, in English Literature of the use of antibiotic-loaded calcium sulphate beads as an adjuvant in the surgical treatment of SA of a native knee joint.

We have previously demonstrated that receptor-interacting serine threonine kinase 1 (RIPK1) is expressed in hair follicle and regulates hair cycle. RIPK1 inhibitors also accelerated the telogen-to-anagen transition and elongated the anagen period in the mouse model. Here, we first investigated the involvement of RIPK1 in alopecia areata (AA). mRNA and protein expression of RIPK1 was increased in the skin of AA mouse model. Single cell RNA sequencing and immunohistochemistry showed that RIPK1 is highly increased in dendritic cells (DCs) and CD8+ T cells. RIPK1 inhibitors (i.e., Necrostatin-1s and GSK2982772) delayed the onset of AA in mouse model, and reduced the DCs and CD8+ T cells in AA skin. RIPK1 inhibitors also increased the hair length in the mouse hair organ culture mimicking AA. Collectively, these results suggest that RIPK1 is involved in AA onset via modulating immune cells, and RIPK1 inhibitors could prevent AA onset.

Utilizing deep learning for semantic segmentation of cropland from remote sensing imagery has become a crucial technique in land surveys. Cropland illustrates diverse morphologies and degrees of fragmentation on the Earth’s surface, underscoring the importance of accurately perceiving the complex boundaries of cropland which are crucial for effective segmentation. This paper introduces a UNet-like boundary-aware compensation model BAFormer. Cropland boundaries typically exhibit rapid transformations in pixel values and texture features, often appearing as high-frequency features in remote-sensing images. To enhance the recognition of these high-frequency features as represented by cropland boundaries, the proposed BAFormer integrates a Feature Adaptive Mixer (FAM) and develops a Deep Wide Large Kernel Multi-Layer Perceptron (DWLK-MLP) to enrich the global and local cropland boundaries features separately. Specifically, FAM adaptively mixes high-frequency and low-frequency features through the advantages of convolution and self-attention; DWLK-MLP expands the convolutional receptive field by deeply decomposing large kernel convolutions. The efficacy of BAFormer has been evaluated on the Vaihingen, Potsdam, and LoveDA public datasets, as well as the Mapcup dataset. It has demonstrated advanced performance, achieving mIoU scores of 84.5%, 87.3%, 53.5%, and 83.1% on these datasets respectively. Notably, BAFormer-T, the lightweight iteration of the model, surpasses other lightweight models on the Vaihingen dataset with scores of 91.3% F1 and 84.1% mIoU. The source code is available at https://github.com/WangYouM1999/BAFormer.

The role of inflammation in the pathophysiology of acute myocardial infarction (AMI) is well established. Recognizing inflammation's pivotal role in AMI, this manuscript systematically traces historical studies, spanning from early attempts to the present landscape. Several anti-inflammatory trials targeting inflammation in post-AMI have been performed. The review includes key trials, examining their designs, patient demographics, and primary outcomes. Efficacy and challenges are analyzed, shedding light on the translational implications of trial outcomes. The article also discusses emerging trends, ongoing research, and potential future directions in the field. Practical applications and implications for clinical practice are considered, providing a holistic view of the evolving landscape of anti-inflammatory interventions in the context of AMI.

This research seeks to solve the multi-faceted problem of waste disposal by analyzing the application of waste plastic and tyre material within non-structural concrete to ensure more sustainability and less environmental degradation. Study focusses on material properties including specific gravity, water absorption and bulk density and characteristics of the concrete that is produced by the utilization of the above waste aggregates including workability, compressive strength, flexural strength and tensile strength. This paper employs secondary data collection and MATLAB in the analysis of the findings pointing to the fact that the mechanical properties reduce with the level of waste content yet emphasizing the green aspect of such materials. Thus, a complex and diverse effect is demonstrated by the life cycle assessments (LCA) for global warming, ozone depletion, terrestrial ecotoxicity, and acidification. Furthermore, the utilization of waste materials decreases the compressive, flexural, and tensile strength, but it provides distinct ecological benefits which prove the importance of proper mix proportions for concrete performance. The outcomes of this research will be useful for further investigation in the application of the concept as well as to call for the development of new ideas for the improvement of bonding of wastes to aggregates in concrete.