Systemic hypertension is not a recognized association in patients with transfusion depended thalassaemia. In patients with high cardio-vascular morbidity due to iron overload and pulmonary hypertension this previously unrecognized disease should not be overlooked.

This research focuses on exploring generalized electron emissions in one-dimensional conducting materials, specifically examining field emission and thermionic emission. Additionally, the study investigates one-dimensional thermal radiation. The current density is derived for both one-dimensional thermionic emission and one-dimensional field emission. A generalized current density in one dimension is devised, taking into account arbitrary accelerating fields and temperature conditions. The formula for the one-dimensional current density is depicted with accelerating field and temperature across various work function materials. The current is also represented as a function of both temperature and electric field for various temperatures.

Researchers have asserted that, as patients rely on external powerful sources to control their disorders, in addition to their own internal powers and virtues, examining their coping mechanisms can lead to a better understanding of the initiation and progression of chronic diseases and uncover potential opportunities to balance and enhance their psycho-spiritual well-being and possibly their treatment and recovery. This review serves as a user’s manual for investigators who choose to use the CSI-SF to conduct their research on coping behaviors. The CSI-SF that measures four coping strategies based on 16 items was first assessed using the Jackson Heart Study (JHS) cohort in 2007. The reliability and construct validity of the CSI-SF was also later assessed among hemodialysis patients across 13 countries. In this study, the CSI-SF was assessed to be a reliable and valid instrument for measuring coping strategies. The CSI-SF serves the purpose of developing an inner voice that can assist with understanding how people cope with everyday life. The information gathered from administration of the CSI-SF can inform investigators about environmental cues and triggers that can also impact individual health.

The Amazonian shrub Myrciaria dubia (Kunth) McVaugh, known as "camu-camu", produces vitamin C-rich fruits of growing commercial interest. However, sustainable utilization requires assessment and protection of the genetic diversity of the available germplasm. We hypothesized that the ex situ M. dubia germplasm bank assembled from eight river basins across the Peruvian Amazon would harbor substantial genetic diversity and have a genetic population structure. This study aimed to (1) develop new polymorphic microsatellite markers, (2) characterize genetic diversity and validate the hypothesis in the ex situ germplasm, and (3) construct a core subset representing maximum allelic variability. Sixteen polymorphic microsatellite loci were developed using an enrichment approach. The evaluation of 336 genotypes from 43 accessions originating from eight river basins of the germplasm bank corroborated this hypothesis, revealing high gene diversity, with observed heterozygosity ranging from 0.468 to 0.644 and expected heterozygosity from 0.684 to 0.817 at the river basin level. Analysis of molecular variance showed a higher genetic variation within accessions and river basins, at 73% and 86%, respectively, than among accessions and river basins, at 27% and 14 %, respectively. Bayesian clustering detected the presence of ten genetic clusters, with several degrees of admixture among river basins. The Putumayo River basin showed a clear genetic homogeneity. A core collection of 84 genotypes was constructed, thus covering 86.7% of the global allelic diversity. These results have important implications for M. dubia conservation strategies and breeding programs, in demonstrating a need for genetic connectivity between populations but preserving unique genetic resources in isolated basins. These results validate the expected levels of diversity and population subdivision in a crop and stress the need to secure genetically diverse germplasms, underscoring the importance of thorough genetic characterization for ex situ germplasm management.

The integration of connected autonomous vehicles into the transportation system introduces significant enhancements in driving experience and convenience. Yet, it simultaneously raises important concerns regarding the security and privacy of passenger data. As individuals increasingly depend on these connected vehicles, particularly rental cars with advanced infotainment systems, safeguarding their personal information becomes a paramount imperative, presenting significant challenges that must be addressed to maintain user trust and comply with privacy regulations. This paper investigates risks associated with personal information stored in connected vehicle technology, emphasizing the importance of robust security and privacy practices. In the advent of cyber threats and the need for data protection, we examine a risk management framework (RMF) as suggested by NIST, which aims to address these quintessential concerns preemptively. Our research moves beyond the limitations of manual safeguarding methods, supporting the necessity for an advanced automated technical solution. To address these issues, we introduce and meticulously assess the efficacy of “Vehicle Inactive Profile Removal” (VIPR), an innovative technical solution conceived to actively eliminate the risks and potential oversights that stem from the prevalent tendency of renters to leave personal data unerased in the infotainment systems of rented connected vehicles. We provide a thorough evaluation of VIPR through various scenarios, including vehicle return to a rental depot, subsequent rentals, and ridesharing contexts. Our proof of concept includes an array of experiments to demonstrate how VIPR proficiently and autonomously removes previous renters’ “Inactive” profiles from a simulated infotainment system. The results highlight how VIPR constitutes a critical step towards enhancing the privacy and security of personal data, thereby promoting a safer, more responsible use of connected vehicle technology in society.

(1) Background: The increasing demand for sustainable practices in the textile industry has led to the exploration of natural dyes and eco-friendly dyeing processes. This study focuses on the potential of used coffee grounds (UCG) as an eco-friendly natural dye for Nylon 6.6 woven fabrics. (2) Methods: Five dyeing processes were evaluated, varying in the use of mordants and acids, to assess their impact on color saturation, colorfastness to laundering, and crocking resistance of Nylon 6.6. fabric. The processes included a control with no mordant or acid and others that incorporated tannic acid, acetic acid, and ferrous sulfate heptahydrate. (3) Results: The results demonstrated that Process 4, which combined tannic acid pre-mordanting with acetic acid in the dye bath, provided the best balance between color saturation and colorfastness. Process 2, utilizing only tannic acid, offered some durability in laundering and crocking tests. Process 5, being the least eco-friendly process demonstrated high color saturation, but it performed poorly in colorfastness to crocking, which means that it releases the UCG-based dye after rubbing of the dyed Nylon 6.6. fabric. (4) Conclusions: The findings confirm that UCG can be an effective and sustainable natural dye for Nylon 6.6, with pre-mordanting and acid treatment significantly enhancing dye uptake and retention. However, further research is needed to optimize color intensity and expand the application of UCG in textile dyeing.

Manual handling is a major risk factor for work-related musculoskeletal disorders and one of the leading causes of disability-adjusted life years globally, necessitating multifaceted risk reduction measures. One potential intervention for manual handling tasks is work technique training assisted by augmented feedback on biomechanical exposures. However, there is a research gap regarding its effectiveness specifically for manual handling tasks in both real work environments and controlled settings, as well as its ability to induce retained reductions in biomechanical exposure. The gap was investigated using a rapid review comprising a literature search using two databases and 11 reviews/overviews to identify studies from the past 20 years, up to studies published by June 1, 2024. Sixteen studies were identified, with 14 of them being of high or moderate methodological quality and were included. Three studies were conducted in real work environments and eleven in controlled settings. Most studies (n = 9) used auditory feedback, followed by vibration feedback (n = 6). In real work environments, the evidence for the effectiveness of sensor-based augmented feedback in reducing biomechanical exposure during administration was considered to be inconsistent and very limited directly after administration. For longer periods after administration, ranging from one week to more than six months, there is currently no evidence demonstrating the effectiveness of the feedback. In controlled settings, there was strong evidence for its effectiveness during and immediately after administration, and limited evidence for effectiveness up to six months post-administration when considering the tasks included in the training. Future research needs are discussed.

In recent years, there has been an increase in the number of illegal radio stations that exceed power limits or use illegal frequencies. There has been a radio monitoring system called the DEURAS system to control these illegal radio stations. This system monitors illegal radio stations by receiving illegal radio waves using sensor stations installed in various locations. However, these systems sometimes do not work well when there is no line of sight from the sensor stations to the illegal radio stations or when there are many reflected waves generated due to the surrounding environment. In this study, we proposed a radio wave source estimation using HAPS, an unmanned aerial vehicle flying at high altitude. The simulation results show that estimation from high altitude provides higher estimation probability and estimation accuracy than the conventional method.

Sclerotinia minor Jagger is a phytopathogenic fungus that causes lettuce drop, a serious problem of lettuce (Lactuca sativa L.) production. The control of this pathogen is challenging because of the presence of resistance of sclerotia, which can survive in the soil under favourable conditions. In Bulgaria, the management of lettuce drop relies primarily on the strategic application of synthetic fungicides. To find alternative methods for disease management, four bacterial isolates were screened for antagonism against S. minor. This study reports the in vitro evaluation of the antifungal activity of Bacillus subtilis, Priestia megaterium, Bacillus safensis, and Bacillus mojavensis against S. minor. The molecular identification of the isolates involved in the activity was examined by 16s rRNA sequencing. Isolated bacterial strains produced indole-3-acetic acid (IAA) in a medium supplemented with 0.1% L-tryptophan. The ability of those strains to increase the mobility of phosphorus and zinc has been elucidated. The production of siderophores has been confirmed on CAS (Chrom azurol S) medium. The inhibitory action showed by the filtrates of the bacterial growth broths against S. minor and points out the nature of the molecules involved. The evaluation of the antifungal activity was carried out in vitro and pot experiments. This study determined the effect of growth-promoting rhizobacteria on the development of lettuce. This research focuses mainly on the development of biocontrol strategies for the management of lettuce drop in greenhouses.

Smart technology is essential for integrating circular economy principles. This study examines how conflict management strategies including collaboration, accommodation, avoidance, compromise, and competitions impact smart technology adoption, particularly blockchain, in Jordanian SMEs, a context that has been under-explored. Additionally, the research examines the moderating role of customer-centric green supply chain management in the relationship between blockchain adoption and the implementation of circular economy principles. Data were collected from 421 senior managers, department heads, and executives of Jordanian SMEs. Primarily, this study employed robust methodological approach, a structural equation modeling (SEM) technique, to test the hypotheses and analyze complex relationships. This research provides valuable insights into the adoption of blockchain technology in Jordanian SMEs, highlighting the importance of strategic conflict management, a context that is rarely explored. Incorporating customer-centric green supply chain management as a moderating factor enhance the sustainability and competitiveness of SMEs through blockchain technology. This approach offers new insights into how blockchain technology aligns with circular economic principles, highlighting the strategic role of conflict management in driving technological innovation and sustainability. The study results indicate that conflict management strategies have a significant impact on the adoption of blockchain technology among Jordanian SMEs. The results also revealed a unique finding that collaboration and compromise are the most influential strategies. Furthermore, the study found that blockchain adoption positively impacts the implementation of circular economy principles. This study provides theoretical and practical insights for SMEs, especially in the context of Jordan.

One of the most common solutions to access underground is through the construction of circular shafts. At preliminary design stage analytical expressions are often used to estimate the horizontal earth pressure acting on the lining. However, most of these solutions are based on active state conditions and do not consider the restrictions imposed on the shaft lateral displacements by the rigidity of its lining or by the construction method employed. To overcome this limitation a new methodology to account with the shaft lateral displacements in the determination of the horizontal earth pressure acting on circular shafts is developed and validated against the results of relevant physical scale models. The obtained results show good agreement with the measured data, reproducing adequately the reduction of the horizontal earth pressure from its initial value to an active state condition with the increase of the shaft lateral displacement. Following this approach more realistic predictions of the earth pressure acting on the lining of circular shafts can be performed.

This text deals with exploring random solutions for generalized nonlinear variational inequality problems. Using the Fan-KKM theorem and Aumann's measurable selection theorem, we are able to prove the existence and uniqueness of random solution sets under the conditions of monotonicity and convexity. Additionally, we use Minty's lemma to demonstrate the compactness and convexity of the random solution sets.

Machine Learning (ML) has revolutionized healthcare by enhancing diagnostic capabilities because of its ability to analyze large datasets and detect minor patterns often overlooked by humans. This is beneficial, especially in pain recognition, where patient communication may be limited. However, ML models often face challenges such as memorization and sensitivity to adversarial examples. Regularization techniques like mixup, which trains models on convex combinations of data pairs, address these issues by enhancing model generalization. While mixup has proven effective in image, speech, and text datasets, its application to time-series signals like electrodermal activity (EDA) is less explored. This research uses ML for pain recognition with EDA signals from the BioVid Heat Pain Database to distinguish pain by applying mixup regularization to manually extracted EDA features and using a Support Vector Machine (SVM) for classification. Results showed that this approach achieved an average accuracy of 75.87% using leave-one-subject-out cross-validation (LOSOCV) compared to 74.61% without mixup. This demonstrates the mixup’s efficacy in improving ML model accuracy for pain recognition from EDA signals. This study highlights the potential of mixup in ML as a promising approach to enhance pain assessment in healthcare.

Critical phases in socio-technical systems (STS) design are the definition of functional requirements for automated or software supported human activities while also addressing social and human interaction issues. To define automation support for human operations, STS designers need to ensure that specifications will satisfy not only non-functional requirements (NFR) of the system but also of its human actors such as human reliability/workload. However, such human factors aspects are not addressed sufficiently with traditional STS design approaches which could lead to systems failure or rejection. This paper proposes a new STS design method that addresses this problem and introduces a novel type of requirements namely Task Support Requirements (TSR) that assists in specifying the functionality that the system should have to support human agents in undertaking their tasks by addressing human limitations. The proposed method synthesizes a requirements/software engineering approach to STS design with functional allocation and an HCI perspective which facilitate application of human factors knowledge in conceptual models and evaluation through VR simulation. A case study methodology is employed in this work that allows in-depth, multi-faceted explorations of the complex issues that characterize STS.

Abstract: The conundrum of Latin American countries in the green transition unveils new opportunities and old structural problems. There is a clear advantage in the energy grid for Argentina, Brazil, and Mexico, coming mainly from hydropower, solar, and wind energy. In 2022, Brazil had the largest solar photovoltaic energy generation capacity in Latin America and the Caribbean, with over 24 gigawatts. Mexico ranked second, with some 9 gigawatts, and Chile ranked third, with a capacity of around 6 gigawatts. The window of opportunity for the industrialized Latin American countries resides chiefly in greening the natural resources industries and agriculture and husbandry, mainly for Brazil and Argentina. Latin America faces the dual challenge of promoting renewable energy and upgrading its value chains while managing the socioeconomic dynamics inherent in its developmental stages. It poses an enormous challenge for Latin American countries because of the phenomenon of "time compression" (Whittaker et al., 2020). Developing country's innovation and technology-intensive segments coexist with informal labor and low-skilled workers. Far from disappearing with economic growth, such coexistence becomes reinforced through an increasingly wide array of temporary and insecure jobs. Time compression condenses different stages of industrial and economic development within the same space and time, directly affecting the indicators related to the reduction of environmental impacts. Instead of locking in one path for this transition, Latin American countries had better not cling to panaceas but consider the threats, the risks, and the need to experiment with multiple paths and solutions simultaneously. However, this change implies the formation of a genuine developmental and environmental coalition guided by rigorous strategic considerations not captured by immediate interests.

Tobacco mosaic virus (TMV) was the first virus to be studied in detail and for many years TMV and other tobamoviruses, particularly tomato mosaic virus (ToMV) and tobamoviruses infecting pepper (Capsicum spp.), were serious crop pathogens. By the end of the twentieth and for the first decade of the twenty-first century tobamoviruses were under some degree of control due to introgression of resistance genes into commercial tomato and pepper lines. However, tobamoviruses remained as important models for molecular biology, biotechnology and bio-nanotechnology. Recently, tobamoviruses have again become serious crop pathogens due to the advent of tomato brown rugose fruit virus, which overcomes tomato resistance against TMV and ToMV, and the slow but apparently inexorable worldwide spread of cucumber green mottle mosaic virus, which threatens all cucurbit crops. This review discusses a range of mainly molecular biology-based approaches for protecting crops against tobamoviruses. These include cross-protection (using mild tobamovirus strains to ‘immunize’ plants against severe strains), expressing viral gene products in transgenic plants to inhibit the viral infection cycle, inducing RNA silencing against tobamoviruses by expressing virus-derived RNA sequences in planta or by direct application of double-stranded RNA molecules to non-engineered plants, gene editing of host susceptibility factors, and the transfer and optimization of natural resistance genes.

This paper addresses the parameter identification of a one-link flexible manipulator based on the experimental measurement of the inputs/outputs, the finite element model, and the application of evolutionary algorithms. A novel approach is proposed to find the values of inertia, stiffness, and damping parameters by minimizing the difference between the numerical model’s outputs and the testbed’s outputs, considering the joint position and acceleration of the link’s tip. The dynamic model is initially obtained using the finite element method and the Lagrange principle. A prototype of a single one-link flexible manipulator is used in the experimental application wherein the servomotor applies the input torque, and the outputs are the joint angle and the link’s tip acceleration. Then, an optimization problem minimizes the difference between numerical and experimental outputs to determine the set of parameters using evolutionary algorithms. A comparative analysis to obtain the identified parameters is established using genetic algorithms, particle swarm optimization, and differential evolution. The proposed identification approach permitted the determination of the dynamic parameters based on the complete dynamic model of the flexible-link manipulator, which is different from the approaches reported in the literature that identify a simplified model. This information is essential for the design of the motion and vibration control laws.

The review emphasizes the importance of the petroleum refining industry in Iraq, highlighting the environmental obstacles it presents. The industry's significant role in the country's economy necessitates a crucial evaluation and improvement of the environmental impact of oil refining in Iraq. The study scrutinizes the progression of crude oil refining techniques, from basic distillation to advanced technologies like vacuum distillation, thermal cracking, and catalytic cracking, all of which are associated with environmental damage. The refining processes produce hazardous gases such as carbon dioxide, carbon monoxide, sulfur oxides, and nitrogen oxides. Refineries also consume substantial amounts of water and discharge effluent, causing environmental issues. It highlights that significant solid waste is produced throughout the refining process, contributing to the environmental difficulties caused by the petroleum industry in Iraq. The paper proposes several approaches to enhancing the environmental performance of Iraq's petroleum refining processes through a comprehensive environmental impact assessment, focusing on identifying problematic areas, measuring environmental damage, and proposing solutions. It provides insights into the types of waste generated in Iraqi refineries, such as polluted hydrocarbon liquids, and highlights the environmental impact of leaks and spills from oil and byproducts, affecting groundwater and soil quality. The paper suggests reusing consumable auxiliary agents and mineral elements in other industrial fields to minimize waste generation, as well as employing advanced technologies like artificial intelligence and machine learning for efficient refining processes and reduced effluents. By offering suggestions to policymakers, industry stakeholders, and environmental regulators in Iraq, the study aims to inform sustainable practices and environmental management strategies within the petroleum refining sector, contributing to improved environmental outcomes and sustainable development in the industry.

Improving the quality of petroleum products and refining processes through the use of artificial intelligence and machine learning techniques is the topic of this article. It shows that expert knowledge and conventional empirical models can't get you where you want to go in a refining process. To effectively capture complex interactions and forecast fuel qualities, machine learning techniques such as principal component analysis (PCA), support vector machines (SVM), artificial neural networks (ANN), and partial least squares (PLS) are suggested. Gasoline and other petroleum products, as well as property prediction, process control, product quality, and operational efficiency in refineries, can all be improved with the help of machine learning applied to spectral or distillation curve data. An exciting new direction in optimizing operations, meeting environmental norms, and precisely estimating gasoline quality is offered by advanced machine learning algorithms.

Understanding the genotype x environment (GE) interaction for traget trait is valuable for growers, agronomists, breeders and the seed industry. The objective of this study was to see and explore the genotype x environment interaction for grain yield of wheat to better guide genotype selection and breeding. The genotype main effect and GE interaction effect (GGE) biplots resolved the following three genotypes: 85 (ETW17-377), 82 (ETW17-374) and 13 (ETW17-281) for being the best yielding and stable. The Kulumsa site planted on 4^th of July 2017 (environment 7=E7) and the DebreZeit site on 20^th of July 2017 (E3) were the most effective environments in distinguishing the test genotypes and are thus suitable environments for future evaluation and selection with specific adaptation. Melkasa site planted on 4^th July (E9) was a representative environment where genotypes can be evaluated for wide adpaptation. The genetic covariate biplot indicated that thousand kernel weight (TKW) and plant height (PH) had significant positive associations with grain yield implying the possible concurrent indirect selection of both traits for exploiting GE interaction for grain yield of wheat.

Abstract Plant fungal pathogens are frequently found as one of limiting factors for crop production. More than 10,000 species of fungi can cause disease in plants. To control the diseases, many farmers are still rely on the use of chemical fungicides, however most synthetic fungicides can cause acute toxicity, and some cause chronic toxicity as well. Thus, an appropriate technological improvement towards a more effective use of natural resources is required in agriculture to develop environmentally friendly sustainable farming system. This paper highlights the potential of extracts of tropical plants as antifungal agent to control plant fungal diseases. Information and data presented in this paper are mainly derived from selected and related references that previously published in the scientific journals. Many higher plants of tropical origin with fungicidal activities and their potential for fungal disease control of agricultural crops have been studied, however most of the studies have been done under in vitro condition. Some plant extracts showed strong antifungal activities on in vitro as well as in vivo tests, but some plant extracts showed significant antifungal activities on in vitro test, but did not obvious on in vivo tests. A great variation in antifungal activities were shown by plants extracts of different species and plant parts, in one hand, and on the other hand, variation was also observed on the responses of different fungal species to the same plant extract. Since the purpose of the use of plant extract is to control plant fungal diseases, the field trial is needed to ensure the stability of efficacy of certain plant extract. In addition, isolation and identification of active substances in the extracts is needed to assess possible mode of action and side effect of their use.

Few young Japanese adults regularly consume a well-balanced diet composed of staples, course dishes, and sides. We hypothesised that adopting the recommended planetary health diet with regular breakfast consumption would promote well-balanced diets among young Japanese male undergraduates. This study aimed to examine the structural association between a planetary health diet with regular breakfast consumption and well-balanced diet intake. Participants were 142 male undergraduates who completed a 2022 online questionnaire via Google Forms in a cross-sectional study at a public university in Hyogo Prefecture. A planetary health diet comprises the consumption of the recommended eight items. A covariance structure analysis was performed in a hypothetical model with factors (regular breakfast consumption and eight items) potentially associated with the intake of a well-balanced diet at least twice daily. Acceptable goodness-of-fit was obtained by excluding chicken dishes and nuts from the eight recommended items of the planetary health diet. Frequent consumption of the remaining six recommended items (fish, eggs, soybeans/soybean products, dairy foods, vegetables, and fruits) was significantly positively correlated with regular breakfast consumption and a significant positive path to a well-balanced diet. Among male university students, regular breakfast consumption and a planetary health diet may lead to a well-balanced diet.

Background: Clean cookstove interventions can theoretically reduce exposure to household air pollution and benefit health, but this requires near-exclusive use of the stoves, with simultaneous disuse of traditional stoves. Previous cookstove trials have reported low adoption of new stoves and/or extensive continued traditional stove use. Methods: The Household Air Pollution Intervention Network (HAPIN) trial randomized 3195 pregnant women in Guatemala, India, Peru, and Rwanda either to a liquefied petroleum gas (LPG) stove and fuel intervention (n = 1590) or to control (n = 1605). The intervention consisted of an LPG stove and two initial cylinders of LPG, free fuel refills delivered to the home, and regular behavioral messaging. We assessed intervention fidelity (delivery of the intervention as intended) and adherence (intervention use) through the end of gestation, as relevant to the first primary health outcome of the trial: infant birth weight. Fidelity and adherence were evaluated using stove and fuel delivery records, questionnaires, visual observations, and temperature-logging stove use monitors (SUMs). Results: 1585 women received the intervention, at a median (interquartile range) of 8.0 (5.0–15.0) days post-randomization and gestational age of 17.9 (15.4–20.6) weeks. Over 96% reported cooking exclusively with LPG at two follow-up visits during pregnancy. Less than 4% reported ever running out of LPG. Complete abandonment of traditional stove cooking was observed in over 67% of intervention households. 31.4% removed their traditional stoves upon receipt of the intervention, and among those who retained traditional stoves, the majority did not use them: traditional stove use was detected via SUMs on a median (interquartile range) of 0.0% (0.0%, 1.6%) of follow-up days (median follow-up = 134 days). Conclusions: Fidelity of the HAPIN intervention, as measured by stove installation, timely ongoing fuel deliveries, and behavioral reinforcement as needed, was high. Exclusive use of the intervention during pregnancy was also high.

Introduction: Senescence-resembling alterations on lymphocytes of dialysis patients have been widely described. However, pathophysiology behind these phenomena has not been clarified. In this study, we examine the impact of dialysis prescription and residual renal function on T and B lymphocytes, in dialysis patients. Methods: T and B cell subsets were determined with flow cytometry in 36 hemodialysis and 26 hemodiafiltration patients, according to the expression of CD45RA, CCR7, CD31, CD28, CD57 and PD1 for T cells, and IgD and CD27 for B cells. Immune phenotype was associated with dialysis modality, hemofiltration volume, and mortality. Results: Compared to hemodialysis, hemodiafiltration patients had significantly decreased percentage of CD4+CD28-CD57- T cells [3.8(2.4-5.3) vs 2.1(1.3-3.3)%, respectively, P=0.002] and exhausted CD4+ T cells [14.1(8.9-19.4) vs 8.5(6.8-11.7)% , respectively, P=0.005]. Additionally, hemofiltration volume was negatively correlated to CD8+ EMRA T cells (r=-0.46, P=0.03). Finally, exhausted CD4+ T cells percentage could predict all-cause mortality in dialysis patients, independently of age. Conclusion: Hemodiafiltration, especially with high hemofiltration volume, may have beneficial effects on senescence-related immune phenotype. Immune phenotype may also be a predicting factor for mortality in dialysis patients.

Piscine lactococcosis is a disease caused by Lactococcus garvieae, L. petauri and L. formosensis. It is considered to be an emerging disease and poses a risk to global aquaculture production. Studies of the genetic diversity of Lactococcus spp. strains have epidemiological importance since they provide information on the genetic relationships between isolates and indicate geographic and temporal distribution. The aim of this study was to evaluate the genetic diversity of Brazilian Lactococcus spp. isolates obtained from native fish species using PCR-based DNA fingerprinting techniques. For this, 36 isolates were selected and submitted to gyrB sequencing, molecular serotyping, REP-, BOX-, and RAPD-PCR approaches. The discriminatory power and the congruence between the typing methods were calculated. After the correct taxonomic discrimination of the isolate, a total of 14, 5 and 17 isolates were reclassified as L. garvieae, L. formosensis and L. petauri, respectively. L. garvieae and L. petauri are related to serotype I, while L. formosensis strains were classified as serotype II. The typing methods tested are useful for evaluating the genetic diversity of isolates and a comprehensive indication of the diversity was found. Heterogeneous and homogeneous populations were observed for L. garvieae and L. petauri, respectively. RAPD-PCR demonstrated a greater discriminatory power for L. formosensis and L. petauri, while REP-PCR showed better results for L. garvieae strains, thus these methodologies are recommended for genotyping these bacterial species. In conclusion, Brazilian isolates of Lactococcus spp. obtained from native fish species constitute a genetically diverse population and the techniques used in this study allowed a better genetic characterization of the isolates.

This research evaluates the feasibility of addressing computer vision problems with limited resources, particularly in the context of medical data where patient privacy concerns restrict data availability. (1) Background: The study focuses on diagnosing skin diseases using five distinct transfer learning models based on convolutional neural networks. Two versions of the dataset were created, one imbalanced (4092 samples) and the other balanced (5182 samples), using simple data augmentation techniques. Preprocessing techniques were employed to enhance the quality and utility of the data, including image resizing, noise removal, and blur techniques. The performance of each model was assessed using fresh data after preprocessing. According to the research findings, (2) Methods: the VGG-19 model achieved an accuracy of 95.00% on the imbalanced dataset. After applying augmentation on the balanced data, the best-performing model was VGG-16-Aug with an accuracy of 97.07%. (3) Results: These results suggest that low-resource approaches, coupled with preprocessing techniques, can effectively identify skin diseases, particularly when utilizing the VGG-16-Aug model with a balanced dataset. (4) Conclusions: The study addresses rare skin disorders that have received limited attention in past research, including acne, vitiligo, hyperpigmentation, nail psoriasis, and SJS-TEN. The findings highlight the potential of simple data augmentation techniques moreover, explainable AI: Grad-CAM interpreted the model outcome by showing image contours visually as well as identifying uncommon skin conditions and overcoming the data scarcity challenge. The implications of these research findings are significant for the development of machine learning-based diagnostic systems in the medical field. Further investigation is necessary to explore the generalizability of these findings to other medical datasets.

There is a lack of studies on the level of stress and health of families of workers in road transport. This study aimed to evaluate the perceived stress level and Family Effectiveness of family members of road transport workers. A quantitative study was carried out with family members of road transport workers in the southern region of Brazil. For the data collection, a sociodemographic form, the Perceived Stress Scale (PSS), and the Evaluation of Family Effectiveness Strategies were used. The data were analyzed by simple frequency, Spearman correlation coefficient (ρ) (p < 0.05), and descriptive analysis from the perspective of the Systemic Organization. 49 family members of road transport workers were participants. Perceived stress was higher in family members who had more than nine years of schooling, the targets of Control and Growth were higher in the evaluation of Family Effectiveness. The Control target was also higher in families with more than three people. The Coherence dimension had higher levels in families with health problems and whose income was less than $792.00 US dollars per month. In the Individuation dimension, income over $792.00 had better results. The Maintenance dimension of the system was higher in families whose drivers’ activity was as an intercity driver, as well as in the Stability target. In conclusion, the social factors such as schooling, income, diseases, type of worker activity in road transport, and number of people in the family influenced perceived stress and Family Effectiveness, which demonstrates the need to increase the promotion of health care of the families of road transport workers.

The study conducts a comparative analysis of solar radiation estimation methods, assessing the efficacy of the Heliosat-2 algorithm against ground measurements across seven distinct countries: Netherlands, Spain, Japan, Namibia, South Africa, Saudi Arabia, and India. To achieve this, it utilizes two distinct satellite data sources—Himawari-8 for Japan and Metosat Second Generation-MSG for the rest of the countries—spanning the years Jan 2022 to April 2024. A robust methodology for determining albedo parameters specific to Heliosat-2 was developed. During cloudy days, the estimates provided by Heliosat-2 generally exceeded the ground measurements in all the countries. Conversely, on clear days, there was a tendency for underestimation, as indicated by the median value of the mean bias (MB) across most of the countries. The Heliosat-2 model slightly underestimates daily radiation values, with a median MB ranging from −27.5 to +10.2 W.m⁻². Notably, the median root mean squared error (RMSE) on clear days is significantly lower, with values ranging from 24.8 to 108.7 W.m⁻², compared to cloudy days where RMSE values lie between 75.3 and 180.2 W.m⁻². In terms of R² values, both satellites show a strong correlation between the estimated and actual values, with a median value consistently above 0.86 on a monthly scale and over 92% of daily data points falling within ±2 standard deviation.

Viruses are the most common congenital infections and an important cause of fetal malformations, morbidity, and mortality. The effects of these infections, which are transmitted in-utero (transplacentally), during childbirth or in the puerperium depend on the timing of the infections. These vary from miscarriages (usually with infections in very early pregnancy), congenital malformations (where the infections occur during organogenesis) and morbidity (with infections occurring late in pregnancy, during childbirth or after delivery). The most common of these viruses are cytomegalovirus, hepatitis, parvovirus B-19, rubella, varicella and zika. There are currently very few efficacious antiviral agents licensed for use in pregnancy and therefore for most of these infections, prevention by means of immunisation (where there is a vaccine), administration of immunoglobulins to those exposed to the virus to offer passive immunity or appropriate measures to avoid being infected would be options to minimise the infections and their consequences. In this review, we discuss the current understanding of some of the congenital infections and their consequences on both the mother and fetus and their management focusing mainly on prevention.

This article critically examines existing wastewater management legislation, highlighting its detrimental impact on environmental and public health. It emphasizes the urgent need for a mentality shift among water managers, advocating responsible stewardship of water resources. By promoting collaboration between regulatory bodies and scientific communities, the article seeks tailored solutions for complex challenges. It underscores the necessity of integrating technological innovation, rigorous monitoring, and public engagement for sustainable management. Additionally, it calls for the implementation of easy-to-adopt technological solutions, alongside rigorous monitoring and financial support. Ultimately, the article advocates for a paradigm shift in wastewater management policies towards accountability, innovation, and environmental sustainability. Directives governing wastewater management in Europe, particularly in Spain, have faced scrutiny due to significant shortcomings. Legislation has inadvertently facilitated untreated or poorly treated wastewater discharge, resulting in widespread pollution. Despite EU efforts, punitive measures have proven ineffective, with fines often borne by the public sector, although private sector responsibility. The lack of robust monitoring mechanisms and technical support exacerbates the situation, perpetuating a regressive mindset among water managers. Urgent reforms are needed to enforce strict discharge prohibitions, promote reuse, and provide financial and technical assistance. However, systemic barriers and entrenched mentalities pose formidable challenges to meaningful change.

Using pumps operating as turbines (PATs) offers the possibility of increasing the sustainability of water and energy systems by recovering the excess of energy that would be otherwise lost in pressure reducing valves or head loss chambers. In on-grid applications, there are many research works and implementations of PATs. However, there is still limited research for the optimization of efficiency and stability of PATs operating in off-grid systems. This work contributes to the development of stable direct current (DC) off-grid electric systems based on PATs, by optimizing and analysing the energy efficiency and stability for a wide range of operation. A self-excited induction generator (SEIG) coupled to the PAT is provided to cover the conventional type of electric ma-chines typically coupled to the hydraulic pumps. In this context, a methodology is proposed, based on the hydraulic, mechanical and electric subsystems, to define the PAT-SEIG operational area, to maximize energy conversion and the system efficiency. This will guarantee stable electric output quantities when operating in a DC off-grid system. In addition, an analytical model is proposed to estimate the PAT-SEIG operation under specific conditions. With this, water managers are suitable to design and optimize an off-grid PAT-SEIG system and to define the best hydraulic machines, electronic equipment, and all different control elements to maximize energy conversion within the target of operational limits. To develop and verify this new methodology, two micro PAT-SEIG setups were implemented in the hydraulic laboratory of IST/CERIS under typical operating conditions. The proposed solution proved to be resilient under a variable load regime. The system's maximum efficiency can be adapted using different capacitor values for the excitation of the SEIG, and the operational range is limited to the electric components. Considering the nominal efficiencies of the system’s components, the maximum p.u. efficiency obtained for each PAT-SEIG system were between 0.7 and 0.8 p.u.

Globalization and digitalization have been associated with a number of challenges and opportunities. The purpose of this chapter is to analyze the impact of globalization and digitization on the Sustainable Development Goals (SDGs) and provide recommendations for achieving sustainable development. The study used a multidisciplinary approach that combined diverse research methods. This included a literature review, case studies, and empirical research findings. The findings suggest that while globalization has had both positive and negative impacts on the SDGs, it also poses challenges to social and environmental sustainability. Globalization's impact on sustainable development goals is complex and multifaceted, requiring policymakers to implement diverse measures to mitigate negative effects and promote sustainable development, particularly in less developed regions and marginalized communities. In conclusion, this chapter highlights the need for a coordinated and holistic approach to sustainable development that prioritizes the Sustainable Design Goals in policy and decision-making.

Drug development is a lengthy process with a high failure rate. Increasingly, machine learning is utilized to facilitate the drug development processes. These models aim to enhance our understanding of drug characteristics, including their activity in biological contexts. However, a major challenge in drug response (DR) prediction is model interpretability as it aids in the validation of findings. This is important in biomedicine, where models need to be understandable in comparison with established knowledge of drug interactions with proteins. drGAT, a graph deep learning model, leverages a heterogeneous graph composed of relationships between proteins, cell lines, and drugs. drGAT is designed with two objectives: DR prediction as a binary sensitivity prediction and elucidation of drug mechanism from attention coefficients. drGAT has demonstrated superior performance over existing models, achieving 78\% accuracy (and precision), and 76\% F1 score for 269 DNA-damaging compounds of the NCI60 drug response dataset. To assess the model's interpretability, we conducted a review of drug-gene co-occurrences in Pubmed abstracts in comparison to the top 5 genes with the highest attention coefficients for each drug. We also examined whether known relationships were retained in the model by inspecting the neighborhoods of topoisomerase-related drugs. For example, our model retained TOP1 as a highly weighted predictive feature for irinotecan and topotecan, in addition to other genes that could potentially be regulators of the drugs. Our method can be used to accurately predict sensitivity to drugs and may be useful in the identification of biomarkers relating to the treatment of cancer patients.

Due to the convenience of implementing without additional transmitter, radar communication integrated systems using conventional communication signals have become an effective means of monitoring unmanned aerial vehicles and other aircraft. However, the non-ideal radar signal form causes the strong signal to cover the weak signal, and the clutter suppression is required to expose the target. As the target signal is extremely low, and the performance of conventional clutter suppression methods is limited, the residual clutter remains after the time-varying clutter is suppressed, which leads to many false alarm points on the processed RD spectrum. The two-dimensional distribution of these false alarms in range-Doppler is very similar to the target and difficult to be distinguished, which seriously affects the target detection and tracking. To reduce false alarm points and improve the performance of target detection, the difference of spatial-temporal stationarity between target signal and clutter in a short time is discussed in this paper, a radar false alarm suppression method based on target spatial-temporal stationarity is proposed by using the difference of stationarity between target signal and false alarm points in range, Doppler, energy and azimuth. In this algorithm, to ensure the short-time stationarity of the target, the RD spectrum of sub-frames with short time interval is obtained by using the sliding matching filtering method and extract the peak points. Then, the Mahalanobis distance between the peak points of each sub-frame is used to eliminate false alarm points. Finally, false alarm points are further eliminated through the target tracking and retain the real target information, so as to improve the radar target detection performance. The simulation experiments show that this method can eliminate more than 90% false alarms points while maintaining the detection performance of the target. The processing of actual data in the field experiment shows that the after suppression false alarm by this algorithm can obtain more ideal target detection results, and realize the tracking of the UAV and other aircraft track.

Climate change has significant impacts on the distribution of orchids. The endemic and endan-gered orchids are more susceptible to climate change than widely distributed orchids. To date, lit-tle is known concerning the response of endangered Changnienia amoena, endemic to China, to different climate scenarios. Here, we chose RF, MaxEnt, and GBM to build an ensemble model af-ter using ten models from Biomod2 to project its potential distribution in China. The outcomes showed that the four key environmental factors influencing its distribution were mean diurnal temperature range, minimum temperature of the coldest month, temperature seasonality, and precipitation of the warmest quarter. This orchid was mainly distributed in western Hubei, the junction between Shaanxi and Sichuan, eastern Guizhou, northwestern Hunan, and the junction among Anhui, Henan, and Hubei provinces in the current. The total suitable area of C. amoena was 58.33 × 104 km2, only accounting for 6.08% of China's total territory, which is larger than known. However, only 4.48% of the suitable area is located within national nature reserves, and 3.33% within provincial nature reserves, respectively. During the Last Inter Glacial and mid-Holocene, its suitable areas were larger than the current. Under six future climate scenarios, its suitable areas may averagely decrease by 2.26% relative to the current, with severe habitat fragmentation. Col-lectively, the centroid of C. amoena is expected to migrate towards the southeast in the future. Therefore, our findings demonstrate that climate change takes an adverse effect on its potential distribution. We recommend expanding protected areas or establishing new conservation sites for C. amoena. Furthermore, our study can help to inform the development of conservation manage-ment strategies for other endangered orchids in China under climate change.

A 23-month-old neutered male dog of unknown ancestry presented with a history of progressive neurological signs that included anxiety, cognitive impairment, tremors, seizure activity, ataxia, and pronounced visual impairment. The clinical signs were accompanied by global brain atrophy. Due to progression in the severity of disease signs, the dog was euthanized at 26 months of age. Examination of tissues collected at necropsy revealed dramatic intracellular accumulations of autofluorescent inclusions in the brain, retina, and cardiac muscle. The inclusions were immunopositive for subunit c of mitochondrial ATP synthase, and their ultrastructural appearances were similar to those of lysosomal storage bodies that accumulate in some of the neuronal ceroid lipofuscinosis (NCL) diseases. The also dog exhibited widespread neuroinflammation. Based on these findings, the dog was deemed likely to have suffered from a form of NCL. A whole genome sequence analysis of the proband’s DNA revealed a homozygous C to T substitution that altered the intron 3 – exon 4 splice site of CLN6. Other mutations in CLN6 cause NCL diseases in humans and animals including dogs. CLN6 protein was undetectable with immunolabeling in the tissues of the proband. Based on the clinical history, fluorescence and electron-microscopy, immunohistochemistry, and molecular genetic findings, the disorder in this dog is classified as an NCL resulting from the absence of CLN6 protein. Screening the dog’s genome for a panel of breed-specific polymorphisms indicated that its ancestry included numerous breeds, with no single breed predominating. This suggests that the CLN6 disease variant is likely to be present in other mixed breed dogs and at least some of the ancestral breeds.

This research aims to explore factors that influence sustainable music education from a psychological perspective, and innovatively constructs a combination of qualitative and quantitative approaches. For first time, combines stochastic nonlinear system of control theory and identifies relevant factors affecting sustainable music learning by establishing a data system for questionnaire answering and a quantitative model for actual academic performance. Existing research mainly looks at several factors that affect fragmented music education from perspective of teachers' teaching rules, emphasizing phased teaching state and learning goals of teachers as main body, thus ignoring sustainable music education and students, who are a large group of music learning objects. The research fills gap in this type of research object, focusing on sustainable music education from an emotional perspective and students who occupy a dominant position in music learning, so as to more accurately explore impact of their emotional influencing factors on sustainable music learning. The most prominent innovation of this study lies in construction of stochastic nonlinear system for psychological influencing factors of sustainable student music learning and a quantitative model for actual learning effect. Through analysis of nonlinear system, weight values of several negative emotions on sustainable music learning are obtained.

In recent times, there has been significant enthusiasm for the development of all solid state batteries. This interest stems from a dual focus on safety—addressing concerns related to toxic and flammable organic liquid electrolytes—and the pursuit of high energy density. \cite{SCHNELL2018160,Zheng2018}. While liquid electrolyte batteries constitute for now the vast majority of commercial cells, solid electrolyte batteries show great promise. In parallel with experimental research, computational models \cite{GrazioliEtAlCM2016} clarify the several fundamental physics \cite{LiMonroeARCBM2020} that take place throughout battery operations. We review some classical models, emphasizing the conceptual advancements documented in the most recent works.

In this paper, a new full-Newton step feasible interior point method for convex quadratic programming is presented and analyzed. The idea behind this method is to replace the complementarity condition with a non-negative variable weight vector and use the algebraic equivalent transformation for the obtained equation. Under the selection of appropriate parameters, the quadratic rate of convergence of the new algorithm is established. In addition, the iteration complexity of the algorithm is obtained. Finally, some numerical results are presented to demonstrate the practical performance of the proposed algorithm.

Breast cancer develops upon sequential acquisition of driver mutations in mammary epithelial cells; however how these mutations collaborate to transform normal cells remains unclear in most cases. We aimed to reconstitute this process in a particular case. To this end, we combined the activated form of the PI 3-kinase harboring the H1047R mutation with the inactivation of the histone lysine methyl-transferase KMT2D in the non-tumorigenic human mammary epithelial cell line MCF10A. We found that PI 3-kinase activation promoted cell cycle progression, especially when growth signals were limiting, as well as cell migration, both in a collective monolayer and as single cells. Furthermore, we showed that KMT2D inactivation had relatively little influence on these processes, except for single cell migration that KMT2D inactivation promoted in synergy with PI 3-kinase activation. Combination of these two genetic alterations induced expression of the ARPC5L gene that encodes a subunit of the Arp2/3 complex. ARPC5L depletion fully abolished the enhanced migration persistence exhibited by double-mutant cells. Our reconstitution approach in MCF10A has thus revealed both the cell function, single cell migration, and the underlying Arp2/3-dependent mechanism, which are synergistically regulated when KMT2D inactivation is combined with the activation of the PI 3-kinase.

Intraprocedural rupture (IPR) is a severe complication of coil embolization (CE) for intracranial aneurysms, yet the clinical and hemodynamic features of rupture risk areas remain unclear. From January 2013 to December 2023, 435 saccular cerebral aneurysms (316 unruptured, 119 ruptured) underwent CE at our institution. Included were cases where extravasation or coil protrusion occurred during CE. The rupture point was determined from postoperative data, and computational fluid dynamics (CFD) analysis was conducted to assess hemodynamic features. IPR occurred in six aneurysms (1.3%; three ruptured, three unruptured; dome size: 4.7±1.8 mm, D/N: 1.5±0.5, locations: four internal carotid artery [ICA], one anterior cerebral artery, one middle cerebral artery). ICA aneurysms were treated using adjunctive techniques (three balloon-assisted, one stent-assisted). Two aneurysms (M1M2 and A1) were treated simply, yet had relatively small and misaligned domes. CFD analysis identified the rupture point as a flow impingement zone with maximum pressure (Pmax) in five aneurysms (83.3%). Time-averaged wall shear stress (WSS) was locally reduced around this area (1.3±0.7 [Pa]). Hemodynamically unstable areas indicated fragile, thin walls with rupture risk. Microcatheter insertion along the inflow zone, directed towards the caution area, was practiced. The presence of a flow impingement zone with Pmax and low WSS suggests potential aneurysm rupture in CE, particularly with adjunctive techniques. Small aneurysms, especially those with axial misalignment, warrant careful consideration due to the risk of excessive flow impact stress on the aneurysm wall.

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

Abstract Background: Analysis of different type of consolidation in dogs and cats relay on parenchymal and vascular criteria. Hypothesis: the vascular criteria of the consolidation it’s possible to perform in dogs and cats and vascularisation is different in shred, tissue, nodule, wedge sign and mass. Animals: 347 cats and 634 dogs with different types of consolidations. Methods: Retrospective study between 2018-2023. Lung ultrasound performed in dogs and cats with different types of consolidations. Characteristic of bronchogram: air dynamic and static bronchogram, fluid bronchogram or absence of bronchogram. Types of vascularisations in consolidation: tree like type, residual, chaotic vascularisation and penetrating from the chest wall to consolidation and “vascular sign”. Conclusion and Clinical Importance: Analysis of bronchogram and type of vascularisation in consolidation is possible to perform in dogs and cat. The analysis of mentioned criteria had an impact on subsequent diagnostic and therapeutic decisions

Ovarian cancer is the deadliest gynecological malignancy of the reproductive organs in the United States. Cyclin-dependent kinase 1 (CDK1) is an important cell cycle regulatory protein that spe-cifically controls the G2/M phase transition of the cell cycle. RO-3306 is a selective, ATP-competitive, and cell-permeable CDK1 inhibitor that shows potent anti-tumor activity in multiple pre-clinical models. In this study, we investigated the effect of CDK1 expression on the prognosis of patients with ovarian cancer and the anti-tumorigenic effect of RO-3306 in both ovarian cancer cell lines and a genetically engineered mouse model of high-grade serous ovarian cancer (KpB model). In 147 patients with epithelial ovarian cancer, overexpression of CDK1 was significantly associated with poor prognosis, compared with a low expression group. RO-3306 significantly inhibited cellular proliferation, induced apoptosis, caused cellular stress, and reduced cell migration. Treatment of KpB mice with RO-3306 for four weeks showed a significant decrease in tumor weight under obese and lean conditions without obvious side effects. Overall, our results demonstrate that inhibition of CDK1 activity by RO-3306 effectively reduces cell proliferation and tumor growth, providing biological evidence for future clinical trials of CDK1 inhibitors in ovarian cancer

As the global overprovisioning of electronic devices continues to rise, the management of electronic waste (e-waste) has developed as a significant challenge. E-waste is comprised of hazardous materials; moreover, the toxic constituents in them can impact the environment and public health through improper disposal. On the beneficial side, this e-waste also contains some valuable materials. If the valuable materials are recovered, they can reduce the dependence on new, raw materials. Conventional linear models of production and consumption have led to a momentous buildup of e-waste, posing environmental and economic risks. This paper examines how the adoption of a circular economy approach can improve e-waste recycling in Nairobi by evaluating its economic recovery potential and e-waste residuals. It introduces metrics to assess the development of circular economy practices to help in the handling of e-waste residuals and examines the economic consequences associated with the adoption of circular strategies.

Acne Vulgaris is a multifactorial skin disorder affecting many adolescents involving a complex array of factors. Previous extensive research has delved into the correlation between acne outbreaks and specific diet consumption, and while there have been a variety of treatment indications for decreasing acne in patients, many do not involve the significant impact a holistic approach can take. This case reports on the relationship between cystic acne and dietary habits, presenting a possible treatment approach for acne vulgaris by eliminating dairy consumption.

Hydrogen sulfide (H₂S) is a novel gasotransmitter, sucrose (SUC) is both cellular energy and signaling molecule. However, their signaling interaction in maize thermotolerance is poorly known. In this study, using maize seedlings as materials, metabolic and functional interaction of H₂S and SUC in maize thermotolerance was investigated. Data show that under heat stress survival rate and tissue viability were increased by exogenous SUC, while malondialdehyde content and electrolyte leakage were decreased by SUC, indicating SUC could upraise maize thermotolerance. Also, SUC-upraised thermotolerance was enhanced by H₂S, while weakened by inhibitor (propargylglycine) and scavenger (hypotaurine) of H₂S and SUC-transport inhibitor (N-ethylmaleimide). To figure out underlying mechanism of H₂S-SUC interaction-upraised thermotolerance, redox parameters in mesocotyls of maize seedlings were measured before and after heat stress. The data display that activity and gene expression of H₂S-metabolizing enzymes were up-regulated by SUC, whereas H₂S had no significant effect on activity and gene expression of SUC-metabolizing enzymes. Besides, activity and gene expression of catalase, glutathione reductase, ascorbate peroxidase, peroxidase, dehydroascorbate reductase, monodehydroascorbate reductase, and superoxide dismutase were reinforced by H₂S, SUC, and their combination under non-heat and heat conditions in varying degrees. Similarly, content of ascorbic acid, flavone, carotenoid, and polyphenol was increased by H₂S, SUC, and their combination, whereas production of superoxide radical and hydrogen peroxide level were impaired by these treatments to different extent. These results imply that the metabolic and functional interaction of H₂S and sucrose exists in the formation of maize thermotolerance through redox homeodynamics.

The nickel-chromium-molybdenum alloy 625 exhibits an excellent combination of mechanical properties and corrosion resistance. However, the high temperature plastic deformation process and the heat treatment represent critical aspects and the adoption of non-optimal parameters can result in excessive grain coarsening and loss in mechanical strength. This detrimental behavior is worsened by the presence of a narrow region of temperatures and strain rates for complete recrystallization and the absence of phase transformation temperatures. In this alloy, the presence of sufficient amounts of niobium, titanium and aluminum permits slow age-hardening response upon single-aging treatments. Therefore, when the soft- or solution-annealed condition is associated with insufficient mechanical properties, this potentiality can be exploited to improve the mechanical strength and prevent waste of material and resources. Since the precipitation kinetic of the hardening γ^'' phase can be accelerated by prior nucleation treatment, different time-temperature combinations of double aging at 732 °C and 621 °C are investigated to reduce the overall heat treatment time. However, the simultaneous formation of Cr-rich carbides can dramatically affect the intergranular corrosion resistance. For this reason, a performance map is developed to compare all the tested conditions and identify the best compromise in terms of mechanical strength and corrosion rate.

Epidemiological studies point cholesterol as a possible key factor for both prostate cancer incidence and progression. So, it could represent a targetable metabolite as the most aggressive tumors also appear to be sensitive to therapies designed to decrease hypercholesterolemia, such as statins. However, whether and how cholesterol, through its dietary uptake and its metabolism, could be important for early tumorigenesis, remains unknown. Oncogene clonal induction in Drosophila melanogaster accessory gland allows to reproduce tumorigenesis from initiation to early progression, where tumour cells undergo basal extrusion to form extra-epithelial tumors. Here we show that these tumors accumulate lipids, and especially esterified cholesterol, as in human late carcinogenesis. Interestingly, high cholesterol diet displays limited effect on accessory gland tumorigenesis. On the contrary, cell-specific downregulation of cholesterol uptake, intracellular transport or metabolic response, impairs the formation of such tumors. Furthermore, in this context, high cholesterol diet suppresses this impairment. Taken together, these results reveal that during early tumorigenesis, tumour cells strongly increase their uptake and use of dietary cholesterol to specifically promote the step of basal extrusion. Interestingly, expression data from primary prostate cancer tissues indicate an early signature of redirection from cholesterol de novo synthesis to uptake. Altogether, these results suggest by which mechanism reduction of dietary cholesterol could lower the risk and slow down the progression of prostate cancer.

Matrices and their inverses allow accurate calculations to be produced quickly and compactly and can be used to control any process represented by a system of equations. Singular matrices require extensive workarounds (costing time and money) to solve these systems of equations because they have no inverse. Finding their inverse involves division by zero. Nevertheless, recently a new number set called semi-structured complex numbers was developed to enable division by zero in algebraic equations. The aim of this research was to demonstrate that the inverse of a singular matrix can be found using semi-structured complex numbers. This research reveals (1) Singular matrices and their inverses can be used to find a unique solution to a pair of simultaneous equations that appear to have infinitely many solutions or appear to have no solution; (2) Singular matrices map coordinates in projective space to coordinates in Euclidean space and their inverse maps coordinates in Euclidean space to coordinates in projective space; (3) the inverse of a singular matrix proves that parallel lines in Euclidean space do not intersect but in projective space intersect at a “point at infinity” and (4) collinear lines that intersect at infinitely many points in Euclidean space only intersect at one point in projective space.

Background The operator's ability in performing carotid stenting (CAS) has improved clinical outcomes. However, more than 3% of patients need to be treated again after CAS. Most of the cases requiring further intervention are affected by hyperplasia. The recommended procedure is the DEBalloon. On the other hand, the literature reports a small number of carotid neoatherosclerosis cases and is recommended to be treated using elective Micronet-covered stent. Discriminating between the two types of in-stent-restenosis ISR (hyperplasia or neoatherosclerosis) is critical for a positive outcome.Case summary We describe a case in which a patient treated with carotid stenting 8 years before, was diagnosed with ISR. Due to the development of neurological symptoms and progressive increases in Peak Systolic Velocity (PSV) eight years following carotid stenting, a DEBalloon was used in a carotid in-stent-restenosis (ISR) standard procedure. About ten minutes after the procedure, the patient developed hemiplegia consistent with the treated carotid territory. The implantation of a MicroNet-covered stent excluded the thrombus and reverted symptoms with a normal MR control at 24 h. Conclusion This case illustrates that when in-stent stenosis evolves years after the stent implantation, neoatherosclerosis should be assessed, and a MicroNet-covered stent should be considered.

The Philippines has the highest HIV incidence rate in the Asia-Pacific region. Previous studies identified multilevel factors contributing to this phenomenon. This scoping review describes the extent of the literature on HIV contributory factors and identifies gaps to guide directions for HIV prevention research. The keywords HIV incidence, individual, sociocultural, environmental factors, and Philippines, were used to search for evidence on Google Scholar, PubMed, Scopus, and Web of Science databases. A total of 1,017 peer-reviewed articles published in the English language from 2014 to 2024 were reviewed using the Joanna Briggs Institute Scoping Review protocol. Thirty articles were included in this review. The majority of the available evidence was quantitative studies published between 2020 and 2024. Individual, sociocultural, economic, educational, political, and environmental factors shaped the Philippines’ HIV epidemic. Low levels of HIV knowledge, low testing rates, high-risk sex behaviors, stigma, lifestyle, location, lack of HIV education in schools, conservative society, and restrictive policies are HIV contributing factors. The Philippine HIV epidemic is multifactorial. Identifying the contributory factors is crucial in developing targeted interventions for high-risk populations. A dedicated HIV research program is needed to provide evidence-based information to guide the development of HIV preventive programs and research.

The objective of the present work was to determine the productivity and costs of timber harvesting and skidding during the first commercial thinning of a Scots pine stand. The analyzed harvesting set consisted of a mini-excavator (34 kW) with an stroke harvester head (gripping range: 4–30 cm), and a farm tractor coupled to an logging trailer with a hydraulic crane. Merchantable timber (roundwood with a minimum diameter of 5 cm inside bark) was harvested from a 25-year old planted Scots pine stand growing on a grid of 1.4×1.8 m. The study showed the productivity of the mini-harvester range from 3.09 to 3.47 m3∙PMH15 -1, and that of the forwarding set to be 4.07 m3∙PMH15 -1. The analyzed model of productivity as a function of individual tree volume and thinning intensity was statistically significant, but the intensity parameter was significant only on plots located along wide access trails (3.7 m) and insignificant on plots located along the narrow access trial (2.5m). The intertree distance was not found to be significant. The calculated net machine costs for the forwarding set and mini-harvester were 36.12 Euro∙PMH 1 and 52.47 Euro∙PMH-1, respectively. An increase in the utilization rate of the harvesting set to 80% would reduce the timber harvesting and skidding costs to 22.07 Euro∙m-3.

As the coronavirus disease 2019 (COVID-19), which is caused by the novel SARS-CoV-2, continues to spread rapidly around the world, there is a need for well validated serological assays that allow the detection of viral specific antibody responses in COVID-19 patients or recovered individuals. In this study, we established and used multiple indirect Enzyme Linked Immunosorbent Assay (ELISA)-based serological assays to study the antibody response in COVID-19 patients. In order to validate the assays we determined the cut off values, sensitivity and specificity of the assays using sera collected from pre-pandemic healthy controls, COVID-19 patients at different time points after disease-onset, and seropositive sera to other human coronaviruses. The developed SARS-CoV-2 S1 subunit of the spike glycoprotein and nucleocapsid (N)-based ELISAs not only showed high specificity and sensitivity but also did not show any cross-reactivity with other CoVs. We also show that all RT-PCR confirmed COVID-19 patients tested in our study developed both virus specific IgM and IgG antibodies as early as week one after disease onset. Our data also suggest that the inclusion of both S1 and N in serological testing would capture as many potential SARS-CoV-2 positive cases as possible than using any of them alone. This is specifically important for tracing contacts and cases and conducting large-scale epidemiological studies to understand the true extent of virus spread in populations.

Introduction: Management of hypoxic-ischemic encephalopathy (HIE) has been the subject of international recommendations. However, some aspects remain within the realm of service practices and appear to vary greatly from one center to another. Objectives: To achieve a multidisciplinary consensus in France regarding management of HIE newborns that are not covered by international and national guidelines. Methods: This is a prospective, multicenter study using the Delphi method from April 21, 2021, to July 15, 2021. Perinatal professionals were contacted through professional societies. Participants received a questionnaire in three successive rounds. Each proposal was rated using a Likert scale from 1 (completely disagree) to 8 (completely agree). An item was considered consensual if at least 70% of responses were in agreement or disagreement. Results: 58 french neonatal professionals participated. A consensus was reached in the first round concerning the use of passive hypothermia in maternity ward, with 84% agreement and to define the target temperature between 35.0°C and 35.5°C at the third round. Regarding hypoglycemia in the delivery room, 79.2% agreed in the first round to correct blood glucose levels below a threshold of <2.2 mmol/l. Parenteral route was the preferred correction method for 91.4% of respondents, while enteral route via nasogastric tube did not reach consensus. Conclusion: This study highlights the variability in definitions and practices among professionals in the management of HIE. Consensus was found regarding certain care practices performed without formal guidelines. The work to harmonize definitions and practices for the management of HIE likely needs to continue.

There is currently no effective treatment strategy for the recurrent/metastatic adenoid cystic carcinoma (R/M ACC). Furthermore, recent single-agent and combination immunotherapy trials have failed in unselected ACC cohorts unlike non-ACC salivary gland cancers. Genomic profiling revealed no actionable targets but NOTCH1 and KDM6A frameshift and CTCF splice site mutations (no MYB/L fusion) with a low TMB, MSS and negative PD-L1. We recommended anti-PD-1 plus anti-CTLA-4 combination based on TMB 2-fold greater than median TMB in ACC, tumor harboring multiple immunogenic frameshift or splice site mutations, and PD-L1 negativity. Accordingly, we achieved a complete response in a radiotherapy and chemotherapy-refractory patient with locally recurrent lacrimal gland ACC and lung metastasis following personalized immunotherapy in combination with integrative therapeutics. Therefore, it is crucial to assess not only conventional immune biomarkers but also patient-specific parameters.

The textile sector is responsible for a number of environmental impacts. Due to the in-creasing quantities of textiles, their share is rising and a trend reversal from a linear to a circular textile chain is needed. This article presents the background, methodological approach and results of a participatory textile development model. In the commercial B2B sector, three textile prototypes were developed together with users and trialled over several months in three application areas. Textile development took into account the requirements of fibre regeneration in the product design and focused on innova-tive chemical recycling solutions. The three sustainably aligned textiles were subject-ed to spectroscopic and textile-technological tests. Screening life cycle assessments an-alysed their environmental profile and compared it with reference textiles that are used as standard. Overall, it is clear that the three textiles can match conventional ref-erence textiles in terms of quality and have considerable environmental benefits com-pared to the reference textiles. The user survey did identify concerns about a high arti-ficial fibre content, although a general rejection of recycled fibres was not observed. The results show that a transformation is possible, but must start with the fibre com-position; recycling, on the other hand, is of minor importance.

The high genetic diversity of the tomato and its high micronutrient content make this fruit very interesting from an economic and nutritional point of view. The genetic erosion suffered by this crop, due to breeding objectives based on yield and marketing, makes it necessary to return to the origins in search of the nutritional and organoleptic quality lost in traditional varieties. In this study, the agronomic, physical, organoleptic and nutritional characteristics of eighteen F1 hybrids obtained by crossing fourteen traditional varieties, previously selected for their quality, were studied to select genotypes of superior quality that could be candidates for new. All parameters studied were strongly influenced by genotype, showing a wide range between cultivars. Principal Component Analysis revealed the characteristics of each hybrid that made it stand out from the others. Some hybrids (H3, H4, and H6) stand out because of their high concentration of active compounds, others (H16, H15, H10, H17, H9, and H11) because of agronomic performance and high content of β-carotene, and H5 was the only one to contain chlorophyll in their ripe fruits. Finally, the evaluation index allowed the selection of five hybrids with interesting characteristics, combining good yield performance and high quality

Background: After the increasingly common anterior cruciate ligament reconstruction (ACLR) procedure in competitive athletes, rehabilitation is crucial for facilitating a timely return to sports (RTS) and preventing re-injury. This pilot study investigates the biomechanical and psychological outcomes of postoperative rehabilitation in competitive athletes, comparing supervised rehabilita-tion (SVR) and home-based rehabilitation (HBR). Methods: After ACLR, 60 (out of 74 screened) athletes were recruited and equally divided into 2 groups using non-probability convenience sam-pling: 30 HBR and 30 SVR (each group comprising 15 males and 15 females). Rehabilitation out-comes in respective groups were evaluated at 8 months using measures (Tegner-Activity-Scale [TAS], International Knee Documentation Committee subjective knee form [IKDC-SKF], ACL Return to Sport after Injury [ACL-RSI]) and objective parameters (isometric muscle strength, hamstring/quadriceps asymmetry). RTS was evaluated at 9 months; with ACL re-injury rates rec-orded approximately 6 months post-RTS. Results: Both groups exhibited decreased TAS scores (HBR: 8 to 6, SVR: 8 to 7), with the SVR group demonstrating superior post-operative IKDC-SKF scores (81.82 vs. 68.43) and lower ACL-RSI scores (49.46 vs. 55.25). Isometric and isokinetic mus-cle strength, along with asymmetry values, favored the SVR group 8 months post-ACLR (p

Objective: We determined, in stable ambulatory heart failure with reduced ejection fraction (HFrEF) subjects and matched controls, the capability of a novel blood based cardiac-specific cBIN1 Score (CS), which assesses the health of cardiac muscle, to identify patients with known heart failure (HF) and to prognosticate future hospitalization. Background: Limited clinical tools are available in assessing cardiac muscle health in stable ambulatory patients. Cardiac bridging integrator 1 (cBIN1) is a cardiomyocyte t-tubule membrane scaffolding protein which regulates calcium signaling in cardiomyocytes, decreases in failing muscle, and is present in plasma in levels that correlate with cardiac content. We hypothesize that CS, a normalized index of plasma cBIN1 concentration, can function as a diagnostic and prognostic biomarker of HF. Methods: Plasma cBIN1 concentration is measured by an ELISA test, and CS is calculated as the natural log of the ratio of a constant population mean cBIN1 to measured cBIN1 concentration. We determined CS among 125 clinically stable individuals with HFrEF (LVEF ≤ 40%) (mean age 56 ± 10 years old, 79% men) and 125 age, sex matched volunteers with no known history of HF. We obtained plasma concentrations of NT-proBNP, a marker of volume status, as comparison. Baseline co-morbidities and 18-month longitudinal clinical information were obtained through electronic medical records. Results: CS follows a normal distribution with a median of 0 in the control population and median is significantly increased among HFrEF patients to 1.8 (IQR 1.4 – 2.1, p < 0.0001). CS diagnosed HFrEF with a receiver operating characteristic (ROC) area under the curve (AUC) of 0.93 (AUC is 0.98 for NT-proBNP, and combined CS and NT-proBNP AUC is 0.99). Unlike NT-proBNP, CS does not correlate with body mass index (BMI) in either the control or HFrEF population (Pearson’s r = -0.15, p = 0.12; Pearson’s r = 0.003, p = 0.97, respectively). NT-proBNP significantly correlates with renal function (Pearson’s r = -0.37, p = 0.001), while CS also has no correlation (Pearson’s r = 0.03, p = 0.71). During an 18-month follow-up, a high CS ≥ 1.8 at the initial visit predicted future cardiovascular hospitalizations (38% vs. 21%, p = 0.04, hazard ratio 2.0). NT-proBNP did not predict future cardiovascular hospitalizations. Conclusions: Plasma cBIN1 based CS is insensitive to BMI and renal function and differentiates myocardial health between patients with HFrEF versus matched controls. An abnormally high CS reflected poor intrinsic myocardial health and can predict future 18-month cardiac hospitalization in stable ambulatory patients.

The gut microbiota constitutes a complex ecosystem, comprising trillions of microbes that have co-evolved with their host over hundreds of millions of years. Over the past decade, a growing body of knowledge has underscored the intricate connections among diet, gut microbiota, and human health. Bioactive polysaccharides (BPs) from natural sources like medicinal plants, sea-weeds, and fungi have diverse biological functions, including antioxidant, immunoregulatory, and metabolic activities. Their effects are closely tied to the gut microbiota, which metabolizes BPs into health-influencing compounds. Understanding how BPs and gut microbiota interact is critical for harnessing their potential health benefits. This review provides an overview of the human gut microbiota, focusing on its role in metabolic diseases like obesity, type II diabetes mellitus (T2DM), non-alcoholic fatty liver disease (NAFLD), and cardiovascular diseases (CVD). It explores the basic characteristics of several BPs and their impact on gut microbiota. Given their significance for human health, we summarize the biological functions of these BPs, particularly in terms of im-munoregulatory activities, blood sugar, and hypolipidemic effect, providing a valuable reference for understanding the potential benefits of natural BPs in treating metabolic diseases. These properties make BPs promising agents for preventing and treating metabolic diseases. The comprehensive understanding of the mechanisms by which BPs exert their effects through gut microbiota opens new avenues for developing targeted therapies to improve metabolic health.

The comprehensibility of good predictive models learned from high-dimensional gene expression data is attractive because it can lead to biomarker discovery. Several good classifiers provide comparable predictive performance but differ in their abilities to summarize the observed data. We extend a Bayesian Rule Learning (BRL-GSS) algorithm, previously shown to be a significantly better predictor than other classical approaches in this domain. It searches a space of Bayesian networks using a decision tree representation of its parameters with global constraints, and infers a set of IF-THEN rules. The number of parameters and therefore the number of rules are combinatorial to the number of predictor variables in the model. We relax these global constraints to a more generalizable local structure (BRL-LSS). BRL-LSS entails more parsimonious set of rules because it does not have to generate all combinatorial rules. The search space of local structures is much richer than the space of global structures. We design the BRL-LSS with the same worst-case time-complexity as BRL-GSS while exploring a richer and more complex model space. We measure predictive performance using Area Under the ROC curve (AUC) and Accuracy. We measure model parsimony performance by noting the average number of rules and variables needed to describe the observed data. We evaluate the predictive and parsimony performance of BRL-GSS, BRL-LSS and the state-of-the-art C4.5 decision tree algorithm, across 10-fold cross-validation using ten microarray gene-expression diagnostic datasets. In these experiments, we observe that BRL-LSS is similar to BRL-GSS in terms of predictive performance, while generating a much more parsimonious set of rules to explain the same observed data. BRL-LSS also needs fewer variables than C4.5 to explain the data with similar predictive performance. We also conduct a feasibility study to demonstrate the general applicability of our BRL methods on the newer RNA sequencing gene-expression data.

Background: Vitamin B 12 deficiency has been implicated in endothelial dysfunction and cardiovascular disease via hyperhomocysteinemia. Coronary tortuosity (CorT) is a common coronary angiography finding. The etiology, clinical implication and long term prognosis are still not well clarified. This study was conducted with the aim to evaluate the relationship between CorT and vitamin B12. Subjects and Method: The medical records of consecutive patients, who underwent coronary angiography, were retrospectively reviewed. The study group consisted of 1624 patients. Taking into consideration the inclusion criteria, 212 patients with CorT and 210 patients with normal coronary angiographies (control group) were included in the study. Vitamin B12, other biochemical parameters, clinical and echocardiographic parameters, and CorT score were evaluated in all patients. CorT is defined as fixed 3 bends during both systole and diastole, with each bend ≥45 °. Results: Patients with CorT had higher prevalence of older, female gender, hypertension, current smoking. Vitamin B12 was significantly decreased in patient with CorT (134.7±47.8 vs 239.6±53.8 p<0.001). On multivariate analysis age, female gender, hypertension, diabetes mellitus and vitamin B12 were independent predictors for CorT (OR 1.56; 95% CI: 1.247–1.962; p < 0.001, OR 1.628; 95% CI: 1.376-2.048; p<0.001, OR 1.865; 95% CI: 1.387-2.695; p<0.001, OR 1.362; 95% CI: 1.184-1.726; p<0.001, OR 1.862; 95% CI: 1.486-2.674; p<0.001, respectively). Conclusion: In our study, we have founded a significant relationship between vitamin B12 deficiency and CorT.

Classroom resource allocation is a challenging problem in educational institutions. The use of cloud computing can aid in the optimization of this process by allowing resource providers to allocate resources to users in a timely and efficient manner. In this paper, we propose a modified Salp Swarm Algorithm (SSA) to optimize the allocation of classroom resources. The proposed approach involves generating a population of salps representing potential solutions to the resource allocation problem and updating the positions of the salps iteratively based on a fitness function and mathematical equations. We conducted simulations of the proposed algorithm on various numbers of tasks and sources to evaluate its performance. The results indicate that the modified SSA algorithm can efficiently allocate classroom resources, achieving lower completion time of tasks (makespan) with lower costs. This paper contributes to the literature by introducing an effective approach to solve the classroom resource allocation problem.

Soil microorganisms plays an important role on plant development and the homogenization of soil microbiome is agri-environmental harmful. It is essential that agricultural practices are carried out taking soil microbiome preservation in consideration. Agroforestry systems are one of the most environmentally friendly agrosystems and its plant diversity influence directly the soil microbiome diversity. In addition, many technologies have been developed and used by applying inoculants in agriculture. However, most of the inoculants are developed with only one or a little number of microbial species to supply a specific plant nutritional demand. In this study we tested the efficacy of the microbial consortium (MC) obtained from compost and the cyanobacteria Arthrospira platensis (AP) comparing with the application of the vermicompost tea (VT) and bokashi (BK) in arugula, lettuce, beetroot, and carrot in an agroforestry system in two seasons. Soil changes on chemical properties and β-glucosidase and arylsulfatase were measured over time. The vegetables yield was measured and for arugula and lettuce the nutritional traits and plant root system were also evaluated. Greater plant yield, nutritional values, and plant root development was observed in the CM treated plants, AP and BK had similar results. Our results show that both CM and AP have potential to become a sustainable product for agricultural production

Background: Autonomic balance measurement (ABM) is a valuable tool in clinical practice for assessing the functioning of the autonomic nervous system (ANS), which controls involuntary bodily functions. Here we summarize the current understanding of the utility of autonomic balance measurement in clinical practice. Methods: A narrative review of relevant literature was conducted, focusing on studies investigating the use of autonomic balance measures in various clinical settings. Results: ABM holds potential benefits in clinical practice, including aiding in diagnosing autonomic dysfunction, monitoring treatment efficacy, and predicting potential health complications. ABM can be crucial for diagnosing various conditions, including cardiovascular diseases (Heart failure, arrhythmias, and hypertension), neurological disorders (Parkinson's disease, Alzheimer's disease, and diabetic neuropathy), in sports medicine athletes can undergo ABM to optimize training regimens, enhance performance, assessing the impact of training stress, preventing overtraining, and promoting overall well-being and probably to assess potential health risks, in metabolic syndromes ABM can aid in the early detection of autonomic neuropathy, a diabetes-related complication, allowing for timely intervention and better management of the disease, and psychiatric disorders (Anxiety, depression, and post-traumatic stress disorder). In addition, ABM can be helpful in monitoring of treatment efficacy, by tracking changes in autonomic balance, clinicians can assess if treatments for conditions impacting the autonomic nervous system are effective. ABM can also be helpful in predicting outcomes by facilitating early recognition of future health complications in certain populations. However, limitations exist, including non-specificity, variability in measurements, and limited evidence for routine use in specific clinical settings. Conclusion: Incorporating autonomic balance measurement into routine clinical assessments provides a more comprehensive understanding of a patient's health. It complements traditional diagnostic methods, offering additional insights into the body's regulatory mechanisms. While autonomic balance measurement offers potential benefits, careful interpretation and consideration of its limitations are crucial for its effective use in clinical practice. ABM remains a valuable and versatile tool in clinical practice for a deeper understanding of the autonomic nervous system, enabling early detection, targeted interventions, and improved management of various medical conditions across different specialties.

In this paper, a method of charging lead acid battery with solar power by flyback converter is proposed. The basic system consists of a buffer circuit, auxiliary power circuit, control circuit, voltage feedback circuit and current feedback circuit. The system is capable of monitoring battery voltage and the current, charging the coupling inductor and automatic control of output duty cycle, overcharging and over-discharging protection. The experiment proves that the charging efficiency is improved by about 83% at full load.

This narrative review challenges the recent World Workshop Consensus (WWC) conferences failure to classify aggressive periodontitis (AP) in young adults as distinct from adult periodontitis. Deficiencies in the consensus process, results of these deficiencies, and their impact on disease classification are presented. Support for retaining localized aggressive periodontitis (LAgP) in adolescents as a unique disease, focuses on the; 1) age of onset, 2) rate of bone loss in those affected, and 3) unique microbiological etiological associations. Examples of, 1) unique clinical signs and symptoms of LAgP are presented, and 2) the microbial subgingival consortia that precedes these clinical signs and symptoms are described. Tables show, 1) decreased publications for AP since publication of WWC guidelines and 2) Bradford-Hill guidelines that support the unique etiological consortia replicated in clinically well-defined longitudinal studies. The review describes major deficiencies in the WWC as compared to other medically-related well-run consensus conferences that, 1) set pre-conference standards for 70 – 80% agreement of expert participants, and 2) published the dissenting point of view. In contrast, the WWC conference was decided by a mere majority, and never presented the dissenting view. The review concludes that averaging of mean pocket depth reduction, or attachment gain can misrepresent disease and/or success of treatment in aggressive diseases. In contrast, clinical assessment of time to recurrence of subgingival re-infection with possible translocation of oral microbes to distant sites is presented as an alternative measurement of success. Other questions and future directions are presented.

Granulites and associated dykes from the less well studied southern Ivrea-Verbano Zone (IVZ; around Ivrea town) are characterized by combining field, macro, micro and chemical (major and trace-element mineral composition) data in order to identify chemical and rheological variations in the lower crust that could be relevant for geodynamic implications. The Ivrea granulites are similar to those belonging to the Lower Mafic Complex of the central IVZ. The mafic lithologies experienced a stealth metasomatism (pargasitic amphibole and An-rich plagioclase) that occurred at suprasol-idus conditions by a pervasive reactive porous flow of mantle-derived orogenic (hydrous) basaltic melts infiltrated along relatively few, likely deformation-assisted, channels. Similar metasomatic melts are already reported in the central and northern IVZ, suggesting a massive regional hydration of the lowermost Southalpine mafic crust during the Early Mesozoic. The plastic behavior of the lowermost part of the crust, induced by its metasomatic hydration, could have promoted the Tri-assic extension of the Variscan crust and the beginning of the Alpine cycle.

In Kenya, reports of Rift Valley fever (RVF), one of the worst climate-sensitive zoonosis, have been common. Despite the fact that several empirical studies have demonstrated that Machine learning techniques perform better than time series models in forecasting time series data, there is little evidence of their use in predicting disease outbreaks in Africa. Recently, the literature has mentioned a number of other uses of machine learning to support intelligent decision-making in the healthcare industry and public health but there is limited knowledge on the use of the XGBoost model in the prediction of disease outbreaks. Among the Kenyan provinces, Rift valley fever cases were more pronounced in Rift valley (26.80%) and Eastern (20.60%) regions. The study explored the relationship be-tween RVF incidence and various climatic factors, such as humidity, clay content, elevation, slope, and rainfall. The strongest correlation, a meager 0.02903 for rainfall, was found in the correlation matrix, which showed weak linear relationships between various climatic factors and RVF cases. These climate variables were used to train the XGBoost model, which showed remark-able performance with an AUC of 0.8908, accuracy of 99.74%, precision of 99.75%, and recall of 99.99%. Rainfall was found to be the most important predictor in the feature importance analysis. These findings are consistent with other research showing how important weather conditions are to RVF outbreaks. According to the study's findings, the use of sophisticated machine learning models that take a variety of climatic factors into account can greatly improve RVF outbreak prediction and control.

In this study, AlSi12 alloy was fabricated using the selective laser melting (SLM) technique to produce high density components with complex and customized parts for railway applications. Nonetheless, the production of dense samples necessitates the optimization of production process parameters. As a statistical design of experimental technique, the response surface methodology was used to optimise different combination of SLM parameters. The results were analysed using analyse of variance (ANOVA) and the signal-to-noise(S/N) ratios. The relationship between the hardness response to the process parameters (scanning speed and lase power) for determining the optimal processing conditions were examined. A hardness value of 133 HV was obtained. The process parameters were successfully optimized and the relationship between the parameters and the structures of the fabricated samples were reported.

Direct-acting antivirals (DAAs) revolutionized therapeutics of chronic hepatitis C. The emergence and transmission of HCV variants with resistance-associated substitutions (RASs) can undermine HCV treatment. This study aimed to assess the prevalence and temporal trends of RASs in HCV with a particular focus on the clinically-relevant RASs (cr-RASs). Near-complete HCV GenBank sequences archived in the Los Alamos HCV Database were analyzed. The study period was divided into two phases; before 2011 and 2011 onwards. Identification of RASs across three DAA classes; NS3, NS5A, and NS5B inhibitors was based on the 2020 EASL guidelines. The AASLD-IDSA recommendations were used to identify cr-RASs for three HCV genotypes/subtypes (1a, 1b, and 3) for four DAA regimens; ledipasvir/sofosbuvir; elbasvir/grazoprevir; sofosbuvir/velpatasvir; and glecaprevir/pibrentasvir. The final HCV dataset comprised 3443 sequences and the prevalences of RASs were 50.4%, 60.2%, and 25.3% in NS3, NS5A, and NS5B, respectively. In subtype 1a, resistance to ledipasvir/sofosbuvir was 32.8%, while resistance to elbasvir/grazoprevir was 33.0%. For genotype 3, resistance to sofosbuvir/velpatasvir and glecaprevir/pibrentasvir was 4.2% and 24.9%, respectively. Significant increase in cr-RASs was observed across the two study phases as follows: for ledipasvir/sofosbuvir in subtype 1a, cr-RASs increased from 30.2% to 35.8% (p=0.019); for elbasvir/grazoprevir in subtype 1a, cr-RASs increased from 30.4% to 36.1% (p=0.018); In subtype 1b, neither ledipasvir/sofosbuvir nor elbasvir/grazoprevir showed any cr-RASs in the first phase, but both were present at a prevalence of 6.5% in the second phase (p&lt;0.001); for sofosbuvir/velpatasvir in genotype 3, cr-RASs increased from 0.9% to 5.2% (p=0.006); and for glecaprevir/pibrentasvir, cr-RASs increased from 12.0% to 29.1% (p&lt;0.001). Rising prevalence of HCV RASs and cr-RASs was discernible. This highlights the necessity for ongoing surveillance and adaptation of novel therapeutics to manage resistance effectively. Updating of the clinical guidelines and treatment regimens are recommended to counteract the evolving resistance to DAAs.

Abstract: Aim: This study aimed to evaluate the antibiotic-prescribing attitudes of dental students during the management of pulpal and periapical pathologies. Material and methods: This cross-sectional descriptive study concerned all the students in clinical training registered the 5 faculties of dental medicine in Morocco. A self-administered questionnaire was used, and completed via email addresses. Data were analyzed with Jamovi software. Quantitative variables are expressed as mean and standard deviation and qualitative variables as numbers and percentages. X2 and ANOVA tests were performed. The significance level was set at p < 0.05. Results: Three sixty-five students participated in this study. The average duration was 5.87 ±1.45 No statistical difference was found between the different universities p >0.05. Of most students 83.8% prescribe Amoxicillin first ; Amoxcilin in combination with clavulanic acid is prescribed by 9.2%. For patients with penicillin allergy, clindamycin was the most prescribed 53.9%, after followed by azitromycin at 20.9%. Antibiotics are prescribed for all pulp and periapical pathologies, whether acute or chronic. For acute pulpitis, a difference was statistically significant (P=0.01). Regarding acute apical periodontitis, a statistically significant difference between the different faculties was noted (P= 0.03). For the apical abscess, the antibiotic prescription was higher at the public faculty of Casablanca 92.8% than in the other faculties (P = 0.02) Conclusion: It appears from this study the need for faculties of dentistry to develop innovative teaching models to improve the level of knowledge of students on antibiotics and their indications in endodontics.

From the existing millions of fungal species only a few cause disease. In this study, we investigated invasive fungal infections in the head and neck over a 19-year period (2005 to 2024) at a large academic healthcare system. Among 413 cases, 336 were noninvasive, and 77 were invasive. The highest incidence of invasive infections occurred in the sinonasal cavities, with a 15-fold difference compared to other sites. Most infections affected adults over 40 years old. The most common organisms were Mucorales (51%), hyaline molds (29%), and Candida (11%). Risk factors included malignancy, transplant, diabetes, and illicit drug use. Mortality was high in patients with malignancy and/or transplant. Infections affecting the mandible were usually a complication of osteoradionecrosis and were associated with coinfection of Candida and Actinomyces. At other sites, infections were rare and were usually the result of penetrating injuries or immunosuppression. Treatment typically involved a combination of antifungals and surgical procedures.

In the context of the Korean building structure standard KDS2022, wind loads can be readily calculated in terms of the along-wind, across-wind, and torsional components, based on a single wind direction. However, this approach falls short in accurately and safely evaluating the impact of winds from various directions. Therefore, in this study, we developed a Wind Load Calculation Program (WCP) capable of predicting wind loads with relative precision during the preliminary design phase. First, wind tunnel tests were conducted to identify the essential factors necessary for calculating wind loads and the variables influencing these factors. The building shape considered was a square, and the wind force coefficients and power spectral density were measured by combining 4 ground roughness values, 11 side ratios (), 4 aspect ratios (), and wind directions ranging from 0° to 90°. Subsequently, a generalized formulation was devised to facilitate wind load calculations across diverse conditions and a method for calculating load combination coefficients, considering response correlation and load simultaneity, was presented. Finally, the wind loads obtained from the wind tunnel tests were compared with those predicted by the WCP using an actual project model.

This article deals with the denoising of signals from sensors (current and voltage) in a photovoltaic conversion chain using wavelet theory. The idea is to reduce the noise as much as possible without distorting or losing the original signal information. To see the reliability of this proposed method, a comparative study between our proposed method (WDD) and the classic low-pass filter method was presented. The contribution in this work is the improvement of the DC-DC boost converter via the use of wavelet theory, however, that’s why we set two objectives: the first is to obtain reliable control of the DC-DC boost converter via the proposed WDD and the second to increase the overall system efficiency. The experimental results show a very great improvement in the quality of the signals of the proposed method compared to the classic method of filtering by low-pass filter.

Intrusion Detection Systems play a crucial role in a network. They can detect different network attacks and raise warnings on them. Machine learning-based IDSs are trained on datasets that, due to the context, are inherently large, since they can contain network traffic from different time periods and often include a large number of features. In this paper, we present two contributions: the study of the importance of feature selection when using an IDS dataset, while striking a balance between performance and the number of features; and the study of the feasibility of using a low-capacity device, the Nvidia Jetson Nano, to implement an IDS in a low-capacity network. The results, comparing the GA with other well-known techniques in feature selection and reduction, show that the GA has a higher F1-score of 76%, although the time to find the optimal set of features surpasses other methods; the reduction of the number of features reduces the processing time without a significant impact in f1-score. The Jetson Nano allows the classification of network traffic with an overhead of 10 times in comparison to a traditional server.

This study explores the stability of electrodeposited copper catalysts utilized in electrochemical CO2 reduction (ECR) across various amine media. The focus is on understanding the influence of different amine types, corrosion ramifications, and the efficacy of pulse ECR methodologies. Employing a suite of electrochemical techniques including potentiodynamic polarization, linear resistance polarization, cyclic voltammetry, and chronopotentiometry, the investigation reveals useful insights. The findings show that among the tested amines, CO2-rich monoethanolamine (MEA) exhibits the highest corrosion rate. However, in most cases the rates remain within tolerable limits for ECR operations. Primary amines, notably monoethanolamine (MEA), show enhanced compatibility with ECR processes, attributable to their resistance against carbonate salt precipitation and sustained stability in potential over extended durations. Conversely, tertiary amines such as methyldiethanolamine (MDEA) present challenges due to the formation of carbonate salts during ECR, impeding their effective utilization. The study highlights the effectiveness of pulse ECR strategies in stabilizing ECR. A noticeable shift in cathodic potential and reduced deposit formation on the catalyst surface through periodic oxidation underscores the efficacy of such strategies. These findings offer insights for optimizing ECR in amine media, thereby providing promising pathways for advancements in CO2 emission reduction technologies.

Software project estimating (SPE) is a powerful but necessary step in the production of projects for software. Software project estimating includes several components, estimating software time, software resources, software costs, and software effort estimation (SEE). SEE seeks to estimate the number of "hours-person" and "months-person" of work required for developing or managing a software program. During the initial phase of the development of software, future efforts are impossible to predict. To estimate effort, many deep learning (DL) and machine learning (ML) models were previously created. For estimate, single-model techniques and ensemble methods were investigated in this research. Ensemble methods involve a combination of multiple separate models. Bagging, boosting, and voting were the ensemble methods looked into for estimation. Datasets considered for estimation were COCOMO81, COCOMONasa I, COCOMONasa II, Albrecht, Maxwell, China, Desharnais, Desharnais_1_1, Kitchenham, Boehm, and Belady. The evaluation criteria used MAE, RAE, RMSE, RRSE, and CC. The result from previous studies is that the total size of the set of data utilized is small in predicting the effort, as the largest size of the set of data used is the China dataset containing only 499 projects. So, in this study, the dataset has been merged to increase the total size of the set of data and increase the accuracy of prediction. The results indicated that the suggested merged dataset increased effort estimation accuracy.

When examining the crucial role of civic competence in fostering sustainable societies, economies, and ecosystems, its significance for cultivating responsible and engaged citizens in our ever more interconnected global landscape must be recognized. Notably, education influences the development of civic competence throughout the learning experience at all levels of education. This research aims to explore the evolution of students’ self-assessment in terms of civic competence and its three sub-competences. The objective is to comprehend the present status of students’ self-assessments and explore their potential impact on the formation of a sustainable society. Data were collected for this study using an assessment tool elaborated in the ESF project “Development and Implementation of the Education Quality Monitoring System” for students’ generic competences. The tool, presented in a digital form using the QuestionPro platform, measures six generic competences, including civic competence. A total of 1,166 bachelor students and 354 Master’s students participated in the study, representing 22 Latvian higher education institutions. The study’s findings indicate that civic competence in the sample is considerably lower than the other generic competences measured, with a greater dispersion of results. The results also highlight specific areas within universities that require improvement during the learning process to cultivate individuals who are sustainable and socially responsible members of society.

In this paper, we introduce and investigate a new family of sequences called the generalized Fibospinomials (or the generalized Fibonacci polynomial spinors or Horadam polynomial spinors). Being particular cases, we handle with $(r,s)$-Fibonacci and $(r,s)$-Lucas polynomial spinors. After a short history on spinors and quaternions, we present Binet's formulas, generating functions and the summation formulas for these polynomials. In addition, we obtain some identities of generalized Fibonacci polynomial spinors, $(r,s)$-Fibonacci polynomial spinors and $(r,s)$-Lucas polynomial spinors. Moreover, we give some special identities such as Catalan's and Cassini's identities and we present matrices related with these polynomials.

Remote video imagery using shoreline edge detection is widely used in coastal monitoring in order to acquire measurements of nearshore and swash processes. Some of these systems have very limited flexibility due to their rigid structures and require considerable investment in hardware. As such, there is a need for an autonomous low-cost system (~€500) that can be rapidly deployed in the field, while still producing the outcomes required for coastal monitoring. This research presents a sensitivity analysis of time-lapse intervals for two low-cost cameras located in a remote coastal area, overlooking a dissipative beach-dune system. The analysis shows that RMSD in the detected shoreline is similar to other studies for intervals ranging between 1 second and 30 seconds (i.e., RMSDmax for Camera 1 = 2.8 m and Camera 2 = 1.3 m). The increase of the sampling interval from 1 s to 30 s is shown to have no significant adverse effect on the precision of shoreline detection. The research found that the limitations of battery life and memory storage substantially decrease when sampling intervals increase without an effect on the data quality. This also has a positive effect for low-cost systems and removes the requirement for external battery and memory storage. On the systems used, the battery life increased from ~10 days to ~100 days when the sampling interval was increased from 1 to 5 s., As such, the current standard of producing Timex images from 1 s sampling intervals over 10 minutes can be changed depending on the application and requirements needed for analysis.

In this study, we consider the valuation of rainbow options using unsupervised machine learning methods. In particular, we consider the pricing of multi-asset (rainbow) European and American options, using Physics Informed Neural Net- works (PINNS). After developing the PINNS architecture, we benchmark the method by using it to price vanilla and exotic options with one and two un- derlying assets. We then use the methodology to price a multi-asset European option, followed by the pricing of the multi-asset American option by solving the linear complementarity problem. We compare our results to those obtained using preexisting numerical methods and note excellent agreement. Unlike con- ventional numerical methods, we note that this methodology does not suffer from the ’curse of dimensionality’. The time complexity of our method is con- siderably less than that of the conventional techniques. Thus PINNS may offer a faster more efficient solution to the pricing of rainbow options.

The coal mining process generates wastes such as overburden and tailings, the disposal of which is a significant environmental challenge. The circular economy model of eliminat-ing waste and regenerating the economy and ecosystems can minimise the impact of coal mining wastes. Another resource-intensive industry is the construction industry, which is responsible for significant resource consumption, waste generation, and emissions. Reus-ing and recycling coal mining wastes as secondary or value-added building materials in the construction industry per circular economy principles will help minimise the impact and have environmental and economic benefits. Several studies have demonstrated the use and reuse of coal mining wastes in the construction industry. This article conducts a systematic literature review to understand the state-of-the-art utilisation of coal mining waste in the construction industry. After a series of refinements using the PRISMA approach, 54 articles were included in the review. The articles were further divided based on using coal mining wastes in the construction industry as end products/application areas. The review results show that coal mining waste has been successfully used to make bricks and aggregates (fine and coarse), replace cement, and develop geopolymers, concrete, roads, and embankments. The studies suggest the utilisation of coal mining waste primarily in non-structural components, with very few studies suggesting using coal mining waste in structural applications. The systematic literature review helped in understanding the knowledge development related to the possible application of coal mine wastes in the construction industry, identifying the gap in the field, and suggesting future research directions.

With the widespread adoption of 3D scanners, reverse engineering methods for obtaining CAD drawings from physical objects have become increasingly utilized. When converting point data obtained from a 3D scanner into a mesh structure, a smoothing process is typically applied because the point data often contains a lot of noise. However, this can result in the loss of important high-frequency details, or in cases where significant high-frequency information remains, there are limitations in representing the object with basic geometric shapes. In this paper, a method to address this issue by using environment mapping and supervised learning techniques is proposed. By mapping the object to an environment and then using supervised learning to analyze the data, it can accurately identify the basic geometric shapes. This approach reduces the working time and allows for easier design of complex surfaces that were previously difficult to model.

The cultural transmissions of a society like famous paintings, popular music, good poetry, and enduring religious rituals/doctrines/texts serve as mediums for the embodiment of algorithms that interact with the mind and body in looping, mostly unconscious interactions that are mutually beneficial to the participants of the society and the algorithms themselves. Useful algorithms are also found in the mind and bodies of other people, and individuals in a group can be used to outsource specialized tasks to benefit the society as a whole. Since useful algorithms must be embodied in a medium to work, there are physical limitations to the computations that can be performed in our universe.

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

Liver cancer imposes a pervasive global health challenge, ranking among the most prevalent cancers worldwide. Its prevalence and mortality rates are on a concerning upward trajectory and exacerbated by the dearth efficacious treatment options. The Hippo signaling pathway, originally discovered in Drosophila, comprises four core components: MST1/2, WW45, MOB1A/B, and LATS1/2. This pathway regulates the cellular localization of the transcriptional coactivator YAP/TAZ (Yes-associated protein/PDZ-binding motif) through a series of enzymatic reactions. The Hippo/YAP/TAZZ pathway maintains a balance between cell proliferation and apoptosis, regu-lates tissue and organ sizes, and stabilizes the internal environment. Abnormalities of any genes within the Hippo signaling pathway, such as deletion or mutation, disturb the delicate balance between cell proliferation and apoptosis, creating a favorable condition for tumor initiation and progression. Mutations or epigenetic alterations in the Hippo signaling pathway components can lead to its inactivation. Consequently, YAP/TAZ becomes overexpressed and activated, promoting excessive cell proliferation and inhibiting apoptosis. This dysregulation is closely associated with the development of liver cancer. This review discusses the pivotal role of the Hippo signaling pathway in the pathogenesis and progression of liver cancer. By elucidating its mechanisms, we aim to offer new insights into potential therapeutic targets for effectively combating liver cancer.

An online survey assessed the awareness of Nelson region’s residents, comparing responses between those who experienced the installation and those who did not. Findings indicated that eco-art installations can significantly enhance public understanding of ALAN's environmental and health impacts. The installation successfully increased specific knowledge about ALAN, with 92% of visitors reporting learning something new, however overarching knowledge and levels of concern were largely similar between the groups. Emotional engagement was high, with 62% of viewers stating that the installation invoked an emotional response. While the study did not measure behavioural changes, 94% of festival participants found it at least ‘somewhat appropriate’ and 64% ‘very’ or ‘entirely appropriate’ to raise awareness of these issues at such events, suggesting a potential foundation for future action. This research underscores the promise of art-science collaborations in environmental education and public engagement.

The present paper reports exact results for the muon self-energy and anomalous g-factor. A unique to the muon cut-off terms that remove the radial singularity in the system and regularize the corresponding electrodynamics are presented. Equations of motion and a transcendental equation satisfied by the muon anomalous g-factor are derived, with solution a_m=0.001165920162(198). The obtained value matches the latest experimental one found in the literature to about 0.43 ppb ruling out a possible tension between theory and experiment.

Introduction: Rural Americans face significant health disparities due to limited access to healthcare services. Telehealth has emerged as a promising solution to mitigate these disparities by facilitating remote access to medical care. This paper comprehensively reviews the advantages, challenges, and implementation strategies of telehealth in rural areas.Method: A narrative review methodology was employed to synthesize existing literature and case studies on telehealth in rural settings. Databases such as PubMed, Google Scholar, and JSTOR were searched for relevant studies published in the last three decades. Keywords such as "telehealth," "rural health," and "healthcare disparities" guided the search process. The review focused on studies assessing the impact of telehealth on healthcare access, cost-effectiveness, and quality of care in rural areas.Results: The review findings indicate that telehealth significantly enhances healthcare access by reducing travel distances, increasing availability of specialist consultations, and promoting patient engagement. It improves chronic disease management and patient satisfaction by enabling convenient access to healthcare services at home. Despite initial setup costs and challenges such as broadband access and regulatory issues, telehealth demonstrates long-term cost savings through reduced travel expenses and improved healthcare efficiency. Quality of care is enhanced through improved communication between healthcare providers and patients, leading to better treatment adherence and health outcomes.Conclusion: Telehealth represents a transformative approach to address healthcare disparities in rural areas. It offers a cost-effective solution to improve access to healthcare services, enhance patient outcomes, and reduce healthcare costs in the long run. However, successful implementation requires addressing infrastructure challenges, refining reimbursement policies, and ensuring regulatory support. Future efforts should focus on expanding broadband infrastructure, refining policy frameworks, and conducting further research to optimize telehealth's impact on rural healthcare delivery.

This paper presents the five-parameter Exponentiated Weibull Weibull (EWW) distribution with Weibull base failure rate parameterization, developed to model the dropout times of students in an online class module. Parameters of the model were estimated using both Maximum Likelihood and Bayesian estimation procedures to determine the most effective estimation method. Simulation results indicate that, across various hazard types and censoring levels, Bayesian estimates are more accurate and precise than Maximum Likelihood estimates. Real-life data analysis of online students module presentation supports these findings, showing significantly lower standard deviations for Bayesian estimates compared to Maximum Likelihood estimates, highlighting the efficiency of Bayesian estimation for the EWW distribution. Additionally, a comparative goodness-of-fit analysis demonstrated that the EWW distribution better fits the student dropout times dataset than Weibull Weibull (WW), Weibull Exponential (WE), and Exponential Weibull (EW) distributions. The median dropout time estimated using the EWW distribution is 24 days, suggesting that instructors should closely monitor student attendance around this period.

Cancer is a highly heterogeneous disease and remains a global health challenge affecting millions of human lives worldwide. Despite advancements in conventional treatments like surgery, chemotherapy, and immunotherapy, the rise of multidrug resistance, tumor recurrence, and severe side effects necessitates innovative therapeutic approaches. The complex nature of the tumor microenvironment (TME) further compromises the efficacy of traditional chemotherapy drugs. Recently, stimulus-responsive nanomedicines designed to target TME characteristics (e.g., pH, redox, enzymes) have gained attention for their potential to enhance anticancer efficacy while minimizing adverse effects. Among various nanocarriers, hydrogels are an interesting class of nanocarriers used in cancer therapy due to their high water content, adjustable mechanical characteristics, and ability to respond to external/internal stimuli. These properties make hydrogels an ideal nanocarrier for controlled drug release within the TME. This review comprehensively surveys the latest advancements in stimulus-responsive hydrogels for cancer therapy, exploring various stimuli-responsive mechanisms, including biological (e.g., pH, redox), chemical (e.g., enzymes, glucose), and physical (e.g., temperature, light), as well as dual- or multi-stimuli responsiveness. This review will offer novel perspectives on the development of stimulus-responsive hydrogels for cancer therapy.

Affinity reagents, or target-binding molecules, are quite versatile and are major workhorses in molecular biology and medicine. Antibodies are the most famous and frequently used type and they have been used for a wide range of applications, including laboratory techniques, diagnostics, and therapeutics. However, antibodies are not the only available affinity reagents and they do have significant drawbacks, including laborious and costly production. Aptamers are one potential alternative that have a variety of unique advantages. They are single stranded DNA or RNA molecules that can be selected for binding to many targets including proteins, carbohydrates, and small molecules – for which antibodies typically have low affinity. There are also a variety of cost-effective methods for producing and modifying nucleic acids in vitro without cells, whereas antibodies typically require cells or even whole animals. While there are also significant drawbacks to using aptamers in therapeutic applications, including low in vivo stability, aptamers have had success in clinical trials for treating a variety of diseases and two aptamer-based drugs have gained FDA approval. Aptamer development is still ongoing, which could lead to additional applications of aptamer therapeutics, including antitoxins, and combinatorial approaches with nanoparticles and other nucleic acid therapeutics that could improve efficacy.

This paper presents a novel framework for Collaborative Learning Networks (CLNs) in classrooms, aiming to optimize resource utilization and enhance educational services. The study addresses the inefficiencies of traditional education models, particularly the underutilization of computational resources and limited access to specialized software. The proposed framework employs a mix of task-based and random allocation strategies to distribute computational tasks within the CLN, considering each device’s capabilities and task requirements. The findings show significant improvement in resource utilization, facilitated by device-to-device collaboration and task sharing, thus enriching students’ learning experiences. This work contributes to optimizing resource utilization in education, advancing the field of CLNs.

Verifying the accuracy of a 3D-printed object involves using an image processing system. This system compares images of the CAD model of the object to be printed with its 3D-printed counterparts to identify any discrepancies. It is important to note that the integrity of the accuracy measurement is heavily dependent on the image processing settings chosen. This study focuses on this issue by developing a customized image processing system. The system generates binary images of a given CAD model and its 3D-printed counterparts and then compares them pixel-by-pixel to determine the accuracy. Users can experiment with various image processing settings, such as grayscale to binary image conversion threshold, noise reduction parameters, masking parameters, and pixel-fineness adjustment parameters, to see how they affect accuracy. The study concludes that the grayscale to binary image conversion threshold has the most significant impact on accuracy and that the optimal threshold varies depending on the color of the 3D-printed object. The system can also effectively eliminate noise (filament marks) during image processing, ensuring accurate measurements. Additionally, the system can measure the accuracy of highly complex porous structures where the pores size, depth, and distribution are random. The insights gained from this study can be used to develop intelligent systems for the metrology of additive manufacturing.

In the quest for robust and efficient digital communication, this paper introduces cutting-edge key exchange protocols leveraging tropical semirings’ computational prowess and block matrices’ structural resilience. Moving away from the conventional use of finite fields, these protocols deliver markedly faster processing speeds and heightened security. We present two implementations of our concept, each utilizing a different platform for the set of commuting matrices: one employing tropical polynomials of matrices and the other employing Linde-de la Puente matrices. The inherent simplicity of tropical semirings leads to a decrease in operational complexity, and using block matrices enhances our protocols’ security profile. The security of these protocols relies on the Matrix Decomposition Problem. We also provide a comparative analysis of our protocols against existing matrix block-based protocols in finite fields. This research marks a significant shift in cryptographic protocol design, specifically tailored for demanding engineering applications, and sets a new standard in secure and efficient digital communication.

A substantial spike in the need for quality healthcare has resulted from people being more cognizant of health issues. With blockchain, healthcare providers may safely share patient information electronically, which is especially important given the sensitive nature of the data contained inside them. However, flaws in the current blockchain design have surfaced since the dawn of quantum computing systems. The study proposes a novel quantum inspired blockchain system (Qchain) and constructs a unique entangled quantum medical record (EQMR)system with an emphasis on privacy and security. This Qchain relies on entangled states to connect its blocks. Automated production of the chronology indicator reduces storage capacity requirements by connecting entangled BloQ (blocks with quantum properties) with controlled activities. One qubit is used to store the hash value of each block. A lot of information regarding quantum internet is included in the protocol for the entangled quantum medical record (EQMR). EQMR can be accessed in Medical Internet of Things (M-IoT) systems may be kept private and secure, and their where-abouts can be monitored in the event of an emergency. The protocol also uses quantum authentication in place of more conventional methods like encryption and digital signatures. Mathematical research shows that the quantum converged blockchain (QCB) is highly safe against attacks such as the External attack, Intercept-Measure-Repeat attack, and Entanglement-Measure attack. The reliability and auditability evaluations of the entangled BloQ, as well as the quantum circuit design for computing the hash value, are presented. There's also a comparison between the suggested approach and several other quantum blockchain designs.

The efficacy and cost-effectivness of Multigene Panel Next-Generation Sequencing (NGS) in directing patients towards genomically matched therapies remains uncertain. This study investigated metastatic colorectal cancer (mCRC) patients who underwent NGS analysis on formalin-fixed paraffin-embedded tumor samples. Data from 179 patients were analyzed, revealing no mutations in 39 patients (21.8%), one mutation in 83 patients (46.4%), and two or more mutations in 57 patients (31.8%). KRAS mutations were found in 87 patients (48.6%), including KRAS G12C mutations in 5 patients (2.8%), PIK3CA mutations in 40 patients (22.4%), BRAF mutations in 26 patients (14.5%). Less common mutations were identified: ERBB2 in 5 patients (2.8%) and SMO in 4 patients (2.2%). Additionally, MAP2K1, CTNNB1, and MYC were mutated in 3 patients (2.4%). Two mutations (1.1%) were observed in ERBB3, RAF1, MTOR, JAK1, and FGFR2. No significant survival differences were observed based on mutation numbers. 40% of patients had druggable molecular alterations, but only 1.1% received genomically guided treatment, suggesting limited application in standard practice. Despite this, expanded gene panel testing can identify actionable mutations, aiding personalized treatment strategies in metastatic CRC, although current eligibility for biomarker-guided trials remains limited.

AbstractLarge numbers of urban lakes are facing issues related to cyanobacteria, commonly known as blue-green algae, resulting in significant threats to humans and animal health due to their production of microtoxins and Anatoxin-A. These toxins induce acute effects including gastrointestinal distress, and respiratory problems, underscoring the need to protect urban lakes to safeguard community health and ecosystem well-being. A comprehensive monitoring program is a critical step to address cyanobacteria-related issues. Monitoring results of dissolved phosphorous, nitrates(N), Total Kjeldahl Nitrogen (TKN), total phosphorous (TP), TSS, and Escherichia coli (E. coli) concentrations, are used to understand the importance of different pathways events during dry conditions (no prior rainfall) and wet events (rainfall during or proceeding monitoring events) plus a microbial source tracking procedure, to understand impacts on growth characteristics of cyanobacteria and microtoxins. The Mann-Kendall Trend Test is used to characterize trends of various parameters (using transitions of ‘wet’ and ‘dry’ to the Lake and within the Lake), to identify the impact of various management strategies and/or the impact of various sources from overland land uses for a) wet events to the Lake, b) dry events to the Lake, c) wet events in the Lake, and d) dry events within the Lake. Microbial source tracking methods are used along the shoreline of Fairy Lake to interpret elevated levels of fecal pollution and E. coli bacteria in water samples, aiding in the identification of pollution sources. Blue-green algae have been observed at least once each year from 2018 to 2023. Microtoxins and Anatoxin-A were present in nine samples with a maximum of 1.72 mg/L and 0.02 mg/L, respectively. Frequent beach closures have occurred every year, raising public concerns regarding both ecological and recreational aspects for Fairy Lake.