Antimicrobial resistance (AMR) is a public health problem exacerbated by the overuse and misuse of antibiotics and an inadequate capacity of laboratories to conduct AMR surveillance. This study assessed the capacity of laboratories in seven faith-based hospitals to conduct bacteriology, AMR testing and surveillance in Zambia. This multi-facility, cross-sectional exploratory study was conducted from February 2024 to April 2024. We collected and analysed data using the self-scoring Laboratory Assessment of Antibiotic Resistance Testing Capacity (LAARC) tool. This study found an overall score of 39%, indicating a low capacity of laboratories to conduct AMR surveillance. The highest capacity score was 47%, while the lowest was 25%. Only one hospital had full capacity (100%) to utilise a Laboratory Information System (LIS). Three hospitals had good capacity to perform data management with scores of 83%, 85%, and 95%. Only one hospital had full capacity (100%) to process specimens, and only one hospital had good safety requirements for a microbiology laboratory, with a score of 89%. This study demonstrates that all the assessed hospitals have a low capacity to conduct AMR surveillance. Therefore, there is an urgent need to strengthen the microbiology capacity of laboratories to enhance surveillance in faith-based hospitals in Zambia.

Background and Objective: Patients with spinal muscular atrophy (SMA) treated with a dis-ease-modifying therapy (DMT) are often classified as responders or non-responders based on at-tainment of a specific improvement threshold on validated functional scales. This categorization may significantly impact treatment reimbursement in some countries. The aim of this research is to evaluate the perception of treatment benefit by patients considered as responder or non-responder. Methods: In this non-commercial multicenter study, 99 post-symptomatically treated SMA type I-III patients with a median age of 11.2 (0.39-57.4) years at treatment initiation were stratified into three groups based on their treatment outcomes, i.e. those exhibiting clinically significant improvement (N=41), those with non-clinically significant improvement (N=18), or those show-ing no improvement (N=40). Fifteen months after treatment initiation patients or patients’ care-givers were assessed using a patient-rated scoring system based on the Patient Global Impression of Change (PGIC) scale, comprising 22 questions targeting important aspects and tasks in the daily life of patients with SMA. Results: We found no statistical difference in patient perception of treatment benefit in 17 out of 22 domains across patient groups. Conclusion: Our results suggest that functional motor scales do not recapitulate patients and pa-tients’ caregivers experience about the effect of nusinersen treatment in SMA.

Environmental degradation leads to a totally unsustainable food system. In addition to this is-sue, the consumption of foods that improve people's health and well-being is recommend-ed. These guidelines create new challenges and the demand for alternatives to meet these needs in the short term. One of the alternatives is undoubtedly the use of by-products of winemaking, namely in the form of grape pomace flour (GPF), which is obtained in a totally sustainable way, requires no further water consumption or agricultural land, and is an ingredient that boasts all the characteristics that meet the circular economy and has colossal potential in promoting nutrition and healthy eating. To verify the benefits of using the Touriga Nacional and Arinto (Vitis vinifera L.) flour varieties, analytical determinations were made to identify and quantify different components, such as nitrogen and extractable phosphorus, ash, moisture, proteins, fats, dietary fibers, total sugars, and phenolic content. GPF could become a valuable ingredient due to its nutritional quality and high content in various polyphenols namely flavonoids and pyranoanthocyanins.

Naturally fermented dairy products are an important component of the human diet. They are a valuable source of nutrients as well as vitamins and minerals. Their importance as a source of probiotic bacterial strains should not be overlooked. A number of studies highlights the positive effects of species of the Lactobacillacea on the intestinal microbiota, the overall homeostasis of the body, as well as a complementary treatment for some diseases. However, data on the effects on intestinal epithelial cells of postmetabolites released by bacteria are incomplete. This is likely due to the fact that these effects are species and strain specific. In the present study, we investigated the effects of postmetabolites produced by a pre-selected candidate probiotic strain Limosilactobacillus fermentum on HT-29 intestinal epithelial cells. Our data showed a pronounced proliferative effect, evaluated by flow cytometry, quantification of cell population, and determination of mitotic index. This was accompanied by stabilization of the cell monolayer, measured by increase of TEER (trans epithelial electric resistance) and reorganization of actin filaments. The data obtained are a clear indication of the positive effects, that the products secreted by L. fermentum strain 53 have on the intestinal epithelial cells.

Background/Objectives: Alcohol consumption during pregnancy poses notable risks to maternal and foetal health, yet its prevalence and impact in Nigeria remain understudied. This commentary article aims to review the current state of alcohol intake by expectant mothers in Nigeria and provide an overview of Foetal Alcohol Spectrum Disorder (FASD) and recommendations for addressing this public health issue. Methods: Drawing on existing literature, we highlight the prevalence of alcohol intake by expectant mothers in Nigeria and explore the socio-cultural factors influencing drinking behaviours during pregnancy. Results: Despite limited empirical evidence, anecdotal reports suggest that alcohol consumption during pregnancy is not uncommon in Nigeria, driven by factors such as social norms, lack of awareness, and limited access to prenatal care and education. The implications of prenatal alcohol exposure for maternal and child health are substantial, with potential consequences including FASD, birth defects, and mortality. Conclusions: We provide recommendations for policymakers, healthcare providers, and community stakeholders to enhance awareness, prevention, and support systems for pregnant women and their families. Key recommendations include implementing public health campaigns to raise awareness concerning the dangers of alcohol use by expectant mothers, integrating alcohol screening and counselling into routine prenatal care services, strengthening regulations on alcohol advertising and availability, and providing support services for women struggling with alcohol use disorders during pregnancy. By addressing the gaps in knowledge and practice surrounding alcohol consumption during pregnancy, Nigeria can take meaningful steps toward promoting maternal and child health and preventing the lifelong consequences of FASD and related conditions.

We describe a method of determining three-body and four-body Laplace-like phase angles with the potential to librate about a mean value in multiplanet extrasolar systems. Unlike in past searches of N-body results, this method relies on global mean-motion resonances (MMRs) and takes into consideration the location of the most massive planet which defines the 1:1 global MMR in each (sub)system. We compile lists of potentially librating phase angles and prevalent MMRs in 35 real multibody systems, and we discuss their properties in conjunction with recent investigations of librations discovered in sophisticated N-body simulations. We hope that our results will facilitate systematic libration searches in dynamical models of compact systems with three or more orbiting bodies.

Objectives: The Implicit Prosody Hypothesis (IPH) posits that individuals generate internal prosodic representations during silent reading, mirroring those produced in spoken language. While converging behavioral evidence supports the IPH, the underlying neurocognitive mechanisms remain largely unknown. Therefore, this study investigated the neurophysiological markers of sensitivity to speech rhythm cues during silent word reading. Methods: EEG was recorded while participants silently read four-word sequences, each composed of either trochaic words (stressed on the first syllable) or iambic words (stressed on the second syllable). Each sequence was followed by a target word that was either metrically congruent or incongruent with the preceding rhythmic pattern. Results: Single-trial event-related potentials (ERPs) and time-frequency representations (TFRs) triggered by the target words were examined using a hierarchical mass univariate general linear modeling approach to investigate the effects of metrical expectancy and stress type. ERP findings revealed significant differences based on expectancy, with words carrying unexpected stress eliciting larger negativities between 240-628 ms after word onset. TFRs further revealed that different frequency bands were sensitive to distinct aspects of rhythmic structure in language. Alpha activity, linked to attention, reflected expectations about the rhythmic pattern. Theta and beta activity, associated with processing rhythm and timing, were sensitive to both the expected rhythm and the specific location of stressed syllables within words. Conclusions: These findings highlight comparable neurophysiological responses to both implicit and explicit speech rhythm, further supporting the IPH and emphasizing the role of prosody in silent reading.

Bean is a remarkable crop widely used as food worldwide, underscoring the significance of scientific research conducted on this plant. Developing a strong root system in the early stages of growth, which can spread wider and deeper, along with the consequent development of the shoot system crucial for photosynthesis, enhances the plant's productivity and resistance to diseases and pests. In the current study, the reactions of cultivars Önceler-98 and Topçu beans to different concentrations of IBA (indole-3-butyric acid) during the seedling stage were investigated. Three different concentrations, namely 0 (Control), 50, 100, and 150 µM IBA, were applied, and their effects on root length (RL), root fresh weight (RFW), root dry weight (RDW), number of nodules on roots (RNN), shoot length (SL), shoot fresh weight (SFW), and shoot dry weight (SDW) parameters were measured. The pattern of the experiment was a randomized complete block factorial design with four replications and, statistically significant (p≤0.05) and highly significant (p≤0.01) differences were observed among the means of root and shoot traits. As a result, it was found that the cultivar Önceler-98 generally exhibited better responses to IBA solutions, and the optimal concentration range for both varieties was between 50-100 µM, decreasing at 150 µM and higher concentrations, yet still yielding superior means compared to the control.

Bacterial keratitis caused by Pseudomonas aeruginosa is indeed a serious concern due to its potential to cause blindness and its resistance to antibiotics, partly attributed to biofilm formation and cytotoxicity to the cornea. The present study uses meta-analysis of transcriptomics dataset to identify important genes and pathways in biofilm formation of P. aeruginosa induced keratitis. By combining data from several studies, meta-analysis can enhance statistical power and robustness, enabling the identification of 83 differentially expressed candidate genes including fis that could serve as therapeutic targets. The approach of combining meta-analysis with virtual screening and in vitro methods provides a comprehensive strategy for identifying potential target genes and pathway crucial for bacterial biofilm formation and development anti-biofilm medications against P. aeruginosa infections. The study identified 83 candidate genes that exhibited differential expression in the biofilm state, with fis proposed as an ideal target for therapy for P. aeruginosa biofilm formation. These techniques, meta-analysis, virtual screening, and in-vitro methods were used in combination to diagnostically identify these genes which play a significant role in biofilms. This finding has highlighted a hallmark target list for P. aeruginosa anti-biofilm potential treatments.

The maritime environment is the setting for a variety of economic activities, such as offshore wind energy, aquaculture, salt extraction, and oil and gas platforms. While some of these activities have a long-term presence, others require decarbonisation as they head towards their demise. In this context, the aim of this study is to develop a methodology to replace the electrical energy from high-emission industrial processes offshore with clean electricity generated by offshore wind energy. The proposal is structured in three phases: Initiation, which involves the collection of quantitative, technical and geospatial information of the study area; Indicators, where the main indicators are calculated and the best alternative is selected using multi-criteria evaluation methods; and finally, short, medium and long-term scenarios are proposed. The methodology is evaluated in Spain, the best alternative with a nominal power of 225 MW, is capable of avoiding up to 1.44 MtCO₂ by 2050.

Crouch gait is one of the most common gait abnormalities. In the literature, there are few works related to the crouch gait recognition. In this work, we aim at the integration of a framework for multiclass crouch gait recognition using synthetic eight-DoF model gait data in anatomical space. The main contribution of our study is the joint function generator algorithm, which could be used to generate other physiological signals; and the gait data validation stage. In addition, we use a feature selection method and a body region segmentation approach to improve classification performance. We generated a gait dataset containing 25 join functions of each DoF for each gait class (4 crouched and normal). The algorithms that we evaluated for classification were: K-nearest neighbors (kNN), Naive Bayes (NB), discriminant analysis (DA), decision trees (DT), and an artificial neural network (ANN). We calculated the metrics: accuracy (Acc), recall (R), specificity (SP), precision (P) and the F-measure (FM) to asses the classification performance. In general, for this work, the best algorithms were discriminant analysis and artificial neural networks. The main results of the proposed recognition framework provide evidence that can be used in real clinical settings to diagnose and make decisions about treatments related to crouch gait diseases.

The aim of this study is to explore the influence of Environmental, Social, and Governance (ESG) factor on business failure in Brazil by employing advanced machine learning techniques. We collected data from 235 companies and conducted Principal Component Analysis (PCA) on 40 variables already used in the bankruptcy failure literature, resulting in the formation of 7 variables that predict business failure. The results indicate that ESG factor significantly predict business failure in Brazil. This study has implications for investors, policymakers, and business leaders, offering a more precise tool for risk assessment and strategic decision-making.

AI-driven applications are rapidly growing, and more applications are joining the market competition. As a result, the AI-as-a-Service (AIaaS) model is experiencing rapid growth. Many of these AIaas-based applications are not properly optimized initially. Once they start experiencing a large volume of traffic, different challenges start revealing themselves. One of these challenges is maintaining a profit margin for the sustainability of the AIaaS application-based business model, which depends on the proper utilization of computing resources. This paper introduces the Resource Award Predictive (RAP) model for AIaaS cost optimization called RAP-Optimizer. It is developed by combining a Deep Neural Network (DNN) with the simulated annealing optimization algorithm. It is designed to reduce resource underutilization and minimize the number of active hosts in cloud environments. It dynamically allocates resources and handles API requests efficiently. The RAP-Optimizer reduces the number of active physical hosts by an average of 5 per day, leading to a 45% decrease in server costs. The impact of the RAP-Optimizer has been observed over a 12-month period. The observational data show a significant improvement in resource utilization. It effectively reduces operational costs from $2,600 to $1,250 per month. Furthermore, the RAP-Optimizer increases the profit margin by 179%, from $600 to $1,675 per month. The inclusion of the Dynamic Dropout Control (DDC) algorithm in the DNN training process mitigates overfitting, achieving a 97.48% validation accuracy and a validation loss of 2.82%. These results indicate that the RAP-Optimizer effectively enhances resource management and cost-efficiency in AIaaS application, making it a valuable solution for modern cloud environments.

This paper starts with an assumption, that there are strong strategic benefits in having boards with members of different backgrounds, experience, and particularly gender, which is reflected in better corporate governance. The central research question of this paper is: To what extent have Estonian listed companies complied with the requirements of the EU law on Gender Balance on Boards compared to other Nordic countries? In the case of Estonia, all listed companies on the Tallinn Stock Exchange are included in our sample. The authors purposely focus on developing ownership strategies to improve diversity on the boards of corporations with concentrated ownership structures, without regard to arguments that emphasize equal rights or a feminist agenda. The methodology part of the study analyses corporate governance in Estonia, followed by a comparative analysis of Nordic countries (Finland, Sweden, Norway) that have successfully increased female representation on boards, providing valuable insights for Estonia’s efforts to enhance gender diversity. The results of the current research are a valuable analytical resource for the Estonian business community, as well as for policymakers.

The consumption of contaminated water contributes to the global burden of diarrhea and other water-borne diseases, especially among young children. While the decentralized solar water disinfection treatment system remains a viable option to have safe drinking water, our understanding of the effects of household water treatment before use on willingness to accept and adopt a decentralized solar water disinfection treatment system is limited. In this study, a complementary log-log regression analysis of the compositional and contextual factors that systematically vary with willingness to accept and adopt a decentralized solar water disinfection treatment system in the Sawla-Tuna-Kalba (STK) district of Ghana was carried out. Based on our findings, a greater proportion of households’ (97%) are willing to accept and adopt a decentralized solar water disinfection treatment system. Compositional and contextual factors such as age, marital status, education, religion, and geographical location significantly contribute to households’ willingness to accept and adopt a decentralized solar water disinfection treatment system in the STK district of Ghana. Households that treat their water before use (66%, P<0.05) are more likely to accept and adopt a solar water disinfection treatment system compared to those that do not treat their water before use. The findings therefore suggest that scaling up solar water disinfection systems in the STK district is sacrosanct, and highlight the necessity to analyze compositional and contextual factors influencing willingness to accept and adopt solar water disinfection systems. Our findings will inform policies and programs aimed at implementing solar water disinfection systems in the study area to improve the health of communities that must rely on poor quality drinking water sources.

In the present paper and inspired with a similar construction on Hermite functions, we construct two series of Gelfand triplets each one spanned by Laguerre-Gauss functions with a fixed positive value of one of their parameters, considered as the fundamental one. We prove the continuity of different types of ladder operators on these triplets. Laguerre-Gauss functions with negative value of the fundamental parameter are proven to be continuous functionals on one of these triplets. Different sorts of coherent states are considered and proven to be in some spaces of test functions corresponding to Gelfand triplets.

Background: Upper tract urothelial carcinoma (UTUC) presents significant challenges in prognostication due to its rarity and complex anatomy. This study introduces a novel approach integrating perirenal fat (PRF) radiomics with clinical factors to enhance prognostic accuracy in UTUC. Methods: The study retrospectively analysed 103 UTUC patients who underwent radical nephroureterectomy. PRF radiomics features were extracted from preoperative CT scans using a semi-automated segmentation method. Three prognostic models were developed: clinical, ra-diomics, and combined. Model performance was assessed using concordance index (C-index), time-dependent Area Under the Curve (AUC), and integrated Brier score. Results: The combined model demonstrated superior performance (C-index: 0.784, 95% CI: 0.707-0.861) compared to the radiomics (0.759, 95% CI: 0.678-0.840) and clinical (0.653, 95% CI: 0.547-0.759) models. Time-dependent AUC analysis revealed the radiomics model's particular strength in short-term prognosis (12-month AUC: 0.9281), while the combined model excelled in long-term predictions (60-month AUC: 0.8403). Key PRF radiomics features showed stronger prognostic value than traditional clinical factors. Conclusions: Integration of PRF radiomics with clinical data significantly improves prognostic accuracy in UTUC. This approach offers a more nuanced analysis of the tumour microenvironment, potentially capturing early signs of tumour invasion not visible through conventional imaging. The semi-automated PRF segmentation method presents advantages in reproducibility and ease of use, facilitating potential clinical implementation.

The RSA cryptosystem has long been a fundamental component of contemporary public key in- frastructure. However, recent developments in quantum computing and mathematical theory have introduced signicant challenges to its security. A fully operational quantum computer would allow for the application of Shor's algorithm, enabling the ecient factoring of large integers, thereby com- promising the integrity of RSA and other cryptographic methods dependent on discrete logarithms. Although Grover's algorithm poses a comparatively smaller threat to symmetric encryption systems, it still presents a vulnerability by accelerating key search processes. The looming threat from quan- tum technologies has driven a surge in research aimed at developing quantum-resistant cryptography. These eorts focus on cryptographic techniques grounded in error-correcting codes, lattice structures, and multivariate public key systems, all of which leverage the complexity of NP-hard problemssuch as solving multivariate quadratic equationsto preserve security in a post-quantum world. This pa- per reviews the current progress in quantum-resistant encryption methods, particularly emphasizing the role of robust trapdoor functions. Additionally, it provides a comprehensive analysis of critical multivariate cryptosystem frameworks, such as Matsumoto-Imai, Oil and Vinegar, and Polly Cracker schemes. It also examines advances in lattice-based systems like Kyber and Crystals-Dilithium, which are undergoing assessment by NIST for potential standardization. As quantum computing continues to evolve, the demand for cutting-edge cryptographic solutions to safeguard digital communications grows increasingly pressing.

The EXO+ hip protector was developed to minimize the risk of hip fractures in seniors following falls. This study evaluated its usability in a Singapore nursing home from the perspective of staff, focusing on Learnability, Efficiency, and Satisfaction. A mixed-method design was employed, with nursing home residents wearing the EXO+ daily for 8 weeks. Quantitative data on wear time was collected alongside qualitative insights from four repeated focus group discussions with care staff. In total, 15 residents (including 8 residents with cognitive impairments) and 9 staff participated. The analysis revealed six themes and fourteen subthemes related to EXO+’s usability. Residents wore the device for an average of 1.54 hours per day, with those having cognitive impairments showing increased wear times, especially in the mornings and on weekdays. Findings indicated that the EXO+ was learnable and satisfactory for nursing home use, provided there are adequate supporting features. The efficiency of the EXO+ was not fully measured since no falls were reported during the study period. The study offers valuable insights for potential design modifications and workflow adaptations to enhance the implementation of hip protectors in healthcare settings, highlighting facilitators and barriers to support device uptake.

Accurate forecasting and efficient management of commodities are crucial for stakeholders in the industry, given the volatile nature of these markets. This study addresses these needs by leveraging advanced forecasting techniques and machine learning models to predict prices and enhance supply chain efficiency. TThe focus is on utilizing ARIMA, SARIMAX, and LSTM models to analyze historical trading data for commodities such as cocoa, coffee, and sugar sourced from Kaggle’s comprehensive dataset. The research applies ARIMA and SARIMAX models to forecast price trends, overcoming initial challenges related to data index frequency and seasonality. LSTM models are employed for more nuanced demand forecasting, particularly for random-length lumber, demonstrating the model's capability to predict future market trends accurately. The study highlights significant improvements in prediction accuracy and supply chain management through meticulous feature engineering and model optimization, offering valuable insights for strategic decision-making in the supply chain sector.

This paper employs the Finite Element Method (FEM) to simulate and analyze the effects of corrosion defect parameters on the stress and failure pressure of pipelines. It investigates how the boundary conditions of the pipeline model influence stress and examines the sensitivity of failure pressure to corrosion defect parameters. A nonlinear regression equation has been developed from a dataset obtained through simulation experiments to predict the failure pressure of corroded pipelines. To validate the effects of corrosion defect parameters on failure pressure, a hydrostatic test platform for an API 5L X65 pipeline with corrosion defects was established to measure stress levels and failure pressures across varying corrosion defects. The study reveals that failure pressure is negatively correlated with corrosion length and depth, while positively correlated with corrosion width. Among these parameters, corrosion depth exerts a more significant influence on the pipeline's failure pressure than corrosion length and width. Within the range of corrosion defect parameters examined, the maximum deviation of the prediction equation's results from the simulation results is 8.71%, with an average deviation of 5.81%. The standard deviation of the fitted residuals is 0.01837. Additionally, the maximum deviation between the predicted results and experimental measurements is 8.39%, with an average deviation of 7.77%. The strong agreement between the predicted results from the equation and the actual measured data underscores the effectiveness of the nonlinear regression equation.

Ensuring adequate food supplies has become a critical issue with a rapidly growing global population and increasing demand for protein-rich foods. In Baghlan province, Afghanistan, many poultry farms have been forced to cease operations due to insufficient infrastructure and resources, leaving a significant gap in meeting the region's growing demand for poultry products. This study aims to identify the key factors affecting the sustainability and development of broiler farms in Baghlan province as perceived by farm owners. The primary research tool was a researcher-developed questionnaire, whose validity and reliability were confirmed. The study population comprised all broiler farm owners in Baghlan Province, from which a sample of 91 was selected using the Morgan table and complete enumeration sampling method. The findings revealed that approximately 54% of respondents expressed moderate satisfaction with their farms. Importance-Performance Analysis (IPA) was conducted to assess the relative importance and performance of various factors affecting the development and sustainability of the farms. The results indicated that having a qualified expert, the farmer's educational level, the poultry house ventilation system, and being a local resident were considered highly important factors for farm success but were underperforming. Based on limited existing research in the region, these findings provide valuable insights for enhancing poultry farm development and food security in Baghlan province and throughout Afghanistan. The study offers specific recommendations for addressing the identified challenges and improving the sustainability of poultry farming in the region.

Objective: The article presents a methodology for the design and validation of two assessment instruments: one focused on competency development and the other on knowledge acquisition on sustainability, aimed at nutrition students from a Mexican university. Method: These instruments were adapted using the triangulation methodology and administered to a group of 45 students after receiving an intervention based on the systemic approach to nutrition. Following the intervention, an analysis of the results was carried out to validate the reliability and validity of the instruments, and to evaluate the impact of the intervention compared to a control group. Results: The findings indicated that the dimensions used in the instrument are suitable for evaluating competency development, additionally, it was found that both instruments are reliable tools for assessing competency development and knowledge in sustainability among nutrition students, concluding that they have the potential to be used for evaluating education in sustainability. Furthermore, the results reveal that the application of an intervention based on the systemic approach to nutrition has a positive impact on the development and acquisition of sustainable nutrition education. The students who received the intervention achieved significantly higher scores in all sustainability sub-competencies (W=100, p=0.019), as well as in the total percentage of sustainable knowledge (w=53, n=0.01). Conclusions: The results of this study pave the way for the operationalization of competency and knowledge dimensions in food sustainability, which can contribute to the evaluation of sustainability education and its impact on the training of nutrition students committed to sustainability.

The permeability, heat conductivity and reaction rate will be varied with the change of natural gas hydrate saturation, when thermal fluid injected into the natural gas hydrate reservoirs. In order to characterize the variation of physical field parameters with hydrate saturation, DDF-LBM was applied to simulate the hydrate dissolving process by thermal fluid injection under pore scale modelling. Based on the forced conjugate heat transfer case, the relaxation frequency of the thermal lattice in the pores is corrected. Based on the P-T phase equilibrium relationship of hydrates and considering the heat absorbed by the hydrate reaction, the solid-liquid state of the hydrate lattice is judged in real time, and the dynamic simulation of the heat flow solidification multi-physics field is realized. The simulation results show that the dissolution rate of hydrates by thermal fluid injection was higher than that by heating the hydrate surface alone, and was positively correlated with the hydrate saturation. On the basis of the above results, this paper provided exponential fitting equations between different hydrate saturations and average permeability, effective thermal conductivity, and inherent reaction rate. The fitting results show that saturation has a negative correlation with relative permeability and effective thermal conductivity, and a positive correlation with the inherent reaction rate. The above results can provide a reference basis for accurately describing the heat and mass transfer of natural gas hydrate under macro-scale.

Life Cycle Assessment (LCA) is a widely used tool to measure the environmental sustainability of products or processes. Integrating LCA into the assessment of waste diversion strategies recognizes current waste diversion strategies are insufficient to effectively stem the global impacts of waste particularly with increased pressure to recover organic and inorganic materials to reduce landfill impacts and promote the circular economy. Historically, waste diversion efforts in municipalities and industries focused on higher-profile inorganic wastes, such as plastics and other recyclables. However, organic waste is increasingly identified as a key waste fraction that must be effectively managed and regulated. This research surveys published LCA studies from 2019 to 2023 focusing on the anaerobic digestion (AD) of organic waste. Notable conclusions include the lack of studies comparing AD with the latest treatment options such as co-gasification; the insufficient attention to the LCAs on biogas upgrading methods; and the monetization of LCA results using carbon credits. In addition, more than 50% of reviewed LCA studies concluded the results with a sensitivity analysis which was not a common practice before 2019 in LCA studies in anaerobic digestion. This signifies the increasing need to understand uncertainty in the circumstances governing applying AD to wastes. Finally, neglecting the combined effect of several parameters in sensitivity analysis might have reduced the accuracy of the sensitivity analyses in the reviewed LCAs. Overall, LCAs conducted on AD-related applications vary widely in terms of scope and consistency, implying that the outcomes may not be as applicable as intended. The identified challenges, issues, and other findings related to this research are expected to help standardize LCA procedures as applied to AD to promote greater comparability.

Reticular and cluster materials often feature complex formulas, making a comprehensive overview challenging due to the need to consult various resources. While datasets have been collected for metal-organic frameworks (MOFs), covalent organic frameworks (COFs), and zeolites, among others, there remains a gap in systematically organized information for polyoxometalates. This paper introduces a carefully curated dataset of 1984 polyoxometalate (POM) and related cluster metal oxide formula instances, currently connecting over 2,500 POM material instances. These POM instances incorporate 75 different chemical elements, with compositions ranging from binary to octonary element clusters. This dataset not only enhances accessibility to polyoxometalate data but also aims to facilitate further research and development in the study of these complex inorganic compounds.

Nanopore raw read accuracy has improved to over 99%, making it a potential tool for metabarcoding. For broad adoption, guidelines on quality filtering are needed to ensure reliable taxonomic unit recovery. This study aims to provide those guidelines for a fungal metabarcoding context and to apply them to a case-study of ectomycorrhizae in decaying bark of Fagus sylvatica.We introduce the eNano pipeline to test two standard metabarcoding approaches: (1) reference-based mapping leveraging the UNITE’s species hypothesis system (SH-approach) and (2) constructing 98% OTUs (OTU-approach). We evaluate these approaches using a mock and natural community. Our results demonstrate that both approaches are effective with Nanopore data. When using a reference database, we recommend strict mapping criteria rather than Phred-based filtering. For 98% OTUs, filtering reads at ≥Q25 is recommended. Our case-study reveals that the decay gradient is a primary determinant of community composition, and that specific mycorrhizal fungi colonize decaying bark. Complementing our metabarcoding results with root tip morphotypification, we identify Laccaria amethystina and Tomentella sublilacina as key ectomycorrhizae of saplings on decaying logs. These findings demonstrate that Nanopore sequencing can provide valuable ecological insights and support its broader use in fungal metabarcoding as read quality continues to improve.

Purpose: This pilot ex-vivo study and first clinical experience in Italy evaluate the impact of using pre-implantation crosslinking on all-femtosecond laser-cut corneal allogenic intracorneal ring segments (AFXL CAIRS). Methods: Six human donor eye-bank corneas were used for the preclinical ex-vivo human study. Three corneas were used for AFXL CAIRS. First, they were prepared (D) with the IntraLase™ femtosecond laser (Johnson & Johnson, USA). The allogenic tissue rings were crosslinked before implantation with Riboflavin -UV-A accelerated crosslinking protocol (ACXL) with a 0.1% HPMC Riboflavin isotonic solution (Vibex Rapid, Glaukos-Avedro, USA) and the new KXL UV-A emitter (Glaukos-Avedro, USA). Three corneas were used as recipients (R) of the AFXL CAIRS. After completing the ex-vivo phase, the first two Italian patients were treated. A single ACXL CAIRS was implanted in a 51-year-old male with 53.53 D K steep, 363 m minimum corneal thickness (MCT) and a double ACXL CAIRS was implanted in a 46-year-old male patient with 58.30 D K steep and 443 m MCT. The follow-up was at three months. Results: Crosslinking of the segments enhanced tissue strength and grip, facilitating manipulation and CAIRS insertion into the recipient tunnels and the yellowish color of the crosslinked segments improved visibility. The segment’s thickness and volume remained unaltered during the follow-up. Both patients improved UDVA and BSCVA. K steep and HOAs were reduced and MCT increased. Conclusions Pre-implantation ACXL facilitated CAIRS insertion by increasing the viscoelastic consistency without altering physical characteristics and volume during the follow-up, rendering this important step a promising candidate in method standardization. Functional data and MCT improved significantly without adverse events.

Signal approximation, is the basic idea of signal processing and mathematical analysis where complex signals can be represented with simpler ones. This paper explores unifying two mathematical fundamentals, Dirichlet-type problems, and hyperbolic series identities, into signal approximation. In this paper, we address a well-known classic topic in mathematical analysis - the Dirichlet problem - to improve (the approximation of) signal using Fourier series. We also discuss using hyperbolic series representations to increase signal modeling flexibility. In this way, we hope to create new links that span some distance along this dimension and result in a somewhat theoretical view of signal behavior - present with the mathematical rigor needed for speculation but absent from an understanding capable of appropriately handling real-world complexity. This paper provides a theoretical investigation of the mechanics at play and reveals by analogy how these mathematical tools can potentially advance signal processing techniques and understanding. This paper illustrates Fourier series and fuzzy logic methods by two case studies on signal processing, satellite communication and financial risk measurement. The first case study performs signal approximation using the Fourier series and hyperbolic function identities to provide insight into how signals behave and are modeled. The second case study deals with applying fuzzy logic for risk evaluation of investment portfolios, emphasizing the utility in evasion systems solving uncertainty and imprecision problems related to financial decisions.

In developing nations, such as Ekiti State, Nigeria, the use of remote sensing data, especially satellite and UAV imagery, remains significantly underutilized in land administration. This limits the potential of multi-resolution imagery in land governance and socio-economic development. This study examines factors influencing UAV adoption for land administration in Nigeria, mapping six distinct rural, peri-urban, and urban sites, and creating orthomosaics with resolutions ranging from 2.2cm to 3.39cm. Boundaries were manually delineated from multi-resolution orthomosaics to acquire coordinates, and parcel areas were calculated. Using 0.05m UAV imagery as the reference, the Root Mean Square Error (RMSE) was computed to evaluate 0.1, 0.5, and 1m orthomosaics' accuracy for boundary delineation across the six geographical contexts. The Normalized Parcel Area Error (NPAE) was also calculated to determine how the multi-resolution imageries affected the property areas. Results showed a consistent increase in error with increasing resolution (0.1m to 1m) in formal areas (Igedora, Maryland, Okebola) with physical demarcations, from 0.117m to 1.747m RMSE. In contrast, informal settlements exhibited inconsistent error values, indicating a differing impact of resolution on error between formal and informal areas. The research demonstrates the role of physical demarcations in facilitating cadastral mapping, with formal settlements demonstrating the highest suitability. This study recommends the use of imagery resolution specific to each context, offering guidance for improved land governance. Implications and recommendations include promoting awareness of settlement typologies in aerial imagery use for cadastral mapping, leveraging UAVs for high-resolution aerial imagery, and addressing regulatory challenges to promote UAV adoption. Finally, the RMSE and NPAE values can serve as metrics for assessing different resolution images’ fitness for cadastral mapping.

It seems that natural compounds with bioactivity are the food constituents, present in the diets, that may provide protective effects on health due to their biochemical effects. The main benefits appear to be decline in oxidative stress and inflammation that are crucial in the pathogenesis of obesity, diabetes and metabolic syndrome. Apart from anti-inflammatory activity, these molecules also possess antifungal, and anti-infective preventative effects. Further evidence indicate that these bioactive molecules are the constituents, which are known to increase function of the foods. This review aims to emphasize that plant based diet is rich in phytochemicals such as antioxidants, vitamins, minerals, polyphenolics and flavonoids, amino acids and fatty acids, that are able to cause benefits via these compounds. These bioactive molecules can target mechanisms of oxidative stress and inflammation that are crucial in the management of metabolic syndrome. These antioxidant compounds appear to be essential component of plant based diets and are crucial for healthiness. Most investigators have proposed that bioactive compounds act as nutraceuticals which are able to regulate certain patho-physiological mechanisms responsible for metabolic diseases. The bioactive compoundspresent in the plant based diets are primary and secondary metabolites of nutritive and non-nutritive natural components generating health benefits by preventing or managing chronic disease or its symptoms. The bioactive compounds are known for their beneficial effects to health. Despite their small quantity in the diets, these bioactive compounds may trigger certain mechanisms that may cause alteration with improvement in health. It seems that most of the bioactive compounds co-existing in the plant-based diets, may be extremely healthful. However, if they are taken in excess, they can cause toxic effects. There is an urgent need to evaluate each bioactive compound to establish a beneficial quantity and a threshold of toxicity, while treating metabolic syndrome.

The bi- or multi-nucleated Reed-Sternberg cell (RS) is the diagnostic cornerstone of EBV-positive and EBV-negative classical Hodgkin lymphoma (cHL). cHL is a germinal center (GC) derived B-cell disease. Hodgkin cells (H) are the mononuclear precursors of RS. An experimental model has to fulfill three conditions to qualify as common pathogenic denominator: i) to be of germinal center-derived B-cell origin, ii) to be EBV-negative to avoid EBV latency III expression, and iii) to support permanent LMP1 expression upon induction. These conditions are unified in the EBV-, Diffuse Large B-Cell Lymphoma (DLBCL) cell line BJAB-tTA-LMP1. 3D reconstuctive nanotechnology revealed spatial, quantititive and qualitative disturbance of telomere/shelterin interactions in mononuclear H-like cells, further progression during transition to RS-like cells, including progressive complexity of the karytotype with every mitotic cycle, due to BBF (breakage/bridge/fusion) events. The findings of this model were confirmed on diagnostic patient samples and correlate with clincal outcome. Moreover, in vitro signficant disturbance of the lamin AC/telomere interaction progressively occured. In summary, our research over the past three decades identifed cHL as the first lymphoid maligancy driven by a disturbed telomere/shelterin/lamin AC interaction, generating the diagnostic RS. Our findings may act as trailblazer for tailored therapies in refractory cHL.

The medical research field has been tremendously galvanized to improve the prediction of therapy efficacy by the revolution in artificial intelligence (AI). An earnest desire to find better ways to predict the effectiveness of therapy with the use of AI has propelled the evolution of new models in which it can become more applicable in clinical settings such as breast cancer detection. However, in some instances, the U.S. Food and Drug Administration was obliged to back some previously approved inaccurate models for AI-based prognostic models because they eventually produce inaccurate prognoses for specific patients who might be at risk of heart failure. In light of instances in which the medical research community has often evolved some unrealistic expectations regarding the advances in AI and its potential use for medical purposes, implementing standard procedures for AI-based cancer models is critical. Specifically, models would have to meet some general parameters for standardization, transparency of their logistic modules, and avoidance of algorithm biases. In this review, we summarize the current knowledge about AI-based prognostic methods and describe how they may be used in the future for predicting antibody-drug conjugate efficacy in cancer patients. We also summarize findings of recent late-phase clinical trials using these conjugates for cancer therapy.

A cross-sectional study was organised to investigate the range, prevalence, and associated factors for occupational health problems related to dental practice among Cambodian dentists; Participants underwent a face-to-face interview to explore dentists work-related health problems; 106 Cambodian dentists participated in this study, 68.9% were male. Ages ranged from 29 to 71 years, averaging 36.1 years, with the majority (77.4%) in the 29-40 age group. They had 5 to 18 years of practice experience, and worked an average of 52.2 hours per week. Commonly reported health issues included back pain (88.7%), headaches (81.1%), shoulder pain (78.3%), arm/hand pain (57.5%), and eye problems (48.1%). 38.7% of participants felt stressed and 19.8% depressed. Some reported suicidal thoughts and taking medication for depression. Despite these challenges, 91.5% enjoyed practicing dentistry; Findings highlight the need for interventions and strategies to address the physical and mental well-being of Cambodian dentists. By addressing these issues, steps can be taken to enhance the working conditions and professional satisfaction of dental professionals, ultimately benefiting both the practitioners and their patients.

By using the method of high-temperature solid-phase reaction, the new piezoceramic SrBi2Nb2-2xWxSnxO9 was obtained, in which partial substitution of niobium (Nb) atoms with Sn4+ and W6+ atoms in the compound SrBi2Nb2O9 took place in the octahedra of the perovskite layer (B-position). X-ray diffraction studies have shown that these compounds are single-phase SrBi2Nb2-2xWxSnxO9 (x = 0.1, 0.2) and two-phase SrBi2Nb2-2xWxSnxO9 (x = 0.3, 0.4), but they all had the structure of Aurivillius – Smolensky phases (ASPs) with similar parameters of orthorhombic elementary cells, corresponding to the spatial group A21am. The dependences on temperature of the relative permittivity ε/ε0 and the tangent of the loss angle tan d at different frequencies were measured.

(1) Background: Impaired colonic motility is common in patients with ulcerative colitis. The patients often complain the symptoms related to colonic motility dysfunction such as loose stools or diarrhea and these symptoms make therapy more challenging; (2) Methods: This study aimed to examine the movement of the colon in mice with ulcerative colitis and determine if the glial-derived neurotrophic factor (GDNF) may enhance aberrant colonic motility through Connexin 43 (Cx43).A model of ulcerative colitis was created using 32 male C57BL/6 mice. Colonic transit time was measured in all mice, and colon tissues were examined using hematoxylin-eosin staining (HE) and transmission electron microscopy (TEM). The levels of GDNF and Cx43 were determined via immunohistochemistry, immunofluorescence, and RT-PCR techniques. The levels of C-reactive protein (CRP),TNF-α, IL-1β, and IL-6 were quantified using the enzyme-linked immunosorbent assay (ELISA); (3) Results: 1) The UC models were effectively established by administering dextran sodium sulfate (DSS) water. Mice with ulcerative colitis exhibited a delay in colonic transit time. 2) GDNF can improve the delayed movement of the colon in mice with UC by activating the Cx43 protein; (4) Conclusions: GDNF can ameliorate impaired colonic motility in mice with ulcerative colitis, and its mechanism is related to the activity of Cx43. GDNF could potentially be utilized as a therapeutic option for addressing aberrant colonic motility in patients.

Human-robot collaboration is a new trend in modern manufacture. The safety or human protection is of great significance due to the share of the same workshop between humans and robots. To achieve effective protection, in this paper, a contact force sensor based on the 8-shaped wound polymer optical fiber is proposed. The 8-shaped wound structure can convert the normal contact force to the shrinkage of the 8-shaped optical fiber ring. The macro-bending loss of the optical fiber is used to detect the contact force. Compared with conventional sensors, the proposed scheme has the advantage of high flexibility, low cost, fast response, high repeatability, which shows the great promise on actively alarming collision and passively reducing the hurt.

Introduction Delayed intra-operative neuro-monitoring (IOM) events can be defined as loss of responses, after baseline traces were present upon completion of instrumentation and deformity correction. This study reports this subgroup of events, assesses their incidence and evaluates potential causative factors. Methods We reviewed 1881 consecutive patients who underwent spinal deformity correction between 2011 and 2021. Surgeries were performed using a standardised anaesthetic regimen and multimodal IOM protocol. Results 44 patients (2.4%) had an IOM event. 9 patients (6F:3M) had a true positive delayed event after posterior segmental pedicle screw instrumentation. Mean age was 12.5 (2-17) years with mean thoracic Cobb angle of 78° (51-94°). Mean estimated blood loss was 28%. Titanium beam shaped rods were utilised in 3 patients and 5.5mm titanium rods in 5 patients. Motor evoked potentials were lost on average 18 (10-30) minutes after completion of instrumentation. 7 patients had bilateral loss and 2 patients had unilateral loss of MEP traces. Sensory evoked potential amplitude was preserved in all cases. There was a transient latency shift in one patient. MEPs returned within 20 minutes of rod release. The rod was re-contoured to incorporate more scoliosis in 1 patient. 2 patients had beam rods exchanged to 5.5mm titanium rods. 6 patients had rod removal with retention of pedicle screws and staged completion of instrumentation at mean 5 (1-7) days later. No patients had a postoperative neurological deficit. Conclusion Delayed IOM loss represented 15% of all events. Increased cord tension and hypo-perfusion were considered likely mechanisms. Rod release restored MEPs in all cases. The timing of events emphasises the importance of continued monitoring for minimum 30 minutes after completion of spinal instrumentation or until wound closure.

This paper introduces a quantitative methodology for assessing drone pilot performance, aiming to reduce drone-related incidents by understanding the human factors influencing performance. The challenge lies in balancing evaluations in operationally relevant environments with those in a standardized test environment for statistical relevance. The proposed methodology employs a novel virtual test environment that records not only basic flight metrics but also complex mission performance metrics, such as the video quality from a target. A group of Belgian Defence drone pilots were trained using this simulator system, yielding several practical results. These include a human performance model linking human factors to pilot performance, an AI co-pilot providing real-time flight performance guidance, a tool for generating optimal flight trajectories, a mission planning tool for ideal pilot assignment, and a method for iterative training improvement based on quantitative input. The training results with real pilots demonstrate the methodology’s effectiveness in evaluating pilot performance for complex military missions, suggesting its potential as a valuable addition to new pilot training programs.

The amplified production of reactive oxygen species (ROS) or reduced oxidant protection is described as oxidative stress, which contributes to the increase in several disorders, notably obesity and neurodegenerative diseases. Flavonoids are polyphenol compounds found in a variety of foods of plant origin, such as berries, with a basic chemical structure consisting of an aromatic ring linked to a benzene ring and a pyran ring. Flavonoids have neuroprotective activity and promote synaptogenesis and neurogenesis and are also capable of inhibiting oxidative stress and neuroinflammation. Anthocyanins are water-soluble natural pigments that belong to the flavonoid class and have antioxidant and anti-inflammatory properties, helping to reduce chronic inflammation associated with various health conditions, including obesity. In addition, flavonoids and anthocyanins can modulate the expression of genes involved in lipid metabolism, promoting the oxidation of fatty acids and the reduction of lipid synthesis, contributing to the reduction of body fat accumulation. One of the most abundant flavonoids in the natural environment, quercetin, is found mainly in the form of O-glycoside derivatives and rarely as an aglycone. Quercetin has antioxidant properties, against diseases such as cancer; It also acts to combat allergic reactions, inflammatory processes, platelet disorders, obesity, hypertension and atherosclerotic and hypercholesterolemic disorders. Anthocyanins can activate intracellular signaling pathways, such as the AMPK (AMP-activated protein kinase) pathway, which has a crucial role in regulating energy metabolism and glycemic homeostasis. Activation of this pathway increases glucose uptake and fatty acid oxidation, favoring weight loss and improving insulin sensitivity. This review article aims to analyze the effects of using berries in controlling obesity and its role as a reducing factor for mild cognitive decline. A systematic review will be carried out following the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyze. We will search the following electronic databases: PubMed (Medline), Science Direct, Scopus and Web of Science.

Sialyllactose (SL) is a functional human milk oligosaccharide essential for immune support, brain development, intestinal maturation, and antiviral defense. However, despite its established health benefits, the effect of SL on exercise performance and muscle mass in mice remains unknown. Here, we aimed to investigate, for the first time, the effects of 6′-SL on muscle functions. Seven-week-old male C57BL/6J mice were administered 100 mg/kg 6′-SL for 12 weeks, after which exhaustive treadmill performance, muscle strength, and muscle phenotype were examined. The administration of 6′-SL significantly improved exhaustive treadmill performance metrics, including distance and exhaustion time. Grip strength was also increased by 6′-SL administration. Additionally, 6′-SL increased muscle mass in both the gastrocnemius (GAS) and soleus. 6′-SL administration led to an increase in the minimum Feret’s diameter and the protein expression of total myosin heavy chain in the GAS muscle. In conclusion, 6′-SL administration in vivo led to increased running distance and time by increasing muscle mass and strength. These findings collectively indicate that 6′-SL is a potential agent for improving muscle health and exercise performance.

In this article, we consider a class of nonsmooth interval-valued multiobjective semi-infinite programming problems with vanishing constraints (in short, NIMSIPVC). We introduce the VC-Abadie constraint qualification (in short, VC-ACQ) for NIMSIPVC and employ it to establish Karush-Kuhn-Tucker (in short, KKT)-type necessary optimality conditions. Related to NIMSIPVC, we formulate interval-valued vector Lagrange type dual and scalarized Lagrange type dual problems. Subsequently, we establish weak, strong, and converse duality results relating NIMSIPVC and corresponding dual problems. In addition, we introduce the notions of saddle points for interval-valued vector Lagrangian and scalarized Lagrangian of NIMSIPVC. Moreover, we establish the saddle point optimality criteria for NIMSIPVC. Various non-trivial examples are provided to demonstrate the validity of established results. To the best of our knowledge, optimality conditions, Lagrange type duality, and saddle point optimality criteria for NIMSIPVC have not been investigated yet via Clarke subdifferentials.

Traumatic brain injury (TBI) is an important global clinical issue, requiring not only prevention but also effective treatment. Following TBI, diverse parallel and intertwined pathological mechanisms affecting biochemical, neurochemical, and inflammatory pathways can have severe impact on the patient’s quality of life. The current review summarizes evidence for utility of amantadine in TBI in connection to its mechanism of action. Amantadine combining multiple mechanisms of action may offer both neuroprotective and neuroactivating effects in TBI patients. Indeed, use of amantadine in TBI has been encouraged by several clinical practice guidelines/recommendations. Amantadine is also available as infusion which may be of particular benefit in unconscious patients with TBI, due to immediate delivery to the central nervous system and the possibility of precise dosing. In other situations, orally administered amantadine may be used. There are several questions that remain to be addressed: Can amantadine be effective in disorders of consciousness requiring long-term treatment and in combination with drugs approved for treatment of TBI? Do the observed beneficial effects of amantadine extend to disorders of consciousness due to factors other than TBI? Well controlled clinical studies are warranted to ultimately confirm its utility in the TBI and provide answers these questions.

Investigation of functional magnetic resonance imaging (fMRI) data with machine learning (ML) techniques, including also deep learning (DL) methods, have been widely used to study Autism Spectrum Disorder (ASD). This disorder is characterized by symptoms that affect the individual’s behavioral aspects and social relationships. Early diagnosis is crucial for intervention, but the complexity of ASD poses challenges for treatment development. This study compares traditional ML techniques with deep learning (DL) methods in the analysis of functional connectivity measures obtained from the time series of multicentric ABIDE dataset. Specifically, Support Vector Machines (SVM) classifiers, with both linear and Radial Basis Function (RBF) kernels, as well as eXtreme Gradient Boosting (XGBoost) classifiers, are compared against the TabNet classifier, which is a DL architecture customized for tabular data analysis and a Multi Layer Perceptron (MLP). The findings suggest that DL classifiers may not be optimal for the type of data analyzed, as their performance trails behind that of standard classifiers. SVMs achieve performances, in terms of AUC, around 75%, compared to the best TabNet and MLP results, which are 65% and 71%, respectively. Additionally, this work investigates the brain regions that contribute most to the classification task, which are found to be those primarily responsible for sensory and spatial perception, as well as attention modulation, known to be altered in ASD.

This study evaluates the effectiveness of Darknet53 and VGG16 pretrained models to develop a hybrid ensemble model named HybridPlantNet23 deep learning model for effective plant disease classification, using the publicly available dataset ‘A Dataset of Leaf Images’ from Mendeley webpage. The preprocessing of the dataset involved resizing the images to the respective requirements of the pretrained models to develop a combined datastore which served as the input. All experiments were conducted using MATLAB R2023b deep network designer interface. Model performances were evaluated based on the conventional accuracy, precision, recall, and F1-score. VGG16 achieved the highest accuracy (97.25%), precision (96.30%), recall (96.96%), and F1-score (96.55%), outperforming both Darknet53 and the ensemble HybridPlantNet23 model. Despite the advanced architecture of the ensemble model, its performance (accuracy: 95.37%, precision: 93.56%, recall: 94.20%, F1-score: 93.74%) was not significantly superior, thus highlighting scientific reasons behind this development. Recommendations for future works were provided accordingly.

Wolfram syndrome 1 (WS1) is an uncommon autosomal recessive neurological disorder that is characterized by diabetes insipidus, early-onset non-autoimmune diabetes mellitus, optic atrophy, and deafness (DIDMOAD). Other clinical manifestations are neuropsychiatric symptoms, urinary tract alterations, and endocrinological disorders. The rapid clinical course of WS1 results in death by the age of 30. Severe brain atrophy leads to central respiratory failure, which is the main cause of death in WS1 patients. Mutations in the WFS1 gene, located on chromosome 4p16, account for approximately 90% of WS1 cases. The gene produces wolframin, a transmembrane glycoprotein widely distributed and highly expressed in retinal, neural, and muscular tissues. Wolframin plays a crucial role in the regulation of apoptosis, insulin signaling, and ER calcium homeostasis, as well as the ER stress response. WS1 has been designated as a neurodegenerative and neurodevelopmental disorder due to the numerous abnormalities in the ER stress-mediated system. WS1 is a devastating neurodegenerative disease that affects patients and their families. Early diagnosis and recognition of the initial clinical signs may slow the disease's progression and improve symptomatology. Moreover, it is critical to provide genetic counseling to the patient's relatives in order to reduce the risk of transmitting WS1. Regrettably, there are currently no specific drugs for the therapy of this fatal disease. A better understanding of the etiology of WS1 will make possible the development of new therapeutic approaches that may enhance the life expectancy of patients. This review will examine the pathogenetic mechanisms, development, and progression of neuropsychiatric diseases commonly associated with WS1. A thorough understanding of WS1's neurophysiopathology is critical for achieving the goal of improving patients' quality of life and life expectancy.

Hypoxia contributes to tumor progression and metastasis, and hypoxically dysregulated RNA molecules may thus be implicated in poor outcomes. Canine oral melanoma (COM) has a par-ticularly poor prognosis, and some hypoxia-mediated miRNAs are known to exist in this cancer; however, equivalent data on hypoxically dysregulated other non-coding RNAs (ncRNAs) are lacking. Accordingly, we aimed to elucidate non-miRNA ncRNAs that may be mediated by hy-poxia, targeting primary-site and metastatic COM cell lines and clinical COM tissue samples in next generation sequencing (NGS), with subsequent qPCR validation and quantification in COM primary and metastatic cells, plasma and extracellular vesicles (EVs) for any identified ncRNA of interest. Findings suggested that a number of non-miRNA ncRNA species are hypoxically up- or downregulated in COM. We identified one ncRNA, the long ncRNA fragment EN-SCAFT00000084705.1, as a molecule of interest due to its consistent downregulation in COM tissues, hypoxically and normoxically cultured primary and metastatic cell lines, when compared to the oral tissues from healthy dogs. However, this molecule was undetectable in plasma and plasma EVs, suggesting that its expression may be tumor tissue-specific and it has little potential as a biomarker. Here, we provide evidence of hypoxic transcriptional dysregulation for ncRNAs other than miRNA in COM for the first time, and suggest that ncRNA ENSCAFT00000084705.1 is a molecule of interest for future research on the role of the transcriptome in hypoxia-mediated progression of this aggressive cancer.

A field study was conducted at the Eastern Mediterranean Agricultural Research Institute, Adana, Turkey, during the 2022/23 cropping seasons to examine the impact of Mepiquat chloride dosage applied at different intervals to optimize growth, yield characteristics enhancement and improve fiber quality attributes of cotton. The study employed a split-split plot design with three replications. The results revealed that differences among treatments were not statistically significant for most parameters investigated. The observed discrepancies indicated that all treatments tested in 2012 delayed cotton development, whereas in 2023, they led to premium fibers. In 2023, the fiber percentage was highest in the control treatment, and in 2022, it was highest at 400 mL·ha-1. Across all years, the Selin variety exhibited the highest fiber percentage when Mepiquat chloride was applied at the bolling stage. The shoot-root ratio was highest at 400 mL·ha-1 during harvest in 2022 and at 0 mL·ha-1 during the reproductive stage in 2023. Overall, the results demonstrated that the application of Mepiquat chloride altered the physiological characteristics of the cotton plants, leading to bottom-up effects on cotton performance. This implies that the effectiveness of Mepiquat chloride in cotton production to alleviate drought stress is influenced by weather-related conditions.

Due to the manufacturing process of Additive Manufacturing (AM) Parts the mechanical properties cannot be predicted. The scope of the present work is to propose a predictive tool for the mechanical properties of AM parts through the use of Adaptive Neuro-Fuzzy Inference System (ANFIS). The Adaptive Neuro-Fuzzy Inference System (ANFIS) combines neural networks with fuzzy logic to generate a mapping between the inputs and the output. Real data experiments from different AM technologies and materials compose the initial dataset so a detailed investigation is conducted to gain knowledge of how different parameters affect the UTS, the elongation and the Young modulus of each experiment. Experimental data were also use to train, check and verify the methodology. The method is tested for FFF technology, PLA material and 3 point bending stress, with interesting results.

High entropy alloys have excellent wear resistance, so they have great application prospects in the fields of wear resistance and surface protection. In this study, the wear resistance of FeNiCrCoCu high entropy alloy coating was systematically analyzed by molecular dynamics method. FeNiCrCoCu high entropy alloy was used as a coating material to adhere to the surface of Cu matrix. The friction and nano indentation simulation of this coating material were carried out by controlling the ambient temperature. The influence of temperature on its friction properties was analyzed from five aspects: lattice structure, dislocation evolution, friction coefficient, hardness and elastic modulus. The results show that with the increase of temperature, the disorder of the lattice structure increases, which leads to the increase of the tangential force and the friction coefficient in the friction process. At 300K and 600K, the ordered lattice structure of the high entropy alloy coating material is basically the same, and thus their hardness is basically the same. However, the dislocation density at 600K is significantly reduced compared with that at 300K, resulting in the increase of the elastic modulus of the material from 173GPa to 219GPa. At the temperatures of 900K and 1200k, lattice disorder take place rapidly, and dislocation density also decreases significantly, resulting in a significant decrease in the hardness and elastic modulus of the material. When the temperature reaches 900K, the wear resistance of the FeNiCrCoCu high entropy alloy coating decreases sharply. This work is of great value to the analysis of wear resistance of high entropy alloy at high temperature.

Diffuse type of gastric cancer (GC) has an increasing prevalence worldwide, especially in Western countries, is usually diagnosed at advanced stages, and has no efficacious treatment options. Epidemiological studies have reported an increased mortality from GC after occupational exposure to well-studied pro-carcinogen that are metabolically activated by Cyp P450 enzymes through aryl hydrocarbon receptor (AhR). Substantial studies support the involvement of AhR in gastric carcinogenesis. However, little is known about the role of AhR in diffuse GC, as compared to intestinal GC. In a cohort of 29 gastric tumors, we described a significantly increased AhR protein and mRNA expression levels in GCs, independently of subtypes and clinical parameters. AhR and RhoA mRNA expression were correlated in diffuse GC. Further, our study characterized how AhR affects gene expression in diffuse GC. Using qRT-PCR, we compared the expression levels of AhR, Cyp1A1 and Cyp1B1 to the expression of genes in a panel previously described. In diffuse GC, Cyp1A1 expression correlated with genes involved in IGF signalling, EMT (VIM), migration (MMP2). In an in vitro assay using the poorly differentiated KATOIII epithelial cell line, two well-known ligands for AhR (TCDD and BaP) induced mRNA expression of CYP1A1, IL1b, as well as UGT1, NQO1 and AhRR to a lower extent. We also observed a strong increase in Cyp1B1 expression in diffuse GC, along with a lower TCDD-increased Cyp1B1 expression as compared to Cyp1A1 in KATOIII cells, and immunostaining in stromal cells. In intestinal GC, Cyp1B1 inversely correlated with several genes including IDO1 (generating endogenous kynurenin-e AhR ligand). Our data provide evidence for a major role of AhR in GC, as an environmental xenobiotics receptor, through different mechanisms and pathways in diffuse and intestinal GC. Our results support continued efforts to clarify the identities of exogenous AhR ligands in diffuse GC in order to define new therapeutic strategies.

Drought stress severely affects the normal growth and development of wheat, leading to reduced yields and quality. To address the lag and limitations of traditional drought monitoring methods, this paper proposes a drought stress monitoring model for winter wheat during critical growth stages based on deep learning. By acquiring drought stress images of winter wheat during three critical growth stages: rise-jointing, heading-flowering and flowering-maturity, a dataset correlating these images with soil moisture monitoring data was established. The DenseNet121 network model was selected as the base network to extract phenotypic features of winter wheat under drought stress. Variables such as the model's training approach, changes in learning rate, and whether an attention mechanism was included, were used to conduct eight sets of model training and optimization strategy experiments. A drought stress monitoring model for winter wheat during critical growth stages based on an improved DenseNet-121 was constructed. The results showed that the average recognition accuracy of the eighth group in the test set reached 94.67%, indicating good application prospects in the recognition of drought stress levels during critical growth stages of wheat.

Mesoscale eddies wield a significant influence on primary productivity in marine ecosystems by regulating nutrient availability, vertical mixing, and phytoplankton growth dynamics. In this study, we tracked and analyzed the effects of mesoscale eddies on net primary productivity (NPP) across the Bay of Bengal (BoB) using remote sensing data. Dividing the Bay into six zones based on mesoscale eddy frequency and distribution, our analysis identified 1880 anticyclonic eddies and 1972 cyclonic eddies during January 2010–March 2020 monitoring period. Cyclonic eddies predominantly occurred in the western part of the bay - along the east coast of the Indian Peninsula; while anticyclonic eddies were also observed, albeit in lesser proportions. Among eddies lasting one-week, anticyclonic eddies comprised approximately 48.81%, while cyclonic eddies constituted approximately 51.19% of the total. Analysis of the NPP distribution across the months revealed intriguing patterns. Mean values of NPP fluctuated throughout the year, with the highest value observed in August (748.51 mg C/m2/day) and the lowest in May (519.26 mg C/m2/day). The findings indicate a complex relationship between eddy distribution and NPP, with Zones 1 and 2 exhibiting a higher occurrence of cyclonic eddies, correlating with greater NPP and its variability. Conversely, Zones 3 and 5 display increased eddy activity despite fewer cyclonic eddies and lower NPP levels. Zone 6 shows reduced eddy activity and NPP, while Zone 4 presents moderate eddy activity alongside lower NPP. In contrast, Zones 3 to 5 exhibit lower NPP levels despite exhibiting heightened eddy activity. This study contributes valuable insights into the dynamics of ocean circulation and the impacts of eddies on NPP, which can inform fisheries management strategies, advances climate resilience measures, expands scientific knowledge, and guides policies related to conservation and sustainable resource utilization.

The severest form of muscular dystrophy (MD) termed Duchenne MD (DMD) remains to this day an incurable disease, hence the continued effort to further develop supportive therapies. Perturbation of autophagy, a degradative yet protective mechanism activated when tissues face severe and persistent stress, is critically involved in DMD, consequently, treatments harnessing autophagic capacities represent an amenable therapeutic strategy. We studied the effects of the protective osmolyte ectoine in the standard mouse model of DMD, the MDX, zooming in on the autophagy-related proteome. From birth, mice were treated with ectoine in the drinking water (150mg/kg) or through daily intraperitoneal injection (177mg/kg) until 5 weeks old. Hind limb muscles were dissected, performing western blotting for protein quantification and immunofluorescence to investigate tissue distribution. We report significant alterations of total levels of autophagy related 5 (ATG5), Ser366 phosphorylated sequestosome 1 (SQSTM1) and heat shock protein 70 (HSP70), and of activated microtubule-associated protein 1A/1B-light chain 3 (LC3 II) in mitochondrial fractions. Ectoine tends to restore the shifted balance between LC3 II and SQSTM1 levels observed in MDX muscle, and LC3 II upregulation was most pronounced on muscle fibers of MDX treated with ectoine in the drinking water. Findings from this explorative study support a possible beneficial effect of ectoine on the autophagic deficit in the MDX, which warrants its further exploration as a therapeutic supplement for DMD.

Background: Parkinson's disease (PD) is a neurodegenerative disorder that affects both patients and their caregivers, particularly parents who often become informal caregivers. Caring for someone with PD is linked to increased psychiatric morbidity and persistent anxiety-depressive distress. Methods: This study investigates the burden on caregivers of PD patients using multi-dimensional scales and identifies the personological and environmental factors contributing to this burden. Results: 20.6% of participants reported a severe burden according to the Caregiver Burden Inventory (CBI), while 13.8% had a moderate to severe burden according to the Zarit Caregiver Burden Inventory (ZBI). The Family Strain Questionnaire Short Form (FSQ-SF) high-lighted a minority of caregivers needing psychological support, with some cases requiring ur-gent attention, particularly in stress-related symptoms (SR) and urgent support (U). The Depres-sion Anxiety Stress Scales Short Version (DASS-21) results showed a higher prevalence of stress-related symptoms compared to anxiety and depression. Retired caregivers reported higher ZBI scores and lower life satisfaction levels compared to working caregivers. Additionally, years of schooling negatively correlated with the ZBI score. T-test analysis indicated that caregivers of patients with cognitive impairment were at greater risk compared to caregivers of patients with-out cognitive impairment. Conclusions: The results underscore the need for early recognition of risky situations and adequate support for caregivers.

Background: Physiotherapy and chronic low back pain (CLBP) form a broad and fast developing research area. The aim of the article was to holistically, thematically and chronologically analyze and synthesise literature production and identify most prolific research entities and research themes. Methods: The article quantitatively and quantitatively analyzed research literature production harvested from the Scopus bibliometric database. using a triangulation of bibliometric and thematic analysis. For this, Excell and VOSwiever software were used. Results: In the Scopus database, 2843 data sources were found, which were published between 1974 and February 26, 2024. The growth trend has been linearly positive since the beginning of publication, and after 2018, exponential growth began. A review of the most prolific entities showed that the most literature was published in America, Europe and Australasia. The thematic analysis of the information sources identified six main themes, while the chronological analysis revealed three main areas of developments: assessment tools, CLBP processing and study methodology. Conclusions: The results of the bibliometric study presents a good starting point for further research providing Taxonomy and research landscapes as a holistic framework offering multidisciplinary knowledge about CLBP, while chronological analysis provides a basis for identifying prospective research trends.

Falls are considered a significant cause of disability, pain, and premature deaths in older adults, often due to sedentary lifestyles and various risk factors. Combining immersive virtual reality (IVR) with physical exercise, or exergames, enhances motivation and personalizes training, effectively preventing falls by improving strength and balance in older people. IVR technology may increase the ecological validity of the assessments. The main goal of our study was to assess the feasibility of using a KAVE-based VR platform combining simulations of Levadas and a Cable Car to perform a balanced assessment and profiling of the older adult population for high risk-of-falls and the related user experience. A VR-based platform using a Wii balance board and a CAVE was developed to assess balance and physical fitness. Validated by the Biodex Balance System (BBS), 25 older adults participated in this study. The usability and presence were measured through the System Usability Scale and ITC-SOPI questionnaires, respectively. The IVR system showed a high presence and a good usability score of 75. Significant effects were found in the maximum excursion of the Centre of Pressure (COP) on the anterior-posterior axis during the Cable Car Simulation (CCS), correlating with BBS metrics. Multiple discriminative analysis models and the support vector machine classified fall risk with moderate to high accuracy, precision, and recall. The system accurately identified all high-risk participants using the Leave-One-Out method. This study suggests that an IVR-based platform based on simulations with high ecological validity can be used to assess physical fitness and identify individuals at a higher risk of falls.

The research that we are referring to examines the morphological, structural, and optical characteristics of kesterite (Cu1-xAgx)2ZnSnS4 (CAZTS) thin films, which are produced using a process known as thermal evaporation (TE). The study's main goal was to determine how different Ag contents affect the characteristics of CAZTS systems. X-ray diffraction (XRD) and Raman Spectroscopy: were used to confirm the crystal structure of the CAZTS thin films. Using a mathematical model of spectroscopic ellipsometry, the refractive index (n) represented the part of the complex refractive index, the extinction coefficient (k) portrayed the imaginary one, and also the energy band gap of the fabricated films were all calculated. The energy band gap of the thin films was also calculated. This is a crucial parameter for solar cell applications, as it determines the wavelength of light that the material can absorb. The energy band gap was found to decrease from 1.74 eV to 1.55 eV with increasing Ag content. The ITO/n-CdS/p-CAZTS/Mo heterojunction was well constructed, and the primary photovoltaic characteristics of the n-CdS/p-CAZTS junctions were examined for use in solar cells. Different Ag contents of the CAZTS layers were used to determine the dark and illumination (current-voltage) characteristics of the heterojunctions. The study's findings collectively point to CAZTS thin layers as potential absorber materials for solar cell applications.

Androgenetic alopecia, the most common cause of hair loss affecting both men and women, is typically treated using pharmaceutical options such as minoxidil and finasteride. While these medications work for many individuals, they are not suitable options for all. To date, the only non-pharmaceutical option that the United States Food and Drug Administration has cleared as a treatment for androgenetic alopecia is low-level laser therapy (LLLT). Various clinical trials utilizing LLLT devices of various types are available. However, a myriad of other physical treatments for this form of hair loss are reported in the literature. This review evaluated the effectiveness of microneedling, pulsed-electromagnetic field (PEMF) therapy, low-level laser therapy (LLLT), fractional laser therapy, and non-ablative laser therapy for the treatment of androgenetic alopecia (AGA). It also explores the potential of multimodal treatments combining these physical therapies. LLLT has the majority of evidence in the literature as a physical therapy for androgenetic alopecia. However, other physical treatments, such as non-ablative laser treatments, and multimodal approaches, such as PEMF-LLLT, seem to have the potential to be equally or more promising and merit further exploration.

Biodiesel has several drawbacks, such as being prone to oxidation, having reduced stability, and having limited storage time. Antioxidants compatible with biodiesel are being used to address its drawbacks. Utilizing antioxidants effectively improves the quality of biodiesel. Enhancing the quality of biodiesel for use as a clean energy source benefits both the global economy and ecology. Therefore, we believe that our work will contribute to the advancement of the worldwide industry. The study used blends consisting of 20% biodiesel and 80% diesel fuel. Isatin-thiosemicarbazones were tested as additives in blends at a concentration of 3000 parts per million (ppm) using an oxifast device. Comparing them to the chemical antioxidant Trolox. FT-IR, DSC, and TGA were used to characterize these samples. DSC measured sample crystallization temperatures (Tc). Samples with antioxidants showed decreased values compared to the non-antioxidant diesel sample D100. Several DSC tests were conducted to determine the antioxidant strengths of various samples. The results showed that the FT-IR spectrum's antioxidant effect regions grow clearer with antioxidants. The extra antioxidant is effective. Biodiesel's oxidative stability improves with isatin-thiosemicarbazones at varying concentrations. The kinetics of thermal decomposition of isatin-thiosemicarbazones under non-isothermal conditions have been determined using the Kissinger, Ozawa, and Boswell techniques. The activation energies of compounds 1 and 2 were calculated 137–147 kJ mol^-1 and 173–183 kJ mol^-1, respectively.

Sharing confidential information is a critical concern in today’s world. Secret sharing schemes facilitate the sharing of secrets in a way that ensures only authorized participants (shareholders) can access the secret using their allocated shares. Hierarchical secret sharing schemes (HSSSs) build upon Shamir’s scheme by organizing participants into different levels based on priority. Within HSSS, participants at each level can reconstruct the secret if a specified number, denoted as the threshold value (t), or more of them are present. Each level has a predetermined threshold value. If the number of participants falls below the threshold at any level, higher-level participants must be involved in reconstructing the secret at lower levels. Our paper proposes schemes that implement hierarchical access structures and enable the sharing of multiple secrets. Additionally, our proposed scheme includes share verification. We have analyzed potential attacks and demonstrated the scheme’s resistance against them. Through security analysis and comparison with existing schemes, we highlight the novelty and superiority of our proposed approach, contributing to advancements in secure information sharing practices.

Soft robotic manipulators consisting of serially-stacked segments combine actuation and structure in an integrated design. This design can be miniaturised while providing suitable actuation for applications such as minimally invasive surgery and for inspections in confined environments. The control of these robots, however, remains challenging, due to the difficulty in accurately modelling the robots, in coping with their redundancies, and in solving their full inverse kinematics. In this work, we explore a hybrid approach to control serial soft robotic manipulators that combines machine learning (ML) to estimate the inverse kinematics with a closed-loop control to compensate the remaining errors. For the ML part, we compare various approaches, including both kernel-based learning and more general neural networks. We validate the selected ML model experimentally. For the closed-loop control part, we first explore Jacobian formulations using both synthetic models and numerical approximations from experimental data. We then implement integral control actions using both these Jacobians, and evaluate them experimentally. In an experimental validation, we demonstrate that the hybrid control approach achieves setpoint regulation in a robot with 6 inputs and 4 outputs.

Background: The importance of knowing the workload of family nurses lies essentially in the possibility of improving health outcomes, care processes and the nurse’s professional life. There is a lack of studies that fully describe nursing workload in primary care, particularly, in the context of family health nursing, and the ideal metrics to be used remains unknown, making it impossible to characterize and therefor provide the necessary insight to acknowledge the different contributions of several aspects that embodies the global workload of family nurses. The objective of this scoping review was to map the known evidence and characterize the instruments used to assess the workload of family nurses. Methods: Scoping review, according the Joanna Briggs Institute proposed methodology for scoping reviews, consisting of 3 research stages: 1) initial research in Medline and CINHAL; 2) extended search, using keywords and search terms, in the following databases: JBI, CINAHL Complete, MEDLINE, Cochrane and Scopus; 3) Search the reference list of the selected articles. No time limit was defined. Results: Fourteen studies referring to ten as-sessment instruments were included. Nine of them analyze workload as a dimension of a broader instrument, and two studies refer to an instrument that focuses exclusively on workload. Con-clusions: The diversity of professional competencies and contexts, the conceptual complexity of workload and the absence of an theoretical framework, makes it difficult to identify consensual instruments to assess the workload of family nurses. This study was registered in Open Science Framework® with the following registration (https://osf.io/3k6vr).

In the realm of digital transformation, effective leadership and motivation are pivotal for organisations navigating the complexities of today’s systems. This study explores the intersection of intrinsic motivation and mental well-being among mature students—an analogy that sheds light on strategies applicable to organisational contexts. In developed nations like the UK, mental health for mature students is increasingly recognised as a crucial component of their educational journey. Mature students, who typically enrol in higher education after an educational gap and upon turning 21, often face specific challenges that can impact their mental well-being while pursuing academic goals. The primary objective of our study was to assess the relationship between intrinsic motivation and the mental well-being of mature students. Our research revealed a significant positive correlation between these two variables. Confirmatory factor analysis (CFA) results supported a one-factor solution for the sample data from mature students. Additionally, we found that intrinsic motivation is a significant predictor of the mental well-being of mature students. Recognising and addressing the unique difficulties individuals encounter and providing appropriate support can enhance their well-being and contribute to the overall success of the higher education community.

Bistatic radar experiments have been used to study surface characteristics of extra-terrestrial bodies in the Solar System, including the Moon, Venus, Mars, and Titan. This paper proposes a 3D model to characterize the scattered field of a Gaussian rough surface on an extra-terrestrial body for an orbital bistatic radar configuration. Specifically, this model will investigate how the variability of surface roughness impacts the spectral broadening of the received signal using physical optics approximations and ray tracing on a surface model using a facet approach with Gaussian properties. A linear relationship between spectral broadening of the signal and surface roughness was found. This relationship is in line with results obtained by commonly used analytical models for bistatic radar on planetary surfaces.

The study examines projected changes in dry- and wet-spell probabilities in West Africa using a Markov chain approach. Four simulations of regional climate models from the CORDEX-Africa program were used to analyze projected changes in intraseasonal variability. The results show an increase in the probability of having a dry day, a dry day preceding a wet day and a dry day preceding a dry day, and a decrease in the probability of wet days in the Sahel region under anthropogenic forcing scenarios RCP4.5 and RCP8.5. The decrease in wet days is stronger in the far future and under the RCP8.5 scenario (up to -30%). The study also finds that the probability of consecutive dry days (lasting at least 7 days and 10 days) is expected to increase in the Western Sahel, the Central Sahel, and the Sudanian Area under both scenarios, with stronger increases in the RCP8.5 scenario. In contrast, the decrease is expected over the Guinea Coast, with the changes more important under the RCP4.5. These changes in dry- and wet-spell probabilities are important for water management decisions and risk reduction in the energy and agricultural sectors. The study also highlights the need for decision-makers to implement mitigation and adaptation policies to minimize the adverse effects of climate change.

Flexible polymer-based dye-sensitized solar cells (DSSCs) offer promising potential for lightweight, cost-effective and versatile photovoltaic applications. However, the critical challenge in their widespread applications is the weak thermal stability of the majority of polymeric substrates, which can only withstand a maximum temperature processing of 150°C. In this study, we propose a facile and low-cost strategy to develop at low temperature DSSC flexible photoanode based on a polymeric matrix. Highly porous nanocomposites fibrous mats composed of polyethylene terephthalate (PET) and titanium dioxide (TiO2) nanobars were prepared through electrospinning process using different configurations (uniaxial electrospinning, coaxial electrospinning, and electrospray-assisted electrospinning). These techniques enabled precise control of the microstructure and the positioning of TiO2 within the composite nanofibers. Therefore, the as-produced photoanodes were loaded with N719 dye and tested in DSSC prototype using iodide-triiodide electrolyte and platinum (Pt) coated counter electrode. The results show that incorporating TiO2 on the fiber surface through the electrospray-assisted electrospinning enhanced the performance of the nanofiber composite, leading to improved dye loading capacity, electron transfer efficiency and photovoltaic performance.

A canine population’s immune resistance to canine distemper virus (CDV), canine parvovirus (CPV), and infectious hepatitis virus (CAV-1) was evaluated. In this study, a total of 112 sera were analyzed. Animals were considered as vaccinated if, in the last two years, they had re-ceived at least one dose of a vaccine that provides joint protection against CDV, CPV, and CAV-1. Animals that had never received any dose of these vaccines were designated as non-vaccinated. CDV, CPV, and CAV-1 antibodies were detected via a modified solid-phase enzyme-linked im-munosorbent assay (ELISA), which detects IgG antibody levels in sera and provides semi-quantitative results in less than 30 min. In total, 41.1% of the dogs had been vaccinated, and 58.9% of dogs were designated as non-vaccinated. Overall, 90.2%, 92.0%, and 78.6% of the tested dogs had positive results for the presence of IgG antibodies against CPV, CDV, and CAV-1, re-spectively. CPV antibodies were present in 93.5% (43/46) of the vaccinated and 87.9% (58/66) of the non-vaccinated dogs, while CDV antibodies were present in 95.5% (63/66) of the vaccinated and 87.0% (40/46) of the non-vaccinated dogs. Finally, CAV-1 antibodies were present in 84.8% (56/66) of the vaccinated and of 69.6% (32/46) the non-vaccinated dogs.

Digital twin technology is revolutionizing traditional manufacturing paradigms. In modern manufacturing systems, digital twin technology is fraught with challenges due to the scarcity of labeled data. Specifically, existing supervised machine learning algorithms, with their reliance on voluminous training data, find their applicability constrained in real-world production settings. This paper introduces an unsupervised 3D reconstruction approach tailored for industrial applications, aimed at bridging the data void in creating digital twin models. Our proposed model, by ingesting high-resolution 2D images, autonomously reconstructs precise 3D digital twin models without the need for manual annotations or prior knowledge. Through comparisons with multiple baseline models, we demonstrate the superiority of our method in terms of accuracy, speed, and generalization capabilities. This research not only offers an efficient approach to industrial 3D reconstruction but also paves the way for the widespread adoption of digital twin technology in manufacturing.

This paper focuses on environmental policies for mitigating the impacts of climate change on the Taj Mahal, an iconic structure built on the banks of the Yamuna River from 1632 to 1653 with a blend of Persian and Indian architectural styles. The Taj Mahal is threatened by damage caused by climate change, including air pollution resulting in acid rain and floods that lead to erosion and discoloration of the main marble material of the Taj Mahal. This study employs a Systematic Literature Review (SLR) of peer-reviewed journal articles with a primary focus on environmental policy, the impacts of climate change, and the preventive measures undertaken by the Indian government. Based on the analysis of 270 papers, it is concluded that out of the total, 30 papers specifically address objectives relevant to this research. The study period spans from 2014 to 2024. The research findings reveal that air pollution has a significantly adverse impact on the Taj Mahal. Furthermore, nine policies are generally identified from the journal literature review aimed at preserving and protecting the Taj Mahal from the negative effects caused by air pollution.

This study investigates the concentrations of physicochemical and heavy metal contaminants in the groundwater of the Middle Awash Basin, Ethiopia, to inform targeted water management strategies. A total of 32 groundwater samples were collected from 16 stations via piezometers and boreholes at the end of both the dry (June 2021) and wet (October 2021) seasons. Utilizing Inductively Coupled Plasma Mass Spectrometry (ICP-MS), Atomic Absorption Spectroscopy (AAS), and in situ metering, 22 physicochemical and 24 heavy metal parameters were analyzed. The data revealed significant levels of contamination; notably, sample GW11 had extraordinarily high concentrations of total dissolved solids (10,826 mg/L), strontium (908 µg/L), molybdenum (802.4 µg/L), zinc (6060 µg/L), and electrical conductivity (15,645 µS/cm), while GW12 exhibited elevated levels of aluminum (2615 µg/L), zinc (4446 µg/L), and arsenic (117.2 µg/L). Contaminants such as arsenic, vanadium, gallium, lithium, rubidium, chromium, manganese, copper, and zinc were found enriched in groundwater near Lake Beseka, influenced by agricultural runoff from sugarcane plantations and agro-industry activities. Over half of the groundwater sources were unsuitable for drinking, posing significant health risks to local communities that rely heavily on these sources due to limited access to clean surface water. The findings emphasize the urgent need for comprehensive groundwater management and remediation plans in the Middle Awash region to ensure safe and sustainable water use, particularly addressing the variation in contamination levels influenced by Lake Beseka. These measures are critical to protect public health and support local development in the face of ongoing environmental and anthropogenic pressures.

The formation of hydrogen sulfide (H₂S) in petroleum reservoirs by anaerobic microbial activity is called biogenic souring of reservoirs and is a risk in the oil and gas industry as the compound is extremely toxic, flammable and corrosive. In this paper, we present a workflow and the tools to assess biogenic souring from a pragmatic engineering perspective. The retention of H₂S in the reservoir due to the reactions with iron-bearing rock minerals (e.g. siderite) is shown in a theoretical approach here and supported with literature data. Cases are given for two fields under secondary (waterflooding) and tertiary flooding with microbial enhanced oil recovery (MEOR). The use of the Monte Carlo method as a numerical modelling tool to incorporate uncertainties in the measured physical/chemical/ biochemical data is demonstrated as well. A list of studies conducted with different chemicals alone or in combination with various biocides to mitigate biogenic souring provides an overview of potential inhibitors as well as possible applications. Furthermore, the results of static and dynamic inhibition tests using molybdate are presented in more detail due to its promising mitigation ability. Finally, a three-step workflow for the risk assessment of biogenic souring and its possible mitigation is presented and discussed.

In the face of the burgeoning electricity demands and the imperative for sustainable development amidst rapid industrialization, this study introduces a dynamic and adaptable framework suitable for policy-makers and renewable energy experts working on integrating and optimizing renewable energy solutions. While using a case study representative model for Sub-Saharan Africa (SSA) to demonstrate the challenges and opportunities present in introducing optimization methods to bridge power supply deficits and the scalability of the model to other regions, this study presents an agile multi-criteria decision tool that pivots on four key development phases, advancing upon established methodologies and pioneering refined computational techniques, to select optimal configurations from a set of Policy Decision Making Metrics (PDM-DPS). Central to this investigation lies a rigorous comparative analysis of variants of three advanced algorithmic approaches: Swarm-Based Multi-objective Particle Swarm Optimization (MOPSO), Decomposition-Based Multi-objective Evolutionary Algorithm (MOEA/D), and Evolutionary-Based Strength Pareto Evolutionary Algorithm (SPEA2). These are applied to a grid-connected hybrid system, evaluated through a comprehensive 8760-hour simulation over a 20-year planning horizon. The evaluation is further enhanced by a set of refined Algorithm Performance Evaluation Metrics (AL-PEM) tailored to the specific constraints. The findings not only underscore the robustness and consistency of the SPEA2 variant over 15 runs of 200 generations each, which ranks first on the AL-PEM scale but also validate the strategic merit of combining multiple technologies and empowering policymakers with a versatile toolkit for informed decision-making.

The experiment was conducted to evaluate the effect of seed and fertilizer rates on biomass yield and other agronomic treats of two oat varieties (Bona-bas and Bate) at the Kuyu sub-site of Fitche Agricultural Research Center in 2022. Four levels of Nitrogen fertilizer rates (0, 23, 46, and 69 kg ha-1) and three levels of seed rates (60, 80, and 100 kg ha-1) with two oat varieties (Bona-bas and Bate) were using randomized complete block design in factorial arrangement with three replications. Soil samples before and after were collected and analyzed. The analysis of soil samples before planting indicated that the soil pH was strongly acidic (5.16) and after harvest, it raised to a strongly acidic level (5.29). The interaction effect of varieties, seed rates, and fertilizer rates showed highly significant (p<0.001) variation on the number of tillers per plant, leaf area per plant, leaf-to-stem ratio, fresh biomass yield, dry matter yield, days to maturity, and seed yield. The treatment combination of Bona-bas: FR46:SR100 has produced the highest dry matter yield (5.1 t/ha), followed by Bona-bas: FR46:SR60 and Bona-bas: FR69:SR80 which produced the same value of 4.9 t/ha dry matter yield. This study recommends that for the better agronomic performance of oat varieties, 46 kgha-1 N of fertilizer with 60 kgha-1 of seed rate is preferable to use by farmers in the study area and other areas having similar agroecologies and soil types. In addition, it is essential to conduct the year-over location to confirm the present findings and the result needs to be supported with animal evaluation trials.

We explore an idea put forward many years ago by Zeldovich and Novikov concerning the existence of compact objects endowed with arbitrarily small mass. The energy-density of such objects, which we call “Ghost stars”, is negative in some regions of the fluid distribution, producing a vanishing total mass. Thus, the interior is matched on the boundary surface to Minkowski space-time. Some exact analytical solutions are exhibited and their properties are analyzed. Observational data that could confirm or dismiss the existence of this kind of stellar object, is commented.

The development of offshore wind farms requires robust bonding solutions that can withstand harsh marine conditions for the easy integration of secondary structures. This paper investigates the durability performance of two adhesives: Sikadur 30 adhesive and Loctite UK 1351 B25 for use in offshore wind environments. Tensile tests on adhesive samples and accelerated aging tests were carried out under a variety of temperature and environmental conditions, including both dry and wet conditions. The long-term effects of aging on adhesive integrity are investigated by simulating the operational life of offshore installations. The evolution of mechanical properties, studied under accelerated aging conditions, provides an important indication of the longevity of structures under normal conditions. The results show significant differences in performance be-tween the two adhesives, highlighting their suitability for specific operating parameters. It should also be noted that for both adhesives, their exposure to different environments (seawater, distilled water, humid climate) over a prolonged period has shown that i) Loctite adhesive has a slightly faster initial uptake than Sikadur adhesive, but the later reaches an asymptotic plateau with a lower maximum absorption rate than Loctite adhesive; and ii) a progressive deterioration in the tensile properties following an exponential function. Therefore, aging behavior results showed a clear correlation with Arrhenius' law, providing a predictive tool for the aging process. Thus, the aging process of the two adhesives follows Arrhenius kinetics.

Leaf springs are critical components for the railway vehicle safety in which they are installed. Although these components are produced in high-strength alloyed steel and designed to operate under cyclic loading conditions in the high-cyclic fatigue region, their failure is still possible to occur and hence lead to economic and human catastrophes. The aim of this document is precisely to characterise the mechanical crack growth behaviour of the 51CrV4 spring steel representative of leaf springs under cyclic conditions, that is, crack propagation in mode I. The common fatigue crack growth prediction models (Paris and Walker) considering the effect of stress ratio and parameters such as propagation threshold, critical stress intensity factor and crack closure ratio are also determined using statistical methods, which resulted in good approximations with respect to the experimental results. Lastly, the fracture surfaces under the different test conditions were analysed using SEM, with no significant differences to declare. As a result of this research work, it is expected that the developed properties and fatigue crack growth prediction models can assist design and maintenance engineers in understanding fatigue behaviour in the initiation and propagation phase of cracks in leaf springs for railway freight wagons.

The interface of a structural system in the soil is usually unsaturated. A decrease in the shear strength of the soil-structure interface can degrade the structure, especially under humid conditions. Studying the shear characteristics of the silt-concrete interface using a shear apparatus. Suction-controlled ring shear tests were carried out on the silt-concrete interface for three interface roughness values. The influence of the matric suction (s) and interface roughness (R) on the shear strength index of the silt-concrete interface was analyzed. The test results showed that high values of s and R improved the shear strength of the interface, but the result was affected by the net normal stress (NNS). High values of NNS and R improved the strain-hardening characteristics. NNS, s, and R significantly affected the interface shear shrinkage. The internal friction angle of the saturated interface was smaller than that of saturated silt, whereas the opposite occurred in the unsaturated state. The cohesion and internal friction angle of the interface rose with an increase in R, and they were larger than that of silt. A shear strength model of the silt and interface was established.

This article introduces an innovative design method for public lighting systems that surpasses the limitations of conventional approaches, which rely on predefined lamp characteristics and spatial arrangement. By employing a multi-criteria decision algorithm, our study proposes an optimal design methodology considering several key aspects, including the distance between lamps, their type, power, and light distribution. The goal is to achieve optimal illumination that enhances visibility on public roads for drivers and pedestrians while simultaneously minimizing glare, installation costs and maximizing energy efficiency. The proposed methodology is implemented through an algorithm developed in Matlab, with results validated through simulations in Dialux. This information is used to construct a decision matrix, assessed using the CRITIC method across 180 different scenarios within a specific case study. The findings demonstrate the effectiveness of multi-criteria decision-making as a tool for significantly improving the planning and design of lighting in public illumination systems, allowing for selecting the optimal combination of parameters that ensure the best lighting conditions.

Micropollutants belongs to various groups of chemicals. One of the most diverse and large group of them are pharmaceuticals. The presence of pharmaceutical residues in wastewater poses a significant challenge to water quality and environmental health. This paper provides an overview of recent advancements in the removal of pharmaceuticals from water, focusing on various treatment processes and their effectiveness in eliminating micropollutants. Through a review of literature, including studies on ozonation, UV irradiation, sulphate radical-based tech-nologies, and photocatalytic processes, insights into degradation mechanisms and optimal con-ditions for their removal are synthesized. Additionally, with new legislation mandating the monitoring of selected micropollutants and the implementation of quaternary treatment in wastewater treatment plants, the paper discusses prospects for future research and recommen-dations for effective pharmaceuticals removal. Key actions include conducting comprehensive laboratory and pilot trials, implementing quaternary treatment of wastewaters, continuously monitoring of water quality, investing in research and development, and promoting collabora-tion and knowledge sharing among stakeholders. By embracing these strategies, we can work towards safeguarding water resources and protecting public health from the adverse effects of pharmaceutical contamination.

: Image guided superficial radiation therapy (IGSRT) combines superficial radiation therapy (SRT) with full dermal visualization (FDV) via high resolution dermal ultrasound (HRDUS) for the treatment of non-melanoma skin cancer (NMSC). The gold standard for IGSRT delivery includes a comprehensive cancer care model with support for the dermatologists by a multidisciplinary team. When delivered in this model, IGSRT can achieve cure rates of 99% for treatment of NMSC. This paper focuses on the benefits of IGSRT during therapy. Medical records from 7 dermatology clinics of 883 patients with 1,507 cases of NMSC treated with IGSRT be-tween 2017 and 2018 were retrospectively reviewed. 92% of the NMSC lesions showed daily depth fluctuations, 60.32% of lesions did not require changes during therapy, and nearly 40% of lesions required at least one compensatory change during therapy. 83% of NMSC lesions were labeled high-risk based on the 2024 NCCN guidelines. Increasing and decreasing tumor depth measurements during IGSRT inform dermatologists when adaptive changes in energy (kV), TDF and dose will result in more efficacy and less toxicity, respectively.

Abstract: Lung cancer, characterized by its heterogeneity, presents a significant challenge in ther-apeutic management, primarily due to the development of resistance to conventional drugs. This resistance is often compounded by the tumour’s ability to reprogram its metabolic pathways, a survival strategy that enables cancer cells to thrive in adverse conditions. This review article ex-plores the complex link between drug resistance and metabolic reprogramming in lung cancer, offering a detailed analysis of the molecular mechanisms and treatment strategies. It emphasizes the interplay between drug resistance and changes in metabolic pathways, crucial for developing effective lung cancer therapies. The review examines the impact of current treatments on metabolic pathways and the significance of considering metabolic factors to combat drug resistance. It highlights the different challenges and metabolic alterations in non-small cell lung cancer and small cell lung cancer, underlining the need for subtype-specific treatments. Key signalling pathways, including PI3K/AKT/mTOR, MAPK, and AMPK, have been discussed for their roles in promoting drug resistance and metabolic changes, alongside the complex regulatory networks involved. The article evaluates emerging treatments targeting metabolism, such as metabolic inhibitors, dietary management, and combination therapies, assessing their potential and challenges. It concludes with insights into the role of precision medicine and metabolic biomarkers in crafting personalized lung cancer treatments, advocating for metabolic targeting as a promising approach to enhance treatment efficacy and overcome drug resistance. The review underscores ongoing advancements and hurdles in integrating metabolic considerations into lung cancer therapy strategies.

Aim: To investigate the effects of intensive physical exercise on retinal microvascular regulation in healthy volunteers by the adaptive optics retinal camera measurement. Methods: We included healthy volunteers (11 men and 14 women) aged 20.6± 0.9. Heart rate (HR) and systolic and diastolic blood pressure (SBP, DBP) were recorded before and after a submaximal physical exertion of continuously riding a training ergometer. The superior temporal retinal artery measurements were captured using the adaptive optics (AO) camera rtx1TM (Imagine Eyes, Orsay, France) without pupil dilatation. We compared measures of vessel diameter (VD), lumen diameter (LD), two walls (Wall 1, 2), wall-to-lumen ratio (WLR), and wall cross-sectional analysis (WCSA) before and immediately after the cessation of exercise. Results: Cardiovascular parameters: After exercise, SBP, DBP and HR changed significantly from 130.2±13.2 to 159.7±15.6 mm Hg, 81.2±6.3 to 77.1±8.2 mm Hg and 80.8±16.1 to 175.0±6.2 bpm, respectively (p < 0.002). Retinal microcirculation analysis: Before the exercise, the mean LD was 96.0±6.8, VD 118.5±8.3, WLR 0.234±0.02, WCSA 3802.8±577.6. The mean LD and Wall 1 did not change significantly after exercise but slightly decreased. The VD, Wall 2, WLR and WCSA decreased significantly (p

Enhancing energy efficiency within the building sector is imperative to curbing energy losses, given that this sector alone contributes to over 34% of global energy consumption. Employing a building management system, along with its regular updates, presents a strategic avenue to decrease energy usage, enhance building energy efficiency, and more. Tailored control strategies, aligned with the unique characteristics and usage patterns of each building, are essential for achieving energy savings. This article presents an evaluation of HVAC system efficiency in office buildings, utilizing a data-driven approach coupled with simulations conducted in IDA ICE software. The research explores the control strategy of an office building equipped with a constant air volume HVAC system, featuring a regularly controlled air handling unit. The objective is to boost energy efficiency while striking a balance between occupant comfort and energy consumption. Findings indicate that by analyzing measured data and adjusting configurable parameters, building energy consumption can be significantly reduced. Close monitoring of indoor parameters by building operators and corresponding adjustments to the HVAC system can yield energy savings of up to 16%. Leveraging these insights, the paper suggests integrating data-driven and dynamic simulation methods into building management system models to optimize HVAC systems, enhance energy efficiency, and advance ambitious carbon neutrality objectives.

Balsam fir trees are a specialty agricultural species that are largely used as Christmas trees. Soil or tissue sampling is seldom performed to assess balsam fir nutrition needs, which can contribute to over or under fertilization and subsequent environmental challenges. Remote sensing to determine the normalized difference vegetation index (NDVI) has had strong relationships with nutrition in other plants. This research aimed to determine whether there is a relationship between NDVI and nutrition in balsam fir. A random sample of 45 trees was selected in autumn of 2021 and then another 70 trees selected in spring of 2022. Soil and needle tissue samples were analyzed for N, P, K, Ca, Mg, S, Fe, Zn, Mn, B, and Cu. An uncrewed aerial vehicle equipped with a multispectral camera was flown over the orchard to determine the NDVI for each tree. Nutrition and NDVI were analyzed for correlations through all sampled trees. Separate correlation analyses were repeated for mature trees (greater than 5 years old) and young trees (less than 5 years old). Soil nutrients N, P, K had a significant relationship with NDVI in autumn sampling. Only N was correlated consistently in both autumn and spring, with R2 of 37% and 33% respectively. Relationships were much stronger when only mature trees were considered, increasing to 60% and 51% respectively. NDVI was found to offer predictive power for tissue N that can be useful to producers to help optimize fertilizer input. However, further work is needed to refine these relationships.

Fingerprint recognition systems have achieved widespread integration into various technological devices, including cell phones, computers, door locks, and time attendance machines. Nevertheless, individuals with worn fingerprints encounter challenges when attempting to unlock original fingerprint systems, resulting in disruptions to their daily activities. This study explores two distinct methods for fingerprint backup: traditional fingerprint impression and 3D printing technologies. Unlocking tests were conducted on commonly available optical fingerprint lock-equipped cell phones to assess the efficacy of these methods, particularly in unlocking worn fingerprints. The research findings indicated that traditional fingerprint impression method exhibited high fidelity in reproducing fingerprint patterns, achieving an impressive unlocking success rate of 97.8% for imprinting unworn fingerprints. However, when dealing with worn fingerprints, traditional fingerprint impression technique showed a reduced unlocking success rate, progressively decreasing with increasing degrees of finger wear. In contrast, 3D printed backup fingerprints, with image processing and optimization of ridge height, mitigated the impact of fingerprint wear on unlocking capability, resulting in an unlocking success rate of 84.4% or higher. Thus, the utilization of 3D printing technology proves advantageous for individuals with severely worn or incomplete fingerprints, providing a viable solution for unforeseen circumstances.

As technology and multimedia production have advanced, there has been a significant rise in attacks on digital media, resulting in duplicated, fraudulent, and altered data and the infringement of copyright laws. This paper presents a robust and secure digital image watermarking technique that has been implemented in the spatial domain and exploits the erratic and chaotic behaviour of the powerful elementary cellular automata rule-30. The crucial characteristics of the watermarking system, i.e., imperceptibility, capacity, and robustness, have been perfectly balanced by the suggested blind watermarking technique. In this approach, prior to embedding, the grayscale watermark image is downsized to its two Most Significant Bits (MSBs). Then, the 2-MSBs watermark is encrypted using an ECA rule-30 so as to level up the security attribute of the system. Then, the host image is scrambled using ECA rule-30 to distribute the watermark pixels throughout the host image and thus achieve the highest robustness against geometrical attacks. Finally, the encrypted watermark data is embedded into the scrambled host image using the ECA rule-30-based embedding key. The proposed method performs better in terms of imperceptibility, capacity, and robustness when compared to several systems with similar competencies. The simulation's findings demonstrate strong imperceptibility as evaluated by the Peak Signal-to-Noise Ratio (PSNR), which has an average value of 58.3735 dB and a high payload. The experimental outcomes, observed across a diverse range of standardized attack scenarios, unequivocally establish the ascendancy of the proposed algorithm over competing methodologies in the realm of image watermarking.

The quiet-time solar wind electrons feature non-thermal characteristics when viewed from the perspective of their velocity distribution functions. They typically have an appearance of being composed of a denser thermal “core” population plus a tenuous energetic “halo” population. At first, such a feature was empirically fitted with the Kappa velocity space distribution function, but ever since the ground-breaking work by Tsallis, the space physics community has embraced the potential implication of the Kappa distribution as reflecting the non-extensive nature of the space plasma. From the viewpoint of microscopic plasma theory, the formation of non-thermal electron velocity distribution function can be interpreted in terms of the plasma being in a state of turbulent quasi equilibrium. Such a finding brings forth the possible existence of a profound inter-relationship between the non-extensive statistical state and the turbulent quasi-equilibrium state. The present paper further develops the idea of solar wind electrons being in the turbulent equilibrium, but unlike the previous model, which involves the electrostatic turbulence near the plasma oscillation frequency (i.e., Langmuir turbulence), the present paper considers the impact of transverse electromagnetic turbulence, particularly, the turbulence in the whistler-mode frequency range. It is found that the coupling of spontaneously-emitted thermal fluctuations and the background turbulence leads to the formation of non-thermal electron velocity distribution function of the type observed in the solar wind during quiet times. This demonstrates that the whistler-range turbulence represents an alternative mechanism for producing the Kappa-like non-thermal distribution, especially close to the Sun and in the near-Earth space environment.

The main purpose of this article is to identify brain diseases such as Alzheimer's and Parkinson's in an intelligent way using Python software and processing the function of neurons in the neural network areas of the brain, which are classified into different components, and the data is based on the information obtained from FMRI images using algorithms artificial intelligence can intelligently diagnose the type of disease. Therefore, by using spatial clustering based on the deep learning method with noise algorithm (DBSCAN), fMRI brain image and fMRI brain surface complexity are classified, and then image preprocessing will be applied to fMRI images by using artificial intelligence neural networks (ANN) algorithm. became. Then, by using the ANN algorithm and Deep CNN topology of the brain neural network, the type of disease in the brain can be diagnosed by simulating the performance of an artificial neural network (ANN) in Python software. Deep machine learning-based methods, especially algorithms based on neural networks, have shown great success in medical image analysis for various tasks including brain tumor detection, segmentation, and classification, and Alzheimer's and epilepsy. In this research, we propose an integrated model based on a convolutional neural network for simultaneous recognition and classification of brain diseases for intelligent diagnosis of tumors and epilepsy. The average accuracy obtained from our proposed model is 97.89 percent accuracy and 91.66 percent specificity.

Educational institutions must identify students who are academically struggling to provide them with necessary support to improve their performance. In this context, recommendation systems powered by deep learning techniques are vital for detecting and categorizing such students. These systems help students plan their future by uncovering patterns in their historical academic data. This study introduces a new deep learning model designed to classify academically underperforming students in educational settings. The model incorporates a Gated Recurrent Neural Network (GRU) and includes specific neural network features like a dense layer, max-pooling layer, and the ADAM optimization algorithm. The model's training and evaluation were conducted using a dataset comprising 15,165 student assessment records from various academic institutions. The performance of the developed GRU model was benchmarked against other educational recommendation systems, including Recurrent Neural Network models, AdaBoost, and the Artificial Immune Recognition System v2. The proposed GRU model demonstrated remarkable accuracy, achieving an overall rate of 99.70%.

Hereditary polyposis syndromes are rare but potentially devastating conditions which require multidisciplinary care from an early age. Societal guidelines serve as the framework for disease diagnosis, surveillance and treatment. Guidelines are based on expert opinion and ideally are evidence-based; however, recommendations may vary among different societies and can result in discordant care. This paper aims to summarize key differences in management based on societal guidelines for pediatric polyposis syndromes and identify some of the factors which may contribute to divergence in care.

Anthropogenic development can strongly influence natural river processes, leading to environmental changes that negatively affect important habitats and biodiversity, and consequently reduce economically important natural resources. This study investigated the effects of salinity intrusion on the habitat of the clam Corbicula japonica in the Seomjin River estuarine zone. We employed the Environmental Fluid Dynamics Code (EFDC) model, which incorporates topographic data and hydrological changes, to simulate salinity concentrations. Two salinity measurement facilities were installed in Seomjin River estuarine and operated to optimize the EFDC model. The results show that reduced flow rates due to intake have a negligible impact on the increased salinity. Maintaining optimal salinity (15–20 psu) during neap tides at the Seomjin River Bridge requires constant high flow rates, which poses significant challenges. Saltwater stratification is identified as the primary cause of pronounced salinity stratification, particularly during neap tides. Addressing this issue through river discharge and intake facility operation is challenging. Structural measures, including riverbed restoration and underwater barriers, are recommended to improve resistance to seawater intrusion. Future research should aim to develop scenarios to reduce salinity concentrations, quantify the reduction efficiency, and propose region-specific measures.

A semi–numerical approach proposed many years ago for describing gravitational collapse in the post–quasi–static approximation , , , , is modified in order to avoid the numerical integration of the basic differential equations the approach is based upon. For doing that we have to impose some restrictions on the fluid distribution. More specifically, we shall assume the vanishing complexity factor condition, which allows for analytical integration of the pertinent differential equations and leads to physically interesting models. Instead, we show that neither the homologous nor the quasi–homologous evolution are acceptable since they lead to geodesic fluids, which are unsuitable for being described in the post–quasi–static approximation. Also, we prove that, within this approximation, adiabatic evolution also leads to geodesic fluids and therefore we shall consider exclusively dissipative systems. Besides the vanishing complexity factor condition, additional information is required for a full description of models. We shall propose different strategies for obtaining such an information, which are based on observables quantities (e.g. luminosity and redshift), and/or heuristic mathematical ansatz. To illustrate the method, we present two models. One model is inspired in the well known Schwarzschild interior solution, and another one is inspired in Tolman VI solution.

New digital interventions have shown potential in managing asthma and improving patients' quality of life compared with conventional interventions. Our objective was to conduct an exhaustive survey of the application of digital health technologies in evaluating, treating, and self-managing psychological and psychopathological elements linked to asthma. We analyzed a compendium of research papers and systematic reviews pertaining to asthma, encompassing themes such as outdoor air pollution, early life wheezing disorders, atopic dermatitis, digital strategies for asthma self-management, psychiatric conditions and asthma, familial impacts on pediatric asthma, and the utilization of mobile health apps for managing asthma. We scrutinized eight chosen studies to evaluate the capacity of digital health technologies to enhance the man-agement and treatment outcomes of psychological factors related to asthma. The studies under review indicate that eHealth interventions, mixed reality instruments, mHealth technolo-gy-augmented nurse-led interventions, and smartphone apps incorporating Bluetooth-enabled sensors for asthma inhalers can markedly enhance self-management of symptoms, quality of life, and mental health outcomes, particularly in children and adolescents with asthma. Nonetheless, additional research is required to ascertain their efficacy and practicability across diverse populations and environments. Customized interventions that cater to the unique needs and preferences of asthma patients and associated psychological elements are vital to ensure the persistent and equitable adoption of these technologies. We underscore the significance of addressing psychological elements in the management and treatment of asthma and advocate for ongoing research and development in this domain.

TGF-ꞵ signaling is a vital regulator for maintaining articular cartilage homeostasis. Runx transcription factors, downstream targets of TGF-ꞵ signaling, have been studied in the context of osteoarthritis (OA). Although Runx partner core binding factor β (Cbfβ) is known to play a pivotal role in chondrocyte and osteoblast differentiation, the role of Cbfβ in maintaining articular cartilage remains obscure. This study investigated Cbfβ as a novel anabolic modulator of TGF-ꞵsignaling and determined its role in articular cartilage homeostasis. Cbfβ significantly decreased in aged mouse articular cartilage and human OA cartilage. Articular chondrocyte-specific Cbfb-deficient mice (Cbfb△ac/△ac) exhibited early cartilage degeneration at 20 weeks old and developed OA at 12 months old. Cbfb△ac/△ac mice showed enhanced OA progression under the surgical-induced mice OA model. Mechanistically, forced expression of Cbfβ rescued Col2α1 and Runx1 expression in Cbfβ-deficient chondrocytes. TGF-ꞵ1-mediated Col2α1 expression failed despite the pSmad3 activation under TGF-ꞵ1 treatment in Cbfβ-deficient chondrocytes. Cbfβ protected Runx1 from proteasomal degradation through Cbfβ/Runx1 complex formation. These results indicate that Cbfβ is a novel anabolic regulator for cartilage homeostasis, suggesting that Cbfβ could protect OA development by maintaining the integrity of the TGF-ꞵ1 signaling pathway in articular cartilage.

This article presents a unique set of voltage and current data from a public building and acquired using a hybrid measurement solution that combines Python and Grafana. The transversal purpose consists of contributing to the community with a vision of the quality of the supply more oriented to the monitoring of the state of the network, providing a more realistic vision, which allows a better understanding, and the adoption of the best decisions to achieve the efficient energy management and thus optimize the operation and maintenance of power systems. The work focuses on higher order statistical estimators that, combined with exploratory data analysis techniques, improve the characterization of the shape of the stress signal. These techniques and data, together with the acquisition and monitoring system, present a unique combination in the line of low-cost measurement solutions. It also incorporates the underlying benefit of the contribution to industrial benchmarking. The paper also includes a computational comparison between Python and LabVIEW to elicit the performance of the measurement solution.

The Great Barrier Reef (GBR) is currently experiencing widespread crown of thorns starfish (CoTS) outbreaks as part of the fourth wave of outbreaks since 1962. It is believed that outbreaks have become more frequent on the GBR and elsewhere in the Indo-Pacific associated with anthropogenic causes. The two widely accepted potential causes are (1) anthropogenic nutrient enrichment leading to increased biomass of phytoplankton, the food of the planktonic stage of larval CoTS; and (2) overfishing of predators on the juvenile to adult stages of CoTS, for example, commercial fished species such as coral trout. In this study, we show the evidence for the nutrient enrichment causation hypothesis is strong based on a large number of recent studies in the GBR. We also hypothesise that secondary outbreaks in the region between Cairns and Townsville can also be enhanced by nutrient enriched conditions associated with the annual nutrient discharge from Wet Tropics rivers.

Active ageing is defined as the process of optimizing opportunities for physical, social and mental health to enable older people to take an active part in society without discrimination and to enjoy an independent and good quality of life. The World Health Organization assumed this as a process for increasing and maintaining an individual’s participation in activities to enhance his/her quality of life. In this survey, the authors addressed the following question: “Is assistive technology (AT) for mobility contributing to enhancement of lifelong capacity and performance?”. From June 2015 until February 2016, 96 community dwelling adults, AT users for mobility (powered wheelchairs, manual wheelchairs, lower limb prostheses, walkers, crutches and canes), aged 45-97, mean 67.02 +/- 14.24 years old, 56.3% female, were interviewed using the Psychosocial Impact of Assistive Devices Scale (P-PIADS), the Activities and Participation Profile related to Mobility (APPM) and demographics, clinical and questions about AT use and training. The participants’ profiles revealed moderate limitation and restrictions in participation, measured by the APPM (2.03). Most participants showed positive impact of AT; average scores obtained from the P-PIADS subscales were: Self-esteem 0.62, Competency 1.11 and Adaptability 1.10. P-PIADS total was 0.96, with the powered wheelchair users scoring the highest (1.53) and the walker users scoring the lowest (0.73). All subscales and P-PIADS total were positively correlated with the activities and participation profile. There was no relation between age and the psychosocial impact of AT or activities and participation profile. These results encourage the authors to follow these participants up for a lifelong intervention. To accomplish that aim, currently, the protocol is implemented at the AT prescribing centers in Coimbra, Portugal in order to assess the impact of AT on participation in society, one of the domains of the Active Ageing Index, a new analytical tool to help policy makers in developing policies for active and healthy ageing.

Warming of the climate system is unequivocal, and many of the observed changes are unprecedented over five decades to millennia. Globally the atmosphere and ocean is increasingly getting warmer, the amount of ice on the earth is decreasing over the oceans, and the sea level has risen. According to Intergovernmental Panel on Climate Change, the total increasing temperature globally averaged combined land and surface between the average of the 1850-1900 period and the 2003 to 2012 period is 0.78 °C (0.72 to 0.85). But should we prepare for such the relatively small change? The importance is not the mean of the warming but the considerable likelihood of climate change that could trigger extreme natural hazards. The impact and the risk of climate change associated with railway infrastructure have not been fully addressed in the literature due to the difference in local environmental parameters. On the other hand, the current railway network in Malaysia, over the last decade, has been significantly affected by severe weather conditions such as rainfall, lightning, wind and very high temperatures. Our research findings point out the extremes that can lead to asset system failure, degraded operation and ultimately, delays to train services. During the period of flood, the embankment of the track can be swept away and bridge can be demolished, while during drought, the embankment of the track can suffer from soil desiccation and embankment deterioration, high temperature increases the risk of track buckling and high winds can result in vegetation or foreign object incursion on to the infrastructure as well as additional quasi-static burden exerted. This review is of significant importance for planning and design of the newly proposed high speed rail link between Malaysia and Singapore.

Argentina is the current and three-time “World Cup” Champion, as well as the current and most successful team in “Copa América” history. We aimed to evaluate the cardiopulmonary fitness profile of youth soccer players from the first league of the Argentine Football Association (AFA). We evaluated ten highly trained youth soccer players from the AFA by applying an on-field cardiopulmonary exercise test (20-meter Shuttle Run Test - 20m-SRT) with wearable gas analyzer (mix-chamber mode). Mean velocity at maximal oxygen uptake (vO_2max) was 12.3±0.7 km·h^-1. The maximal oxygen uptake (O_2max) on-field was 67.1 ± 5.3 mL·kg⁻¹·min⁻¹. The O₂ at the first and second ventilatory thresholds (VT1 and VT2) were identified at 67.0±3.0% and 84.7 ± 3.7%, respectively. Argentine soccer players exhibit exceptional cardiopulmonary fitness, comparable to that of professional athletes, underscoring their potential for high performance. Support staff, fitness or health professionals, and researchers can now compare cardiopulmonary differences between Argentine youth soccer players and other populations.