In order to conduct an accurate classification of the heterogeneous landscape in Jiului Valley, Romania mining basing, four machine learning algorithms (SVMs) and two common algorithms (MLC and MD) have been compared, using a temporal series of Landsat satellite images from the period 1988-2017. By using independent validation, an accuracy assessment was established together with the analysis of the differences between the classification algorithms used. Although all six algorithms used have shown a high overall accuracy (ranging from 80.29% to 93.14%) and Kappa values (from 0.77 to 0.92), SVM-RBF appears to have a higher overall applicability in describing the spatial distribution and the cover density of each land cover category. Results have indicated a large difference in classification accuracy between the SVM-RBF algorithm and commonly used algorithms, the SVM-RBF algorithms have slightly outperformed the MLC with an overall accuracy of 7.14–8.86% and by 0.0833–0.1033 kappa coefficient. On the other hand, the same algorithm have outperformed the MD by and overall accuracy of 9.71–10.86% and by 0.1133–0.1267 kappa coefficient. By using SVM-RBF, certain classified maps have been developed and used for assessing changes by post classification comparison. The results have shown an average growth of 6.5% in mined areas over the studied period.

Ovulatory infertility is a serious clinical problem whose direct causes are still largely unknown. In addition to pathologies that make it impossible for a couple to establish a pregnancy, there are a number of other factors that have a bearing on fertility, including lifestyle factors, particularly diet. Although numerous studies have been performed linking such factors with ovulatory infertility, most of them lack the necessary clinical significance, focusing instead on observational data and establishing associative relationships. This paper includes a literature review focusing on connections between lifestyle factors such as diet, physical exercise, oxidative stress, sleep, and supplementation, and ovulatory infertility. Special emphasis was given to issues such as obesity and insulin resistance and their mutual relationship with other factors linked to ovulatory infertility. In addition, making use of the conclusions of literature review, the authors propose a classification of relationships between ovulation disorders and lifestyle factors in ovulatory infertility based on the WHO classification of ovulation disorders, and indicate areas that merit further research. The data presented in the paper shows that the issues of proper diet and physical exercise are those that would merit from robust clinical studies focused specifically on ovulation infertility, while studies concerning the relationship between oxidative stress, sleep, and supplementation and ovulatory infertility do not seem to be promising directions as far as clinical significance is concerned.

The seasonal variation in fatty acids and minerals concentrations was investigated through the analysis of Pecorino Romano cheese samples collected in January, April, and June. A fraction of samples contained missing values in their fatty acid profile. Probabilistic Principal component analysis coupled with Linear Discriminant Analysis was employed to classify cheese samples on a production season basis while accounting for missing data and quantifying the missing Fatty acids concentration for the sample in which they were absent. The levels of rumenic acid, vac-cenic acid and omega-3 compounds were positively correlated with the spring season, while the length of the saturated fatty acids increased throughout the production seasons. Concerning the classification performances, the optimal number of principal components (i.e., 5) achieved an ac-curacy in cross-validation equal to 98 %. Then, when the model was tasked to impute the lacking Fatty acid concentration values, the optimal number of principal components resulted in an R2 value in cross-validation of 99.53%

Over the last few decades, the approach to geological and geomorphological surveys has changed remarkably. The advent of digital tools has allowed significant advances in the acquisition and management of survey data. In this paper, we demonstrate the development and testing of a new and effective digital survey method that allows for the fast acquisition and collaborative storage and management of data and information. This method was tested in collaboration with five universities for the mapping and classification of landslides in 249 survey areas in Central Italy and, more precisely, in the municipalities affected by the 2016 Central Italy Earthquake. Geological and geomorphological surveys were carried out in the field with tablet PCs, GPS, and cameras. The survey project for collecting field data was based on the structure of the Italian Landslide Inventory (IFFI) and the Territorial Resilience Central Apennines Earthquake Reconstruction (ReSTART) projects. The structure of the database and input forms were implemented for these aims. Moreover, the data and information were retrieved and organised in detailed records useful to the administrative entities.

This paper presents the findings of an exploratory study on the continuously generating Big Data on Twitter related to the sharing of information, news, views, opinions, ideas, knowledge, feedback, and experiences about the COVID-19 pandemic, with a specific focus on the Omicron variant, which is the globally dominant variant of SARS-CoV-2 at this time. A total of 12028 tweets about the Omicron variant were studied, and the specific characteristics of tweets that were analyzed include - sentiment, language, source, type, and embedded URLs. The findings of this study are manifold. First, from sentiment analysis, it was observed that 50.5% of tweets had the ‘neutral’ emotion. The other emotions - ‘bad’, ‘good’, ‘terrible’, and ‘great’ were found in 15.6%, 14.0%, 12.5%, and 7.5% of the tweets, respectively. Second, the findings of language interpretation showed that 65.9% of the tweets were posted in English. It was followed by Spanish or Castillian, French, Italian, Japanese, and other languages, which were found in 10.5%, 5.1%, 3.3%, 2.5%, and <2% of the tweets, respectively. Third, the findings from source tracking showed that “Twitter for Android” was associated with 35.2% of tweets. It was followed by “Twitter Web App”, “Twitter for iPhone”, “Twitter for iPad”, “TweetDeck”, and all other sources that accounted for 29.2%, 25.8%, 3.8%, 1.6%, and <1% of the tweets, respectively. Fourth, studying the type of tweets revealed that retweets accounted for 60.8% of the tweets, it was followed by original tweets and replies that accounted for 19.8% and 19.4% of the tweets, respectively. Fifth, in terms of embedded URL analysis, the most common domains embedded in the tweets were found to be twitter.com, which was followed by biorxiv.org, nature.com, wapo.st, nzherald.co.nz, recvprofits.com, science.org, and other URLs. Finally, to support similar research and development in this field centered around the analysis of tweets, we have developed an open-access Twitter dataset that comprises tweets about the SARS-CoV-2 omicron variant since the first detected case of this variant on November 24, 2021.

Conclusions: Molecular classification could provide reliable supplementary information for evaluating prognostic and contribute to treatment option decision-making in EEC patients. Fertility-sparing treatment is not recommended for EEC patients with CNH and MSI-H. Furthermore, fertility-sparing treatment can be attempted in EEC patients with CHL, but regular follow-up should be carried out to early detection of EC relapse and prevention of disease progression.

Current diagnoses of mood disorders are not cross validated. The aim of the current paper is to explain how machine learning techniques can be used to a) construct a model which ensembles risk/resilience (R/R), adverse outcome pathways (AOPs), staging, and the phenome of mood disorders, and b) disclose new classes based on these feature sets. This study was conducted using data of 67 healthy controls and 105 mood disordered patients. The R/R ratio, assessed as a combination of the paraoxonase 1 (PON1) gene, PON1 enzymatic activity, and early life time trauma (ELT), predicted the high-density lipoprotein cholesterol – paraoxonase 1 complex (HDL-PON1), reactive oxygen and nitrogen species (RONS), nitro-oxidative stress toxicity (NOSTOX), staging (number of depression and hypomanic episodes and suicidal attempts), and phenome (the Hamilton Depression and Anxiety scores and the Clinical Global Impression; current suicidal ideation; quality of life and disability measurements) scores. Partial Least Squares pathway analysis showed that 44.2% of the variance in the phenome was explained by ELT, RONS/NOSTOX, and staging scores. Cluster analysis conducted on all those feature sets discovered two distinct patient clusters, namely 69.5% of the patients were allocated to a class with high R/R, RONS/NOSTOX, staging, and phenome scores, and 30.5% to a class with increased staging and phenome scores. This classification cut across the bipolar (BP1/BP2) and major depression disorder classification and was more distinctive than the latter classifications. We constructed a nomothetic network model which reunited all features of mood disorders into a mechanistically transdiagnostic model.

The use of Hyperspectral (HS) and LiDAR acquisitions has a great potential to enhance mapping and monitoring practices of endangered grasslands habitats, beyond conventional botanical field surveys. In this study we assess the potentiality of Recursive Feature Elimination (RFE) in combination with Random Forest (RF) classification in extracting the main HS and LiDAR features needed to map selected Natura 2000 grasslands along Polish lowland river valleys, in particular alluvial meadows 6440, lowland hay meadows 6510 and xeric and calcareous grasslands 6120. We developed an automated RFE-RF system capable to combine the potentials of both techniques and applied it to multiple acquisitions. Several LiDAR-based products and different Spectral Indexes (SI) were computed and used as input in the system, with the aim of shedding light on the best-to-use features. Results showed a remarkable increase in classification accuracy when LiDAR and SI products are added to the HS dataset, strengthening in particular the importance of employing LiDAR in combination with HS. Using only the 24 optimal features selection generalized over the three study areas, strongly linked to the highly heterogeneous characteristics of the habitats and landscapes investigated, it was possible to achieve rather high classification results (K around 0.7-0.77 and habitats F1 accuracy around 0.8-0.85), indicating that the selected Natura 2000 meadows and dry grasslands habitats can be automatically mapped by airborne HS and LiDAR data. Similar approaches might be considered for future monitoring activities in the context of habitats protection and conservation

This study explores the application of the Fully Convolutional Network (FCN) algorithm to the field of meteorology, specifically for the short-term nowcasting of severe convective weather events such as hail, convective wind gust (CG) and thunderstorms, short-term heavy rain(STHR) in Gansu. Training data comes from the European Center for Medium-Range Weather Forecasts (ECMWF) and real-time ground observations, the performance of the proposed FCN model based on 2017 to 2021 training datasets demonstrate a high prediction accuracy, with an overall error rate of 16.6%. Furthermore, the model exhibited an error rate of 18.6% across both severe and non-severe weather conditions when testing against the 2022 dataset. Operational deployment in 2023 has yielded an average Critical Success Index (CSI) of 24.3%, a Probability of Detection (POD) of 62.6%, and a False Alarm Ratio (FAR) of 71.2% for these convective events. It is noteworthy that the predicting performance for STHR was particularly effective with the highest POD and CSI, as well as the lowest FAR. CG and hail predictions had comparable CSI and FAR scores, although the POD for CG surpassed that for hail. The FCN model’s optimal performance for hail prediction occurred at the 4th, 8th, and 10th forecast hours, while for CG, the 6th hour was most accurate, and for STHR, the 2nd and 4th hours were most effective. These findings underscore the FCN model's ideal suitability for short-term forecasting of severe convective weather, presenting extensive prospects for the automation of meteorological operations in the future.

This critical review revisited the new classification system for periodontitis, specifically for staging, suggesting modifications, and introducing a new flowchart for a better clinical evaluation. It evaluated articles published between 2018 and 2024 in the English language, which had an educational motivation focused on staging periodontitis. PubMed/MEDLINE, Web of Science, and Embase databases were used to retrieve the articles. The focus questions involved the analysis of all parameters for staging periodontitis. A total of 836 articles were initially found; 388 duplicated were excluded; 448 were evaluated by title and abstract; 26 articles followed for full-text reading, and 6 articles were included in this critical review (k=0.98). All articles included detailed parameters and steps about how to diagnose periodontitis. Therefore, it was possible to observe instability and “gray zones” in the staging step, which was due to a lack of priority and an organized order sequence. This review suggests the severity parameters cannot be overcome by complexity parameters, following a cumulative sequence: CAL (1^st), RBL (2^nd), TLP (3^rd), and, after, complexity parameters. An exception must be permitted only for complexity factors between Stages III and IV that can change the initial Stage (III or IV) obtained through the severity analysis, but only between themselves. Moreover, for patients without tooth loss or with TLP of ≤4 teeth (without need for complex rehabilitation) and presenting any type of drifting or flaring or a secondary traumatic occlusion, it cannot be a justification for moving the diagnosis from Stage III to Stage IV.

There are a number of Anopheles species playing either primary or secondary roles in malaria transmission. Hence, understanding the species composition, their bionomics, and behaviors are all important in designing and implementing vector control intervention tools. Moreover, accurate identification of different species is vital. This study aimed to assess species composition, sporozoite infection rate, and blood meal origins of malaria mosquitoes in two malaria-endemic villages of Boreda district in Gamo zone, southwest Ethiopia. Thirty houses, 20 for Centers for Disease Control and Prevention (CDC) light traps and 10 for Pyrethrum Spray Catches (PSC), were randomly selected for bimonthly mosquito collection from October 2019 to February 2020. Enzyme-linked-immunosorbent assay (ELISA) test was done to detect the blood meal origins and circumsporozoite proteins (CSPs). The entomological inoculation rate (EIR) was calculated by multiplying the sporozoite and human biting rates from PSCs. Anopheles gambiae and An. funestus complexes were further identified into species by polymerase chain reaction (PCR). Anopheles species with some morphological structures confusing with An. gambiae or An. funestus complexes were father confirmed by PCR. A total of 15 Anopheles species were documented, of which An. demeilloni was the dominant one. Only An. arabiensis was positive for P. falciparum CSP. The overall P. falciparum CSP rate of An. arabiensis was 0.54%. The overall estimated P. falciparum EIR of An. arabiensis from PSC was 1.5 infectious bites/person/five months. Of the 145 freshly fed Anopheles mosquitoes tested for blood meal source, 57.9% (84/145) had bovine blood meal, 22 (15.2%) had human blood meal origin alone and 24 (16.5%) had mixed blood meal origins of human and bovine. An. demeilloni mainly fed on bovine blood (102/126 = 80.9%). Among 420 morphologically classified An. demeilloni, 11 (2.6%) were confirmed as An. lessoni (one of the An. funestus complexes) by PCR. A substantial number of morphologically classified An. salbaii, An. maculipalpis and An. fuscivenosus were found to be An. arabiensis by PCR. Regardless of the high diversity of Anopheles mosquitoes, An. arabiensis is playing the primary role in malaria transmission. Morphological misclassification of species could be a challenge in malaria mosquito monitoring and surveillance, and hence it should be supported by more sensitive techniques for confirmation.

Aggressive mature T-cell lymphoma (TCL) is a disease that carries a poor prognosis. The mechanism, at the functional protein level, of the resistance to therapy of TCL has not yet been well clarified. To comprehensively clarify the mechanism, we first reviewed the literature, as mentioned in the Introduction section, on 22 types of representative functional proteins reported as being potentially associated with treatment resistance, mainly in hematologic tumors. Next, we retrospectively analyzed the tumor cell expressions of these 22 functional proteins in 16 randomly selected patients with TCL who had received CHOP (cyclophosphamide, hydroxyl doxorubicin, oncovin, and prednisolone) therapy as first-line treatment. As a result, we propose a new three-group prognostic classification of TCL (the TCL Urayasu Classification) based on the mechanism of resistance to treatment of these tumors. The study subjects included 7 patients (44%) with peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS), 6 patients (38%) with angioimmunoblastic T-cell lymphoma (AITL), and 3 patients (19%) with anaplastic large T-cell lymphoma (ALCL). Immunohistochemistry was performed in paraffin-embedded tumor tissue sections prepared from these patients as antigens using antibodies against the 22 previously reported treatment-resistance-related proteins to determine the tumor cell expression statuses of these proteins. Log-rank test was performed to compare the overall survival (OS) after CHOP therapy of the patients showing positive and negative immunostaining for these proteins, as well as among patients grouped based on other factors. Overall, the median OS of the TCL patients was about 33.5 months. When compared by the disease type, the prognosis was found to be worse in the order of ALCL, AITL, and PTCL. A multivariate analysis showed that expression of glucose-regulated protein 94 (GRP94), a protein that serves as a pro-survival component under endoplasmic reticulum (ER) stress in the tumor microenvironment, was significantly associated with shortened survival. No significant difference was observed for the conventional poor prognostic factors: high-risk International Prognostic Index (IPI) and high-risk Prognostic Index for peripheral T-cell lymphoma, unspecified (PTCL-U) (PIT). However, when GRP94 was combined with six other factors, listed below, as survival-shortening factors, significant differences were observed. The 6 factors were 1) programmed cell death-ligand 1 (PD-L1) and 2) programmed cell death 1 (PD-1), which are involved in immune surveillance; 3) aldo-keto reductase family 1 member C3 (AKR1C3), which is an enzyme that inactivates HO (hydroxyl doxorubicin [H] and oncovin [O]) of CHOP; 4) P53, a tumor suppressor; 5) Glucose-regulated protein 78 (GRP78), an ER stress protein; 6) thymidine phosphorylase (TP), which is an enzyme involved in thymidine catabolism. Based on the combination of GRP94 and the six other factors expressed in the tumors, we propose a new prognostic classification system for TCL (TCL Urayasu classification). Group 1 (group with a relatively good prognosis): GRP94-negative (n = 6; median OS, 88 months; p < 0.01); Group 2 (group with a poor prognosis): GRP94-positive, plus expression of 2 of the 6 aforementioned factors (n = 5; median OS, 25 months; p > 0.05); and Group 3 (group with a very poor prognosis): GRP94-positive, plus expression of at least 3 of the 6 aforementioned factors (n = 5; median OS, 10 months; p < 0.01). Thus, the TCL Urayasu prognostic classification is a simple, useful, and innovative classification that also explains the mechanism of resistance to treatment for each functional protein. Accumulation of more cases and further analyses are expected in the future to enable clinical application, such as use of a stratified treatment approach and treatment using inhibitors.

Background: the nature of alpha-D-mannose - natural aldohexose sugar, C-2 glucose epimer, whose intended use is for preventing urinary tract infections - in the interaction with E. coli is addressed in order to drive the issue of its regulatory classification as a medicinal product or medical device. Methods: PRISMA systematic review approach was applied; Delphi Panel method used to target consensus on statements retrieved from evidence. Results: Based of regulatory definitions and research evidences, the mechanism of D-mannose does not involve a metabolic or immunological action while there is an uncertainty regarding the pharmacological action. Specific interaction between the product and the bacteria within the body occurs, but its nature is inert: it does not induce a direct response activating or inhibiting body processes. Moreover, the action of D-mannose takes place, even if inside the bladder, outside the epithelium on bacteria that have not yet invaded the urothelial tissue. Therefore, its mechanism of action is not directed to host structures but to structures (bacteria) external to the host's tissues. Conclusions: From a regulatory perspective, D-mannose maintains the current medical device classification: new regulations and legal judgments can add further considerations. From a pharmacological perspective, research is driven versus synthetic mannosides: no further considerations are expected on alpha-D-mannose.

Rationale: Major depressive disorder (MDD) is characterized by signaling aberrations in interleukin (IL)-6, IL-10, beta-endorphins as well as mu (MOR) and kappa (KOR) opioid receptors. Here we examined whether these biomarkers may aid in the classification of unknown subjects into the target class MDD.Methods: The aforementioned biomarkers were assayed in 60 first-episode, drug-naïve depressed patients and 30 controls. We analyzed the data using joint principal component analysis (PCA) performed on all subjects to check whether subjects cluster by classes; support vector machine (SVM) with 10-fold validation; and linear discriminant analysis (LDA) and SIMCA performed on calibration and validation sets and we computed the figures of merit and learnt from the data. Results: PCA shows that both groups were well separated using the first three PCs, while correlation loadings show that all 5 biomarkers have discriminatory value. SVM and LDA yielded an accuracy of 100% in validation samples. Using SIMCA there was a highly significant discrimination of both groups (model-to-model distance=87.5); all biomarkers showed a significant discrimination and modeling power, while 10% of the patients were identified as outsiders and no aliens could be identified.Discussion: We have delineated that MDD is a distinct class with respect to neuro-immune and opioid biomarkers and that future unknown subjects can be authenticated as having MDD using this SIMCA fingerprint. Precision psychiatry should employ SIMCA a) to authenticate patients as belonging to the claimed target class and identify other subjects as outsiders, members of another class or aliens; and b) to acquire knowledge through learning from the data by constructing a biomarker fingerprint of the target class.

The Upper Hunter Valley is a major coal mining area in New South Wales (NSW), Australia. Due to the ongoing increase of mining activities, PM10 (air-borne particle with an aerodynamic diameter less than 10 micrometres) pollution has become a major air quality concern in local communities. The early part of this study identified two air quality subregions in the valley. This paper aims to provide a holistic summarisation of the relationships between elevated PM10 pollution in two subregions and the local- and synoptic-scale meteorological conditions for spring and summer, when PM10 pollution is relatively high. A catalogue of twelve synoptic types and a set of six local meteorological patterns have been quantitatively derived and linked to each other using the self-organising map (SOM) technique. The complex meteorology-air pollution relationships have been visualised/interpretated on the SOM planes for two representative locations. It was found that the influence of local meteorological patterns differed significantly for mean PM10 levels vs the occurrence of elevated pollution events and between air quality subregions. In contrast, synoptic types showed generally similar relationships with mean vs elevated PM10 pollution in the valley. Two local meteorological patterns, the hot-dry-northwesterly wind conditions and the hot-dry-calm conditions, were found most PM10 pollution conducive in the valley when combined with a set of synoptic counterparts. These synoptic types are featured with the influence of eastward migrating continental high pressure system and westerly troughs, or a ridge extending northwest towards coastal northern NSW or southern Queensland from the Tasman Sea. The method and results can be used in air quality research for other locations of NSW, or similar regions elsewhere.

Hands-on gardening is linked with healthy eating behaviors, increased outdoor activities, and overall well-being. Although these positive associations are widely established for adults and school-agers, little evidence can be found on how such relationships may extend to early childhood, especially in the preschool years (3-5 years). One recent study conducted in North Carolina (NC) showed that participating in hands-on gardening significantly increased preschoolers’ accurate identification of fruits and vegetables (FV) and FV consumption compared to children who did not participate in hands-on gardening, but no association was found between participation in hands-on gardening and children’s liking (eating preferences) of FV. FV identification and liking during the early years may lead to lifelong healthy eating behaviors and preferences, making hands-on gardening a critical health intervention in early childhood environments, especially in childcare centers where a majority of the 13 million children in the United States between the ages of 0 and 5 spend most of their waking hours. This research aimed to investigate how hands-on gardening in childcare centers may impact preschoolers’ (3-5 years old) FV knowledge (identification) and liking in a semi-arid climate zone with a high concentration of Hispanic families by conducting experimental research with eight childcare centers and one hundred forty-nine children (n=149) in Lubbock County located in West Texas. Findings showed changes in average liking scores are generally positive in the experiment group of children who participated in hands-on gardening (compared to the control non-gardening group), implying improved liking. The findings indicate that the benefits of hands-on gardening in childcare centers for enhancing healthy eating preferences are evident even in a semi-arid climate zone, where high temperatures and limited rainfall present significant gardening challenges. This research underscores the importance of integrating hands-on gardening into childcare programs, highlighting its potential as an effective obesity prevention strategy not only within the U.S. but also in other regions with similar environmental constraints.

Reliable access to electricity has become a necessity worldwide, and without it, no country can operate health systems and achieve sustainable development goals (SDGs). However, while developed countries have been successfully electrified for many decades, most developing countries have fallen short, contributing to significant impacts, particularly the health sector. The present study explores the relationship between electrification and health vulnerability in developing countries between 1990 and 2020 using the Feasible Generalized Least Square (FGLS) approach. The varied electrification across countries motivates the use of non-linear associations between electrification and health vulnerability. We demonstrate that electrification and health vulnerability have an inverted U-shaped association. Further analysis indicates that most developing countries have achieved a mature level of an inverted U-shaped curve, showing a reduction in health vulnerability with increasing electrification. There are few developing countries, mainly African countries, struggling to reach maturity. The results of this study indicate the need for increased access to electrification, in combination with a reduction in the use of biomass and fossil fuel feedstocks, to mitigate health vulnerability in developing countries.

N-Nitroso-3-morpholinosydnonimine 3 was prepared by nitrosation of SIN-1 and characterized by 1H-NMR, 13C-NMR and HRMS. Its structure, confirmed by single crystal X-ray diffraction analysis, was found in agreement with its mesoionic and aromatic character. Unlike SIN-1, which releases both nitric oxide and superoxide radical, decomposition of this nitrosylated sydonimine could yield nitric oxide as the only decomposition product, and thus without the formation of toxic peroxynitrite.

Klippel-Feil disease is a condition characterized by a defect in the spine, consisting in the fusion or non-separation of two or more vertebrae of the cervical tract. It usually affects 1 every 50.000 newborns, and the pathogenesis remains unknown to date, although the role of certain genes deputed in segmentation processes is being studied. A single case of gene Myosin Heavy Chain 3 (MYH3) mutation is described by researchers. Affected patients are often characterized by a relatively short neck and, therefore, poor mobility, a low hairline and obesity and may have other health problems of different nature. The frequent presence of comorbidities worsens the quality of life of these young patients. The following case describes the synergistic effect of Incobotulinum toxin type A and physiotherapy in a small patient with MYH3 mutation-related Klippel-Feil Syndrome (KFS) complicated by bilateral paraplegia, to improve the spasticity condition of the lower limbs. To assess improvements over time, the patient underwent rating scales to determine spasticity (Modified Ashworth Scale: MAS), the neck’s range of motion (ROM), and muscle tone by using MyotonProÒ. Specifically, measurements were taken on the day of the first medical examination (T0), the month after the injection and the startup of therapeutic exercise (T1), at three months (T2), and then once a month for a total of 6 months (T3, T4 and T5). This therapeutic approach led to entirely satisfactory results with the child’s well-being, prolonged until the sixth month and with a total absence of any side effects.

Extracellular electron transfer (EET) is key to the success of microbial fuel cells (MFCs). Clostridium sp. often occurs in MFC anode communities, but its ability to perform EET remains controversial. We have employed Clostridium pasteurianum DSM 525 as biocatalyst in a glycerol-fed MFC, designated MFC_DSM. We have also followed the EET of this biocatalyst in the presence of a mediator, namely soluble neutral red (NR), soluble methyl viologen (MV), neutral red film (FNR), or methyl viologen film (FMV). MFC_DSM provided power and current densities (j) of 0.39 mW.cm^-2 and 2.47 mA.cm^-2, respectively, which evidenced that the biocatalyst performs direct electron transfer (DET). Introducing 150.0 µM NR or MV into the MFC_DSM improved the current density by 7.0- and 3.7-fold (17.05 and 8.45 mA.cm^-2), respectively. After 20 cyclic voltammetry (CV) cycles, the presence of FNR in the MFC_DSM anodic chamber provided almost twofold higher current density (30.76 µA.cm^-2) compared the presence of NR in the MFC_DSM. Introducing MV or FMV into the MFC_DSM anodic chamber gave practically the same current density after 10 CV cycles. The MFC_DSM anodic electrode might interact with FMV weakly than with FNR, so FNR is more promising to enhance C. pasteurianum DSM 525 EET within MFC_DSM.

Background. The multisystemic features of Down syndrome (DS) in children are accompanied by an immunodeficiency, making them susceptible to infections and immune dysregulation with autoimmune, allergic, inflammatory, and hematological complications. This study was aimed at a better understanding of the abnormalities within the B and T cell compartments and their correlations with clinical immunophenotypes. Methods. Medical records of 35 DS children were retrospectively reviewed, referring to clinical symptomatology including history of infections, immune dysregulation disorders, and humoral and cellular immune response. Results. While the etiology of respiratory tract infections included typical viral and bacterial pathogens, SARS-CoV2-induced inflammatory disease and syndromic immunodeficiency contributed significantly to deterioration of the clinical course. Allergic diseases in the form of asthma, allergic rhinitis, and alimentary allergy were the most frequent manifestations of immune dysregulation and were followed by autoimmune disorders, such as Crohn disease, celiac disease, autoimmune thyroiditis, and alopecia, as well as inflammatory disorders, balanitis xerotica obliterans and lymphadenopathy, and a hematological disorder of myelopoiesis. Deficiency of serum immunoglobulin levels, reduced numbers of naïve B cells, and non-switched memory B cells along with low naïve T helper cells and significantly reduced regulatory T helper cells were the most prominent immune abnormalities. Conclusions. The loss of naïvete in B and T lymphocyte compartments with a deficiency of regulatory T cells may be underpinning pathomechanisms for the skewed immune response. The clinical immunophenotype in DS is complex and represents syndromic inborn error of immunity with immune dysregulation.

This study investigates job satisfaction, burnout, and well-being among first-generation migrant physicians in anesthesia and intensive care medicine in Germany, comparing them to their native German counterparts. Utilizing a cross-sectional survey design, the study collected data from 513 physicians, 110 of whom identified as having a migration background. Job satisfaction was measured using the German version of the Warr-Cook-Wall (WCW) Job Satisfaction Scale, burnout was assessed with the Copenhagen Burnout Inventory (CBI), and well-being was evaluated using the WHO-5 Well-Being Index. The job satisfaction ratings revealed no significant differences between migrant and German physicians in most dimensions, including physical workload, freedom to choose work methods, satisfaction with colleagues, responsibility, income, skill utilization, and variety in work tasks. However, migrant physicians reported significantly higher satisfaction with recognition received for their work and lower dissatisfaction with working hours. Burnout assessments showed that migrant physicians experienced higher psychological strain, perceiving every work hour as more exhausting and having significantly less energy for family and friends. Notably, migrant physicians reported higher difficulty and frustration in working with patients. Well-being ratings indicated that migrant physicians felt less energetic and active but found their daily life more filled with interesting activities. The findings underscore the unique challenges faced by migrant physicians, particularly in terms of recognition and patient-related burnout. These results highlight the need for targeted interventions to support migrant physicians, including cultural competence training and flexible working hours to enhance their job satisfaction and overall well-being. Addressing these issues is crucial for maintaining the quality of patient care and the occupational health of migrant physicians in Germany.

As the demand for long-distance flights continues to increase, supersonic transport (SST) has once again become a hot research topic. However, due to its uniqueness, there was few corresponding evaluation methods to compare the supersonic civil aircraft with common subsonic aircraft. This paper proposed an evaluation method for the competitiveness of supersonic civil aircraft based on the Analytic Hierarchy Process (AHP). Key indicators were selected to form a competitiveness evaluation model from the perspective of four criterion factors of economy, comfort, adaptability, and environment. The evaluation method proposed in this paper could reflect characteristics of supersonic civil aircraft and other subsonic aircraft, and effectively compare their competitiveness. This work can be used to evaluate the social acceptance of supersonic civil aircraft and provide reliable decision-making support for airlines' design work.

Food insecurity remains a significant developmental challenge, particularly in rural areas, despite ongoing efforts to mitigate it. To better understand the persistence of this issue, this study investigates the causes of household food insecurity and the coping strategies employed by households in Zorkor Goo, Zorkor Kanga, and Zorkor Kadaare communities within the Bongo District of Ghana’s Upper East Region. A descriptive cross-sectional survey was conducted, sampling 196 households systematically from these communities. Data were collected through a structured questionnaire covering socio-demographic factors, causes of food insecurity, household coping strategies, and dietary intake. The analysis was performed using SPSS (Version 21.0), with food security status and dietary intake assessed via the FANTA Household Food Insecurity Access Scale (HFIAS) and Food Consumption Score, respectively. The study identified key factors contributing to food insecurity, including soil infertility, poor rainfall patterns, food spoilage, extravagant funerals, large family size, high unemployment, and lack of education. Major coping strategies among households included meal skipping, reducing meal size and frequency, migration, asset sales, and basket weaving. The findings revealed that 96.4% of households were severely food insecure, and over two-thirds (67.3%) exhibited poor dietary consumption. Notably, household employment levels, education, and food consumption scores were significantly associated with food insecurity (P&lt;0.001). These findings underscore the need for targeted interventions addressing the root causes of food insecurity and enhancing household resilience in these communities.

This study employed the Allium cepa bioassay to evaluate the toxic effects of contaminants in the Yucatan aquifer. Seven monitoring wells were studied during September and October 2021. Nutrient concentrations showed significant variation between sites, with samples closer to the coast (P3 and P7) presenting higher ammonia and phosphate concentrations. The pesticides found at the highest concentration were δ-HCH and chlorpyrifos, with 141.44 and 175.92 ng/L, respectively. Heptachlor and aldrin were present in sites P4oct and P2sept. Interestingly, DDT values were highly correlated with caffeine concentrations. Acenaphthylene, acenaphthene, fluorene, phenanthrene, and the sum of B(k)fluoranthene and B(b)fluoranthene presented the highest prevalence. B(k)fluoranthene and B(b)fluoranthene were the PAHs found at the highest concentration. The results of the A. cepa bioassay indicated no nuclear abnormalities. The study also found no statistical differences in the mitotic index, root length, biomarkers of oxidative stress, and inhibition of B-esterases between sites and controls. In summary, the wells sampled in the present study had low concentrations of contaminants that can be used as a proxy of anthropogenic discharges; the lack of effect in the biomarkers used at organism, cellular, and biochemical levels indicated no toxic effect on A. cepa roots.

Objectives: Epidemiologic and experimental animal studies have shown that stress can alter tumor growth and that adrenergic fibers from the sympathetic nervous system, acting through stromal β2-adrenergic receptors, promote tumor cell survival during the initial phases of cancer development. Recent epidemiological data suggest that beta blocker intake improves survival of prostate cancer patients. Methods: Signaling mediated by β-adrenergic receptors promotes neuroendocrine differentiation in prostate cancer cell line. Although neuroendocrine cells are a minor population in the epithelial compartment of normal prostate glands, the population of neuroendocrine-like cells that express markers such as SYN, Bcl-2, Dll-3, and AMACR correlates with cancer progression, androgen-independent state, and poor prognosis in prostate cancer patients. As recent studies show that an increase in neuroendocrine cell density occurs before development of benign prostatic hyperplasia (BPH), a non-lethal disease affecting the transition zone, in hypertensive rats, we investigated whether neuroendocrine differentiation is associated with BPH. Results: Our results showed that isobutyl-methyl-xanthine and forskolin leading to sustained activation of the cAMP/PKA pathway led to an increase in neuroendocrine differentiation markers in BPH-1 cell line. Similar results were observed by combining the β-adrenergic receptors agonist isoproterenol with forskolin. These treatments reduced BPH-1 cell proliferation rates by 40% and 60%, respectively. Effects on neuroendocrine markers expression and cell proliferation were fully reversed by the β2 blocker carvedilol. Conclusion: These data suggest that β-adrenergic receptors are linked to the development of neuroendocrine-like cells in BPH. As 20% of the prostate cancer tumors occur in this zone, this observation warrants further investigation.

Polymer-modified fertilizers (PMFs) with prolonged nutrient release present a promising solution to address the challenges associated with conventional fertilization practices, particularly for sensitive crops such as strawberries. This study investigates the effectiveness of biodegradable PMFs in maintaining nutrient availability at optimal levels while minimizing root burn and nutrient losses. A factorial field experiment was conducted to evaluate the impact of varying concentrations of polyvinyl alcohol (PVA) on the nutrient release rates from complex NPK fertilizer and monoammonium phosphate, as well as their subsequent effects on strawberry yield and soil properties. Results indicate that polymer modifications significantly slow down nutrient release, leading to prolonged nutrient retention in the soil at salt levels that are optimal for maximizing yield while remaining low enough to prevent the risk of root burn. Consequently, the application of PMFs not only enhances strawberry yield but also promotes healthier plant growth by ensuring a steady supply of nutrients throughout the growing season without inducing stress. The findings underscore the potential of biodegradable polymers to optimize fertilizer efficiency, reduce environmental impacts, and support sustainable agricultural practices. This research provides valuable insights into the development of more effective fertilization strategies for strawberry cultivation and other sensitive crops using PMFs.

This study presents the development and evaluation of a Multi-Task Long Short-Term Memory (LSTM) model with an Attention Mechanism designed to predict students' academic performance. The model concurrently addresses two tasks: predicting overall performance (total score) as a regression task and categorizing performance levels (remarks) as a classification task. By processing both tasks simultaneously, the model optimizes computational efficiency and resource use. The dataset includes detailed student performance records across various metrics such as Continuous Assessment, Practical Skills, Demeanor, Presentation Quality, Attendance, and Participation. The model's performance was evaluated using comprehensive metrics. For the regression task, it achieved a Mean Absolute Error (MAE) of 0.0249, Mean Squared Error (MSE) of 0.0012, and Root Mean Squared Error (RMSE) of 0.0346. For the classification task, it attained perfect scores with an accuracy, precision, recall, and F1 score of 1.0. These results highlight the model's high accuracy and robustness in predicting both continuous and categorical outcomes. The Attention Mechanism enhances the model's capabilities by identifying and focusing on the most relevant features. This study demonstrates the effectiveness of the Multi-Task LSTM with Attention Mechanism in educational data analysis, offering a reliable tool for predicting student performance and potential broader applications in similar multi-task learning contexts. Future work will explore further enhancements and wider applications to improve predictive accuracy and efficiency.

In this study, Fe, N co-doped biochar (Fe@N co-doped BC) was synthesized by carbonization-pyrolysis method and used as carbocatalysts to activate peroxymonosulfate (PMS) for sulfamethoxazole (SMX) removal. In the Fe@N co-doped BC/PMS system, the degradation rate of SMX (10.0 mg⋅L−1) was 90.2% within 40 min under optimal conditions. The radical quenching experiments, and electron spin resonance (ESR) analysis suggested that sulfate radicals (SO4•−), hydroxyl radicals (•OH) and singlet oxygen (1O2) participated in the degradation process. After the reaction, the proportion of pyrrolic N apparently decreased from 57.9% to 27.1%. Pyrrolic N served as active sites to break the inert carbon network structure and promote the generation of reactive oxygen species (ROSs). Besides, pyrrolic N showed stronger interaction with PMS and significantly reduced the activation energy required for the reaction (∆G = 23.54 kcal/mol). The utilization potentiality of Fe@N co-doped BC was systematically evaluated in terms of its reusability, and selectivity to organics. Finally, the intermediates of SMX were also detected.

The emergence of few-shot learning as a potential approach to address the problem of data scarcity by learning underlying pattern from a few training sample had so far given a mix-result especially on the suitability of model-agnostic meta learning, transfer learning, and optimization strategy to rapidly learn valid information from few sample. In this research, we did an in-depth evaluation of meta- learning to determine their plausibility and suitability for previously unknown cyberattack detection by first retrieving the original research artifacts of current state of the art meta learning to repeat the experiment with original dataset before replicating the experiment with two different malware dataset which had not been previously done with meta-transfer learning. On each of the experiments, meta-transfer learning gave good results on digital character recognition dataset but abysmal result on Malimg and Malevis malware images datasets thereby indicating its unreliability for detecting cyberattacks and the need for an improvement to the state-of-the-art meta transfer learning towards a better attack detection. Transfer learning performance is independent on imbalance and hence does not influence its performance since both malware dataset used for this experiment result in high validation loss and balancing the dataset doesn't result in reduced validation loss,the successful learning transfer seen on digital character recognition dataset is not unconnected to the fact that several languages have similar characters and digits thereby enhancing the successful learning transfer unlike malware datasets, and more importantly the finding that current meta-learning transfer approach doesn't generalize well on malware dataset and hence not suitable for detecting previously unseen out-of-distribution attack.

The minimum ignition energy (MIE) has been extensively studied via experiments and simulations. However, our literature review reveals little quantitative consistency, with results varying for f=1.0 from 0.324 to 1.349 mJ, and for f=0.9 from 0.22 to 0.944 mJ. Therefore, there is a need to resolve these discrepancies. This RANS study aims to partially address this knowledge gap. Additionally, it presents other flame evolution parameters essential for robust combustion design. Using the reactingFOAM solver we predict the threshold energy required to ignite the fuel mixture. For this, the single step using the Arrhenius law is selected to model ignition in the flame kernel of stochiometric and lean CH₄/air mixtures, allowing it to develop into a self-sustained flame. The ignition power density, an energy quantity normalised with volume is incrementally varied keeping the kernel critical radius r_s constant 0.5 mm in the quiescent mixture of two equivalence ratios fs 0.9, and 1.0, for varied operating pressures 1, 5 and 10 bar at constant initial temperature 300 K. The minimum ignition energy is validated with twelve independent 1 bar data sets both numerical and experiments. Finally, a mathematical formulation of MIE is devised, a function of pressure and equivalence ratio shows a slightly curved relationship.

Bicuspid aortic valve(BAV) is a common congenital heart defect often associated with ascending aorta dilation. Current guidelines suggest surgical intervention for diameters ≥45mm in conjunction with surgical aortic valve replacement(SAVR). However, the optimal management of ascending aortas measuring 40-44mm remains controversial. Objectives: This study aims to evaluate the evolution of aortic diameter in patients with BAV and ascending aorta diameters between 40 and 44 mm undergoing AVR, assessing whether a conservative approach is justified. Methods: We conducted a retrospective observational study, analyzing 88 BAV patients who underwent SAVR between January 2012 and December 2018. Patients were divided into two groups based on preoperative ascending aorta diameters: &lt;40 mm (Group 1, n=15) and ≥40 and &lt;45 mm (Group 2, n=11). Follow-up of at least 5 years included aortic dilation rates, survival, and reoperation rates. Results: No significant differences were observed in aortic dilation between the two groups during follow-up, with overall annual dilation rates of 0.2±0.07mm/year. Survival rates were 86.3% for Group 1 and 81.9% for Group 2, with no reoperations required in either group. Conclusions: Patients with BAV and ascending aorta diameters of 40-44 mm do not require prophylactic aortic replacement during AVR, provided they undergo regular follow-up. These findings support current guidelines advocating conservative management for ascending aorta diameters in this range. However, in younger patients or those nearing the 45 mm threshold, surgical replacement may still be considered to preempt future complications.

In this paper, we propose "Synergized Efficiency Optimization for Large Language Models" (SEO-LLM), a groundbreaking approach that integrates advanced data utilization and model compression techniques to significantly enhance the performance, efficiency, and scalability of large language models (LLMs). Our method synergistically combines Adaptive Data Augmentation (ADA), Transfer- Active Learning (TAL), Adaptive Iterative Pruning (AIP), and Synergistic Quantization and Distillation (SQD). These components work together to reduce the training data requirement by 30%, compress model size by 67.6%, and improve inference speed by up to 50%, while preserving or even enhancing model accuracy across various NLP tasks. ADA dynamically adjusts augmentation strategies to optimize model generalization, while TAL leverages pre-trained models to focus learning on the most informative data samples. AIP intelligently prunes less significant weights, and SQD harmonizes quantization with knowledge distillation to achieve high compression rates without significant performance loss. The synergy between these techniques makes SEO-LLM a robust solution for deploying LLMs in resource-constrained environments, maintaining state-of-the-art performance with a fraction of the computational and data resources.

This study investigates the necessity of particle asymmetry for self-propulsion in nanomotors. While conventional wisdom posits that asymmetric designs are crucial for generating phoretic forces or localized bubble propulsion, recent research suggests that symmetrical particles may also exhibit motility. To address this debate, we developed a robust workflow for synthesizing gold grafted silica nanoparticles with precise control over size and shape, enabling the direct comparison of their motile behavior by dynamic light scattering and particle tracking velocimetry. Our results indicate that the inherent asymmetry generated during isotropic gold nanoparticle deposition onto silica surfaces may enable particle motility.

Utilising the metabolic processes (urease activity) of certain microorganisms that result in the precipitation of calcium carbonate (CaCO₃) as a sustainable method for manufacturing self-healing cementitious materials for use in the construction, soil stabilization, and wind-induced erosion industries has garnered considerable attention over the last twenty years. Despite extensive efforts for experimental characterization of the effect of numerous influential factors such as the bacteria type, nutrition type and quantity, and environmental conditions governing this phenomenon, computational modeling of this biochemical process has not advanced significantly due to its complexity and intertwined involved parameters. Among these parameters, pH is of special significance since the initial pH level has an immediate effect on the bacteria's urease activity, which then influences the rate at which calcium carbonate precipitates. Furthermore, during the CaCO₃ precipitation process, pH changes due to the generation of byproducts such as ammonium, which alters the velocity of the bacteria's urease activity continuously.The present study proposes a computational model for calcium carbonate precipitation by urease activity of Sporosarcina pasteurii using COMSOL Multiphysics^®. The theoretical background on governing parameters and chemical reactions involved in the process are discussed. The model takes into account the impact of calcium and urea concentrations, as well as the initial pH level and pH variations caused by the production of by-products during the process. The capability of the model to foresee CaCO3 concentration and ultimate pH level is evaluated by comparing the computational outcomes with empirical data obtained from established literature sources. The influence of the initial pH of the environment on the pH variation of the system during the precipitation process was simulated and compared to actual data available in the literature, suggesting that the model can accurately predict the kinetic. Finally, a parametric analysis is performed to identify the ideal initial environmental pH for calcium carbonate precipitation by the S. pasteurii bacterium.

Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combinations of factors/genes/proteins that might be affecting the pathway under certain conditions. In colorectal cancer (CRC) cells treated with ETC-1922159, many genes were found up and down regu- lated, individually. A recently developed search engine ranked combinations of AN- TXR cell adhesion molecule 2 (ANTXR2)-X (X, a particular gene/protein) at 2nd order level after drug administration. These rankings reveal which ANTXR2-X combinations might be working synergistically in CRC. If found true, oncologists can further test the combination of interest in wet lab and determine the mechanism of functioning be- tween the ANTXR2 and X. In this research work, we cover combinations of ANTXR2 with Collagen (COL), Integrin (ITG), Matrix metalloproteinases (MMP), WNT, TNF and Interleukin (IL) .

(1) Telemedicine in diabetic retinopathy (RD) screening is effective but does not reach the entire diabetes population. The use of portable cameras and artificial intelligence (AI) can help diabetes screening. (2) Methods: We evaluated the ability of two handheld cameras, one based on a smartphone and the other on a smartscope, to obtain images comparing OCT. Evaluation was done in two stages, the first by two retina specialists and the second using an artificial intelligence algorithm that we developed. (3) Results: The retina specialists report that the smartphone images must mydriasis in all cases compared to 73.05% of the smartscope images and 71.11% of the OCT images. Images were ungradable in 27.98% of the retinographs with the smartphone and 7.98% with the smartscope. The detection of any-DR using the AI algorithm showed that the smartphone obtained lower recall values (0.89) and F1scores (0.89) than the smartscope, with 0.99. The detection of Mild-DR using the smartphone also obtained low results (146 retinographs) compared to the smartscope (218 retinographs). (4) Conclusions: The use of handheld devices together with AI algorithms for reading retinographs can be useful for DR screening, although these devices need to improve the ease image acquisition through small pupils.

Probiotics, known for regulating gut microbiota, may aid those with overweight or obesity, but their mechanisms require more research. This study involved 75 overweight or obese young adults, randomly assigned to either a Bifidobacterium brevis BBr60 group or a placebo group. Both groups received diet guidance and took either BBr60 (1×1010 CFU/day) or a placebo for 12 weeks. Re-searchers analyzed body composition, serum glucose, lipids, liver and kidney function, comprehensive metabolome, and intestinal homeostasis before and after the intervention. After 12 weeks, BBr60 significantly reduced weight and BMI compared to pre-treatment levels and outperformed the placebo. The BBr60 group also showed improved blood biochemistry, with notably lower fasting blood glucose (FBG) levels than the placebo group (p < 0.05). Additionally, BBr60 influenced vital serum and fecal metabolites related to three amino acid metabolic pathways and regulated the bacteria Dialister, Klebsiella, and Bacteroides, which correlated strongly with serum metabolites. These findings indicate that BBr60 can safely and effectively regulating BMI, body weight, serum glucose, lipids, and liver function markers, which may involve BBr60's impact on key gut bacteria, which influence metabolites related to the valine, leucine, and isoleucine biosynthesis; glycine, serine, and threonine metabolism; and alanine, aspartate, and glutamate metabolism.

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

This paper assessed rice farmers' perceptions of agricultural and irrigation practices through their choices. A survey was carried out among 278 rice farmers on five irrigation schemes in three agro-ecological zones of Togo, viz. the savannah region, the forest region and the coastal region. The sample of random respondents was selected from a list of rice farmers provided by the managers of these irrigated schemes. Data collected through face-to-face interviews using kobocollect software were analyzed with R 4.2 software, and the Pearson test was validated at the 5% level. The results showed that the socio-economic characteristics of the rice growers, such as access to agricultural credit, experience, non-agricultural activities, membership of a rice growers' cooperative, gender, level of education and training in growing irrigated rice, and farm labor, significantly influenced the choice of agronomic practices and irrigation management. Characteristics such as experience, training and hired farm labor enhanced paddy yields, while access to agricultural credit was unproductive. Chemical clearing combined with stump removal, tiller ploughing, transplanting and high plant density are the agricultural practices that increased paddy yields, while tiller mudding and basal mineral fertilization were inefficient. In the same way, water distribution by demand, water depth above the soil of less than 5 cm, use of water-saving technologies and farmer's knowledge of how to improve water and fertilizer use-efficiency also helped to boost paddy yields. For sustainable rice production in the context of labor shortages and the financial resources required, future studies will focus on the effectiveness of motorized mudding and agricultural credits for rice farmers. Furthermore, considering the crucial role of training in the adopting of good agricultural and irrigation practices, future research will study strategies for wide diffusion of knowledge to rice producers.

Background: Post COVID-19 syndrome or Long Covid (LC) is a novel fluctuating condition with a protracted course in some patients. Specialist LC services have been operational in the UK since 2020 and deal with a high caseload of patients. Aims: To evaluate long-term outcomes in patients discharged from a community-based LC specialist service. Methods: A service evaluation study that included patients who were well engaged in the services [completed the standard Patient Reported Outcome Measures (PROMs) and received intervention from clinician(s)] and had been discharged for at least 3 months from the service. They consented to the study and completed standard PROMs: COVID-19 Yorkshire Rehabilitation Scale (C19-YRS), EQ-5D-5L, and National Institute for Health and Care Excellence (NICE) criteria for Myalgia Encephalomyelitis/ Chronic Fatigue Syndrome (ME/CFS). Results: Out of 460 patients contacted, 112 (average of 37.6 months since infection and 9.8 months post-discharge) completed the PROMs. 90.2% patients continued to experience LC symptoms and disability and had not returned to their pre-COVID health status. The average EQ-5D-5L index score was 0.53 (SD 0.29) highlighting a significant disability and that LC had become a long-term condition (LTC) in majority of patients who responded to the survey. 43% patients met the criteria for suspected ME/CFS. Conclusion: A proportion of LC patients develop Persistent Long Covid (PLC) consistent with a LTC and had a significant overlap with ME/CFS.

Background: Lung cancer remains the leading cause of cancer-related mortality globally, predo-minantly affecting older individuals. Despite the increasing use of immune checkpoint inhibitors (ICIs) like nivolumab in non-small cell lung cancer (NSCLC), the efficacy and safety in elderly pa-tients, particularly those aged 65 and above, remain underexplored due to their underrepresenta-tion in clinical trials. Methods: This retrospective study analyzed data from 60 elderly patients (≥65 years) with metastatic NSCLC who received nivolumab as second-line or later therapy between January 2020 and May 2023. Results: The median age was 67 years, with a predominance of males (78%). Nivolumab was administered for a median of 8 cycles, with 33.3% of patients recei-ving 15 or more cycles. The median OS was 23 months, and the 1-, 3-, and 5-year survival rates were 93.3%, 54.1%, and 18.6%, respectively. Multivariate analysis identified adenocarcinoma histology, fewer than 15 cycles of nivolumab, and non-response to prior therapies as independent predictors of poor OS. Nivolumab treatment was generally well-tolerated, with 45% of patients experiencing at least grade 1 toxicity. Conclusion: Nivolumab is effective and well-tolerated in elderly patients with metastatic NSCLC, providing survival benefits comparable to those observed in younger populations. The number of treatment cycles and initial response to therapy are key determinants of survival, underscoring the importance of continued treatment in this age group.

Osteomyelitis is a significant cause of total carcass condemnation in pigs at the slaughterhouse. The decision for total condemnation of a pig carcass for osteomyelitis is often based on traditional perceptions of the risk of pyemia, leading to controversy among Official Veterinarians (OV) in the industry. This review aims to provide a more comprehensive understanding of the etiopatho-genesis of osteomyelitis in pigs, the microorganisms involved, and the risk factors. It also high-lights the urgent need for a more uniform method to evaluate osteomyelitis cases, which could significantly reduce economic losses in the industry. Lesions originating from tail-biting, tail docking, castration, teeth resection, and raw management are described as risk factors for oste-omyelitis. Osteomyelitis is caused by the entry of pathogens into the bloodstream of the animal through an open wound. The most described pathogens are Trueperella monocytogenes, Staphylo-coccus aureus, and Streptococcus spp. At slaughter, OVs condemn carcasses with osteomyelitis due to pyemia. Signs of acute disease are essential to identify pyemia cases. In chronic cases, total carcass condemnation can be avoided depending on the number of lesions and number of vertebrae af-fected. A clear overall image of the problem would help authorities in various countries adopt a more homogenous approach.

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.

Introduction : In today’s competitive business landscape, organizations strive to maintain a highly committed workforce to enhance productivity, reduce turnover, and achieve long-term success. Organizational commitment, defined as the psychological attachment of employees to their organization, is crucial in fostering employee loyalty and engagement (Meyer & Allen, 1991). However, identifying the factors that influence organizational commitment requires a deep understanding of employee attitudes and behaviors.

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

Let an odd integer \(\mathcal{X}\) be expressed as $\left\{\sum\limits_{M > m} b_M 2^M\right\} + 2^m - 1,$ where $b_M \in \{0, 1\}$ and $2^m - 1$ is referred to as the Governor. In Collatz-type functions, a high index Governor is eventually reduced to $2^1 - 1$. For the $3\mathcal{Z} + 1$ sequence, the Governor occurring in the Trivial cycle is $2^1 - 1$, while for the $5\mathcal{Z} + 1$ sequence, the Trivial Governors are $2^2 - 1$ and $2^1 - 1$. Therefore, in these specific sequences, the Collatz function reduces the Governor $2^m - 1$ to the Trivial Governor $2^{\mathcal{T}} - 1$. Once this Trivial Governor is reached, it can evolve to a higher index Governor through interactions with other terms. This feature allows $\mathcal{X}$ to reappear in a Collatz-type sequence, since $2^m - 1 = 2^{m - 1} + \cdots + 2^{\mathcal{T} + 1} + 2^{\mathcal{T}} + (2^{\mathcal{T}} - 1).$ Thus, if $\mathcal{X}$ reappears, at least one odd ancestor of $\left\{\sum\limits_{M > m} b_M 2^M\right\} + 2^{m - 1} + \cdots + 2^{\mathcal{T} + 1} + 2^{\mathcal{T}} + (2^{\mathcal{T}} - 1)$ must have the Governor $2^m - 1$. Ancestor mapping shows that all odd ancestors of $\mathcal{X}$ have the Trivial Governor for the respective Collatz sequence. This implies that odd integers that repeat in the $3\mathcal{Z} + 1$ sequence have the Governor $2^1 - 1$, while those forming a repeating cycle in the $5\mathcal{Z} + 1$ sequence have either $2^2 - 1$ or $2^1 - 1$ as the Governor. Successor mapping for the $3\mathcal{Z} + 1$ sequence further indicates that there are no auxiliary cycles, as the Trivial Governor is always transformed into a different index Governor. Similarly, successor mapping for the $5\mathcal{Z} + 1$ sequence reveals that the smallest odd integers forming an auxiliary cycle are smaller than $2^5$. Finally, attempts to identify integers that diverge for the $3\mathcal{Z} + 1$ sequence suggest that no such integers exist.

Worldwide the severity of abiotic stress like heat, drought, salinity, cold, waterlogging, etc. are being increased because of global warming. Wheat crops must adapt to harsh environments through physiological, molecular, and genetic manipulation. Because of abiotic stress, reactive oxygen species (ROS) and reactive nitrogen species (RNS) produced in wheat plants break down DNA and RNA bases, protein, lipid, and other macro- and micromolecules, resulting in stunted growth and yield decline. Abiotic stress also induces the crop's molecular functions, physiological, biological, and cellular processes, and their compositions. Different stressors like high light intensity, heat, drought, and salinity also impair photosynthesis damaging various pigments. Antioxidant enzymes, such as catalase (CAT), superoxide dismutase (SOD), peroxidase (POD), and ascorbate peroxidase (APX), etc. synthesized in harsh environments can ameliorate ROS/RNS produced in oxidative stress. Under abiotic stress, heat shock proteins (HSPs) heat shock transcription factors (HSFs) in heat stress, and dehydration-responsive element binding proteins (DREBs) in drought stress are activated to synthesize proteins to mitigate the deleterious effects of those stressors. Na⁺ exclusive genes (NAX1, NAX2) and Na⁺/H⁺ (K⁺) antiporter genes (NHX1, NHX2) hinder those ions from uptake into leaf sheaths and blades of wheat resulting in the declination of salinity in plants. Exogenous hormones like gibberellic acid, salicylic acid, and melatonin, etc., and osmoprotectants like proline, and gene manipulation in plants through genetic engineering are being employed to mitigate the toxic effects of abiotic stress on plants, as portrayed in many literatures. The review depicts how abiotic stress affects phenotypic, physiological, and molecular structures and functions of wheat crops, and their mitigation strategies to lessen their noxious effects on wheat.

Advances in diagnostic procedures have led to an increasing rate of diagnosis of autoimmune encephalitis or paraneoplastic neurological syndrome (AE/PNS) among patients with progressive supranuclear palsy (PSP)-like manifestations. The antibodies involved in these conditions include anti-IgLON5, -Ma2, and -Ri antibodies, each of which has a characteristic clinical presentation. The steps in the diagnosis of AE/PNS in patients with PSP-like manifestations include (i) suspicion of AE/PNS based on clinical presentations atypical of PSP and (ii) antibody detection measures. Methods used to identify antibodies include a combination of tissue-based assays and confirmatory tests. The primary confirmatory tests include cell-based assays and immunoblotting (line or western blot). Treatments can be divided into immunotherapy and tumor therapies, the former of which includes acute and maintenance therapies comprising corticosteroids, intravenous immunoglobulins, plasmapheresis, rituximab, and cyclophosphamide. One of the major challenges of diagnosis is that existing reports on PSP-like patients with AE/PNS include only case reports, with the majority discussing antibodies other than anti-IgLON5 antibody. As such, more patients need to be evaluated to establish the relationship between antibodies and PSP-like manifestations.

Background: Ensemble tree-based models such as Xgboost are highly prognostic in cardiovascular medicine, as measured by the Clinical Effectiveness Metric (CEM). However, their ability to handle correlated data, such as hospital-level effects, are limited. Objectives: The aim of this work is to develop a binary outcome mixed effects Xgboost (BME) model that integrates random effects at the hospital level. To ascertain how well the model handles correlated data in cardiovascular outcomes, we aims to assess its performance and compare it to fixed effects Xgboost and traditional logistic regression models. Methods: A total of 227,087 patients over 17 years of age, undergoing cardiac surgery from 42 UK hospitals between 1 Jan 2012 and 31 Mar 2019 were included. The dataset was split into two cohorts: Training/Validation (n = 157196; 2012-2016) and Holdout (n = 69891; 2017-2019). The outcome variable was 30 days mortality with hospitals considered as clustering variable. The logistic regression, mixed effects logistic regression, Xgboost and binary outcome mixed effects Xgboost (BME) were fitted to both standardized and unstandardized datasets across a range of sample sizes and the estimated prediction power metrics were compared to identify the best approach. Results: The exploratory study found high variability in hospital-related mortality across datasets, which supported the adoption of mixed effects models. Unstandardized Xgboost BME demonstrated marked improvements in predictor power over the Xgboost model at small sample size ranges, but performance differences decreased as dataset sizes increased. Generalized linear models (glm) and generalized linear mixed-effects models (glmer) models followed similar results, with Xgboost models excelling also at greater sample sizes. Conclusions: These findings suggest that integrating mixed effects into machine learning models can enhance their applicability in clinical settings where sample size is small such as rare conditions.

In this paper, we investigate whether greedy algorithms, traditionally used for pedestrian-based crowdsensing, remain effective in the context of vehicular crowdsensing (VCS). Vehicular crowdsensing leverages vehicles equipped with sensors to gather and transmit data to address several urban challenges. Despite its potential, VCS faces issues with user engagement due to inadequate incentives and privacy concerns. In this paper, we use a dynamic incentive mechanism based on a recurrent reverse auction model, incorporating vehicular mobility patterns and realistic urban scenarios using the Simulation of Urban Mobility (SUMO) traffic simulator and OpenStreetMap (OSM). By selecting a representative subset of vehicles based on their locations within a fixed budget, our mechanism aims to improve coverage and reduce data redundancy. We evaluate the applicability of successful participatory sensing approaches designed for pedestrian data and demonstrate their limitations when applied to VCS. This research provides insights into adapting greedy algorithms for the particular dynamics of vehicular crowdsensing.

Background/Objectives: Traditional neurosurgical training involves a steep learning curve. The introduction of advanced simulation technologies, such as virtual reality (VR), provides an alternative method for skill acquisition, allowing for repeated practice and objective assessment. This study focuses on evaluating the learning curve associated with lumbar pedicle Kirschner wire insertion using a virtual fluoroscopic simulator among neurosurgical residents. Methods: Eighteen neurosurgery residents and two interns participated in this study. Participants’ initial skill levels were assessed through a questionnaire. Performance metrics, including insertion accuracy, time, and the number of virtual X-ray scans, were recorded across multiple attempts until a “practical learning plateau” was reached. Statistical analyses were conducted to model learning curves, assess the correlation between pre-training experience and performance. Results: The average number of attempts required to stabilize performance was 5.61. Learning rates varied, with some residents showing rapid improvement while others progressed more slowly. Higher accuracy in wire placement correlated with reduced usage on X-rays over time. However, experienced residents did not necessarily learn faster, indicating potential challenges in adapting to new simulation-based methods. Post-training feedback highlighted the simulator’s utility in increasing confidence and skill levels, although some limitations in anatomical accuracy were noted. Conclusions: This study demonstrates the potential of virtual simulation to enhance neurosurgical training by providing a controlled environment for repeated practice and objective feedback. Simulation-based training can effectively complement traditional methods, though individualized approaches may be necessary to accommodate varying learning rates among residents. Further refinement of simulation tools and their integration into standard training curricula are recommended.

Understanding the relationship between sugar yield and related traits is crucial for successfully breeding this important industrial crop. Thirteen sugar beet monogram cultivars were evaluated during 2018-2019. The study used a split-factorial design with two planting dates as the main plot, while cultivars and harvesting dates were laid out as CRBD as subplots. Significant differences were observed among the cultivars for all measured traits. The genetic correlation coefficients showed the highest positive and significant correlations between sugar yield and white sugar yield (WSY) (0.88**), sugar content (SC) (0.71**), and white sugar content (0.65**). Conversely, the highest negative and significant correlations were found between sugar yield and molasses sugar content (-0.62**), alkalinity coefficient (-0.58**), and root potassium content (-0.52**). In the stepwise multiple regression analysis, white sugar content (WSC), sugar content (SC), root α-amino nitrogen content, and root yield were included in the regression model, explaining 84% of the total variance in the sugar yield. Path analysis revealed that sugar content (1.12) and root yield (1.03) had the highest direct effects on sugar yield. These traits can be used as key criteria for developing superior hybrids with high economic productivity, thereby accelerating the selection process in various breeding programs.

The 20th century, known as the "metropolitan century," saw urban populations exceed half the global populace. By 2035, emerging metropolises, particularly in Asia and Africa, highlight the urgent need for research on urban growth, demographics, and mobility's role in sustainable development. This study explores the multifaceted aspects of mobility essential for sustaining metropolitan regions, examining stakeholders' perspectives through a detailed analysis of interview data. Responses are aligned with convergent subcategories derived from classical urban theories and contemporary metropolitan studies, encompassing territorial aspects, scale, place, urbanization, economy, culture, identity, innovation, networks, climate change, and foresight. The findings highlight the intricate interplay of governance, infrastructure, and socio-spatial dynamics in Greater London, underscoring the imperative for sustainable mobility and inclusive planning that integrates environmental concerns into metropolitan life. This research, through a convergent analysis of interviewees' responses, has identified thirty-one fundamental attributes to enhance our understanding of mobility within metropolitan areas, with a particular focus on the case study of Greater London. Future research directions include extending this framework to other metropolises and a deeper exploration of the attributes identified in London, employing both qualitative and quantitative methods to enrich our understanding of urban sustainability and mobility.

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

To analyze the impact of weather factors on the resilience of airport flight operations, particularly on flight operation, economic performance, and transportation capacity, Kolmogorov-Arnold Network (KAN) model was used to identify key weather features and establish the mapping relationship between weather factors and airport operational resilience. Taking Xi'an Xianyang International Airport as an example, the weights of each route were determined using the Grey Relational Analysis method considering average daily flight volume, flight distance, and airport flow stability indicators. Flight operation records and weather data were utilized to calculate the importance of key weather factors on the resilience of critical flight operation indicators. The results indicate significant differences in average daily flight volume, flight distance, and airport flow stability across different routes, with routes in economically developed areas having a greater impact on airport flow stability. Temperature and wind speed changes are the key factors affecting flight operation and economic indicators, with importance values of 0.35 and 0.32 respectively. Wind direction change and wind speed change have the greatest impact on flight transportation capacity, with importance values of 0.7 and 0.65 respectively. Therefore, when conducting flight scheduling and risk assessment, special attention should be paid to key weather factors such as temperature and wind speed, while also considering the weight of each route to ensure flight safety, efficiency, and economic benefits.

Background: Several trace minerals have been shown to be associated with thyroid cancer. However, there is a paucity of data on the characteristics of thyroid nodules in different mineral rich regions of the Democratic Republic of Congo (DRC). The objective of this study is to establish the spectrum of thyroid nodules in different regions of the most mineral wealthy country in the world. Method: We conducted a cross-sectional study, performing descriptive statistics and logistic regression with a p value < 0.05 deemed of statistical significance. We enrolled 529 patients diagnosed with thyroid nodules between 2005 and 2019 in two of the richest provinces in minerals (Katanga and South Kivu) and the capital city, Kinshasa. Results: The mean age was 44.2±14.6 years with a female predominance, with a female to male ratio of 5.4. 66.5% of patients had a family history of thyroid disease. 74 patients had simple nodules, whereas 455 had multiple nodules. 87.7% of patients with nodules were euthyroid. The nodules mostly presented a solid structure (72.2%), hypoechogenicity (84.5%), a macronodule (59.8%), calcification (14.4%) and lymphadenopathy (15.5%). 22.3% of these nodules had a malignant character. The independent factors associated with this malignancy were age ≥60 years (aOR=2.81), Katanga as province of origin (aOR=8.19), solid echostructure (aOR=7.69), hypoechogenicity (aOR=14.19), macronodule (aOR=9.13), calcification (aOR=2.6) and the presence of adenopathy (aOR=6.94). Conclusion: By the way of this first of its kind study, we demonstrated that thyroid nodules were more likely to be malignant if found in patients originating from mineral rich region of Katanga. This high frequency of malignancy in mineral rich regions requires measures aimed at early detection in this population, lower threshold for suspicion of malignancy and adequate training of doctors involved in the management of these patients. This study paves the way for future, more extensive and well-funded studies to better understand the relationship of heavy metals and thyroid cancer incidence in the richest country in heavy metals in the world.

Forest quality has a significant impact on carbon emissions of the economic system. This paper, based on an analysis of the Environmental Kuznets Curve (EKC) of Forest quality and carbon emission of economic systems, explores effective carbon emission reduction paths from the perspective of symbiosis between Forest quality and economic growth system. This paper carries out empirical analysis based on the provincial-level regional data of China and EKC panel data analysis for several major geographic regions in China. The correlation statistical test shows that the model considering Forest quality is more consistent with the test requirements. It is found that the overall EKC curve of China as a whole shows an inverted U-shape. In light of geographic differences, the EKC curve in the northwest also shows an inverted U-shape; it shows a U-shaped curve in North, Northeast, and East China. Meanwhile, in South Central and Southwest China, there is an approximately linear increasing curve. The data simulation of the symbiotic system of Forest quality and economic growth suggests that simultaneously increasing the proportion of forestry output value and the systematic carbon inhibition coefficient is the most effective pathway for carbon emission reduction. Choosing an appropriate carbon reduction pathway can contribute to the sustainable development of Forest quality and economic growth. The main research highlight of this paper is the application of the symbiotic system model to the EKC analysis of carbon emissions from the economic system with Forest quality.

Background/Objectives: Caregivers play an important role in supporting care recipients to navigate their health needs including adherence to dietary recommendations which are complex and multifaceted. The study aimed to i) describe the nutrition knowledge of caregivers of people with chronic kidney disease (CKD), and ii) explore caregivers’ perception of their role in providing healthy meals and nutrition support for care recipients. Methods: Caregivers (n=78) of people with stage 1-5 CKD or post-transplant were recruited from a single centre. A sequential mixed-methods approach was used. Nutrition knowledge was assessed with the revised General Nutrition Knowledge questionnaire. Theory informed semi-structured interviews in a sub-sample (n=12) explored caregiver perception. Results: Most caregivers were female (75.6%) caring for a male care recipient (87%; aged 74 (66;80) yrs.). Caregivers (75.6%) provided a meal ≥6 times/week to their care recipient and had moderate nutrition knowledge (66.1 (60.5; 73.9) %). Four themes emerged describing caregivers’ perception on meal provision and nutrition support including i) food literacy skills are valued, ii) social support is important, iii) caregivers’ sense of social responsibility, and iv) meal provision is complex and dynamic. Conclusions: Caregivers have moderate nutrition knowledge; they want to provide healthy meals and support to care recipients to adhere to dietary recommendations. Targeted, co-designed nutrition education programs for caregivers may enhance nutrition care delivery to people with CKD.

This study presents an advanced data augmentation framework to enhance diagnostics in centrifugal pumps using vibration data, with a focus on practical industrial applications. Initially, Gaussian noise and signal stretching were employed to generate synthetic data, addressing the common issue of insufficient defect data in industrial settings. These methods simulate varied operating conditions and different rotating speeds. Recognizing the limitations of traditional approaches, we further integrated advanced models—Long Short-Term Memory (LSTM), Autoencoder (AE), and Generative Adversarial Networks (GANs) to augment the dataset comprehensively. This enhanced approach increases the robustness and accuracy of machine-learning models for fault detection and classification. Experimental results show significant improvements in diagnostic algorithm performance, reducing false positives and increasing fault detection rates. The study offers a complete framework for incorporating both traditional and advanced data augmentation techniques into predictive maintenance protocols, ensuring reliable operation of centrifugal pumps in diverse industrial environments.

With real estate cycles being a subset of business cycles, contraction stages are often perceived as short-term, after which a revival of the property market sets in. The worsening Nigerian economy has further complicated the issues of housing supply and affordability in economic nerves like Lagos. This study examined the fundamental and associated factors contributing to rental housing affordability issues between 2019-2023 in Lagos. This article is produced with the intention of providing a market data update on rental housing price movements for both the mainland and island zones of Lagos, as well as comparing their affordability convenience for income-earners or households based on the rent-income ratio. The study adopted a mixed research design and various data sources were consulted in analyzing the fundamentals, rent and affordability trends. The study discovered that affordability convenience ratios for the various income bands had deteriorated further between 2019 and 2023 in Lagos mainland (8%) and island (9%). 67% and 80% of income earners are paying above the optimal 30% of their personal or household income in meeting their rental demand in Lagos mainland and island respectively. The paper suggested the application of fundamental, institutional and environmental strategies to combating the current phenomenon.

This paper presents three approaches to estimating battery parameters of the electrical equivalent circuit model (ECM) based on electrochemical impedance spectroscopy (EIS); these approaches are referred to as (a) least squares (LS), (b) exhaustive search (ES), and (c) nonlinear least squares (NLS). The ES approach is assisted with the LS method for the rough determination of the lower and upper bound of the ECM parameters, and the NLS approach is incorporated with the Monte Carlo run such that different initial guesses can be assigned to improve the goodness of EIS fitting. The proposed approaches are validated using both simulated and real EIS data. Compared to LS approach, the ES and NLS approaches show better fitting accuracy at various noise levels; whereas both in the validation using simulated EIS data and actual EIS data collected from LG 18650 and Molicel 21700 batteries, the NLS approach shows better fitting accuracy than that of LS and ES approaches. In all cases, compared with the ES approach, the computational time of the NLS approach is significantly faster, and compared with the LS approach, the NLS approach shows a minimal difference in computational time and considerably better fitting performance.

Background/Objectives: The anesthetic block of the sensory branches of the superficial cervical plexus (SCP) renders a specific area of the face, head, and anterior neck insensible and painless. Chronic pain in these areas can be difficult to diagnose and treat. In this report, we briefly review the existing evidence on the topic of the SCP block (SCPB) to set the context for our research. We then share our own clinical experience with the SCPB for managing chronic pain syndromes from both cancerous and non-cancerous etiologies. Methods: We first performed a comprehensive literature search and narrative review of clinical cases and studies that utilized the SCPB as an analgesic technique. We then conducted a retrospective case series of all patients who received a SCPB at our pain clinic since 2020. Results: Our literature review found only a few cases reported, with most of them focusing on acute painful emergencies and perioperative pain syndromes and only very few addressing chronic pain. In our pain clinic, 14 patients received one or more SCPBs for chronic pain management. In 42% of these cases, the pain was related to cancer. The most common areas of pain corresponded to the regions supplied by the transverse cervical and greater auricular nerves. The procedures were uneventful in all cases, and patients rated them as effective and worthwhile 71% of the time. Conclusions: Despite the lack of high-quality studies on SCPBs in pain management, the authors’ experience suggests that it is a valid minimally invasive alternative for managing chronic face, head and neck pain.

Perimenopause, the transitional phase leading up to menopause, affects millions of women worldwide, yet remains poorly understood and under-addressed in healthcare. This report investigates the significant impact of perimenopause symptoms on women's lives, emphasizing the often debilitating effects such as anxiety, depression, weight gain, and hot flashes, which collectively cost an estimated $1.8 billion annually in lost work time. Despite the availability of treatment options like Hormone Replacement Therapy (HRT) and non-hormonal alternatives, awareness and utilization of these options vary significantly among women. The report highlights the historical neglect of women's health issues and the need for improved communication between patients and healthcare providers. A cross-sectional survey conducted with 1,000 adults from the United States and Canada reveals widespread dissatisfaction with the quality of healthcare communication, underscoring the importance of personalized and comprehensive insights for women's health. The findings advocate for more at-home solutions and resources to empower women in managing perimenopause and menopause, promoting informed decision-making and reducing stigma. This study aims to enhance awareness and support for women during this critical life stage.

With cities experiencing faster warming rates than their surroundings and two-thirds of the global population projected to be living in urban areas by 2050, studies that help to describe in detail the spatial temperature patterns in the urban environment have the potential to address concerns regarding cities’ livability. The goal of this research was to examine the spatial pattern of temperature across multiple small to medium-sized cities in the state of Iowa, a relatively rural state. The study utilized mobile temperature sensors to collect air temperature data at a high temporal and spatial resolution in ten urban areas in Iowa during the afternoon, evening, and night on days exceeding 32°C from June to September 2022. Using a Random forest algorithm, and independent variables derived from aerial imagery and LiDAR data, we created predicted surface temperature models that demonstrated R² coefficients ranging from 0.879 to 0.997 with a majority of the temperature models reaching a R² higher than 0.95. These strong modeling results and derived highly detailed raster spatial data supports recent research which help explain the influence of morphometric features on temperature patterns in the urban environment. This study expands on previous research by investigating multiple small to medium sized cities in Iowa.

ABSTRACT (words 232) Objectives: Chronic pulmonary aspergillosis (CPA) is a fungal lung infection characterized by slowly progressive destruction of the lung parenchyma and has four main subtypes. The objective of this work was to evaluate the epidemiology of CPA in our area and evaluate the involvement of the different subtypes in mortality. Methods: This was a descriptive longitudinal retrospective study developed in three tertiary hospitals in Spain. Among all patients admitted with a pulmonary aspergillosis diagnosis, we selected those who fulfilled the criteria for chronic aspergillosis according to the criteria of Denning, excluding those with a haematological disorder. Results: Among 409 inpatients recorded as having pulmonary aspergillosis infection, only 76 (18.5%) fulfilled the criteria for CPA, with an estimated incidence of 0.67 cases/100,000 inhabitants/year. The subtypes detected were subacute invasive aspergillosis (SAIA) in 33 (43.4%) patients, simple aspergilloma (SA) in 25 (32.9%) patients, cavitary chronic aspergillosis (CCPA) in 13 (17.1%) patients and chronic fibrosant (CFAP) in 5 (6.5%) patients. The overall three-month mortality was 23%, which was higher in SAIA patients. The predictors of early mortality were age >65 years (OR 3.0 CI 95 1.0–9.5 0.043) and SAIA subtype vs. other subtypes (OR 3.1 CI 95 1.0–9.5 p 0.042). Conclusions: The incidence rate estimated was inferior to that previously reported. Three-month mortality in patients with CPA was high, with older age and SAIA subtype being the variable independent predictors of a worse prognosis.

Malignant lymphoma is an unusual form of gallbladder neoplasm. Almost all of these tumors are diffuse large B-cell lymphomas or mucosa-associated lymphoid tissue-type lymphomas. Herein, we report a rare case of a gallbladder Burkitt’s lymphoma (BL) in a child living with HIV. As far as we are concerned, this is the secondfirst report in the pediatric population and the first one in an individual living with HIV. The patient (a five-year-old black female child) attended the Federal Hospital of Lagoa, Rio de Janeiro, Brazil, underwent cholecystectomy and the postoperative pathological analysis of the gallbladder revealed a diagnosis of BL (EBV-positive). Also, HIV serology was performed and returned positive. She was transferred to the Martagão Gesteira Institute of Pediatrics and Childcare for oncological treatment, dying from sepsis and disease progression about 18 months later. Patient did not undergo ART/cART. Previous cases of gallbladder BL were herein described and analyzed to characterize the clinicopathological features and possible similarities. BL can occur in the gallbladder both in the context of HIV infection and in the pediatric population. Biopsy is mandatory in cases with suggestive findings of lymphoma, and an early diagnosis can change the course of the disease. Furthermore, the case highlights the importance of an early initiation of ART/cART in people living with HIV (PLWH).

The implementation of Emissions Trading System (ETS) poses significant challenges for the Indian maritime sector. This paper investigates the regulatory implications of ETS, focusing on how these regulations impact compliance and operational practices within the Indian shipping sector. It also analyzes the economic effects, including the financial burden and competitive dynamics faced by the Indian shipping companies in a global context. Additionally, the paper explores various adaptation strategies, emphasizing technological innovations and operational adjustments essential for navigating ETS requirements. By integrating recent industry data and case studies, this study offers a comprehensive overview of how the Indian shipping industry can effectively address the evolving regulatory landscape of ETS.

Human Papillomavirus (HPV) related cancers account for a large percentage of cancers. The tumor microenvironment (TME) is a complex structure and contributes significantly to tumor development, progression, and invasion. The TME is particularly pronounced in HPV-related cancers and HPV is known to influence the TME. In cancer research, there is an urgent need for new models to deepen our understanding of the pathophysiology of the disease and to facilitate the development of innovative therapies. In this context, 3D cell culture has emerged as a promising method in cancer research, that offers significant potential compared to conventional approaches. 3D modelling can produce results that are more consistent with clinical outcomes without the ethical concerns and problems of animal models. In this review, our aim is to discuss the tumor microenvironment in HPV-related cancers and demonstrate the importance of advancing 3D cell culture models for this purpose.

Diabetes is one of the diseases that has spread widely in today's societies. Diabetics should constantly measure their blood sugar. There are also several common methods used to measure blood sugar levels, Diabetes is one of the diseases that has spread widely in today's societies. Diabetics should constantly measure their blood sugar. There are also many common methods used to measure blood sugar levels, and this project significantly accelerates diabetes monitoring, academic research and maintenance. Therefore, a comprehensive examination of several intrinsic properties is of fundamental importance because it contains effects that affect the behavior of the aqueous solution and the safety of the diabetic patient. The dielectric constant of the aqueous solution of different samples of raw sugar is also are make able property in this context. The electrical properties of any material vary according to the properties of the insulating material. Accurate measurement so these properties will be useful for many electronics applications, and these measurements of dielectric materials affect the data values of basic design parameters. For example, both the frequency and impedance of microwaves are affected by the properties of the dielectric material. Additionally, this knowledge can be applied to improve the basic design. Through this research, we will discuss the electrical insulation properties in terms of permittivity and permeability. But it is necessary to address microwave resistance. Permittivity and permeability are not fixed values, as they are affected by many factors such as frequency, temperature, direction, mixing, pressure, and different samples of sugar in the aqueous solution

In wheeled vehicles, there exists a challenge concerning the alignment of the front wheels during turns. When both wheels are turned to the same extent, the inner wheel experiences lateral slippage on the road surface, resulting in reduced steering efficiency. Such problems can be solved by using the Ackermann principle. There are two approaches: through the steering mechanism or by employing autonomous wheel control. From a technical standpoint, it is achievable to synthesize a steering mechanism that fulfills the Ackermann condition, ensuring as accurately as possible for any given turning radius. This research paper focuses on synthesizing a steering mechanism, specifically a two-circuit linkage that links the left and right wheels. The structure and kinematics of this mechanism are being investigated. Following the principles of Ackermann, an objective function for the kinematic synthesis of the mechanism has been formulated. The synthesis process of the mechanism is conducted using the analytical method of interpolation in four parameters. A dedicated method has been developed, and kinematic analysis of the mechanism using the synthesized parameters has been performed. The simulation results have demonstrated high accuracy in achieving the desired implementation of the Ackermann condition.

Dye-sensitized solar cells (DSSCs) belong to the group of thin-film solar cells which have been extensively researched for over two decades due to their low cost of fabrication, flexibility and ability to function under low-light conditions. Some issues have to be addressed in terms of energy losses, material integration, weak photocurrent generation and stability to improve the performance of DSSCs. One of the approaches to enhance the performance of DSSCs is through luminescent materials. These are materials which can absorb light and re-emit at different wavelengths which can be used to convert ultraviolet (UV) and near infra-red (NIR) light that are not efficiently utilized by the DSSCs, into visible light that can be absorbed. The main objective of this article is to provide a comprehensive review on the impact of luminescent materials in DSSCs. Research interest in luminescent materials particularly, down conversion, up conversion and quantum dots were analyzed from the “Web of Science”. It reflected a remarkable number of over 200, 000 publications in the past decade. Therefore, the state of the art of luminescent materials for enhancing the performance of the solar cells were reviewed, which showed significant potential in enhancing the performance of DSSCs.

Water samples for bacterial microbiome studies undergo a biomass concentration, DNA extraction and taxonomic identification steps. Through benchmarking, we studied the applicability of Skimmed milk flocculation (SMF) for bacterial enrichment, an adapted in-house DNA extraction protocol, and six 16S rRNA databases (16S-DBs). Surface water samples from two rivers were treated with SMF and Vacuum filtration (VF) and subjected to amplicon or shotgun metagenomics. A mock community standard underwent five DNA extraction protocols, taxonomical identification with six different 16S-DBs and evaluated by the Measurement Integrity Quotient (MIQ) score. In SMF samples, the skimmed milk was metabolized by members of lactic acid bacteria or genera such as Polaromonas, Macrococcus, and Agitococcus, resulting in increased relative abundance (p &lt; 0.5) up to 5.0 log fold change compared to VF, rendering SMF inapplicable for bacterial microbiome studies. The best performing DNA extraction protocols were FastSpin Soil, the in-house method and EurX. All 16S-DBs yielded comparable MIQ scores within each DNA extraction kit, ranging from 61-66 (ZymoBIOMICs) up to 80-82 (FastSpin). DNA extraction kits exert more bias to the composition than the 16S-DBs. This benchmarking study presented valuable insights to inform future water metagenomic study designs.

Maria Valtorta (MV) (1897–1961) – an Italian mystic who claimed to have visions of Jesus’s life – describes Jesus’ crucifixion with many details from which significant data can be retrieved. My investigation based on these data and the topography of northwest Jerusalem in the I century AD, shows that Jesus’ crucifixion place, Golgotha (the “skull”), is neither at the Holy Sepulchre nor in the Garden Tomb area. The paved road to Golgotha she accurately describes arrives at its top with the minimum mean route gradient; its half way defines two distinct tracts with same height and same walking time but different mean route gradient; at half way Jesus is allowed to walk to the top in a spiral country road less steep than the second tract. The paved road leads to a place where few years after Jesus’ crucifixion the Psephinus Tower, described by Flavius Josephus, was erected, in the area known today as the Russian Compound. In conclusion, the Golgotha reveiled by MV’s writings was located at the same site of the Psephinus Tower.

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

To maximize resource usage, minimize disputes, and improve academic experience, professors must schedule teaching activities efficiently. This study provides an optimized automated schedule creation technique. The system generates conflict-free, efficient schedules using evolutionary algorithms and multi-objective optimization. Resource usage, scheduling prob-lems, and faculty/student satisfaction are the goals of the research. The system optimizes scheduling based on room limitations, instructor availability, and student preferences. The project uses system design, model creation, algorithm implementation, and performance analysis to solve the difficult timetable-generating problem. This research should save administrators time, improve academic operations, and improve staff and student academic experiences. Scalability and flexibility allow the system to be used in multiple faculties and incorporate new limits and requirements. This paper presents a complete approach to faculty scheduling, including insights and recommendations for future study and application in educational institutions.

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

Background: Systemic-to pulmonary collaterals (SPC) are common in congenital heart disease (CHD). Particularly in single ventricle anatomy and Fontan circulation, SPC can both complicate the postoperative course and lead to clinical deterioration in the long term. Treatment of SPC is controversial. The aim of our study was (1) to analyze retrospectively patients who underwent SPC embolization using Concerto™ Helix nylon-fibred microcoils (CHM) and (2) to describe the interventional technique. Methods: In this a single-center retrospective observational cohort study, we analyzed clinical and imaging data of all patients who underwent transcatheter embolization of SPC using CHM from January 2016 to December 2023. Results: In 38 consecutive patients (65.8 % male, median age 41 months, range 2 – 490), a total number of 141 CHM had been implanted into 64 SPC in 49 procedures. The majority were arterial SPC (n = 59/64) originating from the thoracic aorta or its branches; 5/64 were veno-venous SPC. Primary closure succeeded in all procedures. The CHM diameters ranged from 3 to 8 mm, with 5 mm being the most commonly used diameter. The mean coil / SPC ratio was 2.6 (range 1.3 – 5.3). CHM implantation was performed via 4 French sheaths. Both detachment and stable positioning was simple and safe. Neither non-target embolization nor coil migration occurred. One complication occurred with a minor vascular injury without clinical relevance. Conclusion: CHM are easy to use and appropriate for SPC embolization in all age groups, including infants, with a low complication rate. The coils are particularly suitable for closure of collaterals with small diameter or tortuous course.

Lakes across the globe, including the Volta Lake in Ghana, face insidious threats from pollutants driven by high dependency on aquatic ecosystems. Cage aquaculture is expanding in Africa due to its potential to address food insecurity, provide livelihoods, and contribute to local economies. However, the uncontrolled expansion of cage aquaculture can have significant negative impacts on water resources, including environmental footprints that threaten biodiversity. Considering the intensification of cage aquaculture on the Volta Lake, we argue for a shift to sustainable alternative aquaculture systems. Deepening stakeholder collaborations are needed to enhance competence in mapping inland aquaculture areas, identifying eco-friendly alternatives, and strengthening aquaculture regulations and their enforcement in general and cage culture in particular on Lake Volta. This approach would promote best management practices. While competence building must be a continuous process to address knowledge gaps, the establishment of workable preparedness plans is needed in the event of emergencies. As the Lake is a hotspot for certain fish pathogens, implementing these strategies can reduce disease risks and subsequently decrease the development of resistance associated with excessive antimicrobial use in farmed fish.

The aim of the study was to explore attitudes towards climate change among wilderness seekers. The subjects were 273 (M=23.15; SD=7.72) adults. The respondents completed four questionnaires: Wilderness Novelty Seeking Scale, Wilderness Self-Efficacy Sale, Wilderness Courage Scale and Attitude Towards Climate Changes Scale. There were three distinct profiles of the respondents: Curious, who are interested in the wilderness but lack the skills and courage to explore dangerous wilderness places; Adventurous, who actively seek experiences in dangerous wilderness places and have survival skills; and Indifferent, who have little interest in the wilderness. The participants in these profiles differed in terms of attitude toward climate change. The Curious and Adventurous groups were significantly more concerned about climate change. In addition, they were more likely to believe that climate change is already having a negative impact on the lives of people in the places where they live. Furthermore, the Curious group experienced positive feelings towards climate change less often than the Adventurous group. On the other hand, the Curious group experienced significantly more negative feelings in relation to climate change. Finally, wilderness seekers (Curious and Adventurous) were statistically more likely to engage in pro-environmental behaviors in the context of climate change compared to the Indifferent group.

This article compares the predictive capabilities of six models namely the Linear Discriminant Analysis (LDA), Logistic Regression (LR), Support Vector Machine (SVM), XGBoost, Random Forest (RF) and Deep Neural Network (DNN) to predict the default behaviour of credit card holders in Taiwan using data from the UCI machine learning database. Python programming language was used for data analysis. Statistical methods were compared with machine learning algorithms using the confusion matrix measured in metric terms of prediction accuracy, sensitivity, specificity, precision, G-mean, F1 score, ROC and AUC. The dataset contains 30,000 credit card user’s information with 6636 default observations and 23,364 non-default cases. The study results found that modern machine learning methods outperformed traditional statistical methods in terms of predictive performance measured by F1 score, G-mean and AUC. Traditional methods like logistic regression were marginally better than linear discriminant analysis and support vector machines in terms of predictive performance measured by area under the receiver operating characteristic curve. In the modern machine learning methods, deep neural networks were better in most of the predictive performance metrics than XGBoost and Random Forest methods.

Purpose: this paper evaluates the effect of thermo-formed aligners with complementary biomechanical attachments (CBA) on periodontal ligaments (PDL) during the expansion process of the maxillary arch using simulations based on the finite element method (FEM). Methods: four types of teeth (canine 3, premolar 4, premolar 5 and molar 6) were modeled using 3D CAD. In addition, aligners, CBAs and PDL were also 3D modeled. With this information, a FEM model was generated, and a clinical rehabilitation scenario was simulated. For the validation of the results, a comparison with medical records was performed. Results: for canine 3, the displacement was 0.134 mm with a maximum stress of 4.822 x10-3MPa in the amelocemental junction. For premolar 4, the displacement was 0.132 mm at a maximum stress of 3.273x10-3MPa in the amelocemental junction. Premolar 5 had a displacement of 0.129 mm and a stress of 1.358 x10-3MPa at 1 mm from the amelocemental junction. Molar 6 had a displacement of 0.124 mm and a maximum stress of 2.440 x10-3MPa. Conclusions: The simulations showed that the use of CBA reduces tooth tipping. The model with Vestibular CBA presented the best results in terms of the dental movement of expansion of the maxillary arch with the use of thermoformed aligners. Significance: Considering the difficulty of performing in vivo tests that evaluate the configuration effectiveness of the different CBA alternatives in thermoformed aligners, FEM techniques were used to reduce time and costs, and get accurate results with minimal errors.

Glioblastoma (GBM) is an incurable primary brain cancer characterized by increased reactive oxygen species (ROS) production. The redox-sensitive tumor suppressor gene TP53, wildtype (wt) for 70% of patients, regulates redox homeostasis. Glioblastoma stem cells (GSCs) increase thioredoxin (Trx) and glutathione (GSH) antioxidant systems as survival redox-adaptive mechanisms to maintain ROS below the cytotoxic threshold. Auranofin, an FDA-approved anti-rheumatoid drug, inhibits thioredoxin reductase 1 (TrxR1). L-buthionine sulfoximine (L-BSO) and the natural product piperlongumine (PPL) inhibit the GSH system. We evaluated the cytotoxic effects of Auranofin alone and in combination with L-BSO or PPL in GBM cell lines and GSCs with known TP53 status. The Cancer Genome Atlas/GBM analysis revealed a significant positive correlation between wtp53 and TrxR1 expression in GBM. Auranofin induced ROS-dependent cytotoxicity within a micromolar range in GSCs. Auranofin decreased TrxR1 expression, AKT(Ser-473) phosphorylation, increased p53, p21 and PARP-1 apoptotic cleavage in wtp53-GSCs, while mutant-p53 was decreased in a mutant-p53 GSC line. p53-knockdown in a wtp53-GSC line decreased TrxR1 expression and significantly increased sensitivity to auranofin, suggesting the role of wtp53 as a negative redox-sensitive mechanism in response to auranofin in GSCs. Auranofin and L-BSO combination synergistically increased ROS, decreased IC50s and induced long-term cytotoxicity irrespective of p53 in GBM cell lines and GSCs. Intriguingly, Auranofin increased expression of glutathione S-transferase pi-1 (GSTP-1), a target of PPL. Combination of auranofin with PPL synergistically decreased IC50s to a nanomolar range in GSCs, supporting the potential to repurpose auranofin and PPL in GBM.

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

In this paper, we present the application of a special soil resistivity model, the Exponentially Graded Multilayered Soil model, to the interpretation of Borehole Resistivity Logging surveys. The model, which was recently introduced by the authors, offers an alternative to the interpretation provided by constant resistivity layer models. The model is applied to synthetic soil surveys as well as to a real survey that is not satisfactorily interpreted using conventional models.

Integrating ecosystem services (ESs) to delineate ecological functional zones (EFZs) is a fundamental work in terrestrial spatial planning and ecosystem management. However, existing studies have largely overlooked the refinement of EFZs at local scales, which hinders targeted and multi-scale ecosystem management. This study introduced a "two-step refinement zoning method" to address this gap, first using the self-organizing feature mapping method to delineate EFZs based on ESs at a township scale, and then applying hotspot overlay analysis to identify hotspots of dominant ESs within EFZs at a village scale. By overlaying the outcomes of these two steps, EFZs can be further graded by designating villages with hotspots of dominant ESs as level I and those without as level II. The proposed method was applied in Wuhan City, dividing it into five types of EFZs with different ESs combinations and land use compositions. Furthermore, 5.23% of villages were identified as Level I of EFZs, serving as advantageous areas of dominant ESs in the study area. On this basis, diversified management strategies and conservation priorities were proposed. This study provides a theoretical and methodological reference for terrestrial spatial planning and sustainable ecosystem management.

Plant growth-promoting rhizobacteria (PGPR) are beneficial bacteria that play a crucial role in sustainable agriculture by enhancing plant growth through various mechanisms. This review examines the contributions of PGPR in improving nutrient availability, producing phytohormones, providing biocontrol against pathogens, and enhancing abiotic stress tolerance. By reducing the necessity for chemical fertilizers and pesticides, PGPR mitigate environmental impacts, enhance soil health, and support long-term agricultural productivity. However, challenges such as inconsistent performance across various soils, regulatory barriers, and limited farmer awareness hinder their widespread adoption. Recent advancements in nano-encapsulation technology, genetic engineering, and bioinformatics present promising solutions for overcoming these obstacles and enhancing PGPR efficacy. The incorporation of PGPR into biofertilizers, biopesticides, and integrated pest management (IPM) strategies offers a sustainable resolution to global agricultural challenges. This review addresses the current state of PGPR research, applications, and future directions for optimizing their use in promoting sustainable agriculture.

The role of coalesced bainite in hydrogen embrittlement of tempered martensitic steels was investigated. Results showed that the coalesced bainite significantly enhanced the hydrogen embrittlement resistance by restricting hydrogen accumulation at the prior austenite grain boundaries, thus shifting the hydrogen-assisted failure mode from intergranular to transgranular fracture.

Deep Reinforcement Learning has achieved state-of-the-art results in the field of autonomous driving, especially in video game simulations. Trained agents were able to beat the best players achieving supremacy in the field. However, these agents often take in their neural network input the geometry of the track during the learning process. Our approach is to create a learning procedure for autonomous driving agents that is not environment-dependent by design to extend Deep Reinforcement Learning to racing games without racetrack information. Our work builds upon the notorious game Trackmania Nations United Forever, an open access 3D realistic Formula 1 simulation game that offers a high degree of customization. We use the Proximal Policy Optimization algorithm, using hybrid raw pixel data and physics sensors vectors as agent observations. The training track is a custom-made circuit carefully crafted from all available blocks in the game. The training track is considered like a standard dataset in machine learning and thus split in several segments in which the agent is randomly put into. We also created two regularization reward functions for the car’s speed and position to increase robustness of the agent in new scenarios.

Molten salt electrolysis stands as an indispensable technique for the production of rare earth metals, where the judicious distribution of the electric field within the electrolytic cell is paramount for ensuring the seamless operation of the electrolysis process. This study introduces a pioneering approach by integrating the Nernst-Planck equation with the Butler-Volmer equation, thereby devising a holographic electric field model tailored for the electrolysis of the NdF3-LiF molten salt system. The paper presents pioneering experimental measurements concerning the potential distribution and voltage drop characteristics within a 6kA rare earth electrolytic cell, elucidating the interplay between decomposition voltage and current density, which substantiates the model's precision. Our findings unveil that, under operational conditions of 4010 amperes, the 6kA electrolytic cell incurs a total voltage drop of 6.3 volts, delineating a melt voltage drop of 4.43 volts, an ohmic voltage drop of 0.248 volts, and an interfacial voltage drop of 1.622 volts at the electrode-electrolyte junction. The discrepancy in error is confined to within 0.3% of the actual measured voltage of 6.28 volts.The simulation outcomes from the holographic electric field model have been confirmed to precisely and comprehensively reflect the characteristics of the electric field distribution during the rare earth molten salt electrolysis process, offering valuable theoretical insights for practical manufacturing applications.

Outlined is a proposal designed to culminate in the foundry fabrication of arrays of singly addressable quantum dot sources deterministically emitting single pairs of energy-time entangled photons at C-band wavelengths; each pair having negligible spin-orbit fine structure splitting; each pair being channeled into single mode pig-tail optical fibers. Entangled photons carry quantum state information among distributed quantum servers via I/O ports having two functions; the unconditionally secure distribution of decryption keys to decrypt publicly distributed, encrypted classical bit streams as input to generate corresponding qubit excitations, and to convert a stream of quantum nondemolition measurements of qubit states into a classical bit stream. Outlined are key steps necessary to fabricate arrays of on-demand quantum dot sources of entangled photon pairs, principal among are 1) foundry fabrication of arrays of isolated quantum dots, 2) generation of localized sub-surface shear strain in a semiconductor stack, 3) a cryogenic anvil cell, 4) channeling entangled photons into single mode optical fibers, 5) unconditionally secure decryption key distribution over the fiber network, 6) resonant excitation of a Josephson tunnel junction qubits from classical bits, and 7) conversion of a quantum nondemolition measurements of a qubit states into a classical bit.

Glass cleaning is one of the major cleaning tasks in malls, offices, and modern households where the human workforce is still used for cleaning. Manual cleaning on façade glasses is a less safe, tedious, ineffective, and complex process. With the advancement of robot technology, it is possible to use robots for glass cleaning to facilitate mankind and improve cleaning efficiency. This work presents the development of a vacuum-type glass cleaning robot for façade cleaning and its performance analysis. The developed square-shaped robot is controlled by an Arduino Mega microcontroller, equipped with vacuuming and cleaning technology. An adhesion mechanism on the wheels makes the robot move forward and backward on the glass. The robot runs on a 24-volt DC with a power rating of 75 watts. There are ultrasonic sensors to detect obstacles and help the robot navigate. The robot uses a random-walk mechanism and works continuously for 90 minutes effectively. The performance of the robot has been analyzed in terms of coverage area, repeatability, and dust collection efficiency.

Hydrogen is increasingly recognized as a crucial element in the shift toward sustainable energy systems due to its high energy density and minimal environmental impact. Biomass, a renewable material derived from plants and animals, presents a promising feedstock for hydrogen production, offering both environmental and economic benefits. This paper examines diverse biomass resources for hydrogen production, including agricultural and forest residues, dedicated energy crops, and organic waste. It reviews key technologies for converting biomass to hydrogen, categorized into thermochemical, biochemical, and hybrid processes. Thermochemical methods like gasification and pyrolysis are discussed for their ability to handle varied feedstocks and produce high-purity hydrogen. Biochemical processes, such as anaerobic digestion and dark fermentation, are analyzed for their efficiency and lower operational costs. Hybrid approaches, which integrate multiple technologies, are explored for their potential to enhance system performance. Industrial applications of biomass-derived hydrogen are highlighted, including its use in transportation, power generation, the chemical industry, and heating. Future perspectives emphasize the need for advanced catalysts, process integration, sustainable feedstock sourcing, and supportive policies to drive commercial viability. The paper concludes by highlighting biomass’s significant promise in advancing sustainable energy systems and achieving global energy security and environmental goals.

The advent of modern satellite technology has transformed observational astronomy and astrophysics, offering unprecedented insights into the large-scale behavior of gravitation and challenging established cosmological models. This technological progress has reinvigorated the study of relativistic cosmology, leading to a critical reassessment of foundational assumptions, particularly the cosmological principle, which posits that the universe is homogeneous and isotropic on large scales. While this principle underpins the Standard Cosmological Model and the Friedmann-Lemaitre-Robertson-Walker metric, emerging data has increasingly been challenging its validity. Central to this investigation are the redshift-distance and light intensity-distance relations, essential for testing cosmological models. The integration of both parametric and nonparametric redshift models provides a more comprehensive analysis, addressing discrepancies in our understanding of the universe's structure and evolution. However, unresolved mysteries, particularly concerning dark matter and dark energy, complicate these models. This research critically examines the cosmological principle using the latest observational data and scrutinizes the Friedmann model's assumptions. The study shows that galaxy formation was most rapid in the early universe, especially within the redshift range of , with notable peaks around at and These two characteristic redshift values, predicted by our standard Friedmann redshift model used in this work, are confirmed by the observational data utilized in this research. The research also highlights that dark matter plays a significantly more critical role than dark energy in the process of galaxy formation. While dark energy primarily affects the large-scale expansion of the universe, dark matter seems to go beyond the domination of local galaxy formation and evolution of cosmic structures. These findings underscore the limitations of current models and contribute to the ongoing refinement of cosmological theories, offering a clearer understanding of the universe’s evolution.

High-grade advanced ovarian, fallopian tube and primary peritoneal (HGAOC) cancer are malignancies in which, apart from surgical treatment, systemic treatment plays an important role. The improvement in treatment results is associated with the introduction of polyADP-ribose polymerase inhibitors (PARPis). The aim of this study is to present a cross-section of patients treated for HGAOC, in one center, including the distribution of treatment principles following the most up-to-date guidelines. We observed patients who reported to the Department of Gyneacological Oncology between January 2018 and December 2023. We analyzed 437 women with a first diagnosis of HGAOC, who had not been treated before. The introduction of combined treatment with bevacizumab and PARPi into the first line of treatment, regardless of any remnants left during cytoreductive surgery, was introduced in November 2022. Since then, 30% (18/60) of patients (14 with olaparib and 4 with niraparib) have used the above regimen. Previously, in May 2021 - October 2022, it was 27% of patients (13/81 with olaparib and 9/81 with niraparib). Among patients starting treatment in January 2018 - April 2021, only 4.7% (5/106) received the platinum + paclitaxel + olaparib regimen. In both groups – with and without BRCA1/2 pathogenic or likely- pathogenic variant, significantly more patients used PC + PARPi or PC + BEV + PARPi in periods II and III than in period I (p<0.01). The introduction of modern treatment in our center very quickly changed the structure of patient treatment. Carriers of BRCA1/2 pathogenic or likely-pathogenic variant had previously received only chemotherapy, disappeared after the introduction of PARPi, in favor of new therapies. Most patients with no BRCA1/2 pathogenic or likely-pathogenic variant, who used PARPi in therapy in our center – received niraparib and therefore they are beneficiaries of molecular diagnostics in the field of HRD.

The past few decades have brought tremendous insight into the molecular and pathophysiological mechanisms responsible for thrombus generation. For the clinician it is usually sufficient to explain the incident deep vein thrombosis (DVT) with provoking factors such as trauma with vascular injury, immobilization, hormonal factors, or inherited or acquired coagulation defects. About half of the DVTs are, however, lacking such triggers and are called unprovoked. Venous stasis and hypoxia at the valve sinus level may start a chain of reactions. In order to better understand the mechanisms behind DVT and reach beyond the above-mentioned simplifications animal models and clinical epidemiological studies have informed of the complex interplay between leukocytes, platelets, endothelium, cytokines, complements and coagulation factors and inhibitors. These pathways and the interplay will be reviewed here.

Brain tumor being one of the major health hazards always needs a prompt diagnosis for the early treatment options to improve the chances of the survival of the patients. Traditional manual assessment of MRI (magnetic resonance imaging) to identify these conditions is a common practice. Hence, automation of the diagnosis practices can considerably improve the quality of the treatment procedures. In recent years almost every method for the automated diagnosis of brain tumor detection uses a deep learning technique. Despite decent works current techniques need a significant improvement to produce better results. Along with failure to produce decent results, the current deep learning models present very complex architectures which sometimes require huge computational resources. Therefore, in this paper we present a novel method that uses a lightweight dual-stream model with dual-input to detect brain tumors on MRI images. Both the inputs use different pre-processing mechanisms based on Contrast Limited Adaptive Histogram Equalization (CLAHE) and White Patch Retinex algorithm in order to enhance the feature learning capabilities of the proposed model. In addition, the proposed method uses a two-fold margin loss to boost training process for better feature learning. The proposed method produces state-of-the-art results for the detection of Glioma, Meningioma and Pituitary tumors. The model presents accuracy of 99%, 98% and 99% respectively for Glioma, Meningioma and Pituitary tumor detection.

Colorectal cancer (CRC) is characterized by a pro-inflammatory microenvironment and features high energy supply molecules that assure tumor growth. A still underestimated macromolecule is the Inorganic Polyphosphate (iPolyP), a high-energy linear polymer that is ubiquitous in all forms of life. Made up of hundreds of repeated orthophosphate units, iPolyP is essential for a wide variety of functions in mammalian cells, including the regulation of proliferative signaling pathways. Some evidence has suggested its involvement in carcinogenesis, although more studies need to be pursued. In this study, we tested the role of iPolyP on CRC proliferation, using in-vitro and ex-vivo approaches, in order to evaluate its effect on tumor growth. We found that iPolyP is significantly increased in tumor tissues compared to the corresponding peritumoral counterparts. In addition, iPolyP signaling occurs through the TRPM8 receptor, promoting CRC cell proliferation. Pharmacological inhibition of TRPM8 or RNA interference experiments showed that the involvement of TRPM8 is essential, greater than that of the other two known iPolyP receptors, P2Y1 and RAGE. The presence of iPolyP drives cancer cells towards the mitotic phase of the cell cycle by enhancing the expression of ccnb1, which encodes for the Cyclin B protein. In-vitro 2D and 3D data reflected the ex-vivo results, obtained by the generation of CRC-derived organoids, which increased in size. These results indicate that iPolyP may be considered a novel and unexpected early biomarker supporting colorectal cancer cell proliferation.

Characterization of fungal spider pathogens lags far behind their insect counterparts. In addition, little to nothing is known concerning ecological reservoir and/or fungal entomopathogen community surrounding infection sites. Five infected spider cadavers were identified in the neo-tropical climate of north central Florida, US, from three of which viable cultures could be obtained. Multilocus molecular phylogenetic and morphological characteristics identified one isolate as a new species of Gibellula, named, G. floridensis, and the other two as isolates of Parengyodontium album. To examine the insect fungal community sampling of different habitats/trophic levels including soil, leaf litter, leaf, and twig from the surrounding area of the infection sites were collected and analyzed by ITS amplicon sequencing. These data revealed broad but differential distribution of entomopathogenic fungi between habitats and some variation between sites, with members of genera Metarhizium and Metacordyceps from Clavicipitaceae, Purpureocillium and Polycephalomyces from Ophiocordyceps, and Akanthomyces and Simplicillium from Cordycipitaceae, predominating. However, no sequences corresponding to Gibellula or Parengyodontium, even at the genera levels could be detected. Potential explanations for these findings are discussed. These data highlight novel discovery of fungal spider pathogens and open the broader question regarding the environmental distribution and ecological niches of such host specific pathogens.

In this digital era, online learning and teaching is an important platform for college and university learners which required many technical facilities and in depth knowledge to have learning process effectively. Students and teachers in Higher Education Institutions (HEIs) were forced to grapple with the inevitability of shifting to online classes due to the advantages of effective distance learning or for large learning group. In the case of Oman, there have been some efforts to integrate alternative learning mechanisms such as online and mobile education in the past but not followed the scale in the post pandemic world. This study seeks to utilize the principles of Total Quality Management (TQM) through the implementation of Quality Function Deployment (QFD) as a way to better understand the experiences of Omani teachers and students from HEIs. A voice of customer from teachers and students have been collected and recorded. In this regard, 165 staff and 355 students from HEIs of Engineering participated in the survey. After the survey, a house of quality analysis was done to analyze the requirements based on how strongly they are related to the standard learning outcomes expected from HEIs in Oman. The results show that more attention is required in online education in the Sultanate of Oman before it is implemented as a major part in learning and teaching.