We evaluated performance of two American surveillance systems for monitoring the spread of antimicrobial-resistant (AMR) gonorrhea: the Gonococcal Isolate Surveillance Project (GISP) and the enhanced Gonococcal Isolate Surveillance Project (eGISP) which includes the non-urethral isolates in addition to the urethral ones utilized in the original surveillance system. A continuous-time agent-based model of gonorrhea transmission among the US men who have sex with men (MSM) population was developed and used for this purpose. The model accounts for susceptible and resistant strains of N. gonorrhoeae, symptomatic and asymptomatic infection and different transmission routes. Overall, eGISP system outperforms the original GISP system and allows to obtain a higher accuracy using a significantly lower number of isolates. Most of time, the original surveillance system determines the moment of switch to a new antibiotic later than necessary which leads to additional gonorrhea cases and spread of the resistance. Informing the gonorrhea treatment guidelines by eGISP estimates instead of the currently used GISP estimates would result in reduction of 622 (95% uncertainty interval: -4,009, 9,099) gonorrhea cases in the simulated cohort of 10,000 US MSM over a 25-year period without reduction in the lifespan of the current first-line antibiotic.

Highly realistic media created through Artificial Intelligence and Deep Learning, commonly known as deepfakes, presents a serious risk to political stability and the integrity of information. We are entering an era where images and videos, which were once considered reliable evidence, are now being altered to spread false information and incite unrest. This threat is especially heightened in developing nations, where misinformation and manipulation of public sentiment could lead to the collapse of the fragile democracies. This study delves deep into the increased vulnerability of these regions to deepfakes due to slower technological advancement and widespread lack of awareness. This study provides a detailed analysis and sets the stage for future investigations, proposing a combined approach to counteract the consequences of deepfakes. This includes combining technical methods for detection of deepfakes with public awareness campaigns and legal frameworks. Reducing the harmful effects of deepfakes requires cooperation among a diverse group of experts.

Our previous studies demonstrated a tight connection between simvastatin (SIM) inhibition of B16.F10 murine melanoma cell proliferation in vivo and in vitro and strong suppression of the subunit α of hypoxia inducible factor 1 (HIF-1) (HIF-1 α) production in these cancer cells. Since the translation of HIF-1α is modulated by signaling pathways coordinated by Akt, SIM effects on the link between these two regulatory factors and cancer cell metabolism under normoxia was investigated, which to our knowledge have never been described before. To achieve the proposed aim, SIM effects on cell metabolism were analysed at mRNA (via RNA-seq and RT-qPCR) as well as protein level (via western blot and catalytic activity of the key enzymes) Our data suggested that SIM might orientate glucose transport and metabolism to ensure replenishment of tricarboxylic acid (TCA) cycle activity. Moreover, it seems that SIM enhanced biosynthetic role of TCA over its energy role to further support synthesis of lipid-derived signaling molecules. Thus, under normoxia SIM treatment orientated lipid metabolism to stimulate isoprenoid and prostaglandin synthesis which might favor melanoma cell survival and aggressiveness.

Zingiber officinale rhizome is widely cultivated in the central region of Sudan (Gezira) and data on the biological properties of this variety grown in the Sudan climate is scarce. The study aims to comprehensively analyze the antibacterial, antioxidant, phytochemical, and GC-MS properties of Zingiber officinale (Ginger rhizome) to explore its potential applications. The in vitro antibacterial assessment of the aqueous extract of the Sudanese ginger revealed moderate activity against Staphylococcus aureus, Salmonella typhi, Pseudomonas aeruginosa, Escherichia coli , and Klebsiella pneumonia, as resulted from the disc-diffusion which ranged between 12.87±0.11 mm to 14.5±0.12 mm at 30 µg/disc. The minimum inhibition concentration ranged between 6.25 to 25 µg/ml, the MBC were between 25 and 50 µg/ml, and the MBC/MIC exhibited its bactericidal effect against all tested bacteria. The phytochemical screening revealed the presence of various chemical constituents such as saponins, flavonoids, glycosides, alkaloids, steroids, terpenoids, and absence of tannins in the Sudanese ginger rhizome. Furthermore, the GC-MS study of ginger rhizome identified 22 chemical compounds with retention times ranging from 7.564 to 17.023 minutes. The identification of 22 chemical compounds through GC-MS analysis further underscores the prospect of harnessing ginger rhizome for the development of novel medications. The computational analyses showed that ginger compounds bind the 1JIJ and 2QZW with high binding affinities, which reach –9.5 kcal/mol. Ginger compounds also established promising molecular interactions with some key residues, that satisfactorily explain the in vitro results and support the pharmacokinetic and experimental findings. This study lays the groundwork for future research and pharmaceutical exploration aimed at harnessing the beneficial properties of ginger rhizome for medicinal and therapeutic purposes, particularly its antimicrobial potential.

This study examines the impact of decentralisation on corruption levels in water utilities across central India, challenging the widely held assumption that decentralisation reduces corruption. Using experience-based measures and a natural experiment approach, the research compares corruption levels between centralised and decentralised water supply agencies Madhya Pradesh and Chhattisgarh. The study employs a longitudinal survey conducted from over five years, focusing on bribery in various aspects of water service provision. Results indicate that decentralised agencies exhibit significantly higher levels of corruption compared to centralised ones, particularly in the initial stages of decentralisation. While a gradual reduction in corruption is observed over time, the difference between decentralised and centralised utilities remains significant in the medium term. These findings contradict the simplistic view of decentralisation as a cure-all for governance issues and highlight the need for a more nuanced understanding of its effects on public service provision. The study contributes to the ongoing debate on decentralisation policies and their implications for corruption in developing countries, offering valuable insights for managers.

Halide perovskite are known by their (1) organic inorganic hybrid and (2) inorganic halide perovskite. Having this in mind, monolithic perovskite/silicon tandem solar cell has already demonstrated extraordinarily high performance in the field of photovoltaic with current efficiency of 29.15%, breaking the efficiency limit for silicon solar cell. Currently, halide perovskites become successful not only in photovoltaic but also in many other related potential optoelectronic applications. Because of this reason, the origin of their multifunctional properties, remarkable energy harvesting and emitting efficiency and the corresponding potential applications in various optoelectronic devices become controversial issues and hot topics of academic research this time. In this review, nano-engineering strategies, microscopic origins and mechanisms are reviewed well to make clarity on what the origin of multifunctional properties such as tunability, ferroelasticity, piezoelectricity, pyroelectricity and thermoelectric properties of halide perovskites. Moreover, the coexistence of multiple properties makes halide provskites possible for synergistic applications and multifunctional perspectives such as emerging energy harvesting, conversion technologies, nano-plasmonic sensing and electromechanical applications, which are now open for the scientific community for further detail investigations. To successfully explore this field, advanced nanometer scale domain characterization tools are highly relevant to understand the microscopic origin of these electrical properties; aid the commercial enterprises and the research institutions. Not only the microscopic origin but also identifying factors and issues affecting the successful understanding and presence or absence of these electrical parameters should be primary task in this review. Finally, the big challenges for operation of halide perovskites owing to temperature, moisture, light, air, etc. induced material degradation and device deteriorations as well as lattice instability, nano scale defects, surface and bulk defects should be considered while dealing with these future research topics.

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

The interest in natural remedies in modern medicine has revealed potent disease-fighting agents, such asCUR(CUR) and nicotinamide (NIC), known for their antioxidant, anti-inflammatory, and immune-boosting properties. This study explores the antibacterial and antimelanoma potential of a CUR-NIC combination and its liposomal formulation. The antibacterial efficacy was assessed against skin infection-causing bacteria, Staphylococcus aureus, while cytotoxicity and migration assays were conducted on the melanoma B16 cancer cell line. The CUR-NIC combination (1:1) demonstrated an inhibition zone of 18±0.8 mm against S. aureus and a MIC of 31.25 µg/ml, outperforming CUR alone. Whereas Lip-CUR-NIC showed an inhibition zone of 12±0.8 mm against S. aureus and a MIC of 37.5 µg/ml. Notably, the cytotoxicity assay revealed that the CUR-NIC duo synergistically inhibited melanoma cell proliferation (CI<1). Liposomal preparations further enhanced this effect, with Lip-CUR-NIC showing a remarkably low IC50 of 8.5 ± 0.3 µM compared to CUR (IC50= 9.8 ± 2.2 µM) and NIC (IC50=135.95 ± 10.2 µM). These findings highlight the synergistic potential of CUR and NIC, especially in their liposomal form, offering a promising strategy for more effective cancer treatment.

Amazona amazonica, popularly known as the mangrove parrot, belongs to the order Psittaciformes and is widely distributed across South America, occurring in all regions of Brazil. Although this species is commonly admitted to veterinary care, detailed hematological data are scarce. These data are crucial for guiding clinical interventions, monitoring specimens in human care environments, and providing research references. Hematological reference intervals were established for man-grove parrots kept in a fauna management establishment located in the Amazon biome in the state of Pará, Brazil. The study included 32 Amazona amazonica parrots. Average values for the analyzed analytes in the complete blood count (CBC) were: red blood cells (RBC) 2.42 million/µL; hemoglobin 15.92 g/dL; hematocrit 50.31%; MCV 205.47 fL; MCHC 31.7%; MCH 66.73 pg; white blood cells (WBC) 6,281.4 cells/µL; heterophils 2,671.1 cells/µL; lymphocytes 3,452.27 cells/µL; eosinophils 0 cells/µL; basophils 22.63 cells/µL; monocytes 106.41 cells/µL; thrombocytes 29.44; PPT 6.71 g/dL. Morphological characteristics of the cells were similar to those of other parrots. This research is pioneering in deter-mining hematological reference intervals for mangrove parrots under human care in a holding facility in the Amazon biome. These data can be used as a reference for animals of the species raised under similar conditions.

This article explores the use of vegetation indices (VIs) in remote sensing for estimating standing wood volume and volume increments in forest ecosystems. The study investigates the correlations between various VIs and wood volume across different forest stand age classes, focusing on the effectiveness of commonly used indices such as Squared Ratio Simple Red Edge (SQSR) and Red-Edge Ratio Vegetation Index (RERVI), as well as other typical indicators including Enhanced Vegetation Index (EVI), Red-Edge EVI (RE-EVI), and Green Normalized Difference Vegetation Index (GNDVI). Additionally, the analysis considers a pair of indicators based on blue and near-infrared channels, resembling the Blue-normalized Difference Vegetation Index (BNDVI). The research reveals that quadratic and power-law models of SQSR and RERVI demonstrate higher correlations (>0.40) with wood volume, especially in younger forest stands (>0.80). Furthermore, the study highlights the potential of NDII and NDWI indices for estimating volume increments in certain classes of forest stands (0.70). However, correlations vary across different forest stand classes, suggesting the need for further investigation into the reliability of VIs for monitoring wood volume and volume increments in forest ecosystems. Overall, this research contributes to the understanding of the applicability of VIs in remote sensing for forest management and ecosystem monitoring.

This review article explores the innovative application of software sensors for monitoring and controlling biosystems, emphasizing their advantage in managing the complexities of various biological processes. Biosystems—from cellular interactions to ecological dynamics—are characterized by intrinsic nonlinearity, temporal variability, and uncertainty, posing significant challenges for traditional monitoring approaches. A critical challenge highlighted is that what is typically measurable may not align with what needs to be monitored. Software sensors bridge this gap by combining hardware sensor data with advanced computational modelling techniques to indirectly infer hard-to-measure target variables, such as stress level, animal and human health indicators, and chemical soil properties. The article outlines advancements in sensor technologies and their integration into model-based monitoring and control systems, leveraging the capabilities of Internet of Things (IoT) devices, wearables, remote sensing, and smart sensors. It provides an overview of common methodologies for designing software sensors, focusing on the modelling process. The discussion contrasts hypothetico-deductive (mechanistic) models with inductive (data-driven) models, illustrating the trade-offs between model accuracy and interpretability. Specific case studies are presented, showcasing software sensor applications such as the use of Kalman filter in greenhouse control, remote detection of soil organic matter, and sound recognition algorithms for early detection of respiratory infections in animals. Key challenges in designing software sensors, including the complexity of biological systems, inherent temporal and individual variabilities, and the trade-offs between model simplicity and predictive performance, are also discussed. This article positions software sensor as remarkable tool for advancing biosystem management, driving forward the potential for sustainable practices in various sectors such as agriculture, healthcare, and environmental monitoring.

In the World one of the main issues with public health is the lack of fermented cereal grain. One of the solutions is understanding the physicochemical properties of millet grains is optimizing their processing and fermentation. This study examines the effects of fermentation time on the physicochemical properties of finger millet flour batter, using four varieties: Axum, Meba, Padet, and Quncho Teff. Batters were fermented for 24, 48, and 72 hours at room temperature. The pH decreased from 5.8 to 4.5, while titratable acidity (TA%) increased from 0.5% to 1.2%. Viscosity decreased significantly from 420 cP to 190 cP over 72 hr. The number of eyes (NOE) increased from 170 to 240 eyes per 2x2 cm², indicating enhanced porosity. Sensory attributes (SAI%) improved, reflecting better acceptability. These results confirm that fermentation time significantly influences the physicochemical and microbiological characteristics of finger millet flour batter, enhanced quality for finger millet-based food products.

The study aims to explain the relationship of financial literacy with spending habits and behavior. Financial behavior is a mediator of Generation Z. Financial literacy is a very important dimension in making financial decisions, and this research highlighted the direct and indirect impacts that financial literacy exerts on spending behavior. Data is collected from 317 respondents using a structured questionnaire measuring financial literacy, financial behavior, and spending behavior. Correlation, regression, mediation analysis, and Structural Equation Modeling have been used in evaluating these relationships. Financial literacy directly reflects a strong relationship with spending behavior, and its coefficient is 1.167. Financial behavior, therefore, exercises a strong mediation effect on this relationship with a mediation coefficient of 0.797, thus indicating that financial behavior is an essential tool in successfully translating financial knowledge into responsible spending. From these findings, greater financial literacy and responsible financial behavior may increase the aggregate financial efficacy of Generation Z. Although the current study heavily focuses on the fact that financial literacy and behavior are highly associated with financial well-being, it shows that targeted financial literacy education programs on either behavior or financial literacy enhance healthier spending behavior. The scope of future studies involves considering the role of income and personality characteristics as well as an elongation of the time horizon to uncover the impact of financial literacy on shifts in the individual's financial well-being.

Dietary intake of Wistar rats of biofortified with 8-hydroxy-7-iodo-5-quinolinesulfonic acid (8-OH-7-I-5QSA) kale of both cultivars ‘Oldenbor F1’ and ‘Redbor F1’ for 8 weeks resulted in sig-nificantly higher iodine concentrations in the urine and kidneys of rats, which proves iodine bio-availability. Rats diets with 'Oldenbor F1' and 'Redbor F1' kale non- and -biofortified with 8-OH-7-I-5QSA had a significantly lower or a tendency for lower concentration of TSH, triglyc-eride, total and direct bilirubin, TBARs, uric acid, aspartate aminotransferase (AST) and alanine aminotransferase (ALT) concentrations in serum. Dietary intake of 'Oldenbor F1' and 'Redbor F1' kale biofortified with 8-OH-7-I-5QSA significantly increased the total antioxidant status (TAS). The study confirms that kale biofortified with iodine in organic form iodoquinoline 8-OH-7-I-5QSA is bioavailable and well absorbed by the Wistar rat and has a positive effect on selected biochemical parameters.

Femtosecond laser two-photon polymerization (TPP) technology, known for its high precision and capability to fabricate arbitrary 3D structures, has been widely applied in the production of various micro-nano optical devices, achieving significant advancements particularly in the field of photonic wire bonding (PWB) for optical interconnects. Currently, research on optimizing both the optical loss and production reliability of polymeric photonic wires is still in its early stages. One of the key challenges is that inadequate metrology methods cannot meet the demand for multiphysical measurements in practical scenarios. This study utilizes in-situ scanning electron microscopy (SEM), for the first time, to monitor the working PWBs fabricated by TPP technology at the microscale. Optical and mechanical measurements are made simultaneously to evaluate the production qualities and to study the multiphysical coupling effects of PWBs. The results reveal that photonic wires with larger local curvature radii are more prone to plastic failure, while those with smaller local curvature radii recover elastically. Furthermore, larger cross-sectional dimensions contribute dominantly to the improved mechanical robustness. The optical loss deterioration of the elastically deformed photonic wire is only temporary, which can be fully recovered when the load is removed. After further optimization based on the results of multiphysical metrology, the PWBs fabricated in this work achieve a minimum insertion loss of 0.6 dB. In this study, the multiphysical analysis of PWBs carried out by in-situ SEM metrology offers a novel perspective for optimizing the design and performance of microscale polymeric waveguides, which could potentially promote the mass production reliability of TPP technology in the field of chip-level optical interconnection.

In case of a discrete-time Hopfield neural network of two neurons with two delays and no self- connections at 23 bifurcation values 23 voltage trajectories appear.Among the 23 voltage trajectories 14 voltage trajectory are not what we call orbits in classic sense. The geometrical aspect of these trajectories suggest that they are fractals. Our conjecture is that the 14 trajectories in discussion are fractals having real couterpart. In case of a discrete-time Hopfield neural network of five neuron with delay and ring architecture at 9 bifurcation values 9 voltage trajectories appear.Among the 9 voltage trajectories we find 5 voltage trajectory which are not what we call orbits in classic sense.The geometrical aspect of these trajectories suggest that they are fractals.Our conjecture is that the 5 trajectories in discussion are fractals having real couterpart.. In case of a discrete-time Hopfield neural network of two neurons with a single delay and self- connections the computed trajectories are what we call orbits in classic sense. In case of a discrete-time Hopfield neural network of two neurons with two delays and self- connections the computed trajectories are what we call orbits in classic sense.

There are many features of homeostasis worth investigating such as how it works (information pathways, set point processing), where its command and control processors are located in the cell, the language used and how homeostasis interacts with all effectors (all aspects of an organism's functional maintenance and development such as cell differentiation). All are necessary and valid inquiries, but this paper examines the implications of ignoring it as a factor of disease causation. Alternatively when properly understood the homeostasis profile becomes a rational explanation for any individual’s range of health; where some areas are very resistant to disease (resilient) and other areas of homeostasis are dysfunctional leading to minor, moderate or very susceptible tendencies to disease (a lack of resilience in homeostasis). An understanding of this phenomenon, its interactions with gene expression (epigenetics) is necessary in order to evaluate the solutions for acute and chronic diseases. By this method diseases can be defined more precisely as specific susceptibilities in homeostasis, leading to a method to restore specific homeostasis functions.

The effects of reaction temperature, reaction time, heating rate, nitrogen flow rate and catalyst quality on the oil yield and oxygen content in the oil were investigated by using the single factor principle. Too high and too low reaction temperature, nitrogen flow rate, heating rate and reaction time have improved the yield of bio-oil, but the oxygen content in bio-oil is higher. The reduction of catalytic dose is beneficial to the increase of oil yield but not conducive to the increase of aromatic compound content. The optimal reaction conditions were observed as reaction temperature of 500℃, nitrogen flow rate of 110 mL/min, heating rate of 20℃/min, reaction time of 15min, mass ratio of catalyst to coconut coat powder was 1, and the oil yield was 4.97%, aquatic yield was 35.80%, and solid yield was 30.78%. The gas yield was 28.45%, the oxygen content was 10.3%, and the aromatic compound content was 89.7%.The analysis of catalytic cracking mechanism shows that most of the oxygen-containing compounds in the bio-oil are generated into water and aromatic compounds by catalytic cracking reaction at high temperature, thus removing oxygen.

In the last two decades, the application of composite structures in many industrial sectors has been made progressively. For the current major scientific and technological challenge, the energy transition with sustainable and eco-friendly initiatives, the polymer matrix laminated composites have been revealed as an excellent alternative for the renewable energy sector. In this context, the knowledge about mechanical behavior of multiphase materials is required. Then, researches related to macro, mesomechanical and multiscale approaches are very important to improve the accuracy of the models to predict effective mechanical properties and to develop a better understanding about complex phenomena such as progressive damage, creep and fatigue. Due to theses necessities, this research is a synthesis of fundamentals and advanced topics concerning to global modeling of laminated composite structures, layerwise analyzes and micro-macromechanical models, in which mainly fiber, matrix, interface and damage characteristics are studied and used in the macroscale approach. A deeper physical and mathematical description of the models is proposed, intead of general and vague reviews typically stated in the specific literature. At the end of this work, some final remarks and a trend analysis are made based on articles consulted, in other words, expectations for the future works are done by the authors of this study.

This paper explores Augmented Reality (AR) as an innovative active methodology for student assessment in the Higher Education Authority of Arcoverde, proposing an alternative to the traditional assessment model. It investigates how AR can transform the educational process, making assessments more interactive, engaging and effective. The study focuses on three main issues: the impact of AR on student motivation and engagement, its effectiveness in improving content comprehension and retention, and the challenges and limitations of its implementation as an assessment method. Employing dialectical methods, action research and literature reviews, this research documents a 34% increase in students’ content comprehension and a 47% increase in their participation in practical activities. These findings suggest that, despite the technical and curricular integration challenges, AR not only improves assessment methodologies, but also enhances learning, promoting a more dynamic and interactive environment. The overall objective is to demonstrate the ability of AR to replace conventional approaches with a more innovative and efficient practice, highlighting the importance of this technology in advancing pedagogical practices and improving student performance. This study sheds light on the relevance of AR in the educational field, encouraging its adoption to enrich the learning and assessment experience.

Due to the deteriorating state of surface waters and the resulting reduction in their availability for the population, efforts have been undertaken to develop a biodegradable or compostable water filtration system capable of reducing the amount of harmful bacteria in this environment. The aim of this research was to develop nonwovens using the classic needle-punching method with commercially available polylactide (PLA) polymer, and subsequently, to select appropriate concentrations of modifiers to create systems with antibacterial properties. The resulting composites could potentially serve as filtration materials for the purification of surface waters. Nanoparticles such as zinc oxide, titanium dioxide, and silver were employed in this research. These compounds exhibit antibacterial properties and are insoluble in water. To confirm the bactericidal activity of the additives, biological tests were conducted, and the distribution of these additives on the structure of the filtration material was examined using scanning electron microscopy (SEM) equipped with an energy dispersive spectroscopy (EDS) detector.

Engineered viral vectors designed to deliver genetic material to specific targets offer significant potential for disease treatment, safer vaccine development, and the creation of novel biochemical research tools. Viral tropism, the specificity of a virus for infecting a particular host, is often modified in recombinant viruses to achieve precise delivery, minimize off-target effects, enhance transduction efficiency, and improve safety. Key factors influencing tropism include surface protein interactions between the virus and host cell, the availability of host cell machinery for viral replication, and the host immune response. This review explores current strategies for modifying the tropism of recombinant viruses by altering their surface proteins. We provide an overview of recent advancements in targeting non-enveloped viruses (Adenovirus and Adeno-associated vi-rus) and enveloped viruses (Retro/Lentivirus, Rabies, Vesicular stomatitis virus, and Herpesvirus) to specific cell types. Additionally, we discuss approaches such as rational design, directed evo-lution, in silico and machine learning-based methods for generating novel AAV variants with desired tropism, and the use of chimeric envelope proteins for pseudotyping enveloped viruses. Finally, we highlight the applications of these advancements, and discuss the challenges and future directions in engineering viral tropism.

Accurate assessment of crop vegetative state is crucial for agricultural management, particularly under varying environmental conditions. However, detailed field experiments face logistical and cost challenges. The Normalized Difference Vegetation Index (NDVI) and other vegetation indices offer non-invasive and large-scale solutions for assessing plant health. The integral of the NDVI curve over time aggregates NDVI values, providing a comprehensive view of vegetation dynamics, surpassing the insights from individual NDVI measurements. Developing robust methodologies to calculate the vegetation activity index (VAI) based on NDVI integral is essential for efficient vegetation evaluation, aiding sustainable agriculture. Biostimulants enhance plant growth and resilience against stressors. Sugarcane, vulnerable to various stresses, can benefit from biostimulants, especially under drought conditions. Studies on biostimulant effects on sugarcane's metabolism, yield, and quality have been limited. Analysis of NDVI data indicated significant variations between treated and untreated areas over time. Polynomial regression models (R² between 0.921 and 0.959) demonstrated a strong ability to explain vegetation variability. Normality tests confirmed that VAI and Tons of Sugar per Hectare (TSH) data follow a normal distribution. There was no significant difference in VAI between treated and untreated areas during the plant development cycle, but a significant increase in VAI was observed in treated areas post-treatment (p=0.021). No significant difference in TSH was found, indicating no impact on sugar productivity. L-alpha biostimulant increased vegetation activity during critical growth periods but did not affect sugar yield. Return on investment analysis suggested high profitability of the treatment.

Transition zones are in the places on the track, with a change in the basic composition of the railway body. There are many sections with a sudden change in the stiffness of the structures built. When the trains are running, a longitudinal shock wave is created from the wheels, which hits these building objects with a higher stiffness and deforms the surroundings of these zones. The greatest attention should be paid mainly to the transition points from the fixed track to the classic track with a track bed, including objects of the railway substructure such as bridges, portals of tunnels, etc. As part of the research sections on the main corridor lines, a long-term inspection and monitoring was carried out with a continuous measurement system using the KRAB trolley. Height changes in the deflections of rails are evidence of their behavior. The measurements took place on a fixed track and a track with a ballast. Changes in the height jumps between the fixed railway track and the track with a gravel bed are significant and have been recorded since the tracks were put into operation. These certain height deflections allow designers to develop new, more durable construction designs.

This research answers the knowledge gap regarding the explanation of the quantum jump of the electron. This scientific paper aims to complete Einstein's research regarding general relativity and attempt to link general relativity to quantum laws.

This study presents a systematic review to evaluate the effectiveness of virtual patients in enhancing clinical reasoning skills in medical education, utilizing a hybrid methodology that combines human reviewers and ChatGPT. By analyzing various studies involving conversational virtual patients, the review examines the impact of these digital tools on student learning outcomes and satisfaction. Consistent with previous systematic reviews, our findings suggest that conversational virtual patients can improve clinical competencies, particularly in history-taking and clinical reasoning. Regarding student feedback, satisfaction tends to be higher when virtual patients’ interactions are more realistic, often due to the use of artificial intelligence (AI) and natural language processing (NLP) in the simulators. Furthermore, the study compares the accuracy of AI-driven reviews with human assessments, revealing comparable results. This research highlights AI’s potential to complement human expertise in academic evaluations, contributing to more efficient and consistent systematic reviews in rapidly evolving educational fields.

Recently, cases of antifreeze poisoning in companion animals, particularly cats, have surged in the Republic of Korea. Ethylene glycol (EG), the toxic primary component of antifreeze, is metabolized into glycolic acid (GA), leading to severe conditions such as metabolic acidosis and acute renal failure. Traditional detection methods, although effective, are often time-consuming owing to complex sample preparation. This study involved a novel analytical method utilizing GC-MS for EG and LC-MS/MS for GA detection, which streamlined the detection process by eliminating the need for derivatization. The method was validated for accuracy and reliability, enabling the rapid and precise identification of EG and GA in biological samples. The study also included the successful application of this method in a case where initial exposure to antifreeze was not apparent, which highlighted the effectiveness of this method in diagnosing poisoning even in cases where clinical history is unclear. The development of this rapid diagnostic approach addresses the urgent need for the efficient detection of antifreeze poisoning, improving animal welfare and supporting forensic investigations.

Conventional approaches in Structural Health Monitoring (SHM) tend to be complex, destructive, and time-intensive. Additionally, they often require a large number of sensors to thoroughly assess structural integrity. In this study, we present a novel, non-destructive SHM framework based on machine learning (ML) for the accurate detection and localization of structural cracks. This approach leverages a minimal number of strain gauge sensors linked via Bluetooth communication. The framework is validated through empirical data collected from 3D carbon fiber-reinforced composites, including three distinct specimens, ranging from crack-free samples to specimens with up to ten cracks of varying lengths and depths. Strain data from five sensors were analyzed using a combination of Shewhart charts, Grubbs Test (GT), and a hierarchical clustering algorithm, specifically designed to evaluate and classify fractures. Our ML-based framework offers a streamlined and efficient alternative to traditional laboratory procedures, delivering precise crack detection with significant potential for applications in the composites industry.

Cancer ranks second among the causes of mortality worldwide,following cardiovascular diseases.Brain cancer, in particular, has lowest survival rate of any kind of cancer. Brain tumours vary in their morphology, texture, and location, which determine their classification. Accurate diagnosis of the tumour category enables physicians to select optimal treatment strategies and potentially prolong patients’ lives. Researchers who also implemented deep learning models for diagnosing diseases in recent years largely focused on deep neural network optimization to enhance neural network performance. This implicates implementing models with the best performance and incorporating various network architectures by configuring their hyperparameters. This paper presents a novel hybrid approach method for improved brain tumour classification by combining CNN and EfficientNetV2B3 as feature extraction, followed by (KNN) for classification. To evaluate the recommended method’s efficacy, two widely known benchmark MRI datasets were utilized in the experiments. The initial dataset consisted of 3064 MRI images depicting meningiomas,pituitary,and gliomas tumours.Images from two classes of normal brain and brain tumour were included in the second dataset, which was obtained from Kaggle.In order to enhance performance even more, the study concatenates the CNN and EfficientNetV2B3 flattened outputs before feeding them into the KNN classifier. The proposed framework run on two different dataset and demonstrates outstanding performance with an accuracy of 99.51% and 99.8% on each dataset.

Artificial Intelligence (AI) is fundamentally reshaping various industries by enhancing decision-making processes, optimizing operations, and unlocking new opportunities for innovation. This paper explores the applications of AI across four key sectors: healthcare, finance, manufacturing, and retail. Each section delves into the specific challenges faced by these industries, the AI technologies employed to address them, and the measurable impact on business outcomes and societal welfare. We also discuss the implications of AI integration, including ethical considerations, the future trajectory of AI development, and its potential to drive economic growth while posing challenges that need to be managed responsibly.

Background: In our recent publications pertaining to 4G model of final unification and based on strong and electroweak interactions, we have proposed the existence of a weak fermion of rest energy 585 GeV. Objective: To confirm the physical existence of the proposed 585 GeV weak fermion by analyzing weak and strong interactions in a unified approach via 4G model of final unification, super symmetry and string theory. Method: Considering the proposed nuclear charge of 2.95e, proton, electron mass ratio, specific charge ratios of proton and electron, Fermi’s weak coupling constant, Reduced Planck’s constant, nucleon magnetic moments, nuclear stability, nuclear binding energy, nuclear mass and neutron lifetime, it is planned to confirm the physical existence of the proposed 585 GeV weak fermion. Results: All proposed logics and formulae clearly establish the physical existence of 585 GeV weak fermion directly and indirectly. Conclusion: Believing in the physical existence of the proposed 585 GeV weak fermion, there is a scope for observing galactic TeV radiation coming by virtue of annihilation of 585 GeV fermions and radiation associated with various astrophysical acceleration mechanisms of 585 GeV fermions. Appeal: As we are beginners of astrophysics domain, we appeal the science community to see the possibility of considering the proposed 585 GeV weak fermion with a charge of $\pm\left(e\right)$ in place of electron and proton.

Knowledge is not merely the characteristic of certain pieces of information being deemed true, believed and justifiable, but rather a process. The sentences that reaches the status of knowledge forms a universe that provides an interpretation of the reality one aims to describe. Language exchanges information through communication and the justification, if concludes successfully with respect to a sentence, it elevates the information to the status of knowledge. We provide a model where we define the fundamental elements of communication and grammar. As a result, knowledge must be \open\ (everyone must be able to acquire it), \free\ (everyone must be able to justifiy) and \tidy\ (everyone must be able to retrace the justification process in all its parts recursively). Ultimately, the development of knowledge processing, referred to as \emph{knowmatic}, represents the evolution of the information technologies (\it).

Neurodegenerative diseases (NDDs) are a class of cognitive and motor disorders that include Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington's disease (HD), amyotrophic lateral sclerosis (ALS), and others. They are caused by lesions in cells and tissues of the central nervous system, resulting in corresponding dysfunctions and consequent decline in cognitive and motor functions. Neural tissues are extremely vulnerable to oxidative stress, which plays critical biological roles in NDDs. Aromatic compounds are found extensively in natural plants and have substantial effects of anti-oxidative stress damage, which not only have a wide range of research applications in cosmetics, foods, etc., but are also frequently utilized in the treatment of various central nervous system diseases. This review summarizes the relevant oxidative stress mechanisms in NDDs (AD, PD, HD, and ALS) and reviews aromatic compounds such as polyphenols, terpenoids, and flavonoids that can be used in the management of neurodegenerative diseases, as well as their specific mechanisms of antioxidant action. This review will serve as a reference for future experimental studies on neurodegenerative illnesses while also offering fresh insights into clinical therapy.

This study profiled morphological and laboratory-derived performance biomechanics by position of American style football players training for the National Football League draft. Fifty-five players were categorized into Big (e.g., lineman; n=17), Big-skill (e.g., tight end; n=11), and Skill (e.g., receiver; n=27) groups. Body fat (BF%), lean body mass (LBM), total body mass, running force and ground contact time (GCT), countermovement jump height (CMJ-JH), CMJ normalized peak power (CMJ-NPP), and reactive strength were measured in a laboratory. Asymmetry was calculated for running force, GCT, and CMJ eccentric and concentric impulse (IMP). MANOVA determined between-group differences, and radar plots for morphological and performance characteristics were created using Z-scores. There was a significant between-group difference, F(26,80)=5.70, p&lt;0.001;Wilk’s Λ=0.123, partial η2=0.649. Fisher’s least squares difference post-hocs showed Skill had greater JH, CMJ-NPP, reactive strength, and running force versus Big but not Big-skill, p&lt;0.05. Big had greater BF%, LBM, total body mass, and GCT than Skill and Big-Skill, p&lt;0.05. Big-skill had greater GCT asymmetry than Skill and Big, p&lt;0.05. Asymmetries in running forces, and CMJ eccentric and concentric IMP were not different, p&gt;0.05. Morphological and performance biomechanics differences are pronounced between Skill and Big. Big-skill possess characteristics from both groups. Laboratory-derived results provide specialized profiles of draft trainees.

In an era of relentless technological change, accelerating the pace of innovation has become a critical imperative for organizations seeking to maintain a competitive edge and drive meaningful societal progress. Yet the complex, multifaceted innovation process - from ideation to commercialization - is often plagued by bottlenecks and challenges that can significantly delay the time-to-market for groundbreaking new products, services, and business models. This pioneering article represents an outstanding contribution to the burgeoning field of innovationology, offering a comprehensive, evidence-based playbook for shortening the innovation lifecycle. Drawing on an extensive review of over 180 cutting-edge academic sources, the paper maps the key stages of the innovation process - idea generation, concept development, prototyping, testing, and launch - and illuminates cutting-edge strategies that innovators can deploy to overcome common barriers and drive transformative innovations to market more quickly. From advanced idea generation techniques like design thinking and AI-powered creativity to innovative approaches to rapid prototyping, virtual experimentation, and data-driven commercialization, this seminal work equips scholars, practitioners, and policymakers with a transformative, globally-relevant toolkit for accelerating the innovation lifecycle. By synthesizing insights from a diverse array of industries and disciplines, the article establishes a new benchmark for innovationology research and empowers organizations to cultivate the conditions needed to translate visionary ideas into game-changing realities with unprecedented speed and efficiency. As the world grapples with ever-more complex, interconnected challenges, the ability to rapidly develop and scale breakthrough innovations has never been more crucial. This paradigm-shifting contribution to the field of innovationology represents a vital resource for driving the bold, disruptive changes needed to shape a more sustainable, equitable future.

Adolescence is a critical period for the development of self-perception, emotional well-being, and health-related behaviors. During this stage, adolescents undergo significant physical, emotional, and social changes that influence their overall health and interactions with their environment. This study analyzes the prevalence of behaviors related to self-perceived health, mood, and substance use among adolescents, identifies risk factors, and draws comparisons between different groups. It also examines how these factors interact with family and community dynamics to inform strategies for improving mental health care. Method: A descriptive cross-sectional study using questionnaires was conducted. Participants were adolescents aged 14 to 18 years from a school in the Baix Llobregat province of Catalonia. Descriptive and bivariate statistical analyses were performed using the chi-square test and p-value calculations with R Project software. Frequency and percentage analyses were employed to describe the health behaviors and emotional states of the participants. Results: The study reveals significant variations in adolescents' self-perceived health, emotional management, and substance use patterns. Adolescents with lower self-perceived health and negative mood states tend to exhibit higher rates of substance use, particularly alcohol and tobacco. Gender differences were observed, with girls generally reporting better health awareness and emotional regulation compared to boys. Conclusions: This study highlights the need for integrated approaches to family, community, and mental health care. Findings emphasize the importance of early intervention programs addressing emotional well-being and substance use in a holistic manner. Public health policies should prioritize comprehensive strategies involving schools, families, and communities to foster healthier behaviors and improve adolescent mental health outcomes.

Measurement in software projects is one of the activities that define engineering. In this article, it is defined as the competence of the computer engineer and its dimensions and indicators are determined. Subsequently, a pre-experiment is carried out where different empirical methods are applied in three moments to monitor the development of the competence.

This article presents a kinematic analysis of a 4-degree-of-freedom (DOF) robotic arm equipped with a 3-finger gripper, specifically designed for harvesting fruits of varying sizes. The kinematic modeling is thoroughly detailed, encompassing the equations of motion, homogeneous transformation matrices, and kinematic solutions required for the efficient operation of the robotic arm. This analysis is crucial for optimizing both the design and programming of agricultural robots, aiming to enhance harvesting efficiency and reduce labor costs. Additionally, an in-depth examination of the 3-finger gripper’s movement is provided, illustrating how MATLAB facilitates the simulation and visualization of its dynamic behavior during the opening and closing processes. The benefits of using MATLAB for this purpose are emphasized, including its ease of implementation, advanced simulation capabilities, and seamless integration with other tools to optimize the performance of robotic systems in agricultural settings. This study makes a valuable contribution to the field of agricultural robotics, offering technical insights that can be applied in the development of sophisticated robotic solutions for the modern agricultural industry.

Background: Parkinson’s Disease (PD) is a long-term condition, which is more common among people aged 65 years or more. People with Parkinson (PwP) tend to lose motor functions and find themselves in more fatigue and pain, sometimes leading to anxiety, distress and in extreme cases, even depression. So far, there is no cure for PD and they need support from family members, healthcare professionals and carers, as the disease progresses. A large proportion of carers for PwP are their spouses. Progression of PD can result in loss of employment, reduced work hours or premature retirement, for both PwP and their carers. This poses a huge financial burden as income reduces on one hand, and expenses increase on the other. There is a lack of evidence on the societal costs of Parkinson's disease, and this study tries to fill that gap. Objectives: To examine the impact of financial distress on quality of life (QoL) and wellbeing of PwP and their carers in the UK. Methods: A mix of online and postal, cross-sectional survey was administered, which received responses from 853 respondents, out of which 776 were PwP and 546 were carers. The survey sought data on management of the socioeconomic, health and living conditions of PwP and their carers. Net effect of financial distress (cost of healthcare use, non-health expenditure, societal costs and other direct / indirect cost components) on QoL (and VAS scores) as well as wellbeing scores (life satisfaction and happiness scores) of PwP and their primary carers are estimated using simple linear regression models. Results: Financial burden of treatment increase whereas quality of life and wellbeing of PwP decrease with progression of PD. Similar association but of a milder magnitude is observed on quality of life of their primary carers. A strong negative association is found between quality of life of PwP, with financial burden, while holding for demographic and socioeconomic factors, and for disease progression and treatment. The QoL of carers increase up to a financial burden threshold, before it starts declining. This could be because of the increased expenditure in initial phases is associated with improved activities of daily living and lesser reliance on carers. Financial distress is more pronounced among PwP having annual income below £20,000. Despite a small representation of persons from ethnic minority (5 per cent representation), it is observed that the impact of financial distress could be more pronounced on PwPs from other ethnicities. Also, it would be more on women than on men, irrespective of ethnicity. Conclusion and Policy Implications: Cost of care cuts both ways, reduced or lost employment as well as increased expenses on healthcare. This results into a considerable financial distress and deterioration in quality of life and wellbeing of both PwP and carers. Most carers are spouses of PwPs, thus their physical and psychological health and wellbeing is found to deteriorate with the progression of PD due to increased burden of care. Therefore, informal / family carers should get support in the form of respite care. This can help revitalise their energy and psychological wellbeing. Financial distress indicators need to be explored further.

Due to satisfactory mechanical properties, low specific weight and other advantages, a substantial utilization of laminated structural composites in many components and equipments of several industry sectors, especially in the past two decades, has been made. This class of materials has a good potential to be an important ally to support scientists and engineers with the main current world challenge: an energy transition process that must occur with sustainability, eco-friendly materials and based on an efficient reduction of greenhouse gas emissions. In this context, a microanalysis (scale length: 10μm to 1mm) of mechanical behavior that considers the multiphase characteristics, at leastwise a matrix and a reinforcement phase, of this kind of composites must be explored, i. e., a heterogeneous material is assumed to predict, for example, the effective tensors of stiffness or compliance by a homogenization process, modeling to damage analyses, studies about progressive delamination and other specific aspects of a composite can be previously investigated using micromechanical approaches. Therefore, the understanding of fundamental micromechanics as well as some advanced models are essential to apply these materials in structural industry components. In this sense, this literature review is designed to investigate and analize intrinsic physical hypothesis and theories behind the main basic and advanced models of polymer laminated composite micromechanics expanding possibilities to the development of more accurate approximations and showing future perspectives to the topic.

The high thrust linear motor used for electromagnetic launch is switched with the anti-shunt thyristor as the switch. During the switching process, different current path states will appear, which will lead to changes in motor parameters and current fluctuation. Aiming at the air-core synchronous linear motor with parallel and series power supply, this paper analyzes the change of current path caused by the change of trigger signal of anti-parallel thyristor in the process of sectionalized stator switching. The motor circuit model of the switching process is derived. A new sectionalized stator switching method is proposed to realize the smooth switching of the sectionalized stator powered linear motor. Finally, the correctness of the analysis and modeling is verified by simulation, and the effectiveness of the sectionalized stator switching method is tested by using the test prototype.

Following the economic and political transformations after 1991, including the autonomy of the Kurdistan Region and the establishment of Iraq's new federal government in 2003, extensive construction, including mosques, has reshaped the region. This requires examinations of evolving architectural features in new mosques and questioning their alignment with religious and aesthetic design objectives. This research focuses on the transformative impact of modern architectural styles on mosque evolution in Sulaymaniyah Governorate. Acknowledging the interplay of cultural evolution, technology, and artistic innovation, the study employs an in-depth case study approach to analyze selected mosques in Sulaymaniyah Governorate that blend traditional Islamic architecture with modern design principles. This synthesis of traditional and modern elements raises questions about religious legitimacy, historical symbolism, and architecture's role in reflecting and shaping religious values. Using quantitative methods including indicator-based impact assessment, the study identifies modern architectural indicators, determines religious objectives of mosque design through Islamic legitimacy references, and analyzes the case study data. By employing person coefficient correlation, the research aims to identify the causal relationship between modern architectural indicators and achieved design objectives. The results reveal that the overall influence of the modern architecture style tends to be more negative on the evol¬ution of mosques.

In this paper, we present a rigorous proof demonstrating the stabilization of percolation probability in supercritical regimes for percolation models. We analyze a sequence of expanding compact balls in ℝ

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

Coal mining activities frequently result in negative effects that spread to neighboring populations, affecting their general health and well-being. The lack of published research on these effects in the Limpopo province motivated the quest to have local communities assess how mining has affected their health. This study explored the perceived health and well-being outcomes of community members residing around a coal mine in the Limpopo Province, South Africa. The study was conducted in three sub-villages closest to Tshikondeni Coal Mine, namely, Mukomawabane, Bileni, and Mutele B. A qualitative methodology, adopting an exploratory design, was employed. Eighty-one participants, aged 18 to 80, were purposively sampled from the selected sub-villages. Data was collected through one-on-one unstructured interviews captured as field notes, complemented by audio recordings. Throughout the study, ethical research principles of approval and permission, informed consent, privacy anonymity and confidentiality as well as avoidance of harm and benefits were strictly adhered to. Content analysis was used to analyze the data. The findings of the study revealed that many members of the communities residing around the Tshikondeni coal mine are suffering from self-proclaimed respiratory-related conditions (fourteen Asthma, ten TB, three sinuses, three coughs, and one chest pain) perceived to be caused by coal dust. Participants expressed concerns about widespread deforestation from coal mine operations with resultant soil erosion and land degradation as well as the dam that the mine left unfenced, where their livestock is hunted and drowned by crocodiles. In addition, the study reveals twenty-four participants’ concerns about the non-compensation of their cracked houses resulting from mine blasting operations. This study highlights that communities closer to coal mines experience physical, mental, and social well-being challenges, emphasizing the urgent need for extensive research to validate the various self-proclaimed respiratory problems. The concerns of communities are justified as deforestation, soil erosion, and land degradation threaten food security due to the loss of soil fertility and reduced livestock grazing area subsequently impacting the community’s livelihoods. The non-compensation of cracked houses causes frustration, anxiety, sadness, and worry among community members, negatively impacting their mental health. The South African Department of Health, Environmental Affairs, and Mineral and Energy should work together, to ensure the implementation of policies to protect communities closer to mines against harmful mining operations, non-compliance, and negligence.

The objective of this research is to evaluate four distinct models for multi-population mortality projection in order to ascertain the most effective approach for forecasting the impact of COVID-19 pandemic on mortality. Utilising data from the Human Mortality Database for five countries Finland, Germany, Italy, the Netherlands, and the United States, the study identifies the generalized additive model (GAM) within the age-period-cohort (APC) analytical framework as the most promising for precise mortality forecasts. Consequently, this model serves as the basis for projecting the impact of the COVID-19 pandemic on future mortality rates. By examining various pandemic scenarios, ranging from mild to severe, the study concludes that projections assuming a diminishing impact of the pandemic over time are most consistent, especially for middle-aged and elderly populations. Projections derived from the superior GAM-APC model offer guidance for strategic planning and decision-making within sectors facing the challenges posed by extreme historical mortality events and the uncertain future mortality trajectories.

Chronic Myeloid Leukemia (CML) is one of the most commonly found types of myeloproliferative neoplasms, characterized by increased proliferation of granulocytic cells without losing their differentiation ability. Imatinib, a Tyrosine Kinase Inhibitor (TKI), can be effectively used as therapy for CML. However, imatinib can affect bone turnover, thus having clinical implications on the bones of CML patients undergoing long-term imatinib therapy. However, parameters that can accurately describe the bone condition in CML patients receiving imatinib still need further study. A combination of imaging techniques such as Bone Mineral Density (BMD) and bone turnover activity markers such as C-terminal telopeptide of type I collagen (CTX-1) and Osteocalcin has the potential to be used as monitoring parameters for bone density abnormalities in CML patients receiving imatinib. This article explains the rationale for using BMD, CTX-1, and Osteocalcin as monitoring parameters of bone remodeling in CML patients receiving imatinib. First, the physiological process of bone turnover will be explained. Then, we describe the role of tyrosine kinase in bone metabolism. Next, the impact of imatinib on BMD, CTX-1, and Osteocalcin will be explained. In conclusion, the assessment of bone health of CML patients on Imatinib should include both BMD tests and bone turnover marker assays such as CTX-1 and Osteocalcin.

This study aims at providing a climatological analysis of annual and long-term variability of dry periods in the Polish Carpathians in the period of 35 years between 1986 and 2020. Aside days with precipitation, dry periods, namely sequences of days with no precipitation, constitute an inherent characteristic of the climate. The input data for the research was formed by daily precipitation totals from 17 meteorological stations acquired from the Institute of Meteorology and Water Management - National Research Institute, and T. Niedźwiedź catalogue of circulation types over southern Poland. Frequency of dry periods of different duration has been presented. The incidence of long-term periods has been related to the atmospheric circulation. An analysis was performed regarding the change of the average quantity and average length of dry sequences in the 35-year period covered. At most stations, there were no statistically significant trends with respect to the analysed dry day characteristics. This is an obvious result of high variability year-to-year, which means that exclusively large changes may achieve statistical significance. Dry days have been evidenced to related to circulation types. A vast majority of dry days is related to anti-cyclonal systems, particularly the anti-cyclonal wedge (Ka) and western anti-cyclonal (Wa) situations.

Background/Objectives: This study investigates the relationship between diet quality and body image disturbance among young women aged 18-24, a crucial period for establishing lifelong health behaviors. Given the increasing exposure to social media, which often promotes unrealistic beauty standards, this research aims to explore how eating behaviors and diet quality, correlates with body image disturbance; Methods: A mixed-methods approach was employed, combining qualitative focus group discussions with quantitative analysis. Focus groups (n=19) explored themes of body image dissatisfaction. The Body Image Disturbance Questionnaire (BIDQ) was administered to 50 participants (young women aged 18-24) to quantitatively assess body image disturbance, while diet quality was evaluated using the Australian Recommended Food Scores (ARFS). The Three-Factor Eating Questionnaire (TFEQ-R18) was also used to assess eating be-haviors, including cognitive restraint, uncontrolled eating, and emotional eating. A social in-fluence questionnaire (SIQ) was administered to measure the effect of social influence. Pearson's correlation coefficient was used to determine the relationship between ARFS, BIDQ, and TFEQ-R18 scores; Results: Qualitative findings revealed persistent dissatisfaction with body shape, largely influenced by social media. Quantitatively, 65% of participants scored above the clinical threshold for body image disturbance (mean BIDQ score = 4.2, SD = 0.8). The correlation between ARFS scores and BIDQ scores was weak and not statistically significant (r = 0.057, p = 0.711). However, a sig-nificant positive correlation was found between time spent on social media and body image disturbance (r = 0.58, p < 0.01). Additionally, TFEQ-R18 results indicated that 45% of participants displayed moderate levels of uncontrolled eating, and 36.5% demonstrated moderate levels of emotional eating; Conclusions: While social media significantly influences body image concerns, its effect on eating behaviours and diet quality shows weak correlations, suggesting that other factors may mediate these outcomes. These results highlight the complexity of the relationship between body image, eating behaviors, and diet quality, indicating that interventions should address the psychological drivers behind these concerns, rather than solely focusing on reducing social media exposure.

This research explores a novel approach to integrating offshore wind farms into smart microgrids through the development of an advanced model predictive control (MPC) strategy for offshore wind turbines. The MPC strategy comprehensively incorporates complex wind dynamics, such as wind speed, direction, and turbulence, ensuring an accurate representation of turbine behavior. Additionally, the research includes models addressing wind farm layout, wake effects, and electrical infrastructure, enhancing the modeling framework's fidelity and applicability to real-world offshore wind energy systems within a smart microgrid environment. The inherent variability of wind power, transmission technology limitations, and the need for seamless transitions between grid-connected and islanded operation modes in smart microgrids necessitate robust control mechanisms. These mechanisms ensure efficient power capture, grid stability, and optimal energy utilization. To tackle these challenges, the research proposes a smart microgrid design incorporating both AC-DC and DC-DC conversion technologies. This design leverages renewable energy sources like solar and wind with AC-DC conversion for efficient power capture, integrates DC energy storage systems to store excess energy during low demand periods, and utilizes power control mechanisms to manage energy flow effectively within the smart microgrid. The study emphasizes the importance of modeling and control strategies for both AC-DC and DC-DC converters within the smart microgrid framework. To enhance the microgrid's cost-effectiveness, priority is given to the utilization of solar PV energy sources. It also highlights the integration of protection systems for DC faults and coordinated control of AC/DC breakers and converters. This comprehensive approach aligns with the core principles of adaptability, reliability, and sustainability in smart grids, aiming to advance smart microgrid technologies and pave the way for a more resilient, efficient, and secure energy future.

Surgical site infections (SSIs) are a major concern in public healthcare, leading to increased morbidity, pro-longed hospital stays length, and significant economic costs. Understanding the epidemiology and risk fac-tors of SSIs is crucial for developing effective prevention and management strategies. In the last century, CDC ‘s HICPAC improved a bundle for prevention of HAIs based on the use of aseptic procedures, disin-fection of the skin and correct use of antibiotics for SSIs. In our country, Gelli-Bianco law introduced the national health guidelines system that include the prophylaxis antibiotic therapy pre- and post-surgery. The aim of our study is to evaluate adherence and appropriateness of the use of antibiotic-preoperative prophylaxis in a province of southern Italy and to identify risk factors that may influence the development of an SSI. The study found many statistically significant associations among the factors examined, i.e. the timing of administration adherent to guidelines, with 47% of the analyzed files administering antibiotics within 60 min before the intervention and 62.7% using intravenous administration.

Background/Objectives: Monoclonal antibodies (mAbs) have revolutionized multiple myeloma (MM) treatment. However, post-marketing data on their neuropsychiatric safety is limited. This study aimed to evaluate neuropsychiatric adverse events (AEs) related to mAbs used for MM through a retrospective pharmacovigilance analysis using the Food and Drug Administration (FDA) Adverse Events Reporting System (FAERS) database. Methods: Individual case safety reports (ICSRs) from 2015 to 2023 with at least one neuropsy-chiatric AE and one of the MM-approved mAbs as the suspect drug (i.e., daratumumab, elo-tuzumab, isatuximab, belantamab mafodotin, teclistamab, elranatamab, and talquentamab) were analyzed using descriptive and disproportionality approaches. Results: Unknown signals of disproportionate reporting (SDR) included cerebral infarction for daratumumab (n = 45; reporting odds ratio (ROR) = 2.39, 95% confidence interval (CI) = 1.79-3.21; information component (IC) = 1.54, IC025-IC075 = 1.05-1.9), elotuzumab (25; 7.61, 5.13-11.28; 3.03, 2.37-3.51), and isatuximab (10; 2.56, 1.38-4.76; 1.67, 0.59-2.4); mental status changes for daratumumab (40; 2.66, 1.95-3.63; 1.67, 1.14-2.04) and belantamab mafodotin (10; 4.23, 2.28-7.88; 2.3, 1.22-3.03); altered state of consciousness for daratumumab (32; 1.97, 1.39-2.78; 1.32, 0.73-1.74) and belantamab mafodotin (6; 2.35, 1.05-5.23; 1.6, 0.19-2.52); Guil-lain-Barre syndrome (GBS) for daratumumab (23; 6.42, 4.26-9.69; 2.81, 2.11-3.3), isatuximab (8; 10.72, 5.35-21.48; 3.57, 2.35-4.37), and elotuzumab (3; 4.74, 1.53-14.7; 2.59, 0.52-3.8); and orthos-tatic intolerance for daratumumab (10; 12.54, 6.71-23.43; 3.75, 2.67-4.48) and elontuzumab (4; 28.31, 10.58-75.73; 5 , 3.24-6.08). Conclusions: Our analysis highlighted several previously unacknowledged SDRs for MM-approved mAbs. Given the complex and not entirely understood etiology of some neuropsy-chiatric AEs, including GBS, further investigations are necessary.

Lithium ascorbate (LiAsc) is a non-toxic lithium salt (LD50>5000 mg/kg) with normothymic and neuroprotective properties. Chemoreactomic modeling of LiAsc predicted possible hepatoprotective effects which were tested here on a rat model of a non-alcoholic fatty liver disease (NAFLD) with multiorgan pathology caused by combined intake of excess saturated fats (palm oil), carbohydrates (fructose) and inorganic iron salt (ferrous sulfate). Dynamics of biochemical parameters (biomarkers of liver function, kidneys, hematopoiesis, inflammation, thrombus formation, etc.) and histological evidence of hemosiderosis in the model upon reproduction of the model are described. Experimental confirmations of the hepatoprotective effects of LiAsc were obtained. Comparison of the effects of LiAsc with the effects of myoinositol (a substance with known hepatoprotective properties) shown a number of differences in pharmacological action. Both LiAsc and MI lowered ferritin, transferrin saturation, serum iron, AST, ALT, serum creatinine, leukocytes (LiAsc to a greater extent) and normalized lowered serum protein and glomerular filtration rate. However, only LiAsc lowered heightened reticulocytes (absolute and fraction) and platelets towards the corresponding reference intervals, normalized levels of vitamin B12 in blood and lowered the number of Kupffer cells in liver that were overloaded with iron (contained Prussian blue in morphometric studies). In a clinical perspective, hepatoprotective properties of a neuroprotector and it’s potential to reduce hemosiderosis might be useful for treating patients with hepatic encephalopathy.

Infective endocarditis (IE) results from infections by gram-positive bacteria, and, in this condition, the redox homeostasis is lost by overproduction of H2O2, leading to overstimulation of the immune system and up- regulation of the production of proinflammatory cytokines. The aim of this preliminary study of three cases with IE, was to evaluate the levels of oxidative biomarkers and of the enzymatic and non-enzymatic antioxidant systems, in subjects with IE. This is a preliminary retrospective analytical study of cases that had undergone aortic valve replacement surgery that was complicated by IE. We determined malondialdehyde (MDA), total antioxidant capacity (TAC), carbonyl groups, glutathione (GSH), thiols and the nitrate/nitrite ratio (NO3−/NO2−) in homogenized tissue of cardiac valves. We also measured the activities of GST, GPx, GR and TrxR. The super oxide dismutase (SOD) isoforms and peroxidase activities were determined using native gels. There were increases in the activities of antioxidant enzymes such as GST, SOD isoforms and peroxidases (p=0.01) and decreases in oxidative stress markers such as GSH, TAC, (p=0.01); however, MDA was increased (p=0.01). The results suggest that loss of the intricate redox homeostasis system in the aortic valve of patients with IE may favor the bacterial process and cause damage probably leading to a fatal outcome.

Perioperative dysglycemia in cardiac surgery is associated with poor outcomes. Glycemic variability rather than glucose levels are a predictor of length of ICU stay, rise in creatinine and acute kidney injury after cardiac surgery. Glycated haemoglobin (HbA1c) values correspond closely with average blood glucose levels and cut off values can be used to define diabetic and pre-diabetic status. These have been correlated with perioperative events. This narrative review discusses the role of glycated hemoglobin in cardiac surgery.

Background: Empathy and assertiveness are two essential soft skills for health professionals such as nurses or physiotherapists. Both professions are characterised by their contribution to preserving and improving health and human care, and for their vocation of service. One of the achievements of the training is the voluntary, conscious, and individually inspired career choice; Objetives: This study aimed to evaluate the empathy, assertiveness, and care vocational levels among undergraduate students enrolled in the Nursing and Physiotherapy Degree programmes and to investigate the relationship between these soft skills and the care vocation; Methods: A cross-sectional study of nursing and physiotherapy students was conducted in the 2022/2023 academic year. The Interpersonal Reactivity Index scales for empathy, the Rathus test for assertiveness, and the “Vocation of Service for Human Care” questionnaire were used as study tools. Finally, 226 students participated in the study; Results: The empathy, assertiveness and vocation of service for human care of the students was found to be acceptable. Significant differences were also observed according to the gender variable or career in the students, with female students presenting better results in empathy subscales or vocation (p < 0.05). Students who are working or have clinical experience in other professions score lower on the empathy personal distress subscale (p < 0.05); Conclusions: Both nursing and physiotherapy students demonstrate satisfactory levels of empathy, assertiveness and vocation for human care. However, gender and previous work experience do notably influence these scores. It would be beneficial to conduct long-term studies with educational interventions to train and develop empathy, assertiveness and vocation for human care.

The construction of an anaerobic digester coupled with post-digestion low level aeration for agricultural wastewater treatment is described. The digester employs underwater speakers to accelerate the anaerobic digestion process while retaining solids to reduce the strength of the effluent. The digester effluent is sent to a holding tank and fed at a low flow rate to an aeration tank to effect partial nitrification of the wastewater. The outlet of this tank is sent to a settling tank to retain biomass that developed in the aeration tank and the effluent is sent to a small, constructed wetland to further reduce wastewater nitrogen and phosphorus. The wetland was planted with the broadleaf cattail, Typha latifolia, and hence to a retention basin. The system has reduced energy consumption due to both the use of underwater sonic treatment and low-level aeration that is not designed to achieve full nitrification/denitrification but rather to achieve a mixture of ammonium, nitrite, and nitrate that might foster the development of a consortium of organisms (i.e. nitrifiers, Anammox bacteria) that can remediate wastewater ammonium at low cost. Biogas is sent to an inground condenser where different treatments may be tested to remove carbon dioxide and hydrogen sulfide. The system is meant to serve as a complex where various technologies and practices can be evaluated to improve the treatment of agricultural wastewater.

The present studies are specifically aimed at investigating the sensitivity of different dynamic stall models when exposed to various excitation frequencies targeted at the blade edgewise vibrations. The work is done on a modified version of the IEA 15 MW reference wind turbine employing a wind turbine design tool Bladed. The state-of-the-art dynamic stall models for wind turbine applications such as the ye model, Beddoes-Leishman (BL) model and the newly developed IAG model are evaluated. The beginning of the research work starts by evaluating different dynamic stall model effects on rigid blade section forces against known airfoil datasets. Then, the blade flexibility is considered to enable systematic evaluations of the blade flexibility influences in comparison to the rigid blade cases. It is observed that the range of the angle of attack grows depending on the excitation frequency and the adopted dynamic stall model. The critical excitation frequency range and the effects of twist distribution are then identified from the studies, which can be useful as a rough guidance when designing wind turbine blades.

Effective communication in automated chat systems hinges on the ability to understand and respond to context. Traditional models often struggle with determining when additional context is necessary for generating appropriate responses. This paper introduces Context-Aware BERT (CA-BERT), a transformer-based model specifically fine-tuned to address this challenge. CA-BERT innovatively applies deep learning techniques to discern context necessity in multi-turn chat interactions, enhancing both the relevance and accuracy of responses. We describe the development of CA-BERT, which adapts the robust architecture of BERT with a novel training regimen focused on a specialized dataset of chat dialogues. The model is evaluated on its ability to classify context necessity, demonstrating superior performance over baseline BERT models in terms of accuracy and efficiency. Furthermore, CA-BERT's implementation showcases significant reductions in training time and resource usage, making it feasible for real-time applications. The results indicate that CA-BERT can effectively enhance the functionality of chatbots by providing a nuanced understanding of context, thereby improving user experience and interaction quality in automated systems. This study not only advances the field of NLP in chat applications but also provides a framework for future research into context-sensitive AI developments.

The way in which a filtration membrane is woven influences its lifespan and purification performance. This study explores the impact of the weaving method on the hydrodynamic sta-bility of a membrane made from banana pseudotrunk fibres. After extraction, the yarns were characterised. Three membranes were obtained using different weaving methods (plain, twill and satin), and the hydrodynamic study was used to determine the surface density, porosity, per-meability and membrane resistance. Filtration tests were carried out on a suspension of 30 NTU, and the yarn obtained had a twist number of 10, a diameter of 0.75 mm and a tex of 168. The surface density and the percentage of surface voids for the three membranes, plain, twill and satin, were 8.82 g/m2 and 50%, 12.54 g/m2 and 30% and finally 15.77 g/m2 and 12.54%. The uni membrane showed the best permeability, i.e. 5.60.10-10m2, but lower particle retention with a residual turbidity of 12 NTU, compared with the twill and satin membranes, which have per-meability values of 5.31.10-10m2 and 4.80.10-10m2 and 9 and 7 NTU.The twill membrane appears to be the most suitable for filtration.

Background: Gestational trophoblastic neoplasia (GTN) is a rare tumor with an excellent prognosis. This study aimed to refine the FIGO risk score by incorporating immune nutritional factors. Methods: A retrospective analysis was conducted on women diagnosed with GTN between 2005 and 2019 who received chemotherapy (CT). Statistical tests (Wilcoxon, univariate, multivariate analyses) were used to assess associations with response to CT, and Cox regression models were applied to identify factors influencing overall survival (OS). immune nutritional FIGO (in-FIGO) risk score was calculated using significant variables and compared to the original FIGO score using ROC curves. Results: A total of 160 GTN patients were included. There was a positive association between CR and prognostic nutritional index (PNI) (p <0.0001) and neutrophils lymphocyte ratio (NLR) (p <0.001). In multivariate analysis, only PNI had a significant association (p= 0.001). A significant association was found between higher PNI (HR 0.95-IC 0.91-0.99, p= 0.019) and OS. ROC curves of the in-FIGO risk and FIGO risk scores were compared. For CR to chemotherapy, the in-FIGO score had higher sensitivity (71.6 vs. 61.5%), specificity (74.3 vs. 62.9%), and AUC (0.719 vs. 0.633) than the original FIGO score. For OS, the in-FIGO score demonstrated higher sensitivity (92% vs. 72.4%) but lower specificity (45.0% vs. 61.8%) and AUC (0.691 vs. 0.710) compared to the original FIGO score. Conclusions: PNI is a significant predictor of both CR and OS in women with GTN receiving CT. The in-FIGO score demonstrated improved sensitivity and specificity in predicting complete response to CT compared to the original FIGO score.

Treatment with herbal medicine continues to enjoy widespread popularity not only in the developing world, because of low costs, easy availability, and low risk of adverse effects. One such plant is Curcumin longa L., or turmeric, which has a long history of both culinary and medicinal uses throughout Asia, spanning thousands of years. In addition to traditionally being used as a dye, as a flavouring and colouring agent in foods, and, e.g., marriage rituals, turmeric is also notable for its long history of use to treat a variety of medical conditions, including inflammatory, bacterial, and fungal diseases and infections, jaundice, tumours, and ulcers among others. In light of this long history of use, it is not surprising to find that also modern biochemistry and clinical research studies show that a significant component of turmeric, curcumin, may have a multitude of therapeutic benefits, much of it attributed to the herb’s antioxidant qualities which are crucial for the prevention and treatment of, e.g., chronic inflammatory illnesses, conditions that often act as precursors for other serious diseases, including cancer and neurological disorders, such as Parkinsons and Alzheimer’s. Furthermore, investigations on the safety profile and toxicity of curcumin have shown that it is generally safe even at large dosages, although caution is necessary as curcumin also have documented anticoagulant effects. This article is focused on providing a better perspective into molecular mechanisms for possible actions along with an in-depth review of recent studies of curcumin, its beneficial role and therapeutic applications in chronic health conditions, with a focus on its cancer, inflammatory bowel disease, osteoarthritis, atherosclerosis, peptic ulcer, Covid19, psoriasis, vitiligo, and depression.

Background/Objectives: The global push to eliminate tuberculosis (TB) as a public health threat is in-creasingly urgent, particularly in high-burden areas like O.R. Tambo District Municipality, South Africa. Drug-resistant TB (DR-TB) poses a significant challenge to TB control efforts and is a leading cause of TB-related deaths. This study aimed to assess DR-TB transmission patterns and predict future cases using geospatial and predictive modeling techniques. Methods: A retrospective cohort study was conducted across five decentralized DR-TB facilities in O.R. Tambo District Municipality from January 2018 to De-cember 2020. Data were obtained from Statistics South Africa, and patient GPS coordinates were used to identify clusters of DR-TB cases via DBSCAN clustering. Hot Spot Analysis (Getis-Ord Gi) was performed, and two predictive models (Linear Regression and Random Forest) were developed to estimate future DR-TB cases. Analyses were conducted using Python 3.8 and R 4.1.1, with significance set at p < 0.05. Results: A total of 456 DR-TB patients were enrolled, with 56.1% males and 43.9% females. The mean age was 37.5 (±14.9) years. The incidence of DR-TB was 11.89 cases per 100,000 population, with males dispropor-tionately affected. Key risk factors included poverty, lack of education, and occupational exposure. DR-TB types included RR-TB (60%), MDR-TB (30%), Pre-XDR-TB (5%), XDR-TB (3%), and INHR-TB (2%). Spatial analysis revealed significant clustering in socioeconomically disadvantaged areas. A major cluster was identified, along with a distinct outlier. Predictive models indicated differing trends, with the Random Forest model predicting stabilization at 30 cases per year by 2022, while the Linear Regression model projected a decline to zero by 2026. Conclusions: The study highlights the need for targeted interventions in vulnerable populations to curb DR-TB transmission and improve treatment outcomes.

Coastal zones, although vital for human livelihoods and environmental services, are increasingly vulnerable to climate change, especially communities dependent on marine ecosystems. These areas face significant threats from urbanization, population growth, and climate impacts, leading to biodiversity loss and ecosystem degradation. Recognizing these challenges, the United Nations declared 2021--2030 the Decade for Ecosystem Restoration, led by the UNEP and FAO, with the aim of restoring degraded ecosystems to achieve sustainable development goals (SDGs). The link between healthy ecosystems and disaster risk reduction (DRR) has gained global prominence, especially through the Sendai Framework for Disaster Risk Reduction (SFDRR), which emphasizes the importance of ecosystems in mitigating risks. Despite this, many tools for assessing risks in different ecosystem scenarios remain inaccessible to key stakeholders in disaster management. Advances in spatial modelling have supported ecosystem management, policy-making, and ecosystem health assessment, particularly following the Millennium Ecosystem Assessment. These models have been applied in regions such as the Great Bahamas, Haiti, and the Republic of Congo, helping shape ecosystem-based approaches to development. Improving access to spatial tools and models is crucial to enabling effective ecosystem-based DRR and climate adaptation strategies.

A study was conducted to investigate the occurrence of Trypanosoma vivax infection in 72 Murrah buffaloes from Soure, a municipality located on Marajó Island. The aim of the research was to identify the presence of T. vivax in the buffalo population. Blood samples were collected, followed by DNA extraction and polymerase chain reaction (PCR) analysis using the primers TviCatL1 and DTO 155. T. vivax DNA was detected in one 36-month-old female with a body condition score of 2 (on a scale of 1 to 5), although the animal exhibited no clinical signs of infection. These findings suggest that T. vivax is not frequently circulating among the tested buffaloes, or that the animals may be in the chronic phase of infection, which is often characterized by low parasitemia levels. The identification of T. vivax in a clinically asymptomatic animal underscores the importance of using molecular diagnostics for early detection and monitoring of the parasite in buffalo herds, especially in regions where the presence of T. vivax may be underestimated. This study contributes to understanding the epidemiological status of T. vivax in buffaloes on Marajó Island, highlighting the need for ongoing surveillance in endemic areas.

Communication pathways are an underestimated source of synthetic particles in the environment. This study investigated the impact of traffic volume on microplastics (MPs) and tire wear particles (TWPs) pollution in road snow. An examination was conducted in a medium-sized city situated in northeastern Poland, known for being one of the cleanest regions in the country. MPs and TWPs were found at all 53 sites, regardless of the intensity of traffic. The average concentration for all samples was 354.72 pcs/L. Statistically significant differences were found between the average values of particles concentration on low, medium, and heavy traffic roads amounted 62.32 pcs/L, 335.97 pcs/L and 792.76 pcs/L respectively. Within all three studied groups of roads, MPs and TWPs with the smallest size, ranging from 50 to 200 μm, were prevalent. In all studied groups of roads, 4 analyzed shapes of particles were found, with irregular fragments being the most abundant form (89.23%). The most frequently recorded color among the collected samples was black (99.85%), and the least frequently recorded was blue, constituting only 0.01%. This study suggests that snow cover on the roads may act like temporary storage of pollutants during winter in the temperate climate zone and after thaw can significantly increase the concentration of MPs and TWPs in surface waters. Possible measures to decrease the release of MPs and TWPs into the environment in the city may include reducing traffic volume and speed, implementing street sweeping, utilizing filtration chambers, and installing stormwater bioretention systems or settling ponds.

Introduction & Objectives: Blood cells-(BC)count derived parameters have been inves-tigated in systemic inflammation, cancer, and various cutaneous diseases. Little is known about their usefulness in trichology, especially as prognostic and therapeutic biomarkers. We conducted a retrospective study on patients with not-cicatricial alopecia to assess differences in BC-count. Materials & Methods: Patients with alopecia areata (AA), telogen effluvium (TE), androgenic alopecia (AGA), psoriasis and hidradenitis suppurativa (HS) were included in the study. Baseline complete BC-count and derived parameters (i.e. NLR, PLR, MLR, SII; PIV) were recorded and analyzed for each patient and at different timepoints for AA patients on systemic agents. Results: 170 patients fulfilled inclusion criteria. BC-count and derived values were similar across the not-cicatricial alopecia groups, while general differences were observed compared to psoriasis and HS. TE displayed a closer inflammatory signature to psoriasis with respect to CRP, NLR, MLR, SII and PIV. We also found a significant reduction in RBCs in severe versus patchy-AA and an increase of SII in acute versus chronic AA. Modulation of platelet number and volume could be used as therapeutic marker in AA. Conclusions: In our experience, the BC-count and its derived parameters could represent potential tools in AA and TE.

Reverse osmosis (RO) and nanofiltration (NF) are ubiquitous technologies in modern water treatment, finding applications across various sectors. However, the availability of high-quality water suitable for RO/NF feed is diminishing due to droughts caused by global warming, increasing demand, and water pollution. As concerns grow over the depletion of precious freshwater resources, a global movement is gaining momentum to utilize previously overlooked or challenging water sources, collectively known as “marginal water.” Fouling is a serious concern when treating marginal water. In RO/NF, biofouling, organic and colloidal fouling, and scaling are particularly problematic. Of these, organic fouling, along with biofouling, has been considered difficult to manage. The major organic foulants studied are natural organic matter (NOM) for surface water and groundwater and effluent organic matter (EfOM) for municipal wastewater reuse. Polymeric substances such as sodium alginate, humic acid, and proteins have been used as model substances of EfOM. Fouling by low molecular weight organic compounds (LMWOCs) such as surfactants, phenolics, and plasticizers is known, but there have been few comprehensive reports. This review aims to shed light on fouling behavior by LMWOCs and its mechanism. LMWOC foulants reported so far are summarized, and the role of LMWOCs is also outlined for other polymeric membranes, e.g., UF, gas separation membranes, etc. Regarding the mechanism of fouling, it is explained that the fouling is caused by the strong interaction between LMWOC and the membrane, which causes the water permeation to be hindered by LMWOCs adsorbed on the membrane surface (surface fouling) and sorbed inside the membrane pores (internal fouling). Adsorption amounts and flow loss caused by the LMWOC fouling were well correlated with the octanol-water partition coefficient (log P). In part 2, countermeasures to solve this problem and applications using the LMWOCs will be outlined.

This paper compares Demand Response algorithms suitable for Short-term Forecasting based on Advanced Metering Infrastructure (AMI). It selects ARIMA (AutoRegressive Integrated Moving Average), SARIMA (Seasonal ARIMA), LSTM (Long Short-Term Memory), and SVM (Support Vector Machine) as the primary research algorithms and delves into their theoretical foundations and functional characteristics. The theoretical background section explores each algorithm’s applicability in analyzing time-series data, recognizing nonlinear patterns, and solving optimization and classification problems. The methodology section will detail the methods of AMI data collection, the implementation of the algorithms, and the experimental setup. In the experiments and results section, the performance of each algorithm will be assessed by applying AMI data and presenting the outcomes of comparative analyses, with a focus on evaluating the predictive accuracy of the algorithms. The discussion will interpret the experimental results, analyzing the advantages and disadvantages of each algorithm and their applicability in the AMI system. It will also explore the limitations of the current research and directions for future research. The conclusion will summarize the key findings of the study and affirm how this research contributes to understanding and improving short-term forecasting and demand response systems based on AMI.

Antimicrobial resistance (AMR) is a worsening global health crisis that has arisen due to antibiotic overuse and misuse. Antibiotic resistance renders standard treatments less effective, making it difficult to control some infections, thereby increasing morbidity and mortality. Medicinal plants are sustainable treatment options that are becoming increasingly important as antibiotics lose their efficacies. The present study evaluates the antibacterial activity of aqueous, methanolic and ethyl acetate extracts prepared using Terminalia bellirica and Terminalia chebula fruit against six bacterial pathogens using disc diffusion and broth microdilution assays. The aqueous and methanol extracts of T. bellirica and T. chebula showed substantial zones of inhibition (ZOIs) against S. aureus and MRSA. The activity against those bacteria was determined to be strong, with minimum inhibitory concentrations (MIC) ranging from 94 µg/mL to 392 µg/mL. Additionally, the T. bellirica methanolic extract showed noteworthy antibacterial activity against E. coli and an ESBL E. coli strain (MIC values of 755 µg/mL for both). The aqueous T. bellirica and T. chebula extracts also inhibited K. pneumoniae growth (MIC values of 784 µg/mL and 556 µg/mL respectively), whilst the methanolic extracts of these plants also inhibited ESBL K. pneumoniae growth (MIC values of 755 µg/mL and 1509 µg/mL respectively). Eighteen additive interactions were observed when extracts were combined with reference antibiotics. Notably, strong antagonistic interactions occurred when any of the extracts were mixed with polymyxin B. All extracts were subjected to LC-MS analysis, revealing several interesting flavonoids and tannins, including 6-galloylglucose, 1,2,6-trigalloyl-β-D-glucopyranose, 6-O-[(2E)-3-phenyl-2-propenoyl]-1-O-(3,4,5-trihydroxybenzoyl)-β-D-glucopyranose, propyl gallate, methyl gallate, sanguiin H4, hamamelitannin, pyrogallol, gallic acid, ellagic acid, chebulic acid, and chebuloside II. All extracts were nontoxic when tested in brine shrimp assays. This lack of toxicity, combined with their antibacterial activities suggests that these plant species may be promising sources of antibacterial compound(s) that warrant further study.

Cardiovascular disease (CVD) is a major global health concern. The number of people with CVD is expected to rise due to aging populations and increasing risk factors such as obesity and diabetes. Identifying new molecular markers is crucial for early diagnosis and treatment. Among these, plasma levels of some miRNAs specifically expressed in cardiac and skeletal muscle known as myomiRs, has gained attention for their roles in cardiovascular health. This study analyzed the plasma levels of myomiR-133a, -133b, and -206 in the pathogenesis of cardiovascular diseases. Exploiting of a case-control study design with patients recruited from several nursing homes form Calabria (Southern Italy) characterized by different types of CVD compared to non-CVD controls, we found downregulation of miR-133a in heart failure and miR-133b in stroke, along with the overall decreased expression of miR-133b and miR-206 in CVD patients. In silico functional characterization of their targets and signaling pathways revealed their involvement in critical cardiovascular processes. Although further research is necessary to fully elucidate their mechanisms and clinical utility, the results here reported support the idea that myomiRs could serve not only as biomarkers but also as therapeutic targets, offering new avenues for managing and potentially preventing CVD progression.

Occupational incidents in construction workplaces present a persistent challenge, with severe consequences for both workers and project outcomes. This study aims to examine the specific causal factors contributing to these incidents, focusing on the interaction between worker-related, environmental, and procedural factors. The objective is to identify key contributors to workplace accidents and develop a unified predictive model to mitigate future risks. To achieve this, we employed a machine learning approach on a comprehensive accidentology dataset collected from contractors across multiple construction sites. The dataset includes variables such as worker ex-perience, environmental conditions, adherence to safety protocols, and more. By analyzing direct, indirect, and root causes, the methodology uncovers hidden patterns and interdependencies that traditional analysis might overlook. The study’s findings indicate that worker experience and environmental factors are the most significant contributors to incident occurrence, with a clear interaction effect between these variables. The results not only confirm previous research but also offer enhanced predictive capabilities for future safety measures. This research demonstrates the value of machine learning in generating data-driven insights, ultimately aiding in the develop-ment of targeted interventions to improve safety standards in the construction industry.

In this work, studies were carried out on the extraction of silver and gold from cakes of pressure oxidation leaching of sulfide concentrate containing pyrite and arsenopyrite. The addition of gypsum at a consumption of 0.1 g/g of concentrate helps to increase silver extraction from 13.4 to 95–98% at the cyanidation stage, with no conditioning operation. Gold recovery was 99%. An increase in gypsum consumption contributes to the formation of BFAS with an increased content of sulfate sulfur, and a decrease in the As/S(sulfate) molar ratio in the cake from 3.7 down to 0.88 contributes to an increase in silver extraction at the cyanidation stage up to 98%, and its further decrease from 0.88 down to 0.58 leads to a gradual reducing silver transfer into solution from 98 down to 75.1% at the cyanidation stage. Basic ferric sulfate (BFS) is not formed in this case, since according to EDS mapping the distribution of arsenic and sulfur over iron-containing particles is uniform. According to TCLP analysis, the cake obtained after pressure oxidation leaching with the addition of gypsum and cyanidation is stable and suitable for disposal, since the final concentration of arsenic in the solution was 0.45 mg/dm3.

Acetylcholinesterase (AChE) activity is the most commonly used neurotoxicity biomarker in fish. It is measured mostly in the brain but can also be evaluated in muscle or (in the case of very small fish) in whole body homogenate. AChE activity is measured using standard methods, thus the results obtained by various authors are comparable. AChE seems to be a sensitive neurotoxicity biomarker since in most cases of fish exposure to various toxic agents a decrease in the enzyme activity was observed. The effects of toxic agents on AChE activity are concentration and time-related. An increase in AChE activity occurred very rarely. The results of the analysis showed that most aquatic pollutants may modulate AChE activity. Acetylcholinesterase activity can be also used to evaluate transgenerational neurotoxic effects.

Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combination of factors that might be affecting the pathway under certain conditions. For example, Brancati et al.1 observe that mutations in poliovirus receptor related protein 4 (PVRL4), encoding cell adhesion molecule nectin-4, causes Ectodermal dysplasia-syndactyly syndrome. Interaction with cad- herins also implies an influence of nectin-4 on Wnt signaling, which plays a relevant role in limb development (Brancati et al.1). However, not much work has been done to explore the relation of Wnts and PVR family. In CRC cells treated with ETC-1922159, both were found up regulated. In a recent unpublished work in Open Science Framework, Sinha2, we had the opportunity to rank these unknown biological hypotheses for both up and down regulated genes at 2nd order level after drug administration. The search engine alloted high nu- merical valued rankings to some combinations of PVR-WNT, thus indicating a possibility of high combinatorial synergy also. The in-silico derived influences can be represented graphically as - PVR w.r.t WNT with PVR <- WNT9A; and WNT w.r.t PVR with WNT-7B/9A <- PVR and WNT4 <- PVRL2; In the light of the recent findings of PVR with IFN (interferon) and the known interactions between IFN and Wnts, there might be a possibilty to explore the bridge of PVR, IFN and WNTs. The 3 fold (PVR - IFN; IFN - WNT; WNT - PVR), 2 way cross family analysis might shed light on the possible combinations that might be of import. Here, we present a 2-way cross family analysis of multiple, such in-silico ranked 2nd order synergistic combinations, after ETC-1922159 treatment of CRC cells. Via this 2-way cross family analysis, we are able to discover through majority voting, the combinations that might of interest to biologists and also derive plausible influences of components of combinations among themselves. Note that these form biological hypotheses which indicate whether a particular combination and the direction of influence within the combination, exist synergistically in CRC cells. Wet lab tests will indicate the veracity of these combinations and if proven true, will lead to further study of mechanism between the components.

An untrained couch potato involuntarily forced to undergo a long strenuous exercise towards exhaustion will feel it at the end as a discomforting fatigue. A well-trained athlete will feel the same but after a longer time. Patients of various neurological diseases will do so much earlier. All may also finally experience muscle pain and soreness the next day.Muscle fatigue is an exercise-induced reduction in maximal voluntary muscle force. Muscle fatigability varies with muscle use, age and sex. It is commonly divided into two broad categories: peripheral and central fatigue. The first refers to the many processes in the fatiguing muscle(s), the second to subsequent processes in the central nervous system (CNS). Peripheral muscle fatigue can be caused by numerous different mechanisms, ranging from the accumulation of metabolites within muscle fibers to their damage. Central fatigue involves the inadequacy of the CNS to generate and maintain sufficiently strong motor command. There must of course be a mediator relating the two fatigue types, and this is the collection of small-diameter group III (Aδ) and IV (C) nerve fibers emanating from the muscle and distributing their activation effects throughout the CNS.Muscle fatigue develops during eventually exhausting exercise that goes along with activations of the cardio-vascular and respiratory system to supply oxygen and energy resources. While these functions are supported by a number of sensory afferents, fatigue-activated group III/IV muscle afferents have been proposed to contribute their share to cardio-vascular and ventilatory reflex responses that are mediated in the brainstem. Neural feedback from working skeletal muscle is a vital component in providing a high capacity for endurance exercise because muscle perfusion and O₂ delivery determine the fatigability of skeletal muscle (Amann 2012).The development of chronic muscle fatigue is related to muscle wasting mediated by aging, myopathies, muscle dystrophies, immobilization, insulin resistance, diseases associated with systemic inflammation (arthritis, sepsis, infections), trauma, cardio-vascular (heart failure) and respiratory disorders (chronic obstructive pulmonary disease (COPD)), chronic kidney failure, multiple sclerosis (MS), and, more recently, coronavirus disease 2019 (COVID-19).

This paper addresses a critical concern in intrusion detection within the broader realm of cyber security, particularly focusing on login activity involving the majority of normal users. Utilizing the AdaBoost classifier, the study employs various optimizers to enhance performance by selecting optimal control parameters. A specially tailored version of Crayfish Optimization Algorithm (COA) is introduced to cater to the unique requirements of this investigation. Through a comparative analysis of a simulated publicly available dataset, models optimized by the modified algorithm demonstrate superior outcomes, achieving an accuracy of 94.6128% and displaying an adaptive convergence rate capable of navigating local minima to identify optimal solutions. The best-performing model undergoes SHapley Additive exPlanations (SHAP) analysis to identify key contributing features. Limitations arise from the computational intensity of the optimization process, necessitating consideration of limited populations and smaller numbers of estimators during simulations. Future endeavors will extend the methodology to incorporate additional user actions in classification, with a focus on addressing computational constraints as hardware advancements occur. Proposed, modified algorithm could be applied to deal with various optimization tasks, beyond the scope of this study.

Afghanistan is located on the Eurasian tectonic plate's edge, a highly seismically active region. It is bordered by the northern boundary of the Indian plate and influenced by the collisional Arabian plate to the south. The Hindu Kush and Pamir Mountains in Afghanistan are part of the western extension of the Himalayan orogeny and have been uplifted and sheared by the convergence of the Indian and Eurasian plates. These tectonic activities have generated numerous active deep faults across the Hindu Kush-Himalayan region, many of which intersect Afghanistan, resulting in frequent high-magnitude earthquakes. This tectonic interaction produces ground shaking of varying intensity, from high to moderate and low, with the epicenters often located in the northeast and extending southwest across the country. The reoccurrence and severity of earthquakes have been increasing since 1900. This study maps Afghanistan's tectonic structures, identifying the most active geological faults and regions with heightened seismicity. The increasing trend in recurrence and intensity of the earthquakes are drawn. Historical earthquake data were reviewed, and recent destructive events were incorporated into the national earthquake dataset to improve disaster management strategies. Additionally, the study addresses earthquake hazards related to building and infrastructure design, offering potential solutions and directions to mitigate risks to life and property.

This study examines the historiography of early Islam by assessing the reliability of Qurʾānic exegeses as sources documenting the early history of Islam and the biography of the Prophet Muḥammad in Mecca. Focusing on Qurʾānic exegetical practices from late antiquity to the medieval period, this article argues that there is a significant discontinuity in how Qurʾānic exegeses align with the historical context of the Qur'an, especially in relation to Meccan sūras such as Q 107. Significantly, this challenges the continuity of tradition, the fundamental concept upon which the “Islamic sciences” are based. This discontinuity is herein revealed through a historical-critical analysis of the exegesis of Q 107:4 that demonstrates how Qurʾānic exegetical practices have reinterpreted and reshaped early Islamic history in response to the pressures of new identity formation and made Qurʾānic exegeses more reflective of the social and political contexts of the exegetes than of the historical milieu of the Qurʾān itself. This analysis contributes to the ongoing discussion of the role exegesis played in reshaping the history of Islam. Indeed, it emphasises the importance of critically evaluating these sources in order to uncover a more nuanced historical narrative of early Islam.

This research article investigates the application of artificial intelligence (AI) in human resource management (HRM) within the Australian commercial media sector, addressing a critical gap in scholarly knowledge. Despite increasing interest in AI’s potential, there is a lack of consensus among managers and HR professionals regarding its application. Through qualitative interviews with 22 industry professionals, this study examines perceptions, understanding, and the potential for leveraging AI across key HRM functions. Findings indicate significant potential for AI to enhance HRM processes, particularly in job/work design and staffing. This research contributes to a deeper understanding of AI’s role in HRM and offers insights to optimize organizational efficiency and effectiveness in the Australian commercial media sector.

The issue of suicide has garnered considerable attention in refugee scholarship, where research examines how unique forced migration and resettlement challenges exacerbate risks and vul-nerabilities to suicide. However, there are gaps in understanding the social and cultural factors shaping the lived experience of suicide in refugee communities. Using the example of Afri-can-background young people in Australia, this paper presents a collaboration among two aca-demics and two South Sudanese youth advocates to explore the sociocultural factors impacting suicidality through reflexive discussions and collaborative poetry. This combined approach of-fered a unique and nuanced conceptual and methodological framework to contribute culturally specific narratives to critical suicide studies and challenge western-centric and biomedical per-spectives on suicide. The process highlighted (i) the lack of dialogue about suicide in the South Sudanese community, and (ii) an absence of community-based support structures to address sui-cide. This paper provides useful insights on the culturally specific context of suicide, adding ref-ugee perspectives to the discipline of critical suicide studies.

Robotics offers innovative possibilities at all levels of education, which should be considered when planning the teaching process for certain topics of engineering mathematics in higher education. In this paper, we present a new teaching-learning technique using STEAM-based methods to explore the properties of cycloidal curves for Computer Science Engineering BSc students. Frontal teaching, which is part of the tradition of higher education, has been complemented with teaching methods that meet the generational needs of students: problem-based learning, integration of STEAM-based methods and project-based learning. We have developed a new methodological model that combines the advantages of the traditional teaching method with new forms of teaching that have proven to be effective in teaching Generation Z. The four-stage methodological model uses a variety of didactical approaches because each stage applies a different type of learning technique to make the learning process interesting, motivating and effective. One of the main pillars of the model is the use of Desmos dynamic geometry software to create animations, and the other is the use of educational robotics to support visualisation. The implementation of educational robotics and its potential for teaching cycloidal curves is discussed in detail. The effectiveness of the method was investigated with first-year undergraduate students in computer science engineering. Quantitative studies were carried out to test the effectiveness of the new teaching technique. The experimental results strongly indicate the value of using the four-stage methodological model to implement active learning in the field of cycloidal curves.

The increasing request of digitized data among several fields, including the built environment and Cultural Heritage (CH) one points out the need of proficient ways to access, archive and share 3D data and related information among users. The sector of reality capture produces accurate and reliable products that can support building management and CH maintenance, at the price of heavy and resource-demanding data. An emerging solution to this problem is represented by the web platforms for 3D data management, that promise to relieve users from the costs of archive and hardware, providing effective visualization, access and sharing tools. The panorama of commercial web platforms is analyzed according to the Software as a Service business model and the features of some representative platforms are exposed. The paper discusses the main advantages in terms of diffused access and collaboration and the potential issues concerning the long-term archive and data persistence. The future perspectives of the web platform sector are promising as, according to the current development path, could be able to empower the built environment and CH sector with a diffused, systematic and aware use of 3D data.

This work utilizes the particle swarm optimization (PSO) for the optimal sizing of a solar – wind – battery hybrid renewable energy system (HRES) for a rural community in Rivers State, Nigeria (Okorobo-Ile Town). The objective is to minimize the total economic cost (TEC), the total annual system cost (TAC) and the levelized cost of energy (LCOE). A two – step approach is used. The algorithm first determines the optimal number of solar panels and wind turbines. Based on the results obtained in the first step, the optimal number of batteries and inverters is computed. The overall results obtained is then compared with results from the NGSA-II and hybrid GA – PSO optimization algorithm. An energy management system monitors the energy balance and ensures the load management is adequate using the battery state of charge as a control strategy. Results obtained showed that the optimal configuration consist of solar panels (154), wind turbine (3), inverter (136) and batteries (29). This results in a minimized TEC, TAC and LCOE of 476, 731USD, 301, 947USD and 0.011USD/kWh respectively. The optimal configuration when simulated under various climatic scenarios was able to meet the energy needs of the community irrespective of ambient condition.

Background/Objectives: This review examines the evolution of lyotropic liquid crystals (LLCs) in ocular drug delivery, focusing on their ability to address the challenges associated with traditional ophthalmic formulations. The study aims to underscore the enhanced bioavailability, prolonged retention, and controlled release properties of LLCs that significantly improve therapeutic outcomes; Methods: The review synthesizes data from various studies on both bulk-forming LLCs and liquid crystal nanoparticles (LCNPs). It also considers advanced analytical techniques, including the use of machine learning and AI-driven predictive modeling, to forecast the phase behavior and molecular structuring of LLC systems. Emerging technologies in biosensing and real-time diagnostics are discussed to illustrate the broader applicability of LLCs in ocular health; Results: LLCs are identified as pivotal in promoting targeted drug delivery across different regions of the eye, with specific emphasis on the tailored optimization of LCNPs. The review highlights principal categories of LLCs used in ocular applications, each facilitating unique interactions with physiological systems to enhance drug efficacy and safety. Additionally, novel applications in biosensing demonstrate LLCs' capacity to improve diagnostic processes; Conclusions: Lyotropic liquid crystals offer transformative potential in ocular drug delivery by overcoming significant limitations of conventional delivery methods. The integration of predictive technologies and biosensing applications further enriches the utility of LLCs, indicating a promising future for their use in clinical settings. This review points to continued advancements and encourages further research in LLC technology to maximize its therapeutic benefits.

Background: Stroke is one of the leading causes of death and disability worldwide. The diagnosis of stroke remains largely clinical, yet widely used stroke scoring systems and brain imaging do not satisfactorily allow distinction of ischaemic stroke (IS) patients from stroke mimics (SM). Blood biomarkers are promising tools that could facilitate clinical triage. Methods: This study recruited 66 patients with IS and 24 SM. The levels of Glial fibrillary acidic protein (GFAP), Neuron-specific enolase (NSE), Neurofilament light chain (NfL) and blood-brain barrier (BBB) proteins [Occludin (OCLN), Zonula occludens 1 (ZO-1), Claudin-5] in blood serum were measured by enzyme-linked immunosorbent assay technique. Biomarker levels in IS patients and SM was compared using the Mann–Whitney U test. Multivariable logistic regression analysis was used to evaluate the diagnostic performance of biomarkers in combination with the National Institutes of Stroke Scale (NIHSS) score. Results: Significant differences in circulating GFAP, NfL, OCLN, ZO-1, and Claudin-5 but not NSE in IS patients compared to SM. The combination of circulating ZO-1, Claudin-5, and OCLN with NIHSS score gives the highest diagnostic accuracy, sensitivity, and specificity. Conclusion: A prediction model with Circulating BBB proteins in combination with NIHSS score differentiates between IS patients and SM.

Background/Objectives: Pityriasis rosea (PR) is a self-limiting exanthematous disease associated with the endogenous reactivation of human herpesvirus (HHV)-6 and HHV-7. Classically, the lesions gradually resolve leaving no sequelae. Therefore, the best treatment is reassuring the patient and suggesting a resting period. However, atypical PR cases characterized by extensive, persistent lesions and systemic symptoms may impact the patient’s quality of life, and, therefore, a treatment can be prescribed. There is limited evidence on the comparative effectiveness of pharmacological treatments for PR, therefore we performed a network meta-analysis to compare these interventions. Methods: Overall, 12 randomized control trials (RCTs) were identified. The outcomes were itch resolution and rash improvement. Results were expressed as risk ratio (RR) and 95% confidence interval (CI). We also calculated the relative ranking of the interventions for achieving the aforementioned outcomes as their surface under the cumulative ranking (SUCRA). Results: On network meta-analysis, only oral steroids and the combination of oral steroids+antihistamine resulted significantly superior to the placebo in terms of itch resolution (RR 0.44, CI 0.27-0.72 and RR 0.47, CI 0.22-0.99). Oral steroids resulted as the best treatment (SUCRA 0.90) for itch resolution. In terms of rash improvement, only acyclovir and erythromycin resulted significantly superior to placebo (RR 2.55, CI 1.81-3.58 and RR 1.69, CI 1.23-2.33), and acyclovir outperformed all the other tested interventions. Consequently, acyclovir ranked as the best intervention (SUCRA score 0.92). Conclusions: Acyclovir represents the best option in PR patients with extensive, persistent lesions or systemic symptoms. Steroids and antihistamines seemed the best treatment for itch resolution.

This study critically reviews the situation of Language teachers in China, who transited from on-campus face-to-face teaching to online teaching within one week of the official school opening dates, which were postponed due to the outbreak of the COVID-19 pandemic. Presently, the study is located in an interpretive research paradigm, and hence, it takes the human experiences within educational setups to be subjective and complex. In so doing, to elude this educational transition and its complex dynamics, the research uses such qualitative research methods as document analysis and interviews. Painting a picture of great variety in individual experiences and challenges, strategies, and outcomes not only across these two specific institutions but, certainly, more broadly through the lens of previous case studies. First, it values depth over breadth and tries to unearth the varied experiences, challenges, innovations, and achievements faculty made during their transition into online Language Teaching. The results of the study are believed to be able to contribute quite a rich, detailed, and multi-faceted perspective to the pool of knowledge for the complexities of online Language education in China, which may be of interest to educators, policymakers, and researchers.

Currently, there are few effective methods to monitor patients with gynecologic malignancies, including ovarian and endometrial cancers. The ‘tumor fraction’ of circulating tumor DNA may offer a minimally-invasive method to evaluate tumor aggressiveness via simple blood draw. To date, there is limited data on utility of ctDNA TFx interrogation in gynecologic malignancies, although there is evidence that TFx may be low or undetectable in malignancies primarily confined to the peritoneal cavity. The objective was a descriptive assessment of ctDNA TFx in a cohort of patients with ovarian and endometrial cancer. The association of TFx to continuous and categorical baseline clinicopathologic factors and progression-free survival was assessed. In a large cohort comprised of 210 plasma samples from 78 patients with biopsy-proven ovarian and endometrial cancer, plasma samples collected between 4/2018 and 4/2020 were subjected to shallow whole genome sequencing with determination of TFx. Overall, we demonstrate that TFx was low for most samples with mean TFx for ovarian cancer 5.5% and endometrial cancer 2.4%, yet can be quantified above lower limit of detection in many patients particularly when evaluating serial samples. There was no significant association between TFx level and clinical (primary vs recurrent, best response) or pathologic (e.g. histology, grade) features. Our data demonstrates low TFx levels in ovarian and endometrial cancers likely reflect that tumor types are ‘low ctDNA shed’, suggesting that higher sensitivity assessments may be required for clinical utility.

The paper deals with two new properties of the linear time-optimal control problems with real non-positive eigenvalues of the system. They are the foundation of a new method of synthesizing the time-optimal control without the need to describe the switching hyper-surfaces. The so called “axes initialization” and the synthesis technique are illustrated on the already classical example of the time-optimal control of a double integrator.

A consistent theory of quantum entanglement requires that constituent single-particle states belong to the same Hilbert space, the coherent eigenstates of a complete set of operators in a given representation, defined with respect to a shared continuous parameterization. Formulating such eigenstates for a single relativistic particle with spin, and applying them to the description of many-body states, presents well-known challenges. In this paper, we review the covariant theory of relativistic spin and entanglement in a framework first proposed by Stueckelberg and developed by Horwitz, Piron, et.\ al. This approach modifies Wigner's method by introducing an arbitrary timelike unit vector $n^\mu$ on which one induces a representation of $SL(2,C)$, rather than induce on the spacetime momentum $p^\mu$. Generalizing this approach, we construct relativistic spin states on an extended phase space $\{(x^\mu,p^\mu),(\zeta^\mu,\pi^\mu)\}$, inducing a representation on the momentum $\pi^\mu$, thus providing a novel dynamical interpretation of the timelike unit vector $n^\mu = \pi^\mu / M$. Studying the unitary representations of the Poincar\'e group on the extended phase space allows us to define basis quantities for quantum states and develop the gauge invariant electromagnetic Hamiltonian in classical and quantum mechanics. We write plane wave solutions for free particles and construct stable singlet states, and relate these to experiments involving temporal interference, analogous to the spatial interference known from double slit experiments.

The key towards a sustainable future is the reduction of mankind’s impact on natural systems via the development of new technologies and the improvement of source apportionment. Though days, years and seasons are arbitrarily set, their mechanisms are based on natural cycles driven by Earth’s orbital periods. This is not the case for weeks, which are a pure anthropic category and are known from literature to influence emission cycles. For the first time since it started data gathering operations, CO (carbon monoxide), CO₂ (carbon dioxide), CH₄ (methane) and eBC (equivalent black carbon) values detected by the Lamezia Terme WMO/GAW station in Calabria, Southern Italy have been evaluated via a two-pronged approach accounting for weekly variations in absolute concentrations, as well as the number of hourly averages exceeding select thresholds. The analyses were performed on seven continuous years of measurements, from 2016 to 2022. Moreso, the two results have been combined into a new parameter: the hereby defined WDWO (Weighed Distribution of Weekly Outbreaks) normalizes weekly trends of CO, CO₂, CH₄ and eBC on an absolute scale as percentages, with the scope of providing regulators and researchers alike with a new tool meant to better evaluate anthropogenic pollution and mitigate its effects.

We deal with Ulam stability of second order periodic linear differential equations with periodic damping. Necessary and sufficient condition for the Ulam stability of second order periodic linear differential equations is obtained by giving an Ulam stability theorem and an Ulam instability theorem. These results extend the conclusions in relevant literature. In addition, for the situation of constant coefficients, the minimum Ulam constant is given.

This study investigates the phytochemical profile, cytotoxic effects, and lactogenic activity of Oxystelma esculentum, a plant renowned for its traditional medicinal uses. Preliminary phytochemical screening revealed a rich array of secondary metabolites, including alkaloids, flavonoids, glycosides, and tannins, predominantly in polar solvents such as methanol and ethanol. Spectroscopic analysis further identified key functional groups, suggesting significant bioactivity. The cytotoxic potential was evaluated against ovarian (OVCAR3), breast (T47D), and cervical (HeLa) cancer cell lines, demonstrating a concentration-dependent increase in cytotoxicity, with T47D cells exhibiting the highest sensitivity (IC50 = 42.82 μg/ml). Additionally, the brine shrimp lethality assay indicated low toxicity, while in vivo studies highlighted the extract's ability to enhance milk yield and pup growth in lactating rats, correlating with increased prolactin levels. These findings underscore Oxystelma esculentum as a promising candidate for natural anticancer therapies and lactation enhancement, warranting further exploration of its bioactive compounds and mechanisms of action.

In this paper, we propose a novel stochastic model to study prime distribution in almost-short intervals, inspired by a corollary of the Maynard-Guth theorem. The model uses the equation \[ x_{n+1} = x_n + \frac{y}{\log x_n} + \epsilon_n, \] where \(\epsilon_n\) follows a Laplace distribution, and demonstrates that our new bound surpasses both Walfisz and Maynard-Guth bounds, particularly for large \(x\): \[ \left| \pi(x + y) - \pi(x) - \frac{y}{\log x} \right| \leq C \cdot y \exp(-\sqrt[4]{\log x}). \] Additionally, we explore a modulated Hamiltonian \(\hat{H}(x)\), derived from a potential \(V(x)\) and a test function \(f(x)\), that exhibits behavior akin to the energy levels of the heavy nucleus \(H_{38}\). The gaps between these energy levels resemble the distribution of zeros of the Riemann zeta function near the critical line, suggesting that \(\zeta(0.5 + i \hat{H}(x))\) corresponds to the energy levels of \(\hat{H}(x)\), thus supporting a quantum mechanical interpretation of the Riemann hypothesis. Furthermore, assuming the Elliott-Halberstam conjecture, which implies that prime gaps can be as small as 12, we find that the transition rate \(\Gamma_{p \to p+12}\) between quantum states associated with prime numbers increases with larger primes. This observation indicates that the distribution of prime gaps reflects an underlying discrete structure in the "energy" landscape of primes, offering a novel perspective on the connection between number theory and quantum physics.

Background: Bayesian Network (BN) modeling is a prominent methodology in computational systems biology. However, the incommensurability of datasets fre- quently encountered in life science domains gives rise to contextual dependence and numerical irregularities in the behavior of model selection criteria (such as MDL, Minimum Description Length) used in BN reconstruction. This renders model features, first and foremost dependency strengths, incomparable and diffi- cult to interpret. In this study, we derive and evaluate a model selection principle that addresses these problems. Results: The objective of the study is attained by (i) approaching model eval- uation as a classification problem, (ii) estimating the effect that sampling error has on the satisfiability of conditional independence criterion, as reflected by Mutual Information, and (iii) utilizing this error estimate to penalize uncertainty with the novel Minimum Uncertainty (MU) model selection principle. We vali- date our findings numerically and demonstrate the performance advantages of the MU criterion. Finally, we illustrate the advantages of the new model evaluation framework on real data examples. Conclusions: The new BN model selection principle successfully overcomes per- formance irregularities observed with MDL, offers a superior average convergence rate in BN reconstruction, and improves the interpretability and universality of resulting BNs, thus enabling direct inter-BN comparisons and evaluations.

Background/Objectives: Women's reproductive and overall well-being are intricately associated to vitamin D levels. In Morocco, vitamin D deficiency is a major issue for premenopausal women, yet little data exists on their dietary intake of this nutrient. This study explores vitamin D dietary intake, socio-demographic determinants, and its correlation with serum 25-hydroxyvitamin D [25(OH)D3] in premenopausal Moroccan women. Methods: A cross-sectional study was conducted with 355 women aged 18-49 in Meknes, Morocco, using validated questionnaires to assess dietary intake and sun exposure. Potential covariates such as physical activity and BMI were also assessed . Serum 25(OH)D was measured via chemiluminescence immunoassay. Chi-square tests and Spearman correlation were used to identify socio-demographic factors influencing vitamin D intake. Linear and multivariate regression analysis determined the main predictors of serum 25(OH)D levels. Results :The median (interquartile range) vitamin D dietary intake was 2.8 (2.7) µg/day , with less than 20% of women meeting the recommended 5 µg/day. Major dietary sources were fish (48%), dairy (24.3%), and meat (9.4%), with fortified foods contributing only for 7%. Lower intake was linked to younger age (p=0.004) and rural living (p=0.024). While no significant association was found between dietary intake and serum 25(OH)D ((β(SE): 0.155(0.094), p=0.340 ) , sun exposure positively predicted serum levels(β(SE): 0.343 (0.128), p=0.008) , and BMI negatively affected them (β(SE): -1.128 (0.337), p=0.001). Conclusions: Our findings highlight insufficient vitamin D intake among premenopausal Moroccan women, especially younger and rural women. Nutritional interventions are needed to address this deficiency, promoting better dietary habits, sun exposure, and healthy weight.

This study provides a detailed numerical assessment of the productivity of single-slope solar stills across key cities in the Caspian region, including Aktau, Atyrau, Astrakhan, Makhachkala, Baku, Tehran, and Turkmenbashi. Employing a mathematical model based on heat balance equations and the 4th-order Runge-Kutta method, we simulated the distillation process under various climatic conditions. The results reveal that productivity is significantly influenced by geographic location and local meteorological conditions. Tehran demonstrated the highest productivity across all seasons, with values of 1.75 × 10³ kg/(m²·year) and an efficiency of 0.53, due to its optimal solar irradiation and ambient temperature. In contrast, Atyrau and Makhachkala exhibited lower productivity, particularly in colder months, highlighting the effect of ambient temperature on solar still efficiency. The analysis identified optimal water depth at 2 cm and insulation thickness between 4 and 9 cm for enhancing productivity in continental climates like Aktau. Additionally, the lowest cost of distilled water was $0.024 per kilogram in Baku. These findings align with existing literature, validating the numerical model's accuracy. Future research will explore integrating solar stills with other renewable and fossil fuel-based technologies.

Nocardia is an emerging bacterial disease that often affects patients with compromised immune systems. As survival increases, more and more patients are receiving solid organ transplantations, and more patients are cancer survivors and receiving long-term immune system modifying agents or corticotherapy and immunosuppressive agents, organisms that are not harmful in usual circumstances become a public health problem. Carbapenems are usually a second-line therapy in the setting of Nocardia infections. We present the case of a patient who had an immunocompromised status and was diagnosed with Nocardia farcinica, which showed in vitro resistance to carbapenems. The symptoms were non-specific and had a common presentation, although the infection was disseminated and had severe CNS and ocular involvement. The clinical course was more severe when the carbapenem was withdrawn based on the provided antibiogram and markedly improved when clinical judgment considered it might be beneficial. The first case in our region to describe an „in vitro” carbapenem-resistant Nocardia species, that responded „in vivo” differently and we believe it is important to remind clinicians of this potentially severe disease in difficult cases.

Some philosophers of physics have addressed criticisms of the so-called Received View (RV) of non-individual quantum objects, also called the orthodox view. Dennis Dieks made a very good resume of these criticisms in [1] and Tomasz Bigaj in [2] has a more detailed account. In considering (mainly) these works and with some additional mentions, we hope to dissipate some misunderstandings about the RV and clarify what is happening with such a view. According to Dieks, the RV doesn’t fit the practice of physics since in some situations the physicist assumes that quantum objects can be treated individually, imitating standard objects (individuals) of classical physics. Dieks also proposes an Alternative View (AV), generally called the heterodox view, which would give a view of the fundamental ontology of quantum physics and which would be more by the way physicists usually proceed. In my view, the AV cannot be viewed as the fundamental ontological theory despite being suitable for practical purposes. Furthermore, we think that it does not conflate the RV, but is complementary to it, substituting it when quantum objects are sufficiently apart and can be treated in conformity with the classical way. From our point of view, in the practice of physics, we can adopt AV. Still, the RV is more adequate when we are looking for logical and foundational analyses, at least when the supposed metaphysics comprises non-individuals.