Transitional cell carcinoma of the bladder is currently a very representative model of development in basic, pathogenic and therapeutic research within the framework of modern clinical oncology. The article aims to present a clinical case of transitional cell carcinoma of the bladder, a diagnostic approach that takes into consideration the combination of clinical imaging methods, for which a 67-year-old white male patient with a history of macroscopic hematuria is presented, which is why which is treated and placed under follow-up, demonstrating that a successful diagnostic approach has been, is and will always be a fascinating intellectual challenge for doctors, and an inexhaustible source of satisfaction in their professional life, as long as its bases and procedures are mastered. . , this serves as the uniqueness and novelty of the work that is presented.

In this study, we explored the application of deep learning techniques for credit card fraud detection, aiming to improve the performance and reliability of anomaly detection methods in financial transactions. We first utilized the Isolation Forest algorithm, achieving a detection accuracy of 26% for the top 1000 transactions. Subsequently, we experimented with the Autoencoder algorithm, an unsupervised deep neural network model, which enhanced the detection accuracy to 33.6% in the best case despite some fluctuations. The results demonstrate deep learning models' strong feature extraction capability and adaptability, highlighting their potential to surpass traditional methods. However, the high imbalance in the dataset, with only 0.17% of transactions being fraudulent, poses a significant challenge. This study underscores the necessity for further experimentation and optimization of network structures and hyperparameters to achieve more stable and efficient fraud detection. The findings provide valuable insights and reference points for future research in financial fraud detection using deep learning methodologies.

The first member of the family, visual arrestin-1, was discovered in the late 1970s. Later the other three mammalian subtypes were identified and cloned. The first described function was regulation of GPCR signaling: arrestins bind active phosphorylated GPCRs, blocking their coupling to G proteins. It was later discovered that receptor-bound and free arrestins interact with numerous proteins, regulating GPCR trafficking and various signaling pathways, including those that determine cell fate. Arrestins have no enzymatic activity, they function by organizing multi-protein complexes and localizing their interaction partners to particular cellular compartments. Today we understand the molecular mechanism of arrestin interactions with GPCRs better than the mechanisms underlying other functions. However, even limited knowledge enabled the construction of signaling-biased arrestin mutants and extraction of biologically active monofunctional peptides from these multifunctional proteins. Manipulation of cellular signaling with arrestin-based tools has research and likely therapeutic potential: re-engineered proteins and their parts can produce effects that conventional small molecule drugs cannot.

C-C motif chemokine receptor-8 (CCR8) belongs to class A of G protein-coupled receptors (GPCRs). CCR8 interacts with the specific chemokine ligand CCL1/I-309 in humans, which is produced by various cells, including tumor-associated macrophages and regulatory T cells (Treg). CCR8 is highly expressed on Treg and T-helper 2 (T_H2) cells recruited to the inflammation site and is implicated in allergy, asthma, and cancer progression. The CCR8⁺Treg has been suggested to be an important regulator in the immunosuppressive tumor microenvironment (TME); therefore, it has been desired to develop sensitive monoclonal antibodies (mAbs) for CCR8. This study developed a specific mAb for human CCR8 (hCCR8), which is useful for flow cytometry by employing the Cell-Based Immunization and Screening (CBIS) method. The established anti-hCCR8 mAb, C₈Mab-21, (mouse IgM, kappa) reacted with hCCR8-overexpressed Chinese hamster ovary-K1 (CHO/hCCR8) cells, TALL-1 (acute T lymphoblastic leukemia), CCRF-HSB2 (human T-lymphoblastic leukemia), and natural killer cells, which express endogenous hCCR8 by flow cytometry. Furthermore, C₈Mab-21 demonstrated a moderate binding affinity for CHO/hCCR8 and TALL-1 with a dissociation constant of 6.5×10^-8 M and 2.0×10^-8 M, respectively. C₈Mab-21, which was established by the CBIS method, could be a useful tool for analyzing the hCCR8-related biological response using flow cytometry.

Studies have established the crucial role of sleep among the students which plays a significant role in their mood. This study aimed to examine the mood disorders and sleep quality among undergraduate students at the University of Georgia (UG) and comparison between students’ nationality.This cross-sectional study was a self-reported questionnaire comprised of demographics including age, gender, year of study, current location, lifestyle data (Exercise and smoking), the Depression, Anxiety, and Stress Scale (DAS21) and the Pittsburgh Sleep Quality Index (PSQI). We performed a descriptive analysis, and the Chi-square was statistically significant at p<0.05. The prevalence was at a 95% confidence interval (95% CI) as multivariate analysis examined the multicollinearity. The female students were most of the respondents, below the age of 20 years. The student's mean age was 20.20 (SD± 3.0). DAS report presented 72.7% of students with depressive symptoms, 77.8% with anxiety symptoms, and 62.2% had stress. Georgian students were more at risk of having depressive complaints (95% Cl[1.567-3.788]), anxiety (95% Cl[1.612-4.285]), and stress symptoms (95% Cl[1.743-3.831]). There was a strong relationship between the students who experienced poor sleeping patterns and depressive complaints (aOR 0.10). The students who were smokers (aOR 0.39) were more likely to report anxiety symptoms than the students that do not exercise (aOR 1.68). It was observed that students with depressive symptoms, anxiety, and complaints of stress had a significantly high risk of poor sleep quality. Further studies are recommended to curb psychological symptoms of mood changes in association with sleep disorders among students.

Therapies targeting amyloid, such as monoclonal antibodies like Lecanemab, Aducanumab, and Donanemab, have garnered FDA approval but remain controversial due to limited clinical benefits and significant side effects against Alzheimer. The present theorical work utilized Virtual Screening and Docking techniques to explore potential small natural molecules' interactions with the KIBRA protein, a promising target for drug development. Kibra protein is considered a biomarker for synaptic dysfunction and cognitive decline. Employing Autodock Vina and Pyrx software, on several natural compounds and found Hypericin to exhibit the lowest binding energy of approximately -9 kcal/mol. Previous studies have highlighted Hypericin's strong affinity for Alzheimer's Disease-associated targets like ABAD and BACE1. These findings suggest Hypericin could be as a potential therapeutic agent for Alzheimer's Disease. Further research is warranted to validate its efficacy and safety for clinical use.

Security in the Internet of Things (IoT) is a critical concern due to the growing number of connected devices and the limited resources to implement robust security mechanisms in most of them. Adopting standard assessment frameworks, such as CVSS (Common Vulnerability Score System), allows for systematic vulnerability assessment. At the same time, CVE (Common Vulnerabilities and Exposures) records provide unique identifiers for known security issues, making it easier to share information. However, comparing security solutions is still challenging due to the diverse nature of IoT devices and the ever-evolving threat landscape, requiring continual innovation and collaboration between stakeholders. Furthermore, there still needs to be a simple way to compare technologies and solutions, taking into account the security provided by them. In this sense, in this article, we propose a tool for calculating a security score based on the CVSS index of each existing vulnerability in a given technology. We explain how the tool performs the calculation, discuss some challenges, and propose improvements for future work.

Many studies have demonstrated hydrogen's therapeutic and preventive effects on various diseases. Its selective antioxidant properties against hydroxyl radicals, which are responsible for the indirect effects of ionizing radiation, may make it worthy of attention as a new radio-protector. We recently developed new hydrogen water that is more stable and has higher antioxidant activity by using ultra-fine bubbles. In this study, female C57BL/6J mice given ad libitum access to ultra-fine bubble hydrogen water (UBHW) were subjected to whole-body irradiation (WBI) with X-rays, and the radio-protective effect of UBHW was evaluated. Sub-lethal WBI resulted in a 30-day survival rate of 100% in UBHW-fed mice, compared with 37% in control mice. In the case of lethal WBI, while the control mice died out in about 3 weeks, the 30-day survival rate improved to 40% by UBHW due to the high scavenging activity of hydroxy radicals. Twenty-six serum proteins involved in inflammatory and immune responses were significantly identified in UBHW-fed mice by proteomics, and UBHW may enhance and regulate these functions, resulting in reduced damage in mice exposed to WBI. We conclude that UBHW has good potential in radio-protection, with evidence that warrants further research efforts in this field.

Coastal resources in Lian, Batangas, have undergone significant degradation due to a combination of human activities and natural factors, including sea-level rise, changes in sea-surface temperature, fluctuations in rainfall, increased storm frequency, and sedimentation. This has led to adverse effects on key ecosystems such as corals, mangroves, and seagrasses. To assess the extent of climate change hazards on these coastal resources, a Vulnerability Assessment was conducted in the municipality of Lian, Batangas. The assessment employed a combination of the Coastal Integrity Vulnerability Assessment Toolkit (CIVAT) and Integrated Coastal Sensitivity, Exposure, and Adaptive Capacity to Climate Change (ICSEA-C) guidelines. Additionally, the analysis incorporated detailed mapping using Light Detection and Ranging (LiDAR) data for mangroves and LandSat 8 imagery for corals and seagrasses. The findings at the municipal scale revealed that a staggering 90% of mangroves in Lian, Batangas, were highly vulnerable. This vulnerability was attributed to a high score in intrinsic characteristics and a low adaptive capacity. Similarly, a majority of seagrasses in the area were classified as medium and highly vulnerable at the municipal level. Only 10–20% of seagrasses received low to medium vulnerability scores. Shockingly, almost 100% of the coral reefs in Lian, Batangas, were deemed highly vulnerable, indicating a critical threat to this crucial marine ecosystem.

Species distribution models (SDMs) are usually used to predict current species’ geographic distributions and to forecast the impact of climate change, with different aims, such as conservation, and biodiversity management. SDMs use has been increasing in the last decades, however, they are vulnerable to parametrization and data quality input. Thus, inappropriate input can lead to potential unreliability in results. In this context, the most used data and methodologies in SDM, and putative deviations from the consensual best practices, were identified, by analysing recent literature (2018 to 2022). Results show that the parameters presented more consistently are the chosen algorithm (MaxEnt was used in 98% of the studies), the accuracy measures, and the time windows. Many papers fail to specify other parameters, limiting the reproducibility of the studies. Some papers also fail to provide information about the target species: only a fraction of the species' range is considered, or no justification for including specific variables in the model is provided. These options can decrease reliability in predictions under future scenarios since data provided to the model is inaccurate from the start or there is insufficient information for outputs discussion.

Springs play a significant role in maintaining hydrological balance in the mountain ecosystem. The communities living in the mountain tops rely entirely on spring water for their daily needs. In recent decades, spring water discharges have decreased drastically in the Himalayan regions and become seasonal. Remote sensing and geographical information system techniques have proved to be essential for understanding the sustainable development of groundwater, particularly where people rely solely on spring water in the mountainous regions of the Himalayas. This study employs the Analytical Hierarchy Process (AHP) to identify groundwater potential zones. It involves overlaying seven thematic layers concerning elements of physical features and land-use/land-cover of the region, using the weighted overlay toolbox in ArcGIS, with each layer being assigned a weight based on its importance. Based on the results, groundwater potential zones have been classified into five categories: poor, fair, good, very good, and excellent. The study found that poor groundwater potential zones cover 1.1% of the area, fair zones cover 27.8%, good zones cover 23.3%, very good zones cover 26.6%, and excellent zones cover 21.2% of the total area. The excellent to very good potential zones are associated with perennial springs that have higher discharge (4.30-30.92 l/m), lineaments, rainfall, forested land, and porous soils, favoring the potentiality of the springs. The fair and poor groundwater potential zones are associated with seasonal flowing springs, consisting of barren land, less rainfall, and low fracture density are influencing the potentiality groundwater. The validation of the groundwater potential zones indicated an Area Under Curve (AUC) value of 0.88, which shows good agreement with observed spring water locations. The identified groundwater potential zones are valuable for prioritizing the area for the sustainable development of spring water. Particularly, it is helpful for the stakeholder departments to involved in developing strategies for sustainable water management at the micro-watershed level and safeguarding spring water resources using integrated watershed management.

Motivated by South Africa's need in the transition to a net-zero economy; this study 10 investigates the integration of renewable energy sources (RES) into oil refineries, considering 11 therein unique challenges and opportunities. The research focuses on optimizing RES allocation 12 using Particle Swarm Optimization (PSO), a data-driven approach that adapts to real-time 13 operational conditions. Traditional energy management systems often struggle with the inherent 14variability of RES, leading to suboptimal energy distribution and increased emissions. Therefore, 15 this study proposes a PSO-based renewable energy allocation strategy specifically designed for oil 16 refineries. It considers factors like the levelized cost of energy, geographical location, and available 17 technology. The methodology involves formulating the optimization problem, developing a PSO 18 model, and implementing it in a simulated oil refinery environment. The results demonstrate 19 significant convergence of the PSO algorithm, leading to an optimal configuration for integrating 20 RES and achieving cost reductions and sustainability goals. The optimization result of 4,457,527.00 21 Rand achieved by iterations is much better than the result of 4,829,638.88 Rand acquired using 22Linear Programming as the baseline model. The mean cost, indicating consistent performance, has 23 remained at its original value of 4,457,527.00 Rand, highlighting the convergence. Key findings 24 include the average distance measure decreasing from 4.2 to 3.4, indicating particle convergence; 25 swarm diameter decreasing from 4.7 to 3.8, showing swarm concentration on promising solutions; 26 average velocity decreasing from 7.8 to 4.25, demonstrating refined particle movement; and the 27 optimum cost function achieved at 4,457,527 Rand with zero standard deviation, highlighting 28 stability and optimal solution identification. This research offers a valuable solution for oil 29 refineries seeking to integrate RES effectively, contributing to South Africa's transition to a 30sustainable energy future.

Conventional farming relies on large-scale, intensive crop production using mechanical tillage and synthetic inputs like chemical fertilizers and pesticides, which, despite being economically beneficial, can be environmentally destructive, whereas organic cropping system offers a more sustainable alternative with less harmful environmental impact. But is organic system more beneficial for soil health than conventional? In this study, we focused on evaluating the functional potential of soil for the conversion of carbon, nitrogen, phosphorus, and sulfur, as well as measuring the actual activity of enzymes involved in these cycles. We used Chernozem soil samples from fields that were managed using organic (OCS) and conventional (CCS) methods as the objects of our study. Another objective of the study was to compare two methods: profiling of functional genes of the microbial community and measurement of actual enzyme activity. The comparison of OCS versus CCS indicates that functional gene profiling and enzymatic activity measurements similarly capture qualitative differences between these systems, but diverge in how they assess quantitative differences. A combination of these two methods for assessing soil conditions appears to be promising. Overall, the study demonstrated that the agronomic practices used in the conventional cropping system not only preserved the functional properties of the soil microbiome but also significantly enhanced its microbiological activity for nutrient bioconversion compared to the organic cropping system. However, the question remains as to how long the conventional system can sustain its high potential.

This study assessed the perception and use of digital applications for soil fertility management and conservation strategies among small-scale crop farmers in southwest Nigeria. A total of 376 farmers were randomly selected across the six southwest states. The data collected were analyzed using descriptive statistics and Logit regression. Majority of the farmers relied on perception and other non-conventional approaches such as the appearance of weeds and performance of crops in the previous season to assess soil fertility. Only 1.1% and 0.3% of the farmers assessed soil fertility through soil test and digital application respectively. Most farmers adopted bush fallowing and the use of inorganic fertilizer to improve soil fertility. Although, 4.8% of the farmers indicated that they had digital application on their mobile phones, only 2.9% reported to have used such. More than half (56.4%) of the famers stated that lack of awareness of the existence of digital application and internet enabled telephones were reasons they have not been able to use digital applications. Majority of the farmers (97.3%) indicated their willingness to embrace the use of new farm decision digital application which can provide more information especially on soil fertility if introduced. Logit model results revealed that literacy level, membership of farmers’ association and extension contact significantly increased the likelihood of willing to use soil digital application, but older farmers are less likely to use digital application.

The soils of the high-altitude mountains along the East African Rift Valley are poorly understood. Understanding the potential of soils for agriculture, climate change mitigation, and environmental functioning necessitates an understanding of their relationship to soil-forming factors. Therefore, this study focuses on the volcanic soils of Mount Guna. Eighty-five soil profiles, between 3000 and 4120 m a.s.l., were described and sampled along seven topographic transects. The samples were analyzed for physicochemical characteristics using standard methods and classified according to WRB 2022. The clay mineralogy of six profiles was analyzed with X-ray diffraction. The first four-factor axes, which are related to elevation, parent material, climate, and land use were found by factor analysis, explaining more than 60% of the total variation. The clay portions are primarily composed of trioctahedral chlorite, trioctahedral mica (phlogopite or biotite), vermiculite, kaolinite, some quartz, some amorphous silicates (most likely pyroclastic glass), and minor feldspar. The presence of weatherable minerals (biotite, amphibole, feldspars, and so on) suggests that these soils have not been weathered extensively. The dominant Reference Soil Groups found in the study area are Andosols, Phaeozems, Leptosols, Regosols, Cambisols, Luvisols, and Vertisols. As a result, our findings suggest that altitudinal variation, climate, lithology, and their contributions to the variability of soil characteristics and development along the toposequence cannot be separated in this study; more similar studies in other high-elevation/altitude mountains are required. There have been no other studies of high-altitude mountains in East Africa where so many soil profiles have been examined.

The accumulation of space debris, including defunct satellites, rocket stages, and fragments, poses a severe threat to current and future space operations. Addressing this issue requires innovative approaches for active debris removal. This paper presents a new method for detumbling and deorbiting large, non-cooperative tumbling debris, such as those from Russian 'Kosmos 3M' launchers. We propose a detumbling device combining three harpoons with a nanosatellite, which attaches to debris and uses thrusters to reduce its angular velocity, facilitating subsequent removal. A high-fidelity coupled dynamics model of the system is developed to account for external disturbances and inertial changes due to fuel consumption. Additionally, an adaptive sliding mode control strategy with pulse-width pulse-frequency (PWPF) modulation is proposed to manage uncertainties and disturbances during the detumbling process. Numerical simulations demonstrate the effectiveness and robustness of the proposed method, achieving successful detumbling within 4000 seconds.

This study explores the potential of digital technologies for preserving cultural heritage through the case study of the Raya Gopura, an incomplete monument in Melukote, India. Employing a combination of terrestrial photogrammetry, drone-based photogrammetry and terrestrial laser scanning, the research team captured highly detailed 3D models of the structure. This non-destructive approach offers a comprehensive digital record for future research and preservation. Furthermore, for the conjecture for the Raya Gopura two possible purposes: an entrance to a planned fort or a gateway to the temple are perceived through the collective intelligence (CI) approach. These insights inform a conjectural architectural drawing of the potentially completed monument. The 3D models hold significant potential for various applications, including tourism, education, and architectural research, with the potential for immersive experiences through VR and AR technologies. The study demonstrated the effectiveness of digital tools in heritage preservation, not only safeguarding the Raya Gopura but also offering valuable in-sights for future research and public engagements.

With the increasing incidents of fatal road injuries, there is an urgent need for developing effective road safety management systems. The study aims to develop predictive models based on machine learning to forecast the likelihood of road collisions depending on factors like weather, road condition, time, and driver behaviour in Chicago, USA. A machine learning approach has been applied to the crash dataset to evaluate the factors affecting the prevalence of road accidents. Python programming and the Jupyter Notebook platform have been used for performing descriptive statistics, correlation and three classification algorithms (Random Forest, K-Nearest Neighbor (KNN), Decision Tree and MLP Classification). Obtained accuracy of the KNN classifier is slightly higher than the other two classification models. The research explored insights into collision patterns related to roads, locations, and intersections. The study helps to increase road safety through targeted interventions with resource prioritisation, reducing the frequency and severity of traffic incidents by leveraging historical accident data with diverse spatial analysis techniques.

Reduction of the density operator for quantum subsystems is derived without the use of the Born Rule.

Despite their increasing prevalence epidemiological data on thoracic spine injuries(TSI) is under-presented in the literature. Furthermore, these studies fail to correlatemechanisms of injury to the specific spinal regions injured. Team ball sports appear tobe common activities undertaken in these age groups. A narrative review wasconducted to understand the aetiology of traumatic TSI, with a focus on sporting causes in youth populations. A literature search was conducted through various databases (from inception to April 2024) such as PubMed, CINAHL, Medline, ProQuest Central, Science Direct and SPORTDiscus. Seven studies, including 1670 patients were included in the dataset. TSI, particularly fractures, were found to be more common in adolescent age groups. Common sporting causes of spinal trauma included equestrian and team ball/contact sports. This review found few studies failed to correlate the mechanism of injury and the specific spinal level affected, supporting the need for further research.

Objective: The general surgery match process has changed with the transition to virtual interviews, the use of signaling, and the loss of the USMLE1 scoring. We undertook this study to describe the current state of the surgery match.Design: Describe changes in applicant behavior and characteristics between 2020-2024 and perform a multivariable analysis to measure applicant characteristics that predict obtaining an interview invite.Setting: Applicant-moderated, public database with voluntarily completed survey.Participants: 1117 applicants to general surgery residencyResults: Over the time period, applications submitted per student increased by 32%, from 78.0±3.8 in 2020 to 103±3.2 for all and 26% excluding international medical students (IMGs). The average number of applications submitted by student type - US allopathic (USMD), osteopathic (DO), and IMG - were 75±1.5, 105±4.3, and 159±6.3, respectively. The number of interview invitations (IIs) for US graduates decreased by 31%, from an average of 17.4±0.83 to 13.3±0.60 (p<0.0001). 33% of US applicants received more than 18 IIs. Controlling for all other factors, IIs per applicant decreased (-3.5; p<0.0001), and predictors of the number of IIs included USMLE1 (0.15±0.4), USMLE2 (0.17±0.4) scores, student type and higher class quartile rank (p=0.004), lack of gap years (4.7±2.2, p=0.03), AOA membership (3.1±1.0 p=0.003) and graduating from a top-tiered medical school (2.1, p=0.05). In 2024, USMLE2 had the same predictive weight as the combination of scores compared to the prior years (p<0.0001). Signaled program’s II yield was 68±2%.Conclusions: Over the last five years, substantially more applications per student were submitted, while fewer IIs were received, potentially resulting in poor optimization of candidates matching to programs. Loss of USMLE1 scores made no direct difference in the number of IIs.

The CXC chemokine receptor 5 (CXCR5) is a member of the G protein-coupled receptor family that is highly expressed in B cells and a subset of T cells such as T follicular helper cells. Various types of cancers, including non-small cell lung cancer, breast cancer, and prostate cancer also express CXCR5; therefore, antibodies that specifically bind to CXCR5 could be useful for clarification of the mechanisms of cancer progression. In this study, we aimed to develop high-affinity monoclonal antibodies (mAbs) for flow cytometry against mouse CXCR5 (mCXCR5). The established anti-mCXCR5 mAb, Cx₅Mab-3 (rat IgG_2b, kappa), reacted with mCXCR5-overexpressed Chinese hamster ovary (CHO)-K1 (CHO/mCXCR5) by flow cytometry. Kinetic analyses using flow cytometry indicated that the dissociation constants (K_D) of Cx₅Mab-3 for CHO/mCXCR5 is 7.2 × 10⁻¹⁰ M, indicating that Cx₅Mab-3 is useful for detecting mCXCR5 by flow cytometry with high affinity.

Background/Objectives: Schizophrenia (SZ) is a severe psychiatric disorder characterized by a complex interplay of genetic, developmental, and environmental factors that significantly increase the risk of suicidal ideation (SI) and suicide attempts (SAs). This systematic review synthesizes current research on the developmental predictors of SI in individuals with SZ, aiming to delineate the multifactorial etiology of suicide within this population. Methods: A comprehensive search across Medline, PsycINFO, and EMBASE databases identified 23 eligible studies, emphasizing the varied methodological approaches and the global distribution of research efforts. Results: These studies demonstrate a robust association between early life adversities, particularly childhood trauma such as physical neglect, emotional abuse, and sexual abuse, and the increased prevalence of SI and SAs in SZ. This review also highlights the significant genetic factors associated with development of suicidality in SZ, suggesting that polymorphisms in inflammation-related genes and neurodevelopmental abnormalities may influence susceptibility to SI. Notably, family history of psychiatric conditions may exacerbate the risk of SI through both hereditary and environmental mechanisms. Environmental factors, including socioeconomic status and social support, are also implicated, underscoring the role of broader socio-environmental conditions influencing outcomes. Conclusions: This review supports the integration of biopsychosocial models in understanding SI in SZ, advocating for interventions addressing the complex interplay of risk factors and the need for longitudinal studies to elucidate the dynamic interactions between risk factors over time. This comprehensive understanding is crucial for developing targeted preventative strategies and enhancing the clinical management of SZ, aiming to reduce suicidality in this vulnerable population.

Localized protein translation occurs through trafficking of mRNAs and protein translation machineries to different compartments of the cell, leading to rapid on-site synthesis of proteins in response to signaling cues. The spatiotemporally precise nature of the local translation process necessitates continual developments of technologies reviewed herein to visualize and map biomolecular components and the translation process with better spatial and temporal resolution, and with fewer artifacts. We also discuss approaches to control local translation, which can serve as a design paradigm for subcellular genetic devices for eukaryotic synthetic biology.

With the continuous advancement of digital transformation, digital payments are playing an increasingly important role in the financial industry. This study aims to utilize machine learning models to predict and analyze digital payment behavior. Initially, the background and significance of digital payments in the financial sector are introduced. Subsequently, the current status and trends of traditional digital payment distribution are reviewed, alongside related work on digital payment behavior prediction. Methodologically, principles and applications of machine learning models such as logistic regression, decision trees, and random forests are elaborated, along with experimental design and data preprocessing methods. The experimental results and discussion section illustrates the performance of each model in digital payment prediction and explores their impact on credit decisions. This exploration equips financial institutions with more effective user behavior analysis and risk management tools, thereby fostering future development and application of digital payment technologies.

Gc protein-derived Macrophage Activating Factor (GcMAF), a powerful immunostimulant endowed with anti-cancer and anti-angiogenetic activities, offers significant advantages when combined with radiation therapy. A stronger immune response improves the effectiveness of radiation therapy by allowing the body to better eliminate residual cancer cells after treatment. In addition, by boosting the immune system, GcMAF mitigates some of the immunosuppressive side effects of radiation therapy, leading to faster recovery. In order to fully exploit the potential of GcMAF in cancer therapy, knowledge of the molecular interactions with its receptor is essential. This study proposes the first extracellular domain (residues 1-34) of the CCR1 protein as the GcMAF receptor. The CCR1 gene, expressed in monocytes and 168 other cell types or tissues, encodes this transmembrane protein. Electrostatic and hydrophobic interactions, along with hydrogen bonds mediate the molecular interactions between the TPT^420-GalNAcELAK (or TPK⁴²⁰ELAK) sequences of GcMAF (or Gc2 protein variant) and the TTEDYDTTT sequence of its receptor.

This study provides a comprehensive comparison of the Peter Chew Sum method and Newton's Sum in Quadratic Root Function Computations. Both methods offer significant advancements by alleviating the need for memorization of multiple formulas. However, when confronted with higher-order quadratic root functions such as the calculation of power 33. Newton's Sum method proves cumbersome due to its requirement of all previous answers for final computation, leading to increased steps and error risks. In contrast, the Peter Chew Sum method simplifies the computational process by requiring only a few previous answers, resulting in a more straightforward series of operations. We contextualize our analysis within the historical progression of mathematical techniques, highlighting the transformative leap represented by the Peter Chew Sum method in quadratic root function computations. Peter Chew Sum addressing the limitations of Newton's Sum , especially concerning higher-order functions such as the calculation of power 256. Furthermore, study demonstrates that the innovative approach of the Peter Chew Sum method not only simplifies complex computations but also fosters enhanced conceptual understanding among students. In conclusion, study findings underscore the profound impact of the Peter Chew Sum method on quadratic root function computations.

BACE1 is involved in the production of beta-amyloid peptides, and inhibiting its activity is a potential therapeutic strategy for managing Alzheimer's disease, as excessive beta-amyloid accumulation is a characteristic feature of the disease.Computational results suggest that hypericin has the potential to act as an inhibitor of BACE1. The Molecular Docking results indicate a high binding affinity between hypericin and BACE1, with a notable binding energy score of -10.3 kcal/mol. Additionally, the formation of various chemical bonds, including hydrogen bonds and hydrophobic bonds, further supports the idea that hypericin may effectively interact with the active site of BACE1. It's important to note that while the in silico Molecular Docking results are promising, further in vitro and in vivo studies would be necessary to validate and better understand the inhibitory potential of hypericin against BACE1. Additionally, the practical application of hypericin as a BACE1 inhibitor in a therapeutic context would require careful consideration of factors such as bioavailability, safety, and efficacy.

Docking results suggest that Amentoflavone has a higher affinity for investigated Serine/threonine-protein kinases, indicating potential interactions that may influence the activities of these kinases. The negative values of the binding energies signify strong and stable interactions, suggesting that Amentoflavone could be a key molecule in modulating the functions of these kinases, which play crucial roles in cellular processes such as cell cycle regulation and apoptosis.

This study aims to evaluate the performance of a concrete bridge located near the earthen dam of Masjed Soleiman County. The proximity of bridges to large water structures such as dams can affect their structural integrity due to environmental factors and the dynamic loads imposed by fluctuating water levels and soil movements. This paper analyzes the structural behavior of the concrete bridge using advanced modeling techniques, including finite element analysis and site-specific data. The methodology involves evaluating the bridge’s load-carrying capacity, durability under environmental stresses, and possible degradation due to soil settlement and moisture penetration from the nearby dam.Field tests and simulations were conducted to assess the bridge’s stability and performance under different loading conditions, including traffic loads and external factors like changes in water pressure from the dam. Results indicate that while the concrete bridge remains structurally sound, there are signs of minor degradation in certain areas, such as cracks and moisture-related issues. These findings highlight the importance of regular maintenance and monitoring, especially in structures located near large water bodies or dams.Recommendations for improving the bridge’s performance include applying advanced waterproofing methods, strengthening concrete components in critical zones, and implementing a routine inspection schedule. The results from this case study provide valuable insights into the interaction between bridges and surrounding hydraulic infrastructure, serving as a reference for similar projects in regions prone to environmental and structural stresses.This research contributes to the body of knowledge by offering practical solutions for maintaining and enhancing the durability of concrete bridges in close proximity to dams, emphasizing the need for proactive strategies to extend the lifespan of such critical infrastructure.

The rapid development and integration of Autonomous Mobile Robots (AMRs) have 1 revolutionized industries by enhancing automation capabilities. A critical challenge in this evolution 2 is achieving effective navigation and obstacle avoidance, which are essential for deploying AMRs 3 seamlessly in varied environments. This paper presents a detailed exploration of AMR navigation 4 and obstacle avoidance advancements through the application of reinforcement learning, specifically 5 focusing on small-scale Sri Lankan manufacturing facilities. The study demonstrates the effectiveness 6 of Q-learning in managing dynamic obstacles within a factory environment. The AMR successfully 7 avoided obstacles in 36 out of 50 test runs, achieving a 72% success rate, and maintained an average 8 distance of 12 cm from each obstacle, underscoring the algorithm’s precision in maintaining safe nav- 9 igation paths while dynamically adapting to environmental changes. The continuous monitoring by 10 ultrasonic sensors, combined with iterative learning, enabled the robot to refine its decision-making 11 process and efficiently navigate through the environment. This paper also provides a comprehensive 12 examination of conventional methods, tracing their historical development and assessing their role in 13 addressing real-world challenges. The results highlight the significant improvements brought by rein- 14 forcement learning, particularly when integrated with sensor fusion and motor control technologies, 15 to enhance navigation and dynamic object collision avoidance. The study involves developing an 16 AMR prototype, refining algorithms, and assessing system performance in controlled environments. 17 By automating material transportation and addressing operational constraints in manufacturing 18 settings that predominantly rely on manual labor, this paper advances the practical deployment of 19 AMRs. Additionally, it discusses ethical considerations, potential limitations, and the importance 20 of real-world validation, offering valuable insights for the future development and integration of 21 autonomous mobile robotics in dynamic industrial environments.

As an important part of coastal wetlands, tidal flats provide unique ecosystem services and functions. China's tidal flats are significantly threatened by industrialization, urbanization, aquaculture expansion and coastline reconstruction, and there is an urgent need for sustainable strategies to balance the protection and utilization of tidal flats. Remote sensing technology can realize large-scale spatiotemporal research of tidal flats, comprehensively improve the ecological environment of coastal zones, and help achieve the overall planning goals of major projects for the protection and restoration of important ecosystems in China. In this study, based on GEE (Google Earth Engine) platform, Sentinel-2 (MSI), Landsat 8 (OLI) and Landsat 9 (OLI-2) remote sensing images were used to construct multi-source intensive time series remote sensing image collection, combined with MSIC-OA algorithm. The tidal flat data of China in 2018 and 2023 are extracted and analyzed. The results show as follows: 1) The overall classification accuracy of the tidal flat in 2023 is 95.19%, and the Kappa coefficient is 0.89; In 2018, they were 92.77% and 0.83 respectively. 2) The total tidal flat area in 2018 and 2023 is 8300.34km2 and 8151.54km2, respectively, a decrease of 148.80km2; 3) In 2023, estuarine and bay tidal flats will account for 54.88% of the total area, and most of the tidal flats will be distributed near river inlets and bays; 4) In 2023, the total length of the coastline adjacent to the tidal flat is 10196.17km, of which the artificial shoreline accounts for 67.06%, and the development degree of the tidal flat is 2.04, indicating that most of the tidal flat has been developed and utilized. The research results provide valuable reference for the scientific planning and rational utilization of tidal flat resources in China.

Purpose: To evaluate prognostic factors for visual outcome in patients with diabetes who have undergone vitrectomy (PPV) for severe proliferative diabetic vitreo-retinopathy (PDVR) in at least one eye in the past 15 years. Methods: Medical records of 132 eyes of 66 patients were analyzed (median age 52ys 21-80; patients with type 1/2 diabetes 40/26, median follow-up 38 months 9-125). Correlation between final good visual outcome defined as 0.5≤ best corrected visual acuity (BCVA) and prognostic factors (age, sex, type and duration of diabetes, metabolic status, BCVA, diabetic retinopathy status, data of preoperative management, data of vitrectomy, postoperative complications) were analyzed. Results: BCVA improved significantly in the entire study cohort (from median 0.05 min-max 0.001-1 to 0.32 0.001-1, p <0.000). Visual stabilization was achieved in the majority of patients, and relatively good visual acuity (0.5≤BCVA) was maintained in more than one third of eyes. Multivariable GEE statistics showed that in addition to duration of diabetes and stable HbA1c values, only preoperative tractional macular detachment proved to be an independent significant predictor of visual outcome. Conclusions: Pars plana vitrectomy is a useful tool when performed early before tractional macular detachment. However, long-term visual stability can only be achieved with good metabolic control.

Plant biomass could become a new world-class "currency" because almost everything can be made of it: fuels, foods, feeds, constructing materials, furniture, etc. Currently, growing biomass for energy needs is particularly relevant in so complicated socioeconomical situation. There are important its productivity and the lowest possible ecological footprint. Can increasing crop biodiversity address these challenges? A field experiment was started at the Vytautas Magnus University Agricultural Academy in 2020-2022. Maize, hemp and faba bean were grown as single, binary and ternary crops to determine its effect on soil properties. Studies have shown that most intercrops failed to stabilize soil aggregate composition, NPK levels and pH, and enzyme urease activity. Binary maize-hemp and the ternary crops reduced the amount of the main macroelements in the soil the most because of the highest yields, which were removed from the fields. Regardless of the ecological services provided by faba beans, biomass removal leads to degradation of most soil properties and making the soil vulnerable to erosion. In the future, the cultivation of such multicrops should be investigated under conditions of more abundant fertilization or the effect of green manure should be used by increasing the number of fast- and short-growing interior plant species in the cultivation.

Pathogens such as bacteria, fungi and viruses are important components of soil and aquatic communities, where they can benefit from decaying and living organic matter, and may opportunistically infect human and animal hosts. One-third of human infectious diseases is constituted by sapronotic disease agents that are natural inhabitants of soil or aquatic ecosystems. They are capable of existing and reproducing in the environment outside of the host for extended periods of time. However, as ecological research on sapronosis is infrequent and epidemiological models are even rarer, very little information is currently available. Their importance is overlooked in medical and veterinary research, as well as the relationships between free environmental forms and those that are pathogenic. Here, using dynamical models in realistic aquatic metacommunity systems, we analyze sapronosis transmission, using the human pathogen Mycobacterium ulcerans that is responsible for Buruli ulcer. Our work constitutes the first set of metacommunity models of sapronotic disease transmission, and is highly flexible for adaptation to other types of sapronosis. The importance of sapronotic agents on animal and human disease burden needs better understanding and new models of sapronosis disease ecology to guide the management and prevention of this important group of pathogens.

Aims: To compare traditional versus dry wipes hygiene in hospitalized, bedridden patients with regard to economic and environmental impact and patient and professional satisfaction. Background: Evidence shows that dry hygiene with disposable wipes can reduce the risk of infection compared to traditional hygiene with soap and water. However, there is a need to compare the costs and environmental impact (waste) between the two methods. Design: Environmental impact and patient and user satisfaction were assessed through an ad hoc survey and cost minimization analysis in accordance with the Consolidated Health Economic Evaluation Reporting Standards. Methods: The environmental impact and costs associated with traditional versus dry hygiene were compared in an observational analysis. A single-center, randomized controlled trial was performed in a tertiary hospital to assess patient and professional satisfaction in patients with a urinary catheter recovering from heart surgery between January 2019 and February 2020. Results: Direct hygiene costs were similar using traditional (€15.82) and dry (€14.35) methods. However, the analysis of indirect costs, including those related to the probability of contracting catheter-associated urinary tract infections, showed that the total cost per hygiene session was €1345 higher with the traditional method. When using a high-efficiency energy system from a guaranteed renewable source, dry hygiene does not lead to a higher environmental impact (220.07 g CO2 traditional vs. 215.17g CO2 dry). However, emissions from conventional energy systems are 840.14 g CO2 higher when using traditional hygiene. Both patients and professionals express greater satisfaction with the dry method. Conclusion: Dry hygiene is not inferior to traditional hygiene in terms of satisfaction, and it can provide environmental benefits at a significantly lower cost. Implications for the profession and/or patient care: This study provides evidence supporting the introduction of the dry hygiene method, with is associated with higher user and professional satisfaction, lower economic cost, and similar or lower environmental impact compared to soap and water.

Crop productivity is directly dependent to soil fertility. High soil organic carbon (SOC) content in soil is vital as it leads to improved soil quality, increased productivity, and stable soil-aggregates. In addition, with the signing of the climate agreement, there is growing interest in carbon sequestration in landscapes. This paper looks at how SOC can be increased so that it not only contributes to reduction of CO₂, but also translates to increased food production thereby enhancing food security. This synergy between mitigation and enhancing food security is even more relevant for mountain landscapes of the Hindu Kush Himalayan (HKH) region where there remains huge potential to increase CO₂ sequestration and simultaneously address food security in the chronic food deficit villages. Soil samples were collected from seven transects each in Bajhang and Mustang and from 4 land use types in each transect. Samples of soils were taken from two depths in each plot; 0-15 cm below the soil surface and 15-30 cm below the soil surface to compare the top soil and subsoil dynamics of the soil nutrients. The lab analysis was performed to assess the soil texture, soil color, soil acidity in 'power of hydrogen' (pH), macro-nutrients as soil fertility. Secondary data was used to analyze the level of food deficit in the villages. The result shows that most of the sample soils from Mustang were clay (82.1%) which is 46 samples out of 56. The pH value of soil from Bajhang ranged from 5.29 to 9.09. The pH value of soil ranged from 5.65 to 8.81 in Mustang. SOC contents of sampled soils from Bajhang ranged from 0.20% to 7.69% with mean amount of 2.47% ± 0.17. SOC contents of sampled soils from Mustang ranged from 0.51% to 8.56% with mean amount of 2.60% ± 0.25. By land use type, forest land had the highest carbon (C) content of 53.61 t ha^-1 in Bajhang whereas in Mustang, agricultural land had the highest C content of 52.02 tons ha-1. Based on these data, we can say that there is potential for increasing SOC through improved soil health and crop production and soil. Sustainable soil management should be practiced for higher productivity. Livestock may also provide farmyard manure, which can be used to fertilize cultivated soils, which increases soil productivity. Increasing productivity would aid in increasing the access and availability of food in these mountain villages.

This study explores the potential of Federated Learning (FL) to facilitate the sharing and collaboration of medical data in drug development under the premise of privacy protection. While traditional centralized data processing methods limit effective collaboration across agencies due to data privacy and compliance concerns, federated learning avoids the risk of privacy breaches through a distributed architecture that allows participants to train artificial intelligence (AI) models together without sharing raw data. This paper systematically describes the core mechanism of federated learning, including the key technologies such as model parameter updating, differential privacy and homomorphic encryption, and their applications in drug development and medical data processing. Examples, such as NVIDIA Clara's Federated learning application and COVID-19 resource prediction, show that federated learning improves the efficiency of multi-party collaboration and model performance while ensuring data privacy. In addition, this study explores the scalability and generality of federated learning in the medical field, and points out that the technology is not only suitable for drug development, but also has broad cross-industry application potential, especially in areas such as finance and insurance, where data privacy is critical.

In recent years of technological advancements, the digitization of information has become a crucial factor for the growth and sustainability of small and medium-sized enterprises (SMEs), particularly in developing countries. These enterprises face a growing problem in accessing IT resources due to financial and lack of qualified IT personnel in their regions. This led to a growing interest in database and data warehouse technologies, which serve as foundational tools for organizing, storing, and analyzing vast amounts of data. This systematic review proposes to evaluate the impact of database and data warehouse technologies on organizational performance by employing the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, this review thoroughly synthesizes existing literature to identify the key benefits and challenges associated with the adoption of these technologies in SMEs. The proposed inclusion criteria were bounded by (1) publication date between 2014 and 2024, (2) research written in English, (3) research work focusing on evaluating the impact of database and data warehouse technologies on organizational performance, (4) research with a clear evaluation analysis of database and data warehouse technologies research framework. Following these guidelines, 150 eligible research studies were included. The analysis reveals a wide range of findings concerning the impact of database and data warehouse technologies. Key results include the identification of cost-efficiency trends, improvements in decision-making processes, enhancements in customer relationship management, and gains in operational efficiency. The findings provide actionable guidance for SMEs, policymakers, and IT professionals seeking to leverage these technologies to enhance organizational performance. To the best of our knowledge, this systematic review offers a comprehensive analysis that fills a gap in the existing literature on the impact of these technologies on organizational outcomes.

Background: Falls in older adults are prevalent and can result in an injury affecting their quality of life. Whether a fall is fatal or not, it has a high financial impact. Older adults with dementia are more likely to fall than their cognitively healthy counterparts, increasing their risk of serious injury or even death. This quality improvement project developed, implemented, and evaluated an evidence-based fall prevention bundle to reduce preventable falls in a long-term memory care unit.; Methods: The interventions that were implemented includes fall risk screening, physical exercise, vitamin D supplementation, purposeful hourly rounds, and post-fall huddles. Kurt Lewin’s force field analysis guided the project’s implementation. Nurses, healthcare providers, certified nursing assistants, restorative aides, and life enrichment staff from all shifts at a long-term memory care unit participated in the project. The participants implemented the evidence-based fall prevention bundle based on their scope of practice. They participated for 12 weeks. The frequency of falls was compared three months before, during, and after implementation. Paired t-test and independent samples t-test were used to analyze statistically significant differences between variables.; Results: Thirty staff members attended educational sessions, and 22 residents received interventions. Fall rates dropped 50% during the project's implementation but increased subsequently. The difference in the number of falls before, during, and after implementation was not statistically significant.; Conclusions: Staff working in a long-term care facility may help reduce fall rates using evidence-based interventions, but measures should be taken to sustain the initiative's implementation.

In today’s fast-paced technological landscape, businesses must adapt to evolving business models and harness the power of data to thrive. Small and Medium-sized Enterprises (SMEs) face significant challenges in aligning IT infrastructure with business objectives while navigating digital transformation. This systematic review, conducted using PRISMA 2020 guidelines, investigates the role of Enterprise Architecture (EA) and Information Management (IM) in driving IT growth and sustaining competitive performance in SMEs. Through a synthesis of academic research, industry analysis, and case studies from the last decade, this review identifies key frameworks—such as TOGAF, Zachman, and FEAF—that support the alignment of IT capabilities with organizational goals. The integration of IM within EA frameworks is found to enhance decision-making, resource allocation, and operational efficiency. Emerging technologies like Artificial Intelligence (AI) and cloud computing are highlighted for their transformative impact, enabling real-time data analysis, improved scalability, and enhanced agility. Our review reports that 43.44% of the studies focused on cloud-based solutions, while 24.59% adopted hybrid models, offering SMEs scalable and flexible IT infrastructures. The findings emphasize the necessity of strong governance frameworks to ensure compliance, adaptability, and long-term sustainability in a dynamic environment. This research contributes to a comprehensive roadmap for SMEs, enabling them to leverage EA and IM for sustained growth and competitive advantage in the digital era.

For the first time, this communication is intended to perform the possible biological role of natural compound Hypericin, in the Crystal Structure of dual-specificity tyrosine phosphorylation regulated kinase 2 (DYRK2), by Molecular and Dynamic Simulation. From Docking analysis by Autodock Vina and Autodock 4, Hypericin shows a significant ability to binds to DYRK2 protein, an important receptor for cell growth and development. Indeed, from docking studies Hypericin reports a binding energy value of about -13 kcal/mol and Estimated Inhibition Constant (Ki)of ca 1nM . Regarding MD simulation.From our results, this theoretical research it could has excellent future implications in the fight against cancer disease.

With the widespread application of artificial intelligence technology in various industries, users' attention to privacy and data security has increased significantly. Federated learning, as a new technology paradigm combining privacy-enhanced computing and artificial intelligence, resolves the contradiction between data security and open sharing. This paper presents the benefits of federated learning in terms of privacy, real-time processing, model robustness, compliance and cross-industry applications. At the same time, when combined with Edge AI technology, federated learning promotes the decentralisation of intelligent systems, improving data privacy protection and model accuracy. This paper also discusses the application cases of federated learning in the medical field, through local data processing and model training, effectively protecting user privacy, realizing medical data sharing and model optimization, and promoting the development of artificial intelligence.

This paper systematically discusses the application and development of federated learning in data privacy protection and data value sharing. With the rapid development of global information technology, especially the explosive growth of data from Internet of Things devices, data security and privacy protection are facing unprecedented challenges. This paper first analyzes the growth trend of global data volume and its importance to next generation technologies such as artificial intelligence technologies such as deep learning. Second, the paper provides an in-depth look at the impact of current data privacy regulations on data flows and value creation, particularly the EU's GDPR and China's Data Security and Personal Information Protection Law. Then, this paper introduces in detail federated learning, as a new distributed machine learning paradigm, which effectively solves the contradiction between existing data sharing and privacy protection by protecting individual data privacy and realizing global model collaborative construction. Finally, this paper discusses the combination of blockchain technology and federated learning, and proposes BeFL architecture as a new secure, decentralized and trusted federated learning system, which is expected to provide a comprehensive solution for large-scale data processing and value creation in multi-party scenarios. The research in this paper not only deepens the understanding of federation learning in theory, but also provides important reference and enlightenment for future research and application in related fields.

Artificial intelligence provides a new research concept for digital image processing. However, at present, artificial intelligence is rarely introduced into the teaching of digital image processing in colleges and universities, and there are problems such as obsolete teaching content, single teaching methods, and simple course experiments, which affect the teaching effect and are not conducive to the cultivation of comprehensive and innovative talents. Digital image processing technology brings more possibilities to communication engineering and makes communication more convenient. For example, video calls and photo transmission make people's communication methods in daily life more and more diversified. Time and space limitations allow people to meet online, creating more communication possibilities. However, there are still many problems and methods worthy of in-depth exploration. Therefore, this paper has a comprehensive understanding and mastery of the traditional and deep learning methods of digital image processing to improve the relevant project practice and scientific research exploration ability and refer for similar research conclusions.

Electric Transformers make up some of the most critical components of a power system. Their reliable operation is critical for an efficient power grid as well as economic viability for the transformer operators and owners. Power transformers, like other High Voltage (HV) electrical equipment experience aging and insulation degradation due to chemical, mechanical and electrical forces during their operation. Partial Dischargers (PD) are localized electrical discharges that develop within insulation systems of HV electrical equipment such as transformers. In transformers, PD occurs in different forms and various locations and insulation both internal and external of the transformer. PD originates as small pulses but tends to increase in size and intensity which can result in complete insulation failure. Monitoring partial discharges has proven to provide valuable information on the state of the insulation systems of the power transformer, allowing transformer operators to make calculated decisions for the maintenance and life of plant plans. This systematic literature review aims to systematically examine the use of machine learning techniques in classifying PD in transformers to present a complete indicator to allow for future research in the field. The systematic review surveyed a total of 81 research literature published from 2010 to 2023 that fulfilled a specific methodology which was developed as part of this study. The results revealed that supervised learning has been the most widely used Artificial Intelligence (AI) algorithm utilized, primarily in the form of Support Vector Machine (SVM). Regarding PD, the survey revealed that most researchers tend to use numerous types of PD and compare them to one another. Furthermore, the use of artificial PD defect models to simulate the occurrence of PD is widely used versus the use of actual power transformers. Most of the literature tends to not specify the physical characteristics of PD, such as the magnitude of PD, PD inception voltage and PD extinction voltage.

Given the widespread use of AI language models such as ChatGPT, there is an urgent need 1 to understand their political biases in different languages. This study aims to uncover such biases. To 2 investigate this issue, we applied IDRLabs’ Political Coordinates Test to ChatGPT based on GPT-4 3 in four languages - English, French, German, and Italian. The results confirm previous studies that 4 identified a left-libertarian bias in ChatGPT. There was remarkable consistency in political orientation 5 across all four languages, although minor variations were found. Differences in ChatGPT response 6 distributions across languages could indicate culturally determined differences in interpretation 7 of political questions. The results highlight the need to diversify and optimize data collection and 8 model training methods to ensure greater fairness and neutrality and minimize the influence of bias. 9 Our study highlights the importance of cultural differences in the processing and interpretation of 10 political information by AI models.

In this paper, we introduce a novel controlled quantum communication protocol utilizing a quantum random walk involving one sender, one receiver, and multiple controllers. Inspired by classical random walk theory, quantum random walk serves as the foundation of our proposed protocol. With this protocol, we demonstrate the ability to transfer any n- dimensional quantum state to any party facilitated by any m number of controllers. Furthermore, any of the (m+1) individuals has the freedom to accept the role of the receiver. Rigorous testing of the protocol’s performance is conducted through quantum state tomography. We have run different variations of the circuits in IBMQ QASM simulator. Additionally, we explore the effectiveness of a weak measurement-based protocol designed to mitigate amplitude-damping noise’s detrimental effects on quantum states. By analyzing fidelity versus amplitude damping noise-strength plots for scenarios with and without the weak measurement protocol, we provide valuable insights into its protective capabilities across various levels of noise. These find- ings illuminate the protocol’s potential applications in quantum information processing

The dynamic landscape of AI adoption and its impact on employment in Malaysia demands a multifaceted approach to navigate the challenges and harness the opportunities presented. While the government's commitment to fostering AI adoption through initiatives like the National AI Framework and the Malaysia Digital Economy Blueprint is commendable, it is imperative to address concerns regarding job displacement and ensure inclusive growth. This study identifies the pressing research problem of workforce readiness for the AI-driven future, emphasizing the need to explore strategies for reskilling and upskilling affected workers. By employing critical lenses such as Marxist, feminist, and postcolonial perspectives, scholars highlight the power dynamics and societal implications inherent in AI deployment, guiding the development of policies and interventions that prioritize workers' interests and address social inequalities. Moving forward, a comprehensive approach combining policy interventions, educational reforms, and inclusive strategies is essential to maximize the benefits of AI adoption while mitigating its potential risks. Longitudinal studies, interdisciplinary research, and comparative analyses will further advance our understanding and inform evidence-based policymaking in this evolving field. Ultimately, by prioritizing workforce preparedness, social equity, and sustainable development, Malaysia can position itself as a leader in the AI-driven global economy, fostering a resilient, inclusive, and prosperous society for all.

Scholars and policymakers see sustainability as depending on the interrelationships between the three pillars of the environment, society and the economy. However, it remains less clear how key stakeholders with a responsibility for educating the next generation perceive the concept of sustainability and act accordingly. In order to gain new insights, this research scrutinises participant perceptions on sustainability and climate change in two eco-schools in England for primary children (aged 5-11 years). Our case study involved individual interviews with classroom teachers and headteachers, group interviews with fourth- and fifth-year students (ages 10-12 years), and in-class observations. We also analysed data from student exercise books and photographs of school grounds to understand participants’ self-reported knowledge and perceptions of sustainability and climate change. Within a framework drawing on theories of science communication and transformative learning (a learning approach based on having challenging experiences), the results show that the integration of sustainability into the curriculum was limited and problematic. That is despite the fact that all students and teachers were aware of the environmental dimensions of sustainability, like climate change and the overuse of natural sources. These findings suggest that schools are no different to other institutional settings when it comes to dealing with the challenges of integrating sustainability into daily practice. We conclude that there is a need for in-service teacher education programmes to enable and motivate teachers to provide richer teaching-learning environments so as to enable effective learning in schools about sustainability and climate change.

Despite the recent availability of immune checkpoint inhibitors, not all patients affected by Non-Small Cell Lung Cancer (NSCLC) benefit from immunotherapy. The reason for this variability relies to a variety of factors which may allow the identification of novel biomarkers. Presently, a variety of biomarkers are under investigation, including the PD1/PDL1 axis, the tumor mutational burden, and the Microbiota. This latter is made by all bacteria and other microorganisms hosted in our body. The gut microbiota is the most represented and has been involved in different physiological and pathological events, including cancer. In this light, it appears that all conditions modifying gut microbiota can influence cancer, its treatment, and its treatment-related toxicities. The aim of this review is to analyze all conditions influencing the gut microbiota and, therefore, affecting response to immunotherapy, iRAEs and their management in NSCLC patients. The investigation of the landscape of these biological events can allow novel insights for optimal management of NSCLC immunotherapy.

Background: Atopic dermatitis (AD), a common dermatological condition, is often associated with significant economic and social burdens. Despite extensive studies globally, there is a gap in understanding the comprehensive impact of this condition in Romania. Objectives:This study evaluated the economic burden and quality of life impact of AD in Romania, considering both direct and indirect costs. Materials & Methods: A mixed methods cost of illness study was conducted from April to October 2023. A patient survey, expert reviews, and national health data analysis were used to assess direct medical costs (treatments, medical services), direct non-medical costs (transportation, accommodation), and indirect costs (productivity losses). Quality of life was evaluated using the EuroQol EQ-5D-5L instrument. Results: In 2022, the total cost of atopic dermatitis in Romania was 32.983.498 EUR for adults and 31.573.968 EUR for children. Adult costs included 24.534.603 EUR in direct and 8.429.884 EUR in indirect medical costs, with an average annual cost per patient of 447 EUR. Pediatric costs involved 15.026.781 EUR in direct and 16.516.410 EUR in indirect costs, averaging 290 EUR per patient annually. Conclusion: Care expenditure with AD management in Romania is substantially lower than in other countries, posing a significant economic burden, primarily driven by indirect costs. This study provides a foundation for future policy interventions and highlights the need for targeted management strategies for AD.

Background : Probiotics are composed of microorganisms that have positive effects on health when consumed in prescribed quantities. Lately, studies have attempted to establish this role of probiotics in enhancing health. Aim : The aim of this study was to compare the antifungal activity of 3 different probiotics against Candida albicans.Methodology :This was an in-vitro study. 3 types of commercially available probiotics - Bifilac, Pre pro HS and VSL-3 were cultured separately with Candida albicans and were placed under incubation. The maximum zone of inhibition was recorded for each of the probiotic solutions.Results : In this study, it was found that among the three probiotic products, Bifilac exhibited a maximum zone of inhibition of 13mm at concentration of 100µL against C. albicans, while Pre pro HS and VSL-3 showed zone of inhibition of 11mm at concentrations of 50µL and 100µL, respectively.Conclusion : Within the limits of the present study, This study could prove that probiotics can be considered as an alternative and safe treatment option for treatment of Oral candidiasis infections.

In order to search possible residual minor phases in Y-Ba-Cu-O family, powdered mixtures of Y₂O₃ + BaCO₃ + CuO and, independently, superconducting compound YBa₂Cu₃O_7-x have been treated in evacuated cells and elevated temperatures.YBa₂Cu₃O_7-x was reduced to YBa₂Cu₃O₅ by use of the special home designed Taconis-Knudsen vacuum device. A subsequent doping by oxygen converts produced insulator YBa₂Cu₃O₅ to semiconductor or metal YBa₂Cu₃O_5+x (0<x<0.3). In addition to YBa₂Cu₃O₅, 0.05 volume percent of the minor delafossite phase Y₂Cu₂O₄ was being spotted in the powder mixture 1/2 Y₂O₃ + 2BaCO₃+6Cu₂O, heated up to 818 °C in an inert gas atmosphere. An attempt to prepare the insulating bulk delafossite samples was successful, and subsequent doping by oxygen produces novel metallic phases.

A cell-surface ectonucleotidase CD39 mediates the conversion of extracellular ATP into immunosuppressive adenosine with another ectonucleotidase CD73. The elevated adenosine in the tumor microenvironment (TME) attenuates antitumor immunity, which promotes tumor cell immunologic escape and progression. Anti-CD39 monoclonal antibodies (mAbs), which suppress the enzymatic activity, can be applied to antitumor therapy. Therefore, an understanding of the relationship between the inhibitory activity and epitope of mAbs is important. We previously established an anti-mouse CD39 (mCD39) mAb, C₃₉Mab-1 using the Cell-Based Immunization and Screening (CBIS) method. In this study, we determined the critical epitope of C₃₉Mab-1 using flow cytometry. We performed the PA tag (12 amino acids)-substituted analysis (named PA scanning) and RIEDL tag (5 amino acids)-substituted analysis (named RIEDL scanning) to determine the critical epitope of C₃₉Mab-1 using flow cytometry. By the combination of PA scanning and RIEDL scanning, we identified the conformational epitope, spanning three segments of 275^th to 279^th, 282^nd to 291^st, and 306^th to 323^rd amino acids of mCD39. These analyses would contribute to the identification of the conformational epitope of membrane proteins.

Molecular docking, a computational method predicting how molecules bind to target proteins, is employed in this study to explore Polydatin's interactions with Ras-related protein Rab (Rab) proteins using the Mcule Database. This database validates the docking results and offers insights into the pharmacological properties of Polydatin. Notably, specific Rab proteins like Rab-5A, HRas, and Rab-18 exhibit robust interactions with Polydatin, indicated by favorable binding energies.These findings illuminate the potential roles of Polydatin in therapeutic and biological contexts, emphasizing its interaction with Rab proteins. The Mcule Database enhances the credibility of the study, providing additional perspectives on Polydatin's applications. The observed interactions, particularly with key proteins like HRas, present a promising avenue for modulating cellular signaling pathways. This modulation holds therapeutic potential, potentially addressing conditions associated with pathway overactivity, including specific cancer types.

This qualitative research explores the impact of Supplier Relationship Management (SRM) on e-commerce brand reputation, focusing on the strategic imperatives, operational challenges, and economic implications within digital supply chains. Trust emerges as a foundational element in effective SRM practices, facilitating consistent product quality, timely delivery, and consumer trust, thereby enhancing brand credibility and loyalty. Strategic alignment of SRM with organizational goals enables e-commerce brands to leverage supplier capabilities for mutual growth and innovation. Operational challenges such as technological constraints and logistical complexities underscore the importance of digital integration and proactive risk management strategies to optimize SRM effectiveness. Digital technologies, including AI and blockchain, play a transformative role in enhancing transparency, efficiency, and resilience in SRM strategies, enabling e-commerce firms to mitigate risks and capitalize on market opportunities. Emotional intelligence (EI) competencies among supply chain professionals facilitate effective communication, conflict resolution, and sustainable supplier relationships that bolster brand reputation resilience. Economically, effective SRM practices yield cost efficiencies, improved financial performance, and resilience against supply chain disruptions, supporting sustainable growth in competitive digital markets. This study contributes valuable insights for practitioners and scholars aiming to enhance SRM strategies, optimize supplier relationships, and strengthen brand reputation management in e-commerce. By prioritizing trust, strategic alignment, digital innovation, EI competencies, and proactive risk management, e-commerce brands can navigate complexities, capitalize on opportunities, and foster positive brand reputations in the evolving landscape of digital commerce.

The bivalve mollusc Pinna nobilis, endemic to the Mediterranean Sea, since 2016 is vanishing from the whole basin because of an infection by multiple pathogens that caused mass mortality events. In the Eastern Mediterranean some little populations seem to be resistant to the infection. These individuals could represent the only possibility for the species to recolonize now desert habitats. Thus, according to the recommendations of IUCN, the looking for living specimens of P. nobilis is a priority. As a result of a monitoring activity carried out in 2022-2024, along the Ionian cost of Apulia, in Southern Italy, the species has completely disappeared leaving only empty shells. Concurrent to the vanishing of P. nobilis, its congeneric P. rudis, resistant to the infection, seems to be taking advantage, becoming more common and colonizing habitats once exclusive of P. nobilis. In fact, from different areas of the Mediterranean, the sightings of P. rudis are increasing, together with the discovering of individuals exhibiting morphological traits that are a mixing between the two species. In some cases, these morphological features are not easy to detect, anyway the presence of these hybrids, resistant to the infection, is important for the conservation of the species.

Multilingual classrooms are becoming more common worldwide due to increasing mobility. Teaching foreign languages using the mother tongue in multilingual classrooms has become problematic; some of the significant concerns in Japanese language acquisition – such as the high contextuality of the language, vocabulary and kanji learning – make the direct teaching method confusing for many students, especially at the beginner and elementary levels. This article provides the preliminary results of a study carried out as a component of a more extensive empirical research project on instructional languages in classrooms teaching Japanese as a foreign language. It aims to gather, analyse and synthesise data from a survey conducted at a supranational level. The results of the first two sections of a questionnaire containing closed and open-ended questions were used for a quantitative analysis of the profile of Japanese language instructors with a focus on finding possible connections. The wide geographic distribution of the respondents (274 teachers from fifty-seven countries and regions) made it possible to collect data from a more representative sample and draw meaningful conclusions about the spread of multilingual classrooms that would be impossible in smaller-scale research. The study’s primary results show the proportion of multilingual classrooms among countries in different regions and across different educational stages, the distribution of Japanese language instructors in various educational stages depending on the highest level of formal education attained, and the proportion of part-time instructors working in different academic institutions.

Frost heave, which is the volumetric expansion of frozen soil, has great ecological significance, since it creates water storage spaces in soils at the beginning of the growing season in cold temperate forests. To understand the characteristics of frost heave in seasonally frozen soil and the factors that impact its extent, we investigated the frost heave rates of forest soil from different depths and with different soil moisture contents, using both lab-based simulation and in situ measurement in a broadleaved Korean pine forest in the Changbai Mountains (northeastern China). We found that frost heave was mainly affected by soil moisture content, soil type, and gravitational pressure. Frost heave rate increased linearly with soil moisture content, and for each 100% increase in soil moisture content, the frost heave rate increased by 41.6% (loam, upper layer), 17.2% (albic soil, middle layer), and 4.6% (loess, lower layer). Under the same soil moisture content, the frost heave rate of loam was highest, whereas that of loess was lowest, and the frost heave of the uppermost 15 cm, which is the biologically enriched layer, accounted for ~55% of the frost heave. As a result, we determined the empirical relationship between frost heave and freezing depth, which is important for interpreting the effects of frost heave on increases in the storage space of forest soils and for calculating changes in soil porosity.

Laser Powder Bed Fusion (L-PBF)-based manufacturing is a growing area of research, with a particular focus on evaluating constraints in printed products. This research emphasises the importance of considering various factors—including mechanical properties and support structures—that influence the final product during the design phase. Specifically, the design of microchannel heat exchangers is highlighted, where all constraints are critical. This article will present a method for analysing channel pressure limitations and report findings about mechanical properties loss from examining tube porosity. This includes a summary of results and the establishment of an experimental device system.

Background: Peripheral Artery Disease (PAD) affects tens of millions of Americans, and is characterized by the accumulation of plaque within arteries in the arms and legs. Advanced age is a risk factor for PAD, and early recognition through health literacy is imperative to mitigate limb loss. The influence of health literacy on PAD outcomes remains poorly understood, particularly in minoritized communities. The objective of this study is to examine understanding of PAD within San Francisco’s Chinese-speaking population, while discerning preferences regarding PAD educational methods. Material and Methods: An anonymous 14-question survey in Mandarin and English was designed to gauge basic knowledge of PAD and preferred methods for obtaining health information. It was distributed at San Francisco Chinatown health fairs on May 6 and October 14, 2023. Inferential analysis was concentrated on the associations between age, self-reported gender and English proficiency versus PAD baseline knowledge as well as preferred methods to obtain health information. Results: Combining the two surveyed cohorts, 32% of the survey population (29/91) reported prior knowledge about PAD. individuals aged 65-74 were the most prevalent age group. 29/91 had prior knowledge of PAD. While many survey respondents thought that diabetes was a leading cause for PAD (47/76) and arthritis was thought to be the leading symptom related to PAD (32/65). Half of respondents (44/88) expressed a preference for consulting a family doctor for foot wounds, followed by a podiatrist (27/88) and a general surgeon (19/88). The two cohorts that were surveyed had similar and suboptimal understanding of PAD and its complications which confirmed a health literacy gap among the Chinese-speaking population. The top three methods to receive health information were radio, television and newspaper. Interestingly, 65 years old and above (OR 0.124, 95% CI [0.021- 0.0728]) as well as participants who were proficient in English (OR 0.078; 95% CI [0.012-0.52]) were less preferred using radio to receive health information. Male gender was statistically significant to use newspapers to receive health information as opposed to female (p=0.003, OR 5.26; 95% CI [1.76-15.69]). Discussion: Conclusion: The two cohorts that were surveyed had similar and suboptimal understanding of PAD and its complications, which confirmed a health literacy gap among the Chinese-speaking population in San Francisco. To address this gap, factors such as age, education level, and gender need to be better understood in their relation to the impact of learning methods in this population.

Paper presents proposition of possible extension of The Standard Model Classification of Particles and Fields in way to include gravitation into it.

Advanced Persistent Threats (APTs) are sophisticated cyberattacks aimed at stealing sensitive information or causing damage over an extended period. Detecting APTs is crucial for maintaining cybersecurity, and machine learning (ML) has emerged as a powerful tool in this domain. This paper explores the role of ML algorithms in detecting APTs, comparing their accuracy and effectiveness. We evaluate various algorithms, discuss the challenges in achieving superior accuracy, and suggest strategies for improvement. Our findings highlight the potential and limitations of ML in APT detection, emphasizing the need for continuous advancements in this field.

This study examines the development of effective social media strategies for businesses, focusing on how small and medium-sized enterprises (SMEs) can leverage these strategies to boost revenue and sustain growth. In the context of a dynamic digital environment, SMEs face challenges in maximizing the benefits of social media platforms due to limited resources and expertise. Using the 2020 Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA), this study analyzed sixty-nine research papers to identify key strategies that contribute to successful social media marketing. The review considered factors such as content quality, engagement techniques, platform selection, and targeted advertising. The findings suggest that tailored strategies that align with business objectives and audience needs, coupled with consistent engagement and content relevance, lead to more significant revenue growth and customer retention. However, the lack of data on long-term impacts and industry-specific insights limits the generalizability of these findings. This study provides a foundation for SMEs to develop strategic social media marketing plans, but further research is needed to explore long-term effects and industry-specific variations.

Small and medium-sized enterprises (SMEs) face unique challenges that can be effectively addressed through the adoption of data mining and business intelligence (BI) tools. This systematic literature review scrutinizes the deployment and efficacy of BI and data mining technologies across SME sectors, assessing their impact on operational efficiency, strategic decision-making, and market competitiveness. Therefore, drawing from a methodologically rigorous analysis of 93 scholarly articles published between 2014 and 2024, the review elucidates the evolving landscape of BI tools and techniques that have shaped SME practices. It reveals that advanced analytics such as predictive modeling and machine learning are increasingly being adopted, though significant gaps remain, particularly shaped by economic factors. The utilization of BI and data mining enhances decision-making processes and enables SMEs to adapt effectively to market dynamics. Despite these advancements, SMEs encounter barriers such as technological complexity, high implementation costs, and substantial skills gaps, impeding effective utilization. Our review, grounded in the analysis of business intelligence tools used indicates that dashboards (31.18%) and clustering techniques (10.75%) are predominantly utilized, highlighting their strategic importance in operational settings. However, a considerable number of studies (66.67%) do not specify the BI tools or data mining techniques employed, pointing to a need for more detailed methodological transparency in future research. The predominant focus on the ICT and manufacturing sectors underscores the industrial context sector specific applicability of these technologies, with ICT accounting for 45.16% and manufacturing 22.58% of the studies. We advocate for targeted educational programs, development of user-friendly and cost-effective BI solutions, and strategic partnerships to facilitate knowledge transfer and technological empowerment in SMEs. Empirical research validating the impacts of BI and data mining on SME performance is crucial, providing a directional pathway for future academic inquiries and policy formulation.

Leukocyte migration is an essential function of innate and adaptive immune responses. Chemokines and their receptors control the migration system. The abundance of chemokines is controlled by atypical chemokine receptors (ACKRs), chemokine receptor-like molecules that do not couple to the G protein signaling pathways. Among them, ACKR4 regulates dendritic cell migration by controlling the ligands and is involved in tumor development in mouse models. Because no anti-mACKR4 mAb for flow cytometry has been reported, this study aimed to develop a novel monoclonal antibody (mAb) for mouse ACKR4 (mACKR4). Among the established anti-mACKR4 mAbs, A₄Mab-1 (rat IgG_2b, kappa), A₄Mab-2 (rat IgG_2b, kappa), and A₄Mab-3 (rat IgG_2b, kappa) recognized mACKR4-overexpressed Chinese hamster ovary-K1 (CHO/mACKR4) by flow cytometry. The dissociation constant (K_D) values of A₄Mab-1, A₄Mab-2, and A₄Mab-3 for CHO/mACKR4 were determined as 3.5 × 10⁻⁹ M, 6.0 × 10⁻⁹ M, and 1.6 × 10⁻⁹ M, respectively. Furthermore, A₄Mab-1 and A₄Mab-2 could detect mACKR4 by western blotting. These results indicated that A₄Mab-1, A₄Mab-2, and A₄Mab-3 help to detect mACKR4 by flow cytometry and western blotting and obtain the proof of concept in preclinical models.

This study investigates the effects of chanting Nam-Myoho-Renge-Kyo by a long-term Nichiren Shoshu Buddhist practitioner on sound frequencies (A), brain activity (B), and microbial metabolism(C).A. Analysis of sound frequencies: Spectral analysis of chanting revealed peaks at 8 Hz and 116 Hz, corresponding to the first Schumann resonance and a frequency linked to chloride ion movements, respectively. Additionally, five peaks (417, 528, 639, 741, 852 Hz) corresponding to solfeggio frequencies were identified. These frequencies are known to exert a positive impact on the human endocrine and autonomous nervous systems; improve survival of human brain cells; decrease anxiety in rats; reverse cognitive and endocrine deficits in zebra fish; reduce total concentration of reactive oxidative species in brain tissue. B. Study of Brain activity: Brain activity changes were measured using functional near-infrared spectroscopy. Prefrontal cortex activity increased modestly during chanting compared to a pre-chanting baseline, but significantly increased afterwards. This suggests a shift towards focused attention during chanting and enhanced activity afterward. C. Effects on microbial metabolism: Probiotic cultures exposed to chanting, either directly or indirectly, showed increased metabolic activity. This suggests local and non-local effects, similar to previously reported phenomena. Importantly, this study clarifies that the spiritual practice of Nichiren Shoshu Buddhism transcends mere neurophysiological explanations. While brain activity and other bodily processes may be correlated with the practice, the religious and spiritual experience of chanting Nam-Myoho-Renge-Kyoencompasses a mystical dimension that, though not contradicting science, cannot be fully explained by scientific methods alone.

We used light, electron microscope and teased fiber autoradiography to show that myelinating Schwann cells synthesize phospholipids and proteins in perinuclear cytoplasm and superficial cytoplasmic channels or Cajal bands (SCC/CB). Neither phospholipid nor protein syntheses occur in paranodal loops, Schmidt-Lanterman incisures or adaxonal cytoplasm. Autoradiographic studies also show that in support of local protein synthesis, RNAs move from the nucleus into perinuclear cytoplasm and out along SCC/CB. Unlike other phospholipids, phosphatidylinositol synthesis occurs extensively in axons. This finding indicates differences in the distributions of phospholipid synthesizing enzymes in smooth endoplasmic reticulum of Schwann cell processes and axoplasm.Autoradiographs of nerves at longer survival times show that tritiated phosphatidylcholine and fucose-labeled glycoproteins move from synthetic sites to outermost myelin lamellae. From there, phosphatidylcholine spreads inward reaching innermost lamellae of thicker sheaths in days. Coupled with retention of fucose glycoproteins in outer myelin lamellae is the presence of fucose glycoprotein synthesized during early myelination in inner lamellae of thicker sheaths, a result consistent with growth of myelin sheaths from inner lamellae out. In this review, we revisit these studies because they provide temporal and spatial perspectives needed in understanding dynamic aspects of myelinogenesis and how myelinating cells respond to injury and from altered gene expression.

We demonstrate how a rigorous mathematical approach can be used to extract a current CMB temperature of 2.7276 ±0.0723 K from the 580 type Ia supernova data points in the Union2 database. Using redshift formula: z = Sqrt(Rh/R_t) − 1, incorporated into our new CMB redshift prediction redshift formula, we find that the above current CMB temperature value is a remarkably good match for this particular data set. In addition, we can alternatively start out with the measured current CMB temperature and extract the Hubble constant from all 580 supernovae. We then get an estimated Hubble constant of H0 = 66.8711+0.0019 km/s/Mpc when using the CMB temperature from the recent Dhal et al study [1] in combination with redshifts from all 580 supernovae. Furthermore, we get an estimated Hubble constant value of H_0 = 66.8943+0.0287 km/s/Mpc when using the CMB temperature measured by Fixsen [2]. In summary, our simple model essentially perfectly matches all 580 type Ia supernova redshifts while extracting H_0 or T_0 values. The Hubble constant we extract by this method fits very well with the Hubble constant extracted from the CMB by other studies. We believe that our new approach likely solves the Hubble tension problem, at least inside R_h = ct cosmology.

The Jakarta LARP community, established in March 2020 and primarily active in Gelora Bung Karno, fosters inclusivity, diversity, and physical role-playing activities. It adheres to a policy of excluding ultra-nationalist ideologies and integrates new members despite language barriers. Bruhn (2011) defines this kind of community sharing the same goals, values, and lifestyles, while McMillan & Chavis (1986) outline four aspects of the sense of community.The research discovered that communities endure by utilizing solidarity built on the basis of their peculiarities. This study employed ethnographic methods alongside grounded research to identify the community's culture and their efforts to sustain and utilize it as community solidarity.

The C-C motif chemokine receptor 8 (CCR8) is highly and selectively expressed in regulatory T (Treg) cells and is associated with tumor progression. The massive accumulation of Treg cells into tumors suppresses the effector function of CD8⁺ cells against tumor cells. Therefore, selective depletion of Treg cells using anti-CCR8 monoclonal antibodies (mAbs) reinvigorates antitumor immune responses and improves responses to cancer immunotherapy. Previously, we developed an anti-mouse CCR8 (mCCR8) mAb, C₈Mab-2, using the Cell-Based Immunization and Screening (CBIS) method. In this study, the binding epitope of C₈Mab-2 was investigated using flow cytometry. The mCCR8 extracellular domain-substituted mutant analysis showed that C₈Mab-2 recognizes the N-terminal region (1–33 amino acids) of mCCR8. Next, 1×alanine (or glycine) scanning and 2×alanine (or glycine) scanning were conducted in the N-terminal region. The results revealed that the _17-DFFTAP_-22 sequence is important for the recognition by C₈Mab-2, and Thr20 is a central amino acid of the epitope. These results revealed the involvement of the N-terminus of mCCR8 in the recognition by C₈Mab-2.

AbstractBackground: Empowering Communities, Combating HIV/AIDS: The Indispensable Role of Community-Based Strategies in Awareness and PreventionObjective: Highlight the significance of community-based approaches in addressing the ongoing HIV/AIDS pandemic through enhanced awareness, reduced stigma, and effective prevention measures.Method: This article provides a theoretical analysis of community-based HIV/AIDS interventions, drawing upon key strategies and successful examples from diverse settings.Results: Community-based initiatives emerge as critical factors in empowering individuals and communities to take ownership of their health. Through culturally-sensitive education, accessible testing, and comprehensive support services, these initiatives effectively diminish the social stigma surrounding HIV/AIDS and promote preventative behaviors. Examples of successful programs illustrate the potential of community-driven approaches to achieve measurable reductions in transmission rates and improved health outcomes.Conclusion: The fight against HIV/AIDS necessitates robust community engagement. Community-based interventions, grounded in local knowledge and addressing specific needs, offer a promising pathway towards fostering health equity, reducing stigma, and ultimately curtailing the spread of the virus. Further research and investment in these initiatives are crucial to ensuring their scalability and sustained impact on global HIV/AIDS prevention efforts.

In the wake of increasing digital fraud, this paper introduces an innovative application of Artificial Intelligence (AI) in detecting fraudulent activities across finance, healthcare, and e-commerce sectors. It presents a detailed analysis of machine learning methodologies, specifically focusing on the advantages of supervised, unsupervised, and deep learning techniques. The paper addresses the challenges such as data imbalance, model interpretability, and ethical implications in AI-based fraud detection. It also discusses the necessity of high-quality datasets and advocates for the integration of traditional and advanced machine learning methods to enhance accuracy and adaptability in fraud identification. However, it acknowledges the limitations including computational demands and overfitting risks. The study underscores the importance of collaborative efforts between AI experts and industry professionals to develop ethical, efficient, and reliable AI solutions for fraud detection.

Gradual disability worsening in Multiple Sclerosis (MS) is indicative of confirmed progression that mostly involves progression independent of relapse activity (PIRA) and is associated with neurodegeneration. Insights from pathology reveal grey matter involvement (neocortex, hippocampus, spinal cord, and deep grey matter structures) and microglia activation in early stages of MS. Cortical lesions result in thinning and atrophy and are considered accurate predictors of disability progression and cognitive decline.Neuroimaging biomarkers, such as high-resolution 7T MR images and TSPO-PET, have been examined for the detection of cortical pathology linked to cognitive deficits and thus progression, but are limited in clinical practice due to availability, time constraints, and cost.Electroencephalography (EEG) emerges as a non-invasive, cost-effective tool that reflects cortical activity in MS. Its potential in monitoring cognitive impairment is explored by focusing on nonlinear EEG analysis. The MuST PREDICT^TM project aims to extract linear and nonlinear EEG features, investigating their role in predicting disease progression and optimizing treatment. The study employs retrospective, cross-sectional, and prospective designs, utilizing EEGs from various MS forms, cognitive assessments, serum/cerebrospinal fluid biomarkers and neuroimaging. The methodology involves time-based and spectral feature extraction, employing artificial intelligence classifiers and brain criticality-based approaches.The conceptual framework of an innovative modality is herein presented, that would EEG for early MS progression detection. Standardization of the methodology could lead to the creation of a digital tool for better prognostication and treatment strategy optimization.

Agriculture is the main economic activity of Mozambique and there is lack of information about the quality of agricultural soils. In this paper, five soils from the Manica and Sussudenga districts (Manica province) were sampled in the years 2021/2022 and 2022/2023 (before and after the rainy seasons), were subjected to an agronomical and environmental chemical analysis to assess its quality, either from the fertility and environmental contamination point of view. Standard analytical methodologies from external certified laboratories and local X-ray fluorescence measurements were used. All the studied soils are acidic (pH ranging from 4.5 and 5.4), have no salinity problems (conductivity ranging from 4.2 to 11.8 mS/m), have low amount of soil organic matter (0.90% to 1.81%), and soils from the Sussudenga district have a very low cation exchange capacity (CEC) (average of 3.33 cmolc/kg), while those from Manica district ranges from very low to average CEC (3.59 to 13.11 cmolc/kg). Also, Sussudenga soils have a phosphorous deficiency (values ranging from &lt;20 to 38.5 mg/kg) and there are deficiencies and/or excesses of some macro and micronutrients in all soil samples. Manica soils are contaminated, apparently from geogenic origin, with Cr (280 to 1400 mg/kg), Co (80 mg/kg), Ni (78 to 680 mg/kg) and V (86 mg/kg). Agricultural soils monitoring must be fostered in Mozambique in order to improve food quality and quantity to assure economic and environmental sustainability.

Suitable soil structure and nutrient security are important for plant growth and development, characteristics of soil fractal dimension and distribution of physical and chemical properties and their interactions play an important role in studying the stability of soil structure and water and fertilizer cycles. As a sustainable management model, intercropping has positive benefits for erosion control, spatial optimization of resources, as well as improving system productivity. The effects of four intercropping methods on soil fractal dimension and physicochemical properties were investigated by intercropping Salvia miltiorrhiza with forage and S. miltiorrhiza with forest under typical karst rock desertification habitats in Guizhou. The results showed that soil nutrient content of intercropping was significantly higher than that of monoculture, the organic carbon content of soil grown under forest is higher than other treatments, and there was a non-significant change in soil water content of intercropping compared with monoculture. The soil fine-grained matter of intercropping was significantly higher than that of monoculture, while the soil fractal dimension showed a tendency to become larger with the increase of fine-grained matter. The intercropping planting, due to its component types and spatial and temporal configurations, leads to differences in soil water and fertilizer interactions, which can be combined with other ecological restoration measures to optimize the composite model and jointly promote the restoration and development of ecologically fragile areas.

The human malaria parasite, Plasmodium falciparum, accounts for more than 500,000 deaths worldwide per year. From 2000 to 2019, the malaria global death toll fell from 864,000 to 576,000 deaths; however, progress on reducing this toll has since slowed, with the COVID-19 pandemic contributing to an increase in the mortality rate since 2020. The emergence of widespread resistance in P. falciparum to first-line treatment drugs has highlighted the need for new antimalarial drugs, and smarter approaches, including better combination therapies and mass drug administration. Nevertheless, recent successes of the first malaria vaccine (RTS,S/AS01) and the approval of a second (R21/MM), have renewed the promise of widely applicable vaccines in the future. Since 2015, the Eastern Mediterranean Region has experienced an overall increase in malaria cases and deaths. In the Arabian Peninsula, while most of the Gulf Cooperation Council (GCC) countries have been declared free of indigenous malaria (Bahrain, Kuwait, Qatar, and the United Arab Emirates), malaria is still endemic in two GCC countries (Saudi Arabia and Oman) and its neighbors (Yemen). Furthermore, a number of factors threaten malaria control in this region, at the level of the parasite (antimalarial drug resistance), the mosquito (the emergence of highly invasive species), the environment (climate change and increasing average temperatures), and society (imported malaria). In this review, we assess the current world-wide status of malaria in terms of public health, prevention and treatment, before providing an Arabian regional perspective, with a critical evaluation of the malaria situation in the GCC countries.

The purpose of this paper is to explore the implementation of Retrieval-Augmented Gen- 14 eration (RAG) technology with open-source Large Language Models (LLMs). For this purpose, a 15 web-based application named PaSSER that integrates RAG with three models - Mistral:7b, 16 Llama2:7b: Orca2:7b, is developed. In its development various technologies are used, including 17 Blockchain for managing and storing assessment results of built-in testing modules. PaSSER em- 18 ploys a set of evaluation metrics for LLMs’ performance - METEOR, ROUGE, BLEU, Perplexity, 19 Cosine similarity, Pearson correlation, and F1 score. The specialized knowledge base is in the smart 20 agriculture domain. As an illustration, the paper presents results and analysis of two tests. First one 21 is assessing the performance of LLMs across different hardware configurations, the other is deter- 22 mining the model that delivers most accurate and contextually relevant responses within the RAG. 23 The paper also discusses the integration of blockchain technology with LLMs to manage and store 24 assessment results within a blockchain environment. The tests revealed that Mistral:7b demon- 25 strates the best results across most of the evaluated metrics. The discussion section comments on 26 technical and hardware considerations that affect the performance of LLMs. Future development 27 will focus in leveraging new larger pre-trained open-source LLMs, exploring different finetuning 28 approaches, and further integration with Blockchain and IPFS.

Background. Azithromycin (AZM) is widely used in cystic fibrosis (CF) treatment following clinical trials demonstrating improved lung function and fewer pulmonary exacerbations. While the precise mechanisms remain elusive, immunomodulatory actions are thought to be involved. We previously reported impaired phagocytosis and defective anti-inflammatory M2 macrophage polarization in CF macrophages. This study systematically analyzed the effect of AZM on the functions of unpolarized and M1/M2 polarized macrophages in CF. Methods: Monocytes, isolated from the venous blood of patients with CF (pwCF) and healthy controls (HCs) were differentiated into monocyte-derived macrophages (MDMs) and infected with P. aeruginosa. P. aeruginosa uptake and killing by MDMs in the presence or absence of AZM was studied. M1 and M2 macrophage polarizations were induced and their functions and cytokine release were analyzed. Results: Following AZM treatment, both HC and CF MDMs exhibited a significant increase in P. aeruginosa uptake and killing, however, lysosomal acidification remained unchanged. AZM treatment led to higher activation of ERK1/2 in both HC and CF MDMs. Pharmacological inhibition of ERK1/2 using U0126 significantly reduced P. aeruginosa uptake in HC MDMs. M1 macrophage polarization remained unaffected, however, AZM treatment led to increased IL-6 and IL-10 release in both HC and CF M1 macrophages. AZM also significantly increased the phagocytic index for both pHrodo E. coli and S. aureus in CF M1 macrophages. In CF, AZM treatment promoted anti-inflammatory M2 macrophage polarization, with an increased percentage of CD209+ M2 macrophages, induction of the M2 gene CCL18, along with its secretion in the culture supernatant. However, AZM did not restore endocytosis in CF, another essential feature of M2 macrophages. Conclusions: This study highlights the cellular functions and molecular targets of AZM which may involve improved uptake of both gram-positive and gram-negative bacteria, restored anti-inflammatory macrophage polarization in CF. This may in turn shape the reduced lung inflammation observed in clinical trials. In addition, we confirmed the role of ERK1/2 activation for bacterial uptake.

This study centered on examining 60 different antibiotics by assessing their primary toxicity parameters through the pkCSM database. The parameters included Max. Tolerated Dose (Human) (log mg/kg/day), Oral Rat Acute Toxicity (LD50) (mol/kg), Oral Rat Chronic Toxicity (LOAEL) (log mg/kg_bw/day), and Minnow Toxicity (LC50) (log mM). The results highlight four potential antibiotics—cefotaxime, ceftriaxone, imipenem, and meropenem—that exhibited lower toxicity effects compared to other compounds. These findings imply a relatively favorable safety profile for these antibiotics concerning the specified toxicity parameters. However, it is crucial to interpret these outcomes within the context of the specific clinical application and to consider additional factors for a comprehensive safety assessment. It's essential to acknowledge that evaluating toxicity is intricate, and the determination of what is considered "less toxic" can rely on specific criteria and the context of use. Furthermore, individual responses to antibiotics may vary, and the selection of an antibiotic should be based on diverse factors, including the specific infection being treated, the patient's health, and potential side effects.

In recent years, digital transformation has become a pivotal factor for Small and Medium-sized Enterprises (SMEs), empowering them to adopt emerging technologies for competitive advantage. This systematic literature review examines 60 research papers published between 2014 and 2024, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to explore the impact of digital technologies such as artificial intelligence, cloud computing, blockchain, and the Internet of Things (IoT) on SME performance. The review focuses on key drivers and barriers, including operational efficiency, customer engagement, and market expansion. Methodologies employed in the analyzed studies include quantitative approaches (60%), such as surveys and structural equation modelling, qualitative methods (25%), including interviews and case studies, and mixed methods approaches (15%) that combine both. A geographic distribution analysis highlights contributions from various countries, revealing regional research gaps. The results show that digital technologies have significantly improved SME operations, but challenges like limited resources and strategic focus persist. Additionally, the dominance of quantitative methods indicates a potential bias, suggesting the need for more diverse methodological approaches to fully understand the transformative potential of digital technologies for SMEs.

Metallothioneins are multifunctional proteins implicated in various cellular processes. They have been used as biomarkers of heavy metal exposure and contamination due to their intrinsic ability to bind heavy metals and their transcriptional response to both physiological and noxious metal ions such as cadmium (Cd) and mercury (Hg). In this study, we aimed to clarify the role of iron and reactive oxygen species (ROSs) in the induction of the metallothionein system (Mtt) in the ciliate protozoan Tetrahymena thermophila. We investigated the relative mRNA abundances of the metallothionein genes Mtt1, Mtt2/4, and Mtt5, revealing for the first time their responsiveness to iron exposure. Furthermore, by using inhibitors of superoxide dismutase (SOD) and catalase (CAT), alone or in combination with iron, we highlighted the roles of superoxide ion and endogenous hydrogen peroxide, as well as the complex interplay between the metal and ROSs. These results enhance our understanding of the metallothionein system in ciliates and suggest that ROSs may be a primary evolutionary driver for the selection of these proteins in nature.

IT strategic planning is a critical driver of performance for small and medium-sized enterprises (SMEs), improving operational efficiency, decision-making, and innovation. This systematic review synthesizes findings from eighty studies, highlighting the varied impacts of IT strategic planning on SME performance. Quantitative analysis was used in 45% of studies, while thematic analysis was applied in 25%, showing the growing preference for data-driven insights. Cloud-based solutions, adopted in 11.25% of cases, and hybrid IT setups (17.5%) were identified as key enablers of flexibility and cost-efficiency. SMEs aligning IT strategies with business goals reported up to 20% reductions in operational costs and 15% improvements in customer satisfaction. However, 60% of studies identified resource constraints as a major challenge to IT adoption. Despite these barriers, 60% of studies demonstrated that effective IT planning enhances long-term sustainability and competitive advantage. This review provides actionable insights and strategic frameworks to guide SMEs in optimizing their IT investments for sustainable growth.

This paper explores the application of machine learning (ML) in predicting functional recovery in patients with ischemic stroke. As technology advances, ML shows significant potential in the field of stroke medicine, especially in the areas of big data analytics and personalized medicine. Studies have shown that ML algorithms can improve the accuracy of stroke image analysis, subtype classification, risk assessment, treatment guidance, and prognosis prediction. However, the widespread use of ML still faces challenges such as data standardization, model validation, privacy, and bias. This paper reviews the current application status of ML in the field of stroke, discusses the challenges faced, and looks forward to the future development direction, aiming to promote the practical application of ML technology in the diagnosis and treatment of stroke to improve the prognosis and quality of life of patients.

The COVID-19 pandemic has revealed significant disparities in disease severity and mortality across various population groups and regions. These disparities cannot be attributed solely to healthcare systems, socioeconomic factors, living conditions, or individual immune landscapes; environmental factors also play a crucial role. This work explores how variations in nasal morphology related to age, gender, and race can affect the respiratory system's ability to filter, warm, and humidify inhaled air. These variations may contribute to differences in how cold, dry, polluted, and infected air impact the respiratory system, thus influencing the observed patterns of disparities in COVID-19 outcomes. The analysis suggests that individuals with genetically determined wider nasal cavities, often found in populations originating from warmer climates, have decreased filtration and air conditioning efficiency, leading to increased susceptibility to respiratory infections and disproportionately high mortality rates from COVID-19. Moreover, age-related changes in nasal morphology, such as increased nasal volume and involution atrophy of the mucosa, contribute to impaired air conditioning and filtration, thereby increasing the risk of severe outcomes in the elderly. Gender differences in nasal morphology, with men typically having larger nasal passages compared to women, are linked to poorer air conditioning and filtration, potentially contributing to the higher risk of severe COVID-19 in men. Preliminary statistical analyses of the U.S. population suggest that men with favorable nasal structures may have a longer life expectancy than those with less optimal nasal anatomy; however, this should be regarded as a hypothesis. The findings underscore significant gaps in our understanding and highlight the urgent need for further research to unravel the complex interplay between nasal morphology, respiratory system dynamics, and environmental factors in shaping disease susceptibility and outcomes.

Wicked problems, such as climate change, systemic poverty, and healthcare inequalities, rep- resent the most pressing and intractable challenges facing society today. We propose a novel approach that leverages complex systems theory as a comprehensive framework for understanding and tackling these challenges. By applying concepts such as state space analysis, attractors, and nonlinear dynamics, we offer a sophisticated toolkit for mapping the intricate web of interactions defining wicked problems and exploring potential interventions. Our approach moves beyond tradi- tional reductionist methods, embracing the inherent complexity and dynamism of these issues. We demonstrate how this framework can lead to more nuanced, adaptive, and effective strategies for addressing persistent societal problems. Despite the prevalence of wicked problems and the avail- ability of complex systems theory, practical applications of this approach remain uncommon. This paper aims to bridge this gap, illustrating the potential of complex systems theory to transform our engagement with intractable problems and pave the way for more robust, holistic solutions. By synthesizing insights from mathematics, physics, and social sciences, we present a transdisci- plinary approach that promises to revolutionize how we conceptualize and address society’s most challenging issues.

Cancer-specific monoclonal antibodies (CasMabs) that recognize cancer-specific antigens with in vivo antitumor efficacy are innovative therapeutic strategies for minimizing the adverse effects. We previously established a cancer-specific anti-human epidermal growth factor receptor 2 (HER2)monoclonal antibody (mAb), H₂Mab-250/H₂CasMab-2. In flow cytometry and immunohistochemistry, H₂Mab-250 reacted with HER2-positive breast cancer cells but did not show the reactivity to normal epithelial cells. In contrast, a clinically approved anti-HER2 mAb, trastuzumab, strongly recognizes both breast cancer and normal epithelial cells in flow cytometry. The human IgG₁ version of H₂Mab-250 (H₂Mab-250-hG₁) possesses compatible in vivo antitumor effects against breast cancer xenografts with trastuzumab despite the lower affinity and effector activation than trastuzumab in vitro. This study compared the antibody-dependent cellular cytotoxicity (ADCC) and complement-dependent cellular cytotoxicity (CDC) between H₂Mab-250-hG₁ and trastuzumab. Both H₂Mab-250-hG₁ and trastuzumab showed ADCC activity against CHO/HER2 and breast cancer cell lines (BT-474 and SK-BR-3) in the presence of human natural killer cells. Some tendency was observed that trastuzumab showed a more significant ADCC effect compared to H₂Mab-250-hG₁. Importantly, H₂Mab-250-hG₁ exhibited superior CDC activity in these cells compared to trastuzumab. Similar results were obtained in mouse IgG_2a types of both H₂Mab-250 and trastuzumab. These results suggest the different contributions of ADCC and CDC activities to the antitumor effects of H₂Mab-250-hG₁ and trastuzumab and indicate the future direction for the clinical development of H₂Mab-250-hG₁ against HER2-positive tumors.

Purpose: Investigation for the co-occurrence of two unrelated genetic disorders of muscular dystrophy and Prader-Willi syndrome (PWS) (OMIM # 176270) using joint whole genome sequencing (WGS). Methods: Trio WGS joint analysis was performed to investigate the genetic etiology in a proband with PWS, prolonged muscular hypotonia associated hyperCKemia, and early-onset obesity. The parents were unaffected. Results: Results showed maternal isodisomy uniparental disomy (UPD) in chromosome 15 expanding from 15q11.2 to 15q22.2, including PWS regions at 15q11.2-15q13. Maternal heterodisomy was detected from 15q22.2 to 15q26.3. A pathogenic variant, NM_000070.3(CAPN3):c.550del (p.Thr184fs), was identified at 15q15.1 in a heterozygous state in the mother that was homozygous in the proband due to maternal isodisomy. Conclusion: This is the first study of the concurrent molecular etiology of PWS and calpainopathy (OMIM # 253600) in the same patient. This report highlights the utility of joint analysis and need for the assessment of autosomal recessive disease in regions of isodisomy in patients with complex and unexplained phenotype.

We will demonstrate that the photon energy density parameter Ωγ of the universe can be derived exactly in Rh = ct cosmology. We find that it must be Ωγ = 1/5760π ≈ 5.52621330180192× 10^(−5). This indicates that there is no uncertainty in the radiation density within at least somesub-classes of Rh = ct cosmology.

IMPORTANCE: On December 10, 2021, the FDA published a Determination Letter, along with a Statistical Review and Evaluation Report, and concluded that under the non-informative prior, the local Bayesian optimal interval design (BOIN) design, in its revised form, can be designated fit-for-purpose for identifying the maximum tolerated dose of a new drug, assuming that dose-toxicity relationship is monotonically increasing. Although setting the BOIN design parameter p.tox = 1.4 * target.DLT.rate is recommended in almost all BOIN methodology articles and is the default value in the R package BOIN, it's unclear if the choice of p.tox should only depend on the target DLT rate and if certain range of p.tox could produce the same BOIN boundary table. DESIGN: In this simulation study, following parameters were varied one at a time, using R package BOIN, to explore their effects on the equivalence intervals of p.saf and p.tox: 1) target DLT rate, 2) n.earlystop, 3) cutoff.eli, 4) cohortsize, and 5) ncohort. And a simple 3+3 design was used as an example to explore equivalent sets of BOIN design parameters that can generate the same boundary table. RESULTS: When the early stopping parameter n.earlystop is relatively small or the cohortsize value is not optimized via simulation, it might be better to use p.tox < 1.4 * target.DLT.rate, or try out different cohort sizes, or increase n.earlystop, whichever is both feasible and provides better operating characteristics. This is because if the cohortsize was not optimized via simulation, even when n.earlystop = 12 and cohortsize > 3, the BOIN escalation/de-escalation rules generated using p.tox = 1.4 * target.DLT.rate could be exactly the same as those calculated using p.tox > 3 * target.DLT.rate, which might not be acceptable for some pediatric trials targeting 10% DLT rate. The traditional 3+3 design stops the dose finding process when 3 patients have been treated at the current dose level, 0 DLT has been observed, and the next higher dose has already been eliminated. If additional 3 patients were required to be treated at the current dose in the situation described above, the decision rules of this commonly used 3+3 design could be generated using BOIN design with target DLT rates ranging from 18% to 29%, p.saf ranging from 8% to 26%, and different p.tox values ranging from 39% to 99%. These BOIN parameters also need to satisfy a set of conditions. Meaning: This study demonstrates the importance of interpreting BOIN design parameter p.tox as an interval of toxicity rates that are considered too toxic, rather than one prespecified value that corresponds to the lowest toxicity probability that is deemed overly toxic. When designing a dose-finding trial using BOIN, it is important to perform simulation studies to identify equivalent sets of BOIN design parameters that can generate the same boundary table so that we can better compare the safety properties of different boundary tables.

We will apply Pascal’s hydrostatic pressure law to black holes and also to the Hubble sphere. Haug has recently demonstrated how hydrostatic pressure in water can be utilized to determine the Planck length. One can conceptualize the energy in the Hubble sphere as a type of superfluid, and if this is correct, then Blaise Pascal’s hydrostatic pressure formula may also be applicable to such a superfluid, even within the Hubble sphere. By employing the same method on the Hubble sphere and considering it as a Schwarzschild black hole with the critical Friedmann mass, we can estimate the Planck length based on its hypothetical hydrostatic pressure.

The presence of the mite, Varroa destructor, requires the use of acaricide treatments in most honeybee colonies. Amitraz is one of the most common acaricide substances used by beekeepers. Some Varroa mite populations have developed resistance to amitraz leading to a loss of efficacy of amitraz-based treatments. Two products, Apivar and Supatraz were applied in the same apiary. Both treatments are amitraz-based but have a different galenics. Thanks to field data, a dynamic model was used to simulate the action of Apivar and Supatraz on the mite population. We consider two parameters to compare the products: daily mortality rate and treatment duration. In the field, the percentage of efficacy between the two products was not significantly different but Supatraz kills mite faster and decreases 90% of the mite infestation in 28.4 days compared to 50.9 days for Apivar. Thanks to modelling, we showed the daily impact of the two different products on mite population. Supatraz has a higher daily mortality rate during the first two weeks than Apivar. Supatraz requires a lower efficacy to stabilize the varroa mite population due to its faster release of active ingredient than Apivar, needing a shorter period to achieve the same result. Depending on the model, Supatraz conserve a good efficacy for low resistant mites but not for high resistant mites. These results show that the comparison of the efficacy of two products with different characteristics (duration of treatment and daily mortality rate) should be analyzed with caution.

In the field of delta-sigma modulators, reducing system power consumption without sacrificing accuracy has become a challenge. The summing circuit, as the main part of the cascade-of-integrator-in-feedforward delta-sigma modulator, consumes a significant amount of power in active summing. On the other hand, passive summing circuits have low power consumption but can cause attenuation in the summing results. This article proposes a low-power multi-bit delta-sigma modulator based on passive and attenuationless summation scheme. The summation circuit achieves multiplication of the voltage signal carried on the summation capacitor through bidirectional sampling technique, compensating for the inherent attenuation caused by passive summation, thus eliminating the need for active OTA to achieve perfect summation. A 2-order delta-sigma modulator based on 4-bit 1-order passive noise-shaping (NS) SAR quantizer is designed to verify the scheme. By combining a passive NS SAR as the 4-bit quantizer and the third-stage integrator of the system, and incorporating an energy-efficient cascode floating inverter amplifier (FIA) structure as the core active OTA, these elements have significantly enhanced the energy-efficiency of the modulator. Simulation results show that at a supply voltage of 1.2 V and a bandwidth of 20 kHz, the SNDR reaches 102.62 dB, power consumption is only 148.32 μW, and Schreier figure-of-merit (FoM) of SNDR is 183.92 dB.

This qualitative research explores Supplier Relationship Management (SRM) within the context of subscription-based e-commerce models. Through in-depth interviews and thematic analysis, the study investigates key elements such as trust, collaboration, technology integration, sustainability, risk management, cultural alignment, innovation, cost management, globalization, supplier selection, and performance measurement. Findings reveal that trust is foundational in fostering reliable interactions and open communication between e-commerce businesses and suppliers. Collaboration emerges as crucial for innovation and operational efficiency, facilitating co-development and shared risk management. Technology integration, including AI and blockchain, enhances supply chain visibility and decision-making capabilities. Sustainability considerations drive businesses to engage with suppliers adhering to environmental standards, enhancing brand reputation and consumer loyalty. Effective risk management strategies mitigate disruptions and ensure supply chain resilience. Cultural alignment fosters harmonious partnerships and mutual understanding across diverse global markets. Innovation through supplier collaboration drives product development and market responsiveness. Cost management practices optimize operational efficiency and strategic supplier relationships. Globalization necessitates adaptive strategies to manage diverse cultural and regulatory landscapes. Strategic supplier selection and continuous performance measurement drive ongoing improvement and alignment with business objectives. This study contributes empirical insights and practical implications for enhancing SRM in subscription-based e-commerce, informing strategies to navigate challenges and leverage opportunities in a competitive marketplace.

BACKGROUND: Scoliosis is a widespread musculoskeletal disorder of bending and twisting of spine. In this medical ailment, spine curves to the side and even in severe cases it can twist and can take several bends. For the diagnosis and treatment of scoliotic patients, the Cobb’s angle is a critical marker of the body’s curvature. OBJECTIVE: There are many researches that have been conducted to automate the manual measurement of the angle and every investigation has their own limita- tions. METHODS: This paper presents a method for precisely measuring the Cobb’s an- gle using deep learning based techniques. Mainly it comprises of feature enhance- ment of augmented dataset, a bespoke code for landmark estimation on the spine, segmentation model based on the U-Net architecture and a custom code for Cobb’s angle measurement. These measured angles are then compared to the given angles for the segmentation on biomedical (X-ray) images. RESULTS: The findings demonstrate that the proposed technique offers an auto- mated and impartial way for precisely measuring this angle with an overall accu- racy of 97% and Root Mean Square Error (RMSE) of 4.99, which can lower the variability, facilitate early detection, accurate diagnosis, and monitoring of scolio- sis progression. Additionally, it aids in the treatment planning and evaluating treat- ment outcomes. CONTRIBUTION: By leveraging the applications of Cobb’s angle measurement, healthcare professionals can enhance the quality of care and can improve long-term outcomes for individuals with scoliosis.

South Africa (SA) is grappling with a severe food insecurity crisis exacerbated by socioeconomic challenges. This resulted in the Department of Basic Education (DBE) advocating for the establishment of vegetable gardens at schools. However, information on the suitability of the soils in SA for sustainably growing Beetroot is unavailable. This study aimed to assess the suitability of soils for sustainable Beetroot production at various schools in the Kgakotlou circuit. Four Secondary Schools were selected: Mothimako, Madipoane, Klaas Mothapo and Phomolong. A detailed soil survey was carried out to study soils in vegetable gardens. Soil samples were collected for laboratory analysis of physicochemical properties. The soil suitability was evaluated using the physicochemical properties of the soils and optimal soil requirements for Beetroot production. We found various soils in the schools, including Cambisols (Hutton and Nkonkoni), Leptosol (Glenrosa), Plinthosol (Westleigh), and Acrisols (Tshiombo and Tubatse). Portions of the gardens on Hutton and Westleigh soil were moderately suitable (S2), limited by electrical conductivity (EC). Meanwhile, portions of Tshiombo, Tubatse, and Nkonkoni were marginally suitable (S3), limited by pH and EC. Portions on Glenrosa soil at Klaas Mothapo and Phomolong were not suitable (NS) and were limited by shallow depth, soil pH and EC. The results reveal the importance of considering soil suitability before establishing vegetable gardens in school settings. Indexing soil suitability using the physicochemical properties of soil resources for sustainable beetroot production is proposed for schools in the Kgakatlou circuit and other parts of SA.

Knowledge of water availability in terms of spatial (i.e., depth) and temporal variability of soil water is essential to describe soil water infiltration and storage processes accurately. Therefore, performing simulations of soil water dynamics can be a valuable tool to evaluate different land uses and their impact on water availability. Consequently, this study aimed to model the volumetric soil water content (θ, cm3 cm-3) under agroforestry systems (AFS) with cocoa. For this purpose, the θ was monitored at different depths (0-20, 20-40, 40-60, 60-80, 80-100 cm) in the soil profile of four plots (20 × 50 m) of cocoa under agroforestry systems. Environmental conditions significantly influenced the water balance of the cocoa crop. There were seven moments when evapotranspiration (ET0) was higher than 5 mm d-1. During those moments, the environment exhibited higher PAR values of 1041 µmol m-2 s-1, very low atmospheric relative humidity (RHa) levels (around 45%), higher ambient temperatures of 32.2 ºC and a vapor pressure deficit of 1.6 kPa whose θ levels reached 0.32 cm3 cm-3 and a water balance of -5.7 mm, which presented negative values during most of the study period. The θ obtained from the HYDRUS-1D model presented a slight bias as a high level of coherence between the observed values, based on the model's goodness-of-fit estimators. The above provides accurate simulations of soil water content at various depths, much-needed information for management schemes for cocoa cultivation under agroforestry systems.

Field studies were conducted in 2016, 2017, and 2020 in south-central and the Coastal Bend regions of Texas to determine the effects of various biostimulants and soil additives on corn growth and yield. In south-central Texas, the use of pop-up fertilizer (9-30-0 + Zn) either alone or in combination with either 2% N, bifenthrin, or bifenthrin + pyraclostrobin resulted in the greatest corn vigor but a yield response was only noted with pop-up fertilizer alone at 28062 or 46771 ml ha-1 in one year. In the Coastal Bend region, leaf tissue analysis showed that only Fe was affected with the use of any soil additive. Bacillus licheniformis + bacillus megaterium + bacillus pumilus increased Fe leaf tissue content by 20% over the untreated check. Radicoat seed coating at 438 ml ha-1 reduced corn plant stand by 10% and Pseudomonas brassicaceanum reduced corn height when compared with the untreated check; however, no differences in test weight or yield from the untreated check were noted with any soil additive. Little if any impacts of the use of biostimulants or soil amendments were seen in these studies.

The unravelling of the human genome created new perceptions on the origin and evolution of diseases and for cancer in particular, it established the notion that neoplasia has been a companion of life since its ap-pearance on earth. It is not surprising that neoplasms, in various forms, develop in numerous species of animals and even in plants. Unmistakable accounts of cancer with clinical features as are understood today, begin in the 5th c. B.C. The principles and practice of the Hippocratic and Galenic tradition dominated cancer care virtually into the 20th century. Advanced sequencing technologies at the dawn of the 21st century, generate new ther-apeutic opportunities with immunotherapy, oncolytic virotherapy and gene transfer, the latter especially in cases of hereditary cancer.