The startup ecosystem in Peru has experienced remarkable growth in recent years, driven by technol- ogy and innovation across various industries. This article presents a web application developed in PHP, designed to centralize and facilitate access to information about startups, incubators, and investors in Peru. The methodology employed encompasses several stages, from establishing prerequisites to data preprocessing, model selection and training, rigorous evaluation, and the technical development of the platform. The application offers key functionalities such as exploring a comprehensive database of star- tups, detailed access to information about incubators and accelerators, identification of investors, and advanced analysis tools for market proposal prediction. Additionally, it integrates educational resources and allows users to suggest new startups through detailed surveys. The results demonstrate that the ap- plication improves access to relevant data, fosters collaboration among ecosystem actors, and supports informed decision-making. The platform also stands out for its security and privacy measures, as well as its ability to drive innovation and competitiveness in the market. In conclusion, the application has a sig- nificant impact on the Peruvian entrepreneurial ecosystem, and its continuous improvement is essential for the sustainable growth of startups in Peru and globally.

This study discusses the application of deep learning technology in network intrusion detection systems (IDS) and focuses on a new model named CNN-Focal. First, through the review of traditional IDS technology, it analyzes its limitations in dealing with complex network traffic. Then, the design principle of the CNN-Focal model is described in detail, which uses threshold convolution and SoftMax multi-class classification technology to effectively improve abnormal traffic detection's accuracy and efficiency. The experimental results show that CNN-Focal performs well on the open data set, demonstrating the potential and advantages of its application in the natural network environment and providing a new perspective and method for further research of deep learning in the field of network security in the future.

Objective: Repetitive transcranial magnetic stimulation (rTMS) shows promise as an aphasia treatment, however its physiological mechanisms in people with aphasia (PWA) remain largely unclear. Electroencephalography (EEG) could be used to explore rTMS mechanisms. The present study investigated the acute effects of rTMS in PWA on: 1) an EEG measure of language (N400 event-related potential); and 2) picture naming. Methods: Eleven people with chronic non-fluent or anomic aphasia (64 ± 10.13 years old; 8 males; 37.27 ± 20.64 months since stroke) received one session of 1-Hz rTMS and one session of sham to the right pars triangularis at 80% resting motor threshold (RMT). N400 mean amplitude and picture naming accuracy were compared before and after each session. Results: No acute changes were observed in N400 mean amplitude or picture naming accuracy as compared to sham (p > 0.05; BF₁₀ < 0.33). Conclusions: Results suggest that language networks underlying the N400 are not altered following one session of 1-Hz rTMS in PWA. Significance: Although EEG could be useful for assessing physiological mechanisms of rTMS in PWA, the N400 does not appear to be a viable measure of acute 1-Hz rTMS effects, at least when delivered at 80% RMT in a single session.

Background: Research articles are essential for describing new ideas, techniques, and procedures and consolidating concepts. It creates an opportunity to expose studies and publicly assimilate published knowledge. Therefore, one of them is the most used and accepted index to verify the author’s scientific impact is the H-index or Hirsch index. It provides a robust single metric that combines quality and quantity. Therefore, it presents disadvantages, (1) researchers with shorter scientific carriers may have fewer articles and citations than those with longer scientific life; (2) it allows scientists to receive citations even if no new papers are published; (3) it is only useful for comparing the better scientists in a field; (4) this indicator can never decrease; (5) it is only weakly sensitive to the number of citations received; (6) the major and minor (or no) contributor in the research gained equal h-index. Then, the H-index ignores the total of citations and the time of a researcher's experience (“scientific age” [sa]). Objective: Thus, the goal of this study was to present a new index system (GFsa index ©) based on the two variables completely forgotten (total of citations and “scientific age”), introducing a new terminology “scientific age”, to evaluate the best-ranked researchers in Dentistry. Methods: The AD Scientific Index was used, and all researchers included in this study were cited in the AD Scientific Index (2024) in the Dentistry field and had the Google Scholar page accessible for a consultation to manually obtain the total of citations and calculate the “scientific age”. The information was retrieved by two authors (G.V.O.F. and J.C.H.F.), who assessed the data twice to avoid errors or mistakes. For inclusion: a minimum H-index value of 30; for exclusion: (i) the author did not have a Google Scholar page; (ii) duplicated author; or (iii) presented any suspicious activities about manipulation of articles. Then, a dataset of all researchers who were reported in Dentistry was prepared (name, H-index, i10 index, and publications). The formula developed and applied was GFsa=(total number of citations)/(“scientific age)². The Pearson correlation statistically evaluated the data obtained; the confidence interval was 95%. Results: A total of 9,093 researchers in Dentistry were recorded, of whom 1,020 were included. Then, we collected all 1,020 researchers' Google Scholar data (publications, citations, and indexes), and each researcher's total citations and “scientific age” were registered and calculated. The mean “scientific age” registered was 34.18 ± 13.34 (standard error of mean = 0.4181). With this information and data collected, GFsa index © was calculated, presenting a minimum value of 0.2186 and a maximum of 154.8 (mean = 10.33 ± 11.82; standard error of mean = 0.3706). Then, the data was organized and sorted following the ranking obtained in the GFsa index ©. The Pearson correlation showed that the H-index had a weakly positive association with the researcher’s “scientific age”; this fact means that the H-index will increase according to the “scientific age” increase. Otherwise, a moderately negative correlation between GFsa and “scientific age” was demonstrated, whereby a higher GFsa © had a lower “Scientific age”. This assessment shows that the variables introduced were fairly used. Moreover, a positive correlation was observed between both indexes (H-index and GFsa). Conclusion: It is possible to conclude that the new variable reported (“Scientific age”) and using the total citations brought a fairer and more feasible evaluation among the researchers. Thus, we are launching the new GFsa index © and the new information named “Scientific age” for the first time in the literature. The future perspective of the GFsa © application will be (1) the evaluation of the 9,093 researchers in Dentistry (total initially registered), including more authors/researchers in the Dentistry field, (2) expanding the exposition of this new index for other areas of the knowledge, and (3) provide free access of all data through the website (http://gfindex.us.to).

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.

Epithelial barrier damage plays a central role in the development and maintenance of allergic inflammation. The rise in airways epithelial barrier permeability alters the tissue homeostasis and allows the allergen and other external agents penetration. Different factors contribute to barrier impairment, such as eosinophilic infiltrate and allergens proteases action: the eosinophilic cationic proteins and the allergens' proteolytic activity have a central role in contributing to epithelial damage. In the airways, the allergens proteases degrade the epithelial junctional proteins allowing the allergen penetration and the uptake by dendritic cells. The increase in allergen-immune system interactions induce the release of the alarmins and the activation of type 2 inflammatory pathways causing, or worsening, the main symptoms at skin, bowel and respiratory levels. We aim to highlight the molecular mechanisms underlying allergenic proteases-induced epithelial barrier damage and the role of immune response in allergic asthma onset, maintenance and progression. We further will explore clinical and radiological potential biomarkers of airways remodeling in allergic asthma patients.

Light is dispersive in gravitational field. It results in that, under the condition of strong gravitational field, the observed numbers, size, distance and spectrogram of a celestial object is different from the real ones. Therefore, the observed jets of the Sgr A* and M87 need be understood further with the orbit of the stars around them. And, the observed orbits of the S-stars around the Sgr A* is different from the real ones. It implies that the observed optical images of the distant celestial body with strong gravitational field need be further understood with other observations and physics laws to accurately and precisely know the real physical configuration of 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.

Millions of children in developing countries face preventable deaths due to inadequate vaccination and malnutrition, in part due to insufficient monitoring and the absence of official identification. A reliable fingerprint recognition system can be a practical solution to address this issue. However, the scarcity of longitudinal fingerprint datasets for young children leads to unresolved questions regarding the earliest age for fingerprint biometric use, the frequency of enrollment required for reliable recognition, and the methods to accommodate agerelated changes. A few recent studies introduced high-resolution fingerprint scanners and showed promising recognition performance for young children. However, these studies were conducted on a small dataset over a shorter period with limited diversity; further evaluation of their finding is essential. This study assessed the effectiveness of a high-resolution contactless scanner in a controlled, diverse longitudinal dataset of children (0-15 years). Our results indicate that infants can be enrolled at five days old and reliably recognized after two months with a TAR= 100% @ FAR = 0.1%, and children aged 4-15 years can be recognized after one year with a TAR= 98.72% @ FAR = 0.1%.

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.

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.

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 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.

Diabetic foot is a serious complication that poses significant risks for diabetic people. The resulting reduction in protective sensitivity in the plantar region requires early detection to prevent the imminent ulceration and amputation. The primary method employed for evaluating this sensitivity loss is the 10 g f Semmes-Weinstein monofilament test, commonly used as a first-line procedure. However, the lack of calibration in existing devices often introduces decision errors due to unreliable feedback. In this paper, a new equipment designed to address these limitations and enhance the assessment of the sensitivity loss using the 10 g f monofilament is introduced. The proposed system automates metrological verification and evaluation processes, ensuring accurate and reliable results. Furthermore, it simulates the practitioner’s procedure, serving both as a valuable training tool and a means to provide force-feedback information. An analytical model of the monofilament buckling and deflection is also presented. The obtained results showed that the tested monofilaments had a very high error compared to the 10 g f declared by the manufacturers. It is aimed, by introducing this advanced equipment, to improve the precision and reliability of assessing the sensitivity loss. The integration of automated verification, simulation capabilities, and precise measurements shows great promise for diabetic patients, reducing the likelihood of adverse outcomes such as ulceration and amputation.

This article conducts a bibliometric analysis focusing on the influence of globalization and technology in the academic context. Previous studies have focused on analyzing the relationship between the number of research citations and international collaboration, but no study has explored it in the scope of consumer behavior and retail. Leveraging the Scopus database, the research analyzes a significant corpus of 2,111 documents from 1971 to 2022. The findings reveal a distinct surge in academic output related to the topic of technology, reflecting a growing interest in understanding the interaction between technological factors and consumer behavior. The study also explores trends in international collaboration in publications as well as its impact on citation counts.

The retail industry's shift from traditional to online platforms has heightened the need for effective search engine optimization (SEO) strategies to improve digital marketing outcomes. SEO involves optimizing a website's technical and content features to increase its visibility in search engine results, potentially driving traffic, enhancing brand awareness, and boosting revenue. This systematic review aims to evaluate the impact of various SEO tactics on small and medium-sized enterprises (SMEs) in the retail sector, focusing on key performance metrics such as website traffic, search engine rankings, conversion rates, and overall business growth. A comprehensive search was conducted across major academic databases, including Google Scholar, Scopus, and Web of Science, resulting in 102 studies published between 2014 and 2024. The methodological approach utilized both narrative synthesis and quantitative analysis to evaluate the quality and findings of the selected studies, following PRISMA2020 guidelines. The analysis shows that effective SEO practices, such as robust content marketing, technical SEO enhancements, and precise keyword optimization, lead to significant improvements in digital performance metrics. Specifically, up to a 30% increase in website traffic was observed, and keyword optimization improved click-through rates by 20% to 40%. The impact on conversion rates was more variable, with improvements ranging from 10% to 25%, influenced by factors like SEO quality and market conditions. Furthermore, integrating SEO with digital marketing strategies within e-business models enhanced operational efficiency by up to 15% and increased revenue growth by 25%. However, biases in the literature, such as the selective reporting of positive outcomes, may affect the overall reliability of these findings. While SEO strategies effectively drive website traffic and improve search engine rankings, their direct influence on conversion rates is less consistent. A balanced approach, combining SEO practices with strategic IT alignment, is crucial for sustained business success in the retail industry. Future research should address the variability in conversion outcomes and investigate the conditions under which SEO tactics are most effective.

Cells respond to DNA double-strand breaks by initiating DSB repair and ensuing a cell cycle checkpoint. The primary responder to DSB repair is non-homologous end joining, an error prone repair pathway. However, when DSBs are generated after DNA replication in the G2 phase of the cell cycle, a second DSB repair pathway, homologous recombination, can come into action. Both ATM and ATR are important for DSB-induced DSB repair and checkpoint responses. One method of ATM and ATR working together is through the DNA end-resection of DSBs. As a readout and marker of DNA end-resection, RPA is phosphorylated at Ser4/Ser8 of the N-terminus of RPA32 in response to DSBs. Here, the significance of RPA32 Ser4/Ser8 phosphorylation in response to DNA damage, specifically in S phase to G2 phase of the cell cycle is examined. RPA32 Ser4/Ser8 phosphorylation in G2 synchronized cells is necessary for increases in TopBP1 and Rad9 accumulation on chromatin and full activation of the ATR-dependent G2 checkpoint. In addition, our data suggest RPA Ser4/Ser8 phosphorylation modulates ATM-dependent KAP-1 phosphorylation and Rad51 chromatin loading in G2 cells.

Glial cells provide physical and chemical support and protection for neurons. Secreted neurotrophic factors, scaffolds and vesicles regulate in addition to normal homeostasis, repair following neural tissue injury or degeneration. We previously demonstrated that human CNS glia have revascularization capacity by inducing endothelial cell sprouting and microchanneling driven by glial cell secreted components. The clinical potential of using secreted factors and vesicles generated by glial cells in conditioned media was shown by their re-normalization of pathological blood vessels and 3D tissue wound healing and remodeling capacity. To identify secreted components of glial cells and how CNS and PNS glial cells differentially regulate neural tissue homeostasis, disease and injury response, we developed a novel culture method for various types of glial cells that allow for isolating and characterizing secreted glial components, free of animal factors and contaminants normally associated with serum-based culture conditions. Our culture method will enable to identify secreted components using ultrasensitive RNA, glycoconjugate and protein analysis technologies. The characterization of glial secreted factors involved microchanneling may help in development of new clinical therapies for glial and neural cells or axons to migrate and reform connections.

The convergence of Class Incremental Learning (CIL) and Machine Unlearning (MU) is a rapidly developing field in machine learning, especially relevant in adaptive and privacy-sensitive environments like finance. CIL enables models to learn new data classes over time without losing previously acquired knowledge, while MU focuses on selectively forgetting specific data to comply with privacy laws or mitigate security risks. In this paper, we examine the theoretical foundations and practical applications of both approaches, particularly in the financial domain. We explore how these two paradigms interact and complement each other, discuss key algorithms, and present examples to illustrate their applications in areas such as portfolio management, fraud detection, and data privacy. Finally, we explore challenges and potential future directions in achieving optimal synergy between CIL and MU.

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.

Traditional chemical research often relies on trial-and-error synthesis and characterization methods. Now, modern machine learning (ML) offers data-driven approaches for predicting properties, like water solubility, directly from chemical structure. But with various data representation schemes for molecular structure and model approaches to select from, it can be difficult for non-experts to determine best practices for utilizing ML. To clarify this landscape of choices, this study uses the ESOL molecular solubility dataset to compare the performance of a selection of different models on different data representations. First, we compare three classical regression methods (linear, ridge, LASSO) on three common data representations (RDKit fingerprint, Morgan fingerprint, intuition-selected molecular features). Then, we demonstrate how two distinct deep learning approaches (multilayer perceptron, graph convolution) can achieve accurate predictions even when prior intuition about feature-property correlations are absent. Finally, we outline a modern attention-based approach, inspired by successes in language modeling and fine-tuned by Bayesian optimization, to achieve a prediction methodology that is more general and performant than the previous approaches. This attention-based approach operates directly on the common SMILES string molecular representation, without requiring as many model parameters as other deep learning approaches or preprocessing into a representative fingerprint or vector of intuitively selected features. In short, when selected molecular features are known to likely correlate with a feature of interest, it may be possible to achieve good predictive modeling without turning to massive deep learning approaches. When particular features are not known a priori, graph approaches are a common solution, and we further demonstrate how a modern hyperparameter optimized attention approach can perform even better.

We investigated and quantified bacterial accumulation in facemasks used by the general population, using 16S rRNA (Sanger Sequencing), culture and biochemical analysis along with Rose Bengal staining. Additionally, a systematic overview of the literature on face mask contamination was undertaken. We found an average bacterial load of 4.24 × 10⁴ CFU recovered/mask, with a maximum load of 2.85 × 10⁵ CFU. This maximum is 310 times higher than the limit value for contamination of ventilation system outlet surfaces specified by the German standard VDI 6022. Biochemical and molecular identification predominantly found Staphylococcus species (80%), including Staphylococcus aureus, along with endospore-forming Bacillus spp. Literature reports also indicate contamination of masks by bacterial and fungal opportunists of the genera Acinetobacter, Aspergillus, Alternaria, Bacillus, Cadosporium, Candida, Escherichia, Enterobacter, Enterococcus, Klebsiella (including K. pneumoniae), Micrococcus, Microsporum, Mucor, Pseudomonas, Staphylococcus and Streptococcus. Bacterial counts increase linearly with wearing duration, and prolonged use may affect the skin and respiratory microbiomes, promoting consequential eye, skin, oral and airway conditions. These aspects underscore the urgent need for further research and a risk-benefit analysis in respect of mask use, particularly given their unproven efficacy in disrupting the transmission of respiratory viruses and their adverse social consequences.

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.

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.

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.

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.

Background: Suicidal behavior has been a dilemma in Alzheimer's disease. Many ethical debates have been raised about euthanasia in patients with neurodegenerative diseases, and there is a finding of increased suicide rates in the early stages of dementia. However, little is known about the history of suicide ideations or attempts in people with Alzheimer's disease. We aim to measure questionnaire adequacy and acceptability in this specific study to investigate the history of behaviors and factors associated with suicidal behavior across the lifespan in patients with Alzheimer's disease. Methods: This study is an excerpt from case-control research, where the sample was calculated at 150 participants, 75 for the case group and 75 for the control group. Here, the descriptive statistics of the first third of the case group sample, 25 participants, were made. Adequacy was observed by comprehension of questionnaires, and acceptability was measured by participants' understanding of the study object.Results: The prevalence of suicidal ideation throughout life was 8%, and 4% attempted suicide at some point in their lives. The total score of QLoS had a median of 71, which demonstrated the consideration of a moderate quality of life. The total score of the SAF-quest had a median of 12, which showed a low suicidal behavior history. All patients were included in understanding the questionnaires.Conclusions: This study demonstrated the adequacy, acceptability, and scarcity of suicidality histories throughout life in Alzheimer’s disease patients.

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.

Vitamin D deficiencies are very common in pregnant women, raising concerns about adverse health outcomes in children. This issue has hardly been studied in multiple pregnancies, the prevalence of which has been steadily increasing. Therefore, our study investigated the relationship between newborns’ anthropometric parameters and the concentration of 25(OH)D in maternal blood of women with twin pregnancies and umbilical cord blood. The study included 50 women who gave birth after the 36th week of twin gestation. 25(OH)D was determined in maternal blood collected during the antenatal period and in the umbilical cord blood of 100 newborns. Anthropometric parameters of the newborns (birth weight, length, head and chest circumference) were obtained from hospital records. Data on nutrition and lifestyle during pregnancy were collected from the patients during an interview conducted by a dietitian. No relationship between maternal and neonatal cord blood vitamin D concentrations and any of the anthropometric parameters of the newborns was found. However, only 6% of the mothers and 13% of the newborns had vitamin D deficiency (≤20 ng/mL). The type of pregnancy and maternal height were the main factors associated with neonatal size. Newborns from dichorionic pregnancies were on average 202 g heavier (p<0.001) and 1 cm longer (p=0.006) than newborns from monochorionic pregnancies. Newborns of mothers ≤ 160 cm in height had on average 206 g lower birth weight (p=0.006) and were 3.5 cm shorter (p=0.003) compared to newborns of taller mothers. Therefore, in our study, neonatal size of the twins was not related to the vitamin D status but to other factors such as the type of pregnancy and maternal height.

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.

Background The use of antimicrobials to treat food animals leaves antimicrobial residues in foodstuffs. The World Health Organization (WHO) defines the acceptable daily intakes (ADIs) of these residues as the dose of these antimicrobials that is safe for an average human to consume on a daily basis. We hypothesized that the low dose of ciprofloxacin classified as safe by the WHO could select for ciprofloxacin resistant strains of Klebsiella pneumoniae in a Galleria mellonella model. Objectives We aimed to evaluate if the consumption of peri-ADI doses of ciprofloxacin could select for ciprofloxacin-resistant (Ser464Phe, GyrB, ciprofloxacin MIC of 4 µg/mL) compared to -susceptible (isogenic, ciprofloxacin MIC of 0,047 µg/mL) strains of K. pneumoniae in a Galleria mellonella model. Methods A model of K. pneumoniae infection in G. mellonella larvae was used for the experiment. The larvae were inoculated with K. pneumoniae followed by 10x ADI, 1x ADI, 1/10^th ADI, 1/100^th ADI, 1/1000^th ADI doses of ciprofloxacin. Isolation of K. pneumoniae colonies was then performed on selective agar plates with and without ciprofloxacin (1 µg/mL) plates. The proportion of colonies with ciprofloxacin resistance was then calculated for each group at 24 and 48 hours. Results A significant increase was seen in the proportion of resistance for the 1x ADI and 1/10^th ADI concentration on day 2 compared to the positive control. Conclusion We found that at 48 hours, there was an enrichment of K. pneumoniae colonies with ciprofloxacin resistance in the larvae receiving 1x ADI and 1/10^th ADI concentrations of ciprofloxacin. These results suggest that the ciprofloxacin MSC_select for K. pneumoniae in this model is 1/10^th of the acceptable daily concentration (ADI) dose of ciprofloxacin, which is equivalent to 0.239 ng/µl.

Emerging molecular and computational techniques may allow construction and computational treatment of dynamic digital twins on the scale of populations to individuals. This approach may pave the way for predictive, preventive, and personalized treatment. Because digital twins are independent of geographical location, this may also contribute to global equitable health.

Introduction and importance: Congenital bicuspid aortic valve (BAV) is a common cardiac malformation predisposing individuals to aortic stenosis, regurgitation, and aortopathy. This risk persists even after aortic valve replacement (AVR). Case presentation: We report a case of a male in his 60s with a history of BAV who underwent bioprosthetic aortic valve replacement (BVR) and ascending aorta/root repair in 2014. The patient was noncompliant with follow-up and presented nine years later with severe bioprosthetic aortic stenosis and an 8.0 cm ascending aortic aneurysm with no symptoms. Emergent redo BVR and aneurysm repair were successful. Clinical discussion: It should be emphasized that successful outcomes for BAV patients require a lifelong commitment to surveillance, even after surgical intervention. Noncompliance can lead to life-threatening consequences like bioprosthetic valve deterioration or progressive aortic dilation. Conclusion: This case highlights the importance of early BAV diagnosis for ongoing monitoring, accessible follow-up care after complex cardiac procedures, and addressing patient barriers to treatment adherence.

This study focused on the investigation of myeloperoxidase through a Molecular Docking approach with various natural compounds to identify potential substances for cardiovascular research. Recent research has linked elevated myeloperoxidase levels to the severity of coronary artery disease. The results of the docking analysis revealed Polydatin, Daidzin, Astringin, Ginkgetin, and Amentoflavone as promising compounds with excellent binding capacity. To complement this, toxicity prediction studies were conducted using pkCSM. Overall, all four investigated molecules (Amentoflavone, Daidzin, Ginkgetin, and Astringin) were found to be non-toxic, exhibiting high maximum tolerated doses (human) and favorable values for Oral Rat Acute Toxicity and Oral Rat Chronic Toxicity. Moreover, they showed non-AMES toxicity and non-hepatotoxicity. Astringin, in particular, stood out with the highest positive effects, including a high maximum tolerated dose (human) and favorable values for Oral Rat Chronic Toxicity, along with dual inhibition of hERG II and hERG I. These findings suggest the potential utility of these compounds in cardiovascular research with minimized unwanted effects.

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.

This study explores the relationship between positive and negative habits and their rewards over time. Using a bibliographic and documentary approach, we analyze how the temporality of rewards influences the formation and maintenance of habits. We developed the "habit chart", which shows that positive habits, such as reading, healthy eating, physical exercise, meditation and personal relationships, offer incremental and lasting rewards. In contrast, negative habits such as sugar consumption, excessive use of social media and a sedentary lifestyle provide immediate gratification followed by a sharp decline. The analysis was based on studies such as those by Warburton et al. (2006), Lally et al. (2010), and Hu (2003) for positive habits, and Malik et al. (2010) and Kross et al. (2013) for negative habits. We conclude that practical strategies, such as financial incentives and creating enabling environments, can make long-term rewards more tangible, promoting the adoption of healthy behaviors. This study reinforces the importance of the temporality of rewards in habit formation and suggests evidence-based interventions to improve quality of life and general well-being.

Passive marine listening, involving the acoustic monitoring of underwater environments, has become increasingly vital for scientific research, environmental monitoring, and defense applications. However, the success of such applications critically depends on the ability to extract meaningful information from the often noisy and dynamic underwater acoustic environment. In this context, Mel-frequency cepstral coefficients (MFCCs) have emerged as an essential tool for feature extraction, capturing the spectral characteristics of marine sounds and making them ideal for species identification and sound event detection. This research presents an innovative approach to enhance the robustness of MFCC-based passive marine listening through adaptive noise reduction techniques. The proposed approach utilizes dynamic spectral subtraction to counter underwater noise, resulting in enhanced signal-to-noise ratios for desired sounds. This adaptive system adjusts to dynamic underwater noise, allowing it to concentrate on target sounds and suppress interference effectively. The experimentation further validates the effectiveness of the proposed approach, with results reaching 99% on the full dataset of 570 mammals and vessels, demonstrating the effectiveness of SVM and Random Forests in the classification of underwater audio.

We will outline the relationship between luminosity distance and cosmological redshifts, demonstrating that it is consistent with a new cosmological model recently proposed by Haug and Tatum [1], which appears to resolve the Hubble tension within the Rh = ct cosmology.

A new data processing method based on Machine Learning (ML) algorithms has been developed and tested in clinical environments during COVID-19 trials at the Medical University of South Carolina (MUSC). Breath samples have been collected in 2020–2021 from 100 participants using gas chromatography combined with ion mobility spectrometry (GC-IMS, G.A.S. mbH). Polymerase chain reaction (PCR) tests combined with clinical diagnostics were used as a reference method. Once the ML training-testing cycle is completed, the diagnostics of a single data set may be completed in as little as a few seconds. A convincing difference in the Volatile Organic Compounds (VOS) breath biomarker signature with COVID-19 positive and negative participants has been found. This approach is a generic method that can be applied to data sets collected with other types of hardware, e.g., different settings of GC, GC-MS, GC-GC, or other methods providing high information density in 2-dimensional data sets.

During the development of new medicines mutually exclusive specificity appears to be required but hitherto is most often included at the late clinical trial stage. Here, I describe the use of Hedgehog signalling in Drosophila as a model to represent a mutually exclusive relationship within the context and report the identification of CG43658, a putative Rho guanine nucleotide exchange factor and orthologue of the Charcot-Marie-Tooth disease associated gene ARHGEF10. Furthermore, a Computer that is able to consider the whole and describe the sum of everything in it absolutely in unambiguous terms is a useful tool. Here I describe such a computer with a suggestion for an application to examine any component in the context where a medicine is sought. Taken, together I would like to propose this method as a fundamental necessity and improvement upon current existing methods related not only to Hedgehog signalling but plausibly to life science research and the search for medicines in general.

Corrugated board is a composite material characterized by strong orthotropy in both the elastic and plastic phases. Additionally, due to its layered structure, which consists of alternately arranged flat and wavy layers of paper, its mechanics becomes complicated. Therefore, a precisely selected set of laboratory tests is needed to correctly identify all the necessary material parameters of cardboard. The work attempts to define a full set of laboratory tests in order to determine material parameters describing the linear-elastic, orthotropic behavior of cardboard and the basic parameters defining popular plasticity criteria, such as the asymmetric orthotropic Hoffman criterion. For this purpose, sensitivity analysis and numerical models of several selected basic laboratory tests were used. Since the aim of the work is to choose appropriate laboratory tests necessary to correctly determine the material parameters of corrugated board (not its individual layers), the numerical models do not contain detailed cardboard geometry, but only its homogenized representation in the form of the flat shell models. All numerical models are based on the nonlinear finite element method and constitutive modelling in the form of UMAT subroutines implemented in the ABAQUS software. Based on the analyses, appropriate measurements and laboratory tests were indicated that are most sensitive to the material parameters sought.

Precision farming is transforming how antibiotics get used on farms. It fights the overuse that creates superbugs and harms nature. Farmers now have high-tech tools to use antibiotics ultra-precisely. Drones, AI, and advanced watering equipment help them pinpoint only sick plants and animals needing treatment. This shields crops while avoiding environmental damage. More careful antibiotic use makes livestock healthier and boosts harvests too. And it prevents resistant germs, where antibiotics stop working altogether. The approach helps farms in multiple ways, using fewer resources for even better results. There are some growing pains, like high costs for small farms. And questions around data privacy also cause concern. But overcoming these hurdles could help precision farming spread far and wide. This technology promises safer, greener, and more productive agriculture. Targeted use of antibiotics protects both crops and people from drug-proof germs. And nature benefits when less medicine spreads into soil and water. The future looks bright for farming with precision.

Artifact metrics is a technology for authenticating artifacts based on their unique characteristics. The artifact-metric system offers "clone resistance," i.e., it makes it highly improbable to create another object exhibiting the same measured values as a genuine artifact. However, determined adversaries may still attempt to create imitations or clones with close physical characteristics to those of registered products, even if they cannot perfectly replicate the genuine product. Such clones serve to deceive users into believing they are genuine rather than counterfeits. Thus, in this study, we consider a scenario in which we measure raster-scanning-generated clones via a non-raster scanning method. Further, we employ image processing techniques to generate image data representing the clones and theoretically assess the filtering effects on these images. Our findings reveal that applying filters to specific frequency components in the spatial frequency domain can effectively highlight differences between raster-scanning-created clones and the corresponding genuine artifacts. Thus, we demonstrate directional emphasis filtering in artifact metrics as an effective approach for rejecting raster-scanning-created clones.

There are evidences of shared biological mechanisms between obesity and hypertension during childhood in the adulthood, and loads of research literatures have proven that, it will cost the economies and health of nations profoundly if neglected. The Prevention and early diagnosis of cardiovascular risk factors such as overweight and hypertension is an essential strategy for control, effective treatment and prevention of its’ complications. The aim of the study is to assess the effect of school based Exercise and Life style Motivation Intervention (SEAL-MI) on adolescent's cardiovascular risk factors and academic performance. An experimental study was conducted among 1005 adolescents - 520 and 485 adolescents were randomly selected for control and study group, respectively. Demographic details and the data related to dietary habits, physical activity and sleep quality were collected by a structured interview questionnaire. The study group adolescents were given the SEAL-MI for six months which includes a school based rope exercise for 45 minutes per day for 5 days a week and a motivation intervention related to dietary habits, physical activity and sleep. Post-test 1 and 2 were done after 3 and 6 months of intervention. The prevalence of overweight among adolescents was 28.73% and prehypertension was 9.26%. Among overweight adolescents, the prevalence of prehypertension was found to be very high (32.25%). In post intervention, there was a significant reduction in weight, BP (p=0.000) and improvement in dietary habits, physical activity, sleep (p=0.000) and academic performance. A significant positive correlation was found between BMI and SBP (p=0.000) and BMI and academic performance (p=0.003). The linear regression analyses revealed that the gender (ß: 0.47, 95% CI: 0.39, 0.81), age (ß: 0.39, 95% CI: 0.17, 0.46), family income (ß: 0.2, 95% CI: 0.41, 0.5), residence (ß: 0.19, 95% CI: 0.01, 0.27) and type of family (ß: 0.25, 95% CI: 0.39, 0.02) had the strongest correlate with the BMI of the adolescents. Also, Mother’s education Mother’s education (ß: 0.35, 95% CI: 0.18, 0.59) had the strongest correlate with the SBP of the adolescents. In contrast, the DBP was negatively persuaded by age (ß: -0.36, 95% CI: 1.54, 0.29) and gender (ß: -0.26, 95% CI: 1.34, 0.12) of the adolescents. Regular practice of rope exercise and lifestyle modification such as diet, physical activity and sleep quality among adolescents prevent and control childhood CVD risk factors like overweight, hypertension. The SEAL-MI may lead to age appropriate development of adolescents and improves their academic performance and quality of life. Giving importance to the adolescents from urban habitat, from affluent nuclear family and catching them young will bring significant change in the disease burden.

Sodium valproate is a established antiepileptic drug of first choice, primarily used to treat epilepsy, but also bipolar disorder and prevent migraine headaches. Valproate increases concentrations of gamma-aminobutyric acid in the brain, due to inhibition of enzymes responsible for the catabolism of gamma-aminobutyric acid. Main goal of this research consisted in the optimization and development for a new sensitive spectrophotometric quantitative analysis method of sodium valproate from tablets in the Visible field. Following the quantitative color reaction of sodium valproate with diazonium salt of alpha naphthylamine in alkaline medium, a light yellow azo dye was formed and spectrophotometrically analyzed at the wavelength of 386 nanometers. The amount of pure sodium valproate found on pharmaceutical extended-release tablet was 492.578 miligrams compared to the official declared pure amount of 500 miligrams on prolonged-release tablet. Amount found was assigned to a percentage content of 98.5156 percent pure sodium valproate in a pharmaceutical tablet. Mean average percentage error (deviation) from the officially stated value was about 1.4844 percent, which was included within normal limits, below five percent maximum limit allowed by European and Romanian Pharmacopoeias rules. Spectrophotometric method was subjected to the statistical validation procedure and was successfully validated.

Color change is the most obvious characteristic of tomato ripening stage and an important indicator of tomato ripening condition, which directly affects the commodity value of tomato. To visualize the color change of tomato fruit in mature stage, a gated recurrent unit network with an encoder-decoder structure that dynamically simulates tomato growth and development with time-dependent lines using tomato color and shape as real-time data was proposed in this paper. Firstly, the .json file was converted into a mask.png file, the tomato mask was extracted, and the tomato was separated from the complex background environment, and the tomato growth and development data set was successfully constructed. Then, a network of gated recurrent units with encoder-decoder structure was constructed to predict the future growth trend of tomato under different greenhouse temperatures. The experimental results showed that for the gated recurrent unit network of encoder-decoder structure proposed, when the hidden layer number was 1 and hidden layer number was 512, a high consistency and similarity between the model predicted image sequence and the actual growth and development image sequence would be realized, and the structural similarity index measure was 0.746. It was proved that when the average temperature was 24.93℃, the average soil temperature was 24.06℃, and the average light intensity was 11.26 Klux, the environment was the most suitable for tomato growth. The environmental data-driven tomato growth model was constructed to explore the growth status of tomato under different environmental conditions, thus to understand the growth status of tomato in time. The statement provided a theoretical foundation for determining the optimal greenhouse environmental conditions to achieve tomato maturity. And it offered recommendations for investigating the growth cycle of tomatoes, as well as technical assistance for standardized cultivation in solar greenhouses.

The rapid advancement of 5G networks, coupled with the increasing complexity of resource management, traffic handling, and dynamic service demands, has underscored the need for more intelligent network optimization techniques. This paper comprehensively reviews AI-driven methods applied to 5G network optimization, focusing on resource allocation, traffic management, and network slicing. Traditional models face limitations in adapting to the dynamic nature of modern telecommunications, and AI techniques-especially machine learning (ML) and deep reinforcement learning (DRL)-offer scalable, adaptive solutions. These approaches enable real-time optimization by learning from network conditions, predicting traffic patterns, and intelligently managing resources across virtual network slices. AI's integration into 5G networks enhances performance, reduces latency, and ensures efficient bandwidth utilization. It is indispensable for handling the demands of emerging applications such as IoT, autonomous systems, and augmented reality. This paper highlights key AI techniques, their application to 5G challenges, and their potential to drive future innovations in network management, laying the groundwork for autonomous network operations in 6G and beyond.

Timely screening for breast cancer plays a pivotal role in early detection and treatment; however, Afghan refugee females face significant barriers that hinder their access to these life-saving screening services. Cultural norms, socioeconomic factors, proficiency with the English language, and the challenges of navigating foreign healthcare systems contribute to low screening rates within this population. The current review article seeks to identify barriers to breast cancer screening in the Afghan refugee population in the United States (US) and address possible measures to overcome these challenges.

We use the Stefan-Boltzmann law to derive and investigate what we will call the black hole CMB temperature luminosity. Interestingly, the CMB luminosity is always the same for every black hole at a luminosity distance equal to the Schwarzschild radius, which is fully valid in black hole cosmology, a topic that is actively discussed to this day. The CMB black hole luminosity is always the same , regardless of the mass of the black hole. This is in contrast to the Bekenstein-Hawking luminosity, which is linked to the Hawking temperature, that is mass dependent.

Based on the nonlinear failure criterion and modified tangential technique, the upper bound solutions of supporting pressure on the deep tunnel face were obtained under pore water pressure conditions. The influence of parameters on the supporting pressure and collapse range was investigated according to the unlimited block failure mechanism. It is found that the upper bound solutions of supporting pressure increase with the growth of the nonlinear coefficient and pore water pressure coefficient. The collapse range of the tunnel face scales out with the increase of the nonlinear coefficient and shrinks when increasing the pore water pressure coefficient. Moreover, with the increase of the nonlinear coefficient, the impact strength on supporting pressure and collapse range declines gradually. According to the calculating results, it is manifest that both the pore water pressure and nonlinear criterion factors have negative impacts on the stability of the tunnel face. Thus, more attention should be paid to these parameters to ensure face stability in deep tunnel construction.

In this article, we investigate both the Schrödinger and continuity equations in the presence of nonlocal potential that arising from electron-electron interaction and local one in the context of minimal uncertainty in momentum within commutative and noncommutative frameworks. Furthermore, the Frahn–Lemmer potential type is used. Interestingly, the combined effects of both the phase-space noncommutativity and nonlocality on the current density while considering a minimal uncertainty in momentum are examined. We find that the current density, as conventionally defined, does not fulfil the condition of current conservation, thus, a new definition of the current density that encompasses the aforementioned contributions is given. Thereafter, it is shown that the computed current using the new definition of the current density satisfies the current conservation. Noting that using both the $★$product and linear Bopp-Shift, the noncommutativity is inserted.

This study investigates the application of deep residual networks for predicting the dynamics of interacting three-dimensional rigid bodies. We present a framework combining a 3D physics simulator implemented in C++ with a deep learning model constructed using PyTorch. The simulator generates training data encompassing linear and angular motion, elastic collisions, fluid friction, gravitational effects, and damping. Our deep residual network, consisting of an input layer, multiple residual blocks, and an output layer, is designed to handle the complexities of 3D dynamics. We evaluate the network's performance using a dataset of 10,000 simulated scenarios, each involving 3-5 interacting rigid bodies. The model achieves a mean squared error of 0.015 for position predictions and 0.022 for orientation predictions, representing a 25% improvement over baseline methods. Our results demonstrate the network's ability to capture intricate physical interactions, with particular success in predicting elastic collisions and rotational dynamics. This work significantly contributes to physics-informed machine learning by showcasing the immense potential of deep residual networks in modeling complex 3D physical systems. We discuss our approach's limitations and propose future directions for improving generalization to more diverse object shapes and materials.

Complex-structured polymeric microparticles hold significant promise as advance in next-generation medicine mostly due to demand from developing targeted drug delivery. However, the conventional methods for producing these microparticles of defined size, shape and sophisticated composition often face challenges in scalability, reliance on specialized components such as micro-patterned templates, or have limited control over particle size distribution and cargo release kinetics. In this study, we introduce a novel and reliably scalable approach for manufacturing microparticles of defined structures and sizes with variable parameters. The concept behind this method involves the deposition of a specific number of polymer layers on a substrate with low surface energy. Each layer can serve as either the carrier for cargo or a programmable shell-former with predefined permeability. Subsequently, this layered structure is precisely cut into desired-sized blanks using a laser. The manufacturing process is completed by applying of heat to the substrate what results on sealing the edges of the blanks. The combination of the high surface tension of the molten polymer and the low surface energy of the substrate enables the formation of discrete particles, each possessing semi-spherical or other designed geometries determined by their internal composition. Such anisotropic microparticles are envisaged versatile applications.

Asphaltene precipitation and deposition pose significant operational challenges in both reservoirs and surface facilities by inducing formation damage. To understand the kinetics involved in asphaltene particle nucleation, growth, and aggregation, this study employed confocal microscopy and centrifuge-based methods under a unified set of experimental parameters. The acquired time-resolved particle size images were analyzed using MIPAR image processing software, while asphaltene mass yield was quantitatively evaluated through centrifugation. Experimental conditions were varied to study the effects of heptane concentration, asphaltene concentration, and shear rate. Notably, this investigation introduces, for the first time, the parameter of particle count as an additional variable influencing asphaltene mass production. This study aims to discern how particle count correlates with asphaltene mass yield over time. Results indicate that the minimum mean particle diameter was observed to be 2 microns, which progressively grew up to 80 microns. The mean particle diameter exhibited a positive correlation with both asphaltene concentration and shear rate, whereas an inverse correlation was seen with heptane concentration. Furthermore, the asphaltene mass yield was found to increase with greater asphaltene and heptane concentrations, as well as with higher shear rates. The relationship between mean particle diameter and mass yield was found to be nonlinear. In the early stages, a higher prevalence of small particles contributed to a greater total particle count, while at later stages, particle aggregation led to the formation of larger particles that influenced the overall asphaltene mass. The insights gained from understanding the relationship between particle diameter, count, and mass yield are crucial for advancing the fundamental science of asphaltene precipitation and deposition. These findings may also guide future research that relies solely on particle counts for assessing asphaltene deposition.

Polymeric particles made up of biodegradable and biocompatible polymers such as poly(lactic-co-glycolic acid) (PLGA) are promising tools for several biomedical applications including drug delivery. Particular emphasis is placed on the size and surface functionality of these systems as they are regarded as the main protagonists in dictating the particle behavior in vitro and in vivo. Current methods of manufacturing polymeric drug carriers offer a wide range of achievable particle sizes, however, they are unlikely to accurately control the size while maintaining the same production method and particle uniformity, as well as final production yield. Microfluidics technology has emerged as an efficient tool to manufacture particles in a highly controllable manner. Here, we report on tuning the size of PLGA particles at diameters ranging from sub-micron to microns using a single microfluidics device, and demonstrate how particle size influences the release characteristics, cellular uptake and in vivo clearance of these particles. Highly controlled production of PLGA particles with ~100 nm, ~200 nm and >1000 nm diameter is achieved through modification of flow and formulation parameters. Efficiency of particle uptake by dendritic cells and myeloid-derived suppressor cells isolated from mice is strongly correlated with particle size and is most efficient for ~100 nm particles. Particles systemically administered to mice mainly accumulate in liver and ~100 nm particles are cleared slower. Our study shows the direct relation between the particle size varied through microfluidics and the pharmacokinetics behavior of particles, which provides a further step towards the establishment of a customizable production process to generate tailor-made nanomedicines.

Human number understanding is thought to rely on the analogue number system (ANS), working according to Weber’s law. We propose an alternative account, suggesting that symbolic mathematical knowledge is based on a discrete semantic system (DSS), a representation that stores values in a semantic network, similar to the mental lexicon or to a conceptual network. Here, focusing on the phenomena of numerical distance and size effects in comparison tasks, first we discuss how a DSS model could explain these numerical effects. Second, we demonstrate that DSS model can give quantitatively as appropriate a description of the effects as the ANS model. Finally, we show that symbolic numerical size effect is mainly influenced by the frequency of the symbols, and not by the ratios of their values. This last result suggests that numerical distance and size effects cannot be caused by the ANS, while the DSS model might be the alternative approach that can explain the frequency-based size effect.

Within education, we make assumptions the correct manner of teaching a student, and with that, we assume further that regardless of the intellectual content that a student should engage with, the acculturation for the customary (and always rather traditional) means of religious apprehension for the purpose of an assumedly universal and beneficial role. This article instead promotes the view that such determinations are (1) unsuitable for modern society and (2) causal to a disjunction with a truly proper manner of education in the form of the matheticism – a term rather unfortunately applied in educational theory.

Star copolymer films were studied for confirming the role of film morphology in antimicrobial activity previously reported. With this scope, films of a two-branches copolymer, namely m-PEG-(MMA-ran-DMAEMA)2, containing ≈ 40 % in mol of non-quaternized 2-(dimethylamino)ethyl methacrylate, were produced by using spin-coating, drop-casting and casting deposition techniques, thus obtaining ultra-thin, thin and thick films, respectively. The morphology is generally flat for thin and thick films, but it becomes substrate dependent for ultra-thin films where the film planarization effect is not efficient. Mechanical properties of such films were investigated by Force Volume Maps in both air and liquid. In air, ultra-thin films are in the substrate-dominated zone and thus the elastic modulus E is overestimated, while E reaches its bulk value for thin and thick films. In liquid (water), E follows an exponential decay for all films with a minimum soaked time t0 of 0.37 and 2.65 h for ultra-thin and thin and thick films, respectively. After this time, E saturates to a value in average 92 % smaller than that measured in air due to films swelling. Such results confirm the role of morphology envisaged in literature, suggesting also an additional role of mechanical properties in the antimicrobial activity.

“Energy” is probably the most important term in science and technology. The reason is energy can be converted while it is overall conserved, and thereby correlates changes of a wide variety. Despite its successful use in many areas over more than a century, an analysis of current literature shows that definitions of what energy is and explanations why it is conserved are inconsistent. With regard to energy forms not even a definition was found, and examples given are often wrong. Looking at two interacting objects in the most simple situations, with energy conservation but without conversion, a consistent understanding of energy and its conservation can be derived. Based on it, the systematic set of energy forms with a clear definition of each is then presented.

Introduction: Parameters used for neonatal mortality calculation vary among publications. Mortality in < 33 weeks gestational age at birth display global variations across different healthcare units. Objective: This study aims to explore trends in neonatal mortality among infants born preterm in the different units in Latin America and the Caribbean utilizing the EpicLatino Network Database in comparison to Canadian Neonatal Network (CNN) 2022. Materials and Methods: This study focused on an eight-year period, with a particular attention to mortality rates during the pre-pandemic (2015-2019) and pandemic/post pandemic (2020-2022) periods. Survival rates from CNN 2022 were included in the comparison analysis. Logistic regression analysis was confined to the 2020-2022 period. Adjustments were made for factors including gestational age, small for gestational age (SGA), Snape II score, inborn/outborn status, and center. As major malformations could account for mortality differences among units. The incidence of major congenital malformations, as defined by CNN, was compared among deceased patients. Results: A total of 15,454 records from 2015-2019 and 10,711 records from 2020-2022 were scrutinized. Overall, there were no significant differences in mortality rates between the two time periods (p=0.22). Moreover, the originating unit during the 2020-2022 period significantly influenced all statistical computations. Conclusions: Survival rates among infants < 26 weeks of gestation in Latin America and the Caribbean are on an upward trajectory, with the healthcare unit playing a pivotal role in this outcome within the EpicLatino database. Major malformations do not seem to be a significant contributing factor. These findings underscore the imperative of implementing quality improvement initiatives to elevate neonatal care standards in Latin America and the Caribbean.

This study describes a comparative in vitro study of the toxicity behavior of zinc oxide (ZnO) nanoparticles and micro-sized particles. The study aimed to understand the impact of particle size on ZnO toxicity by characterizing the particles in different media, including cell culture media, human plasma, and protein solutions (bovine serum albumin and fibrinogen). The study used various techniques such as atomic force microscopy (AFM), transmission electron microscopy (TEM), and dynamic light scattering (DLS) to characterize the particles and their interactions with proteins. Hemolytic activity, coagulation time, and cell viability assays were used to assess ZnO toxicity. The results highlight the complex interactions between ZnO NPs and biological systems, including their aggregation behavior, hemolytic activity, protein corona formation, coagulation effects, and cytotoxicity. Moreover, the study indicates that ZnO nanoparticles are not more toxic than micro-sized particles, and the 50 nm particles resulted in general the least toxic. Furthermore, the study found that at low concentrations, no acute toxicity was observed. Overall, this study provides important insights into the toxicity behavior of ZnO particles and highlights that no direct relationship between nanometer size and toxicity can be directly attributed.

The adoption of enterprise social platforms (ESPs) has become increasingly important for small and medium-sized enterprises (SMEs) seeking to enhance communication, collaboration, and knowledge sharing in the digital economy. As digital transformation progresses, the role of ESPs in improving operational efficiency and long-term business performance warrants comprehensive evaluation. This systematic review aims to assess the impact of leading ESPs on SME performance, focusing on their capabilities to improve communication efficiency, interdepartmental collaboration, and cost-effectiveness while highlighting emerging trends such as automation and AI integration. A PRISMA-guided systematic review was conducted, analyzing 18 peer-reviewed studies published between 2014 and 2024. Inclusion criteria comprised English-language publications examining ESP adoption in SMEs, focusing on ESP features and their effects on operational and strategic outcomes. Quantitative analysis of the selected studies revealed that ESPs improve communication efficiency by 20% and enhance interdepartmental collaboration by 25%. While initial implementation costs pose challenges, ESPs contribute to a 15% reduction in operational costs over time. Additionally, trends in automation and AI integration suggest further potential for operational enhancements. The findings indicate that ESPs significantly enhance SMEs' competitiveness and sustainability, offering both immediate operational improvements and long-term strategic growth. Business leaders are encouraged to adopt ESPs to foster agility and innovation while ensuring robust data security measures.

According to Young Minds [1] ‘everyone gets angry sometimes’. Their website offers a number of de-escalating strategies, including staying calm, managing responses and setting limits to help young people who most deem resistive. Yet, Young Minds are not alone because such logical advice is ubiquitous in the literature about challenging behaviour despite the fact that in our experience when faced with high states of arousal, most young people tend to act first and deal with the consequences later. It’s not that they are stupid or non-caring, but they are human capable of great feats as well as stupidity. The same is true for any claims that solution focused (SF) conversations can put right the several decades of psychological theory suggesting resistance requires logic and better cognition. By giving the correct thinking skills, young people will walk away from risk, avoid physical confrontation, handle challenging situations like logical thinkers, and generally discount that the process of growing up is part of the challenge. It is with this in mind that over recent years in our current SF practice (with staff group supervision) we have examined the concept of ‘resistance’ and how it can be put to use as a process of collaboration. To do this, we have revisited SF theory that preoccupied many of its pioneers during the 1980’s and 1990’s and attempted to make it useful for the early 2020’s for professionals having conversations with young people, who, in traditional models, are labelled challenging. We want to introduce key differences of solution focused practice and how Anita started to formulate a 6D-SF model (details, dynamics, dimensions, dispositions, dislocations, descriptions) for contemplating how groups of professionals relate to each other and are triggered by challenging behaviour. We do not claim to have proof, logic or exactness on our side, but we’re happy to suggest how our resistance mirrors what many of the staff teams feel and describe when working through their work.

This study investigates the potential of natural compounds against HIV-1 protease, a critical enzyme in the replication cycle of human immunodeficiency virus type 1 (HIV-1). HIV-1 protease is essential for cleaving large precursor proteins into smaller functional proteins necessary for viral maturation and infectivity. Protease inhibitors, specifically targeting HIV-1 protease, effectively suppress viral replication, aiding in the management of HIV infection. Using the SWISS DOCK Server, several natural compounds were investigated for their potential as inhibitors of HIV-1 protease. Docking results revealed that Amentoflavone showed excellent binding affinity with high Estimated ΔG values, suggesting its potential as an inhibitor of HIV-1 protease. Additionally, Amentoflavone exhibited similar promising results when tested against Roussarcoma virus protease. These findings indicate the potential of Amentoflavone as a natural compound against HIV and warrant further investigation into its therapeutic applications.

Keywords: Response surface methodology; Ultrasonication; marigold flower; polyphenols, microcapsules; antimicrobial activity.

The forest regeneration phase in Sweden commonly involves mechanical soil preparation followed by planting of Scots pine or Norway spruce seedlings. The prepared soil offers planting positions with different properties, including decreased damage by pine weevil (Hylobius abietis L.). Nitrogen fertilization can be applied at the time of planting to aid establishment of the seedlings. In this study we compared the effect of organic nitrogen-fertilization for different planting positions, and seedling sizes on early survival and growth of Scots pine and Norway spruce seedlings. The main planting positions were capped mound, hinge, and mineral soil. Seedlings planted close to organic material were categorized as “low quality positions”, since proximity to organic material increase pine weevil attraction. Higher mortality rates related to pine weevil damage were recorded for seedlings planted in the low-quality positions, regardless of seedling size or N-fertilization. Pine weevil attack rates increased with increasing seedling size. Growth was in general lowest in mineral soil positions. The effect of organic N-fertilization on growth was positive for spruce regardless of planting positions and seedling size, while depending on planting position and seedling size for pine, indicating that effects of organic N-fertilization depend on seedling species, -size and planting position.

Teledermatology, the dermatological consultation and diagnosis via digital communication technologies, has become increasingly important in modern healthcare. The integration of teledermatology into clinical practice has gained significant interest, especially during the COVID-19 pandemic. Numerous studies have shown the effectiveness and reliability of teledermatology, with high levels of patient satisfaction and diagnostic accuracy. Pathpoint® eDerma is a cloud-based dermatology platform that aims to streamline the documentation and management of dermatological conditions. It offers several clinical benefits, including faster diagnosis, reduced unnecessary procedures, improved access to care, and cost savings. The platform follows a comprehensive risk management plan and complies with safety regulations. It undergoes regular recertification and reviews to ensure its safety and effectiveness. The clinical evaluation of eDerma confirms its ability to deliver significant clinical benefits while effectively mitigating risks. The benefit-risk ratio of eDerma is highly acceptable, supporting its use in clinical settings to enhance dermatological care.

Adamantinomatous Craniopharyngioma (ACP) is a rare epithelial tumor located in the craniopharyngeal duct, often associated with high morbidity and a high long-term recurrence rate. ACP is predominantly diagnosed in children below the age of 15 and accounts for a majority of Craniopharyngioma cases in the United States. It is most commonly caused by a mutation in exon 3 of the CTNNB1 gene, which promotes overaccumulation of β-Catenin in the WNT pathway. As β-Catenin accumulates, it over-activates the transcription factor, causing an over-transcription of WNT proteins and uncontrolled cell growth. Current methods of treatment for ACP are centered around surgical intervention and radiotherapy, including gross-total resection (GTR), subtotal resection (STR), and a combination of subtotal resection and adjuvant radiotherapy (STR+XTR). These methods of treatment are associated with high risk due to the proximity of ACP to critical structures near the craniopharyngeal duct. High recurrence rates and morbidity are closely associated with current methods of treatment and other methods have yet to be attempted in craniopharyngioma. This review will explore two possible methods of non-surgical and non-radiological therapeutic interventions. The use of RNAi therapy and CRISPR may provide insight and be a potential way to treat ACP with increased quality of life and decreased recurrence rates.

Polydatin, a polyphenolic compound sourced from various natural origins, has garnered attention due to its potential anti-tumor properties and low toxicity. This research utilized Molecular Docking methods to investigate the molecular interactions between Polydatin and the Crystal Structure of Human UDP-glucuronic acid decarboxylase (UXS1), aiming to uncover the mechanisms contributing to its anti-tumor effects. The study revealed a substantial binding energy value of approximately -10 kcal/mol for the Polydatin-UXS1 interaction, indicating a robust and favorable binding. This suggests that Polydatin holds significant promise in cancer research, offering insights into its ability to interact with and potentially modulate the activity of UXS1. Moreover, the additional step of predicting toxicity properties using the pKCSM server provides valuable insights into the safety profiles of polydatin ( or piceid). From these results, the parameters for polydatin: —Max. tolerated dose (human),- Oral Rat Acute Toxicity (LD50), and - Oral Rat Chronic Toxicity (LOAEL)—suggest a favorable safety profile.These findings position Polydatin as a compelling candidate for further exploration in cancer research, highlighting both its efficacy and safety considerations.

The Dual-Frequency Precipitation Radar (DPR) on the Core GPM satellite provides spaceborne 3-dimensional observations of the precipitation field and surface rainfall rate with quasi global coverage. The present study evaluates the behavior of liquid precipitation intensity, radar reflectivity factor (ZKu and ZKa) and Drop Size Distribution (DSD) parameters (weighted mean diameter Dm and intercept parameter Nw) of the GPM DPR derived products version 07 from 2014 to 2023. Observations from seven Parsivel disdrometers located in different topographic zones in the Western Mediterranean are taken as ground reference. Four matching techniques between satellite estimates and ground level observations were tested, and best results were found for the so-called optimal comparison approach. Overall, GPM DPR products capture well the variability of the observed DSD at different rainfall intensities. However, overestimation of the mean Dm and underestimation of the mean Nw are observed, being much more sensitive to errors in drop diameters larger than 1.5 mm. Moreover, lowest errors were found for radar reflectivity factor and Dm, and highest for Nw and rainfall rate. In addition, the GPM DPR convective and stratiform classification was tested and it was found a substantial overestimation of stratiform cases compared to disdrometer observations.

This article comprehensively explores multiple aspects of cryptocurrencies and their price forecasting. Firstly, the article introduces the definition of cryptocurrency and its development process on a global scale, especially focusing on the launch of Facebook Libra and China's central bank digital currency, highlighting the importance and influence of digital currency in the global financial market. Subsequently, the article analyzes the advantages of digital currencies over traditional currencies, including improving economic transaction efficiency, reducing transaction costs and enhancing transaction transparency. Meanwhile, the article also explores the challenges and risks potentially brought by the development of digital currencies, such as regulatory uncertainty and market volatility. In this context, the article raises the importance of cryptocurrency price forecasting and introduces the forecasting models and techniques commonly used today. Finally, through specific experimental analysis, the effectiveness of using the deep learning model CNN-LSTM to predict the price of Bitcoin is demonstrated, and the directions of future research and optimization strategy are proposed. In summary, this paper comprehensively presents the research status and prospects of cryptocurrency and its price prediction field through systematic introduction and analysis.

Abstract: Social media has become a vital tool in ensuring productivity among Undergraduates, as studies have shown an increased number of users globally. This study assessed the impact of social media usage on undergraduates’ academic performance and working memory. Participants from three different universities such as the University of Georgia (UG), Eastern European University (EEU), and Batumi State University (BSU) completed this study using online google forms. UG and EEU are in the capital city, and BSU is in Batumi City. The study questionnaires were approved by the institutional review board before the data collection. Descriptive analyses were assessed for all study populations, characteristics, frequencies, percentages, means, and standard deviations using Statistical Package for Social Science (SPSS Inc., Chicago, IL, version 25.0 for Windows) software. 41.3% were male students, as most of the participants were foreign students living and studying in Georgia. Male students had the risk of ‘conflict’ with families and friends because of social media. Female students are at increased risk of using social media as an ‘escape’ from negative feelings. 89.7% of students that perform physical activities, considering their academic workload had the risk of preoccupation complaints. 87.9% felt dissatisfied regularly because they wanted to spend more time on social media. Utilization of social media differed based on the characteristics of the participants such as age, gender, and geography. We observed the increase in prevalence of social media addiction, its effect on academic performance and working memory among undergraduate students.

Phase errors in magnetic resonance (MR) techniques, including Nuclear Magnetic Resonance (NMR) spectroscopy and Magnetic Resonance Imaging (MRI), pose significant challenges to data accuracy and interpretation. As MR technologies advance, the demand for more sophisticated phase correction methods continues to grow, enhancing diagnostic precision and analytical outcomes. This review explores the evolution of phase correction models, beginning with simple global phase shifts, progressing through traditional linear statistical models, and culminating in modern machine learning techniques—specifically, neural networks. We also examine a range of optimization functions and optimizers, including both MR data-specific and common statistical approaches, applied in phase error correction. Despite notable progress, challenges remain in developing fully automatic phase error correction methods, particularly given the absence of ground truth in real-world MR data. This review highlights key methods, discusses their limitations, and proposes future pathways, including ensemble learning, that could guide further innovation in phase error correction.

This exploratory study investigates the use of Generative Artificial Intelligence (GenAI) tools in developing strategy practices for nanostores — i.e., small independent grocery retailers — to enhance their competitiveness while contributing to community sustainability. Nanostores, particularly in emerging countries, face intense competition and rapidly changing trends. By leveraging their proximity to consumers in neighbourhoods, these stores adopt various strategies, such as offering complementary services, integrating technology, and adapting business models. While existing literature highlights the broader economic, social, and environmental roles of nanostores, there is limited comprehensive research on how they may simultaneously contribute to their competitiveness and communities’ sustainability. This study employs a 'thing ethnography' approach, using GenAI tools —i.e., ChatGPT 3.5 and Microsoft Copilot— to explore potential strategy practices for these shops, and reflect on their possible use for strategy ideation and implementation. Key findings suggest that prioritising local sourcing, understanding local preferences, flexible inventory management, efficient retail operations, in-store sustainable practices, promoting healthy lifestyles, and diverse community engagement are crucial for success. However, the study also acknowledges the limitations of GenAI tools, including possible bias and the need for result validation. By aligning with these evolving requirements, nanostores can strive in neighbourhoods and decisively promote sustainable communities.

In this note we prove that the Riemann hypothesis is false. The proof is by contradiction based on a criterion of Hu.

Conventional tutoring approaches are confronted with limitations such as restricted availability, inconsistent pedagogical quality, and scalability con-straints. Furthermore, the exclusive use of Large Language Models (LLMs) like ChatGPT in education has its shortcomings, including the potential for incorrect responses and the lack of customized guidance aligned with specific course con-tent. This research proposes an innovative intelligent chatbot tutoring system, in-tegrating the Retrieval Augmented Generation (RAG) approach with a custom LLM. The developed system aims to overcome the limitations of traditional tu-toring and general-purpose LLMs by providing accurate, contextually relevant, and personalized assistance, thereby enhancing student understanding and en-gagement. The system, powered by an intelligent agent, retrieves relevant information from curated academic sources, incorporates interactive features for user feedback, and utilizes machine learning algorithms for ongoing performance enhancement, ensuring a robust and effective tutoring experience. The anticipated outcome is an enriched educational experience for university students, advance-ment in personalized learning, and improved student engagement, retention, and academic performance. Through continuous research and refinement, it is expected that the intelligent chatbot tutor will assume an important role in enhancing and supporting the educational journey of students.

Orificed Hollow Cathodes are electric devices necessary for the functioning of common plasma thrusters for space applications. From their reliability mainly depends the success of a spacecraft’s mission equipped by electric propulsion. The development of plasma models is crucial in the evaluation of plasma properties within the cathodes that are difficult to measure due to the small dimensions. Many models, based on non-linear system of plasma equations, have been proposed in the open literature. These are solved commonly by means of iterative procedures. This paper investigates the possibility to solve them by means of Particle Swarm Optimization method. The results of the validation tests confirm the expected trends for all the unknowns; the confidence bound of the discharge current as function of mass flow rate is very narrow (2÷5V), moreover the results match very well the experimental data except at the lowest mass flow rate (0.08mg/s) and discharge current (1A), where the computations underpredict the discharge current to the utmost by 40%; the highest data dispersion regards the plasma density in the emitter region (± 20 % of the average value) and the wall temperatures (± 50K respect to the average values) of orifice and insert; those of the others variables are very tiny.

The construction sector is a paradigm shift into the sustainable era to solve environmental issues, combat global warming. And the reflection of this is on Green Buildings where energy savings, cost savings, occupant comfort takes priority. The idea of green building efficiency merges sophisticated technologies, creative architectural design templates, and reusable materials into the whole life cycle of a building process in order to render minimum environmental impact. The pilot project of The National Institute of Geophysics and Volcanology (INGV) - Irpinia headquarters, illustrates in this context, innovative technologies of energy transition with a photovoltaic parking with geothermal piles. The project is consistent with the concepts of Nearly Zero Energy Buildings (NZEBs), directed to a high energy performance and very low environmental impact. Incorporating geothermal piles is a major area of advancement in sustainable building ideas that use the Earth's natural heat to create an energy efficient solution in heating and cooling. Project Goals – The project aims to install emission free renewable cleaning energy cost-effective clean production and increase energy efficiency, reduce CO2 emissions and energy security. For this purpose this paper, using as a support the PVGis software, evaluates the potential produced with the photovoltaic system installed in the canopies. With the help of this analysis, it is feasible to determine the amount of electricity produced as well as the system's amortization based on the excess energy sent to the public grid. The project is regarded as creative from the standpoints of energy given the use of geothermal and photovoltaic systems, as well as from an architectural and design perspective due to the use of novel materials. This all-encompassing strategy establishes INGV as a pioneer in the promotion of environmentally friendly technologies and shows a dedication to sustainability and energy efficiency.

We experimentally demonstrate waveguide and microring resonator (MRR) polarizers by integrating 2D graphene oxide (GO) films onto silicon (Si) photonic devices. The 2D GO films with highly anisotropic light absorption are on-chip integrated with precise control over their thicknesses and sizes. Detailed measurements are performed for the fabricated devices with different GO film thicknesses, coating lengths, and Si waveguide widths. The results show that a maximum polarization-dependent loss (PDL) of ~17 dB is achieved for the hybrid waveguides, and the hybrid MRRs achieved a maximum polarization extinction ratio (PER) of ~10 dB. We also characterize the wavelength- and power-dependent response for these polarizers. The former demonstrates a broad operation bandwidth of over ~100 nm, and the latter verifies performance improvement enabled by photo-thermal changes in GO films. By fitting the experimental results with theoretical simulations, we find that the anisotropy in the loss of GO films dominates the polarization selectivity of these devices. These results highlight the strong potential of 2D GO films for realizing high-performance polarization selective devices in Si photonic platform.

Domestic crime, conflict, and instability pose a significant threat to many contemporary governments. These challenges have proven to be particularly acute within modern-day Mexico. While there have been significant developments in predicting intrastate armed and electoral conflict in various contemporary settings, such efforts have thus far been limited in their use of spatial as well as temporal correlations, as well as in the features they have consid- ered. Machine learning, especially deep learning, has been proven to be highly effective in predicting future conflicts using word embeddings in Convolutional Neural Networks (CNN) but lacks the spatial structure and, due to the black box nature, cannot explain the importance of predictors. We develop a novel methodology using machine learning that can accurately classify future anti-government violence in Mexico. We further demonstrate that our approach can identify important leading predictors of such violence. This can help policymakers make informed decisions and can also help governments and NGOs better allocate security and humanitarian resources, which could prove beneficial in tackling this problem. Using a variety of political event aggregations from the ICEWS database alongside other textual and demographic features, we trained various classical machine learning algorithms, including but not limited to Logistic Regression, Random Forest, XGBoost, and a Voting classifier. We considered a mix of CNN + Long Short Term Memory (LSTM) networks to decode the spatial and temporal relationship in the data. The performance of all the black box deep learning models was not at par with the classical machine learning models. We also evaluated the temporal relationships in the data to identify important predictors that fuel anti-government violence along with their directions using Shapley additive values. The voting classifier, utilizing a subset of features derived from LASSO across 100 simulations, consistently surpasses alternative models in performance and demonstrates efficacy in accu- rately classifying future anti-government conflicts. Notably, Random Forest feature importance indicates that some features, including but not limited to homicides, accidents, material conflicts, and positive worded citizen information sentiments emerge as pivotal predictors in the classification of anti-government conflicts. Finally, we conclude the research by analysing the spatial structure of the data using Moran’s I index extended version for spatiotemporal data to identify the global spatial dependency and local clusters followed by modelling the data spatially and evaluating the same using Gaussian Process Boost(GPBoost). The global spatial autocorrelation is minimal, characterized by local- ized conflicts cluster within the region. Furthermore, in Phase 3, the Voting Classifier method demonstrates superior performance over GPBoost, leading to the inference that no substantial spatial dependency exists among the various locations.

This paper presents the perspective that universe is relative. The universe, which changes according to the traveler’s proper time, is dynamic. While the universe generally expands, it could also contract if traveler accelerates sufficiently. The Hubble parameter merely represents that change. If the Hubble parameter is dependent on the proper time, the measurement can only be uncertain. Identifying the factors that influence the proper time could potentially lead to a more accurate measurement of the universe’s age, and it could also offer insights into the state of the early universe and the causes of the accelerated expansion observed in the current epoch.

In recent years, the treatment of locally-advanced rectal cancer (LARC) has been spotlighted in a number of landmark, practice-changing trials, heralding in the age of so-termed “total neoadjuvant therapy” (TNT). However, the lack of a common control arm, standard inclusion criteria, or, indeed, definition of TNT necessitates further study in this field, particularly using real-world data. We conducted a retrospective analysis of all patients treated for LARC at a single Canadian academic cancer centre between July 1st 2019 and July 14th 2022. This includes the June 2020 incorporation of TNT. 290 patients were included in the final analysis, and 124 variables were collected on each patient, including demographics, treatment type, adverse events, response to treatment, surgery versus watchful waiting and survival. Our findings demonstrate that both TNT and non-TNT regimens are highly toxic, with regards to both overall toxicity and grade 2+ adverse effects. 62 (96.9%) of patients on TNT experienced toxicities, compared to 186 (82.3%) of the non-TNT patients. Grade 2+ toxicities were 46 (71.9%) and 143 (63.3%), respectively. TNT, however, was associated with improved rates of complete response, compared to non-TNT treatment regimens (44% vs 24%). These findings suggest that TNT harbours both benefit and risk in a real-world population, and both of these aspects should be considered when deciding on a treatment approach for an individual patient. More work in the area is needed to determine longer term survival, as well as impact of surgery versus organ preservation approach on patient outcomes.

Netrokona is a district of the Mymensingh division in northern Bangladesh. In this study, we analyze the soil profile in Netrokona Sadar using digital soil mapping and land formation using the satellite image technique. This study also analyzes the soil profile using remote sensing data of 4 different points in selected area. The main focus of the study is analyzing the soil profile for the structural compatibility and environmental impact for that. After analyzing the soil we found that the soil of that area is mainly clay loam and clayey loam and the soil contains low coarse fragement which is 5.6-8% but we need al least 15-30% coarse fragement in the soil. The soil of this area needs good management to improve its drainage and bearing capacity.

The fast spread of COVID-19 constitutes a worldwide challenge to the public health, educational, and trade systems, affecting the overall wellbeing of human societies. The high transmission and mortality rates of this virus, and the unavailability of a vaccine or treatment, resulted in the decision of multiple governments to enact measures of social distancing. Thus, it is of general interest to consider the validity of the proposal for keeping a social distancing of at least 2 m from other persons to avoid the spread of COVID-19. The exposure to the bioaerosol can result in the deposition of the pathogen in the respiratory tract of the host causing disease and an immunological response. In the atmospheric context, the work evaluates the effect of aerodynamic diameter (size) of particles in carrying RNA copies of the novel coronavirus. A SARS-CoV-2 carrier person talking, sneezing, or coughing at distance of 2 m can still provide a pathogenic bioaerosol load with submicron particles that remain viable in air for up to 3 hours for exposure of healthy persons near and far from the source in a stagnant environment. The deposited bioaerosol creates contaminated surfaces, which if touched can act as a path to introduce the pathogen by mouth, nose, or eyes and cause disease.

We investigate the physics of a medium-size quantum particle' gases, of the order of hundreds or thousands of fermions/bosons (far below the thermodynamic limit $N \, \rightarrow \, 10^{24}$). Appeal to microcanonical ensemble becomes then unavoidable. The so-called statistical complexity is our protagonist. Interesting facts are uncovered, both for fermions and bosons, yielded by statistical quantifiers.

The critical role of manufacturing operations in enhancing the performance of Small and Medium Enterprises (SMEs) has become increasingly evident in recent years. This systematic review examines how manufacturing operations influence the performance of SMEs, analysing findings from 69 diverse research papers published between 2014 and 2024. Utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, the review focuses on key operational strategies, including lean manufacturing and advanced manufacturing technologies, assessing their effectiveness in improving business outcomes such as efficiency, productivity, and financial performance. The methodology included a thorough review of the research methods used in the studies, identifying potential biases, gaps, and highlighting the need for diverse approaches. While manufacturing operations have led to improved productivity and increased efficiency for many SMEs, challenges such as limited resources, lack of technical expertise, and resistance to change remain prevalent. Notably, a reporting bias was observed, with 48% of the literature composed of quantitative studies, 30% being mixed-method studies, and only 20% and 1% representing qualitative and exploratory studies, respectively. This imbalance suggests an overemphasis on quantitative methods, limiting the range of ideas and understanding available. Key strategies such as lean manufacturing and advanced technologies were shown to enhance productivity by 15% to 25% in SMEs. By adopting a broad approach, this review ensures that the findings are applicable across various sectors and geographies, offering valuable insights into best practices and challenges across diverse economic contexts. The broad scope also allows for the identification of under-researched areas, fostering a more comprehensive understanding of how manufacturing operations contribute to SME success. The review provides a decision-making framework for business leaders and policy recommendations to foster SME growth in a competitive, globalized market.

A fundamental aspect in the evolution of Time-to-Digital Converters (TDCs) implemented within Field-Programmable Gate Arrays (FPGAs), given the increasing demand for detection channels, is the optimization of resource utilization. This study reviews the principal methodologies employed for implementing low-resource TDCs in FPGAs. It outlines the foundational architectures and interpolation techniques utilized to bolster TDC performances without unduly burdening resource consumption. Low-resource Tapped Delay Line, Vernier Ring Oscillator, and Multi-Phase Shift Counter TDCs, including the use of SerDes, are reviewed. Additionally, novel low-resource architectures are scrutinized, including Counter Gray Oscillator TDCs and interpolation expansions using Voltage-Temperature-Consumption stable IODELAYS. Furthermore, the advantages and limitations of each approach are critically assessed, with particular emphasis on resolution, precision, non-linearities, and, especially, resource utilization. A comprehensive summary table encapsulating existing works on low-resource TDCs is provided, offering a comprehensive overview of the advancements in the field.

Immune-cell reprogramming driven by mitochondria-derived reactive electrophilic immunometabolites (mt-REMs—e.g., fumarate, itaconate) is an emerging phenomenon of major biomedical importance. Despite their localized production, mt-REMs elicit significantly-large local and global footprints within and across cells, through mechanisms involving electrophile signaling. Burgeoning efforts are being put into profiling mt-REMs’ potential protein-targets and phenotypic mapping of their multifaceted inflammatory behaviors. Yet, precision indexing of mt-REMs’ first-responders with spatiotemporal intelligence and locale-specific function assignments remains elusive. Highlighting the latest advances and overarching challenges, this perspective aims to stimulate thoughts and spur interdisciplinary innovations to address these unmet chemical-biotechnological needs at therapeutic immuno-signaling frontiers.

The automotive industry is continuously developing to meet the needs of users in terms of efficiency and convenience. The Thailand government has set a policy for Thailand to be a hub for the production of electric vehicles and key components in the region, with a target to produce electric and zero-emission vehicles by 2030. This article is an example of communication in an electric vehicle system. The CAN Bus system is a communication system in electric vehicles that can receive and send communication data via CAN-Hight/Low lines designed for vehicles. CAN Bus is a standard communication system supported by global organizations and used in modern automotive systems. The design of the communication system in electric vehicles uses MRS Developers Studio software to help write programs to receive and send data and prioritize the importance of the received data. The system consists of communication examples studied, such as pressing the start button, turning on/off the headlights, turning on/off the turn signal, turning on/off the brake light, turning on/off the emergency light, sending the horn signal, sending the signal to shift forward/backward, and tuning the motor gear system. From the communication examples mentioned, the communication will be a prototype transport set for small electric vehicles under the scope of the lithium battery energy storage system, NMC type, size 72 V 25 Ah, to test the use, efficiency, and stability in CAN Bus communication up to 1000 times.

Two Iron Age settlements, Tuzusai and Taldy Bulak 2 (ca. 500 BCE to 1 CE), located in Southeastern Kazakhstan on the Talgar alluvial fan north of the Tian Shan range, have yielded a small collection of bone, antler/horn, bronze, and stone artifacts with affinity to nomadic art of the first millennium BCE. Both settlements date within the period of late Saka culture. Two pieces have decorative ornamentations with zoomorphic imagery: a small carved fragment of a with carved images of a wing and an ear and a perforated bone disk with the carving of three birds’ heads. The other artifacts include objects associated with Saka weaponry or nomadic economy such as the two antler/horn psalia (cheek pieces) and a bronze amulet. A carnelian bead will also be described as an imported object. These special finds are found on the occupation floors of mud brick houses and pit houses of settlements, not in grave or burial contexts. The objects are placed in stratigraphic sequence in the settlement sites. Then the method for placing these objects within the chronological framework of “animal-style art” is through comparisons with similar objects found throughout Eurasia—a method used in Soviet and Post-Soviet archaeology. The results show that functional and stylistic elements of the six objects indicate that the Talgar settlements were part of a larger world-system of trade and communication along the early Silk Route(s).

While the link between Sensory-processing sensitivity (SPS) and internalizing symptoms has been well-established, a link to externalizing problems is still to be explored. This study aimed to further examine the relation between SPS and behavioral problems by testing the potential mediating roles of trait emotional intelligence (TEI) and decision-making styles. Pathway analyses were conducted on data from 268 community sample participants (Mage= 25.81, SD=2.41, 61.2% females). Results indicated gender differences in the pathway level outcomes of SPS, as well as potential partial mediators in men and women. SPS both directly and via the mediating effects of well-being factor (TEI) and avoidant decision-making influenced depression, regardless of gender. Direct effects on aggression were, however, obtained only in the male sample. Indirect effects of SPS on aggression were found in spontaneous decision-making for men, and in self-control and sociability factors of TEI for women. Directions for future research were discussed. sensory-processing sensitivity, depression, aggression, emotional intelligence, decision-making style, gender differences

In February of 2024, Dr. Jun Zhuang and Dr. Casey Kennington published a paper (Zhuang and Kennington, 2024) in which they classified large language model (LLM) survey papers into different taxonomies utilizing graph learning. In this paper, I evaluate the dataset they created and used and propose that in its current state, there is not enough samples to classify other survey papers into specific categories.

Apolipoprotein A (ApoA), an integral component of high-density lipoprotein (HDL) particles, plays a vital role in the transport and metabolism of lipids. It is implicated in both cardiovascular (CVD) and neurological diseases. In the context of CVD, ApoAs are pivotal in facilitating the reverse transport of cholesterol, mitigating inflammation, and preserving endothelial function. These char- acteristics highlight the crucial role of ApoA in averting the onset of CVDs. Moreover, ApoA has been implicated in the pathogenesis of several diseases, such as Alzheimer's disease (AD), Parkin- son's disease (PD), and multiple sclerosis (MS). Apart from its role in lipid metabolism, recent stud- ies have indicated the involvement of ApoA in neuroprotection, amyloid-beta clearance, and regu- lation of neuroinflammation. This paper addresses the research question: "How do different isoforms of Apolipoprotein A influence the development and progression of cardiovascular and neurological diseases, and what are the potential therapeutic implications of targeting ApoA in these conditions?" By providing a comprehensive review of current knowledge, this paper empha- sizes the importance of ApoA in understanding the intricate connections between cardiovascular and neurological health, suggesting that ApoA-targeted interventions may help treat diseases in both domains.

A microwave technique prepared a superabsorbent polymer (SAP) by grafting two hydrophilic monomers onto a polysaccharide substrate. The monomers used are acrylic acid (AA) or acrylamide (AM) to graft onto a pullulan substrate (PUL) to form PUL-g- AA (SAP1) and PUL-g- AM (SAP2), respectively. The monomers (AM/AA) graft together onto a PUL substrate to form PUL-g-(AM/AA) (SAP3). Grafting parameters such as grafting efficiency with percentage, conversion of monomer to polymer, gel content, water retention, water adsorption capacity, and swelling kinetics were determined. Additionally, the effect of environmental pH (2, 4, 7, 9, and 12) and sodium dodecylbenzene sulfonate (SDBS) surfactant was evaluated, where SDBS was added by 1,2,3,4 and 5 mM to form (SAP4 to SAP8). FTIR results show that AM grafted on PUL through an aliphatic C-N bond, while AA grafting occurred through a single C-C bond. The grafting efficiency with AM is higher than with AA, as well as their gel contents. Water absorbance capacity increased with grafting of AA or AM separately, and water retention was enhanced. The highest absorbent capacity, water retention, gel content, and grafting parameters values were obtained with 3 mM SDBS content and pH7. The swelling kinetics showed that the increases in the theoretical and experimental swelling equilibriums were 72 % and 82%, respectively, at SAP6 compared to the values of these parameters in SAP3. The water absorption capacity of the hydrogel increases with increasing pH to 7 and then gradually decreases. XRD improves the crystallinity and crystalline size of the hydrogel after grafting polymerization for AM/AA into PUL, in addition to enhancing thermal stability. On the contrary, FE-SEM demonstrated that SDBS improves the porosity and pore size of the hydrogel surface in SAP6.

in this research, new setting is introduced for new SuperHyperNotion, namely, Neutrosophic SuperHyperStable. In this research article, there are some research segments for ``Neutrosophic SuperHyperStable'' about some researches on neutrosophic SuperHyperStable. With researches on the basic properties, the research article starts to make neutrosophic SuperHyperStable theory more understandable. Assume a neutrosophic SuperHyperGraph. Then a ``neutrosophic SuperHyperStable'' $\mathcal{I}_n(NSHG)$ for a neutrosophic SuperHyperGraph $NSHG:(V,E)$ is the maximum neutrosophic cardinality of a neutrosophic SuperHyperSet $S$ of neutrosophic SuperHyperVertices such that there's no neutrosophic SuperHyperVertex to have a neutrosophic SuperHyperEdge in common. A basic familiarity with SuperHyperGraph theory and neutrosophic SuperHyperGraph theory are proposed.

This study investigated how fashion is expressed on social media platforms from a media ecology perspective of sustainable digital space-time, as society evolves into a digital ecosystem. Media ecology concerns how the media environment transforms human experience and impacts society and culture. A theoretical review of media ecology was conducted, and the Instagram account of global fashion influencer Susanna Lau (@susiebubble) was analyzed as a case study. In total, 300 fashion-related images were collected out of 5,817 uploaded to Lau’s Instagram between May 2012 and June 2019. These were analyzed—alongside their titles, content, hashtags, and commentaries—for visual phenomena conveying everyday divisions between spatiality and temporality, public and private, real and virtual, and geography and culture, which demonstrate ambiguous boundaries. The analysis revealed that the images reflect nuances of digital time and space as they emerge in social media, and represent a nonboundary of style across the binaries of work and leisure, public and private, real and virtual, and geography and culture—signifying a sustainable digital lifestyle. These findings illustrate how our changing daily lives are visualized through fashion on a sustainable digital platform, and suggest ongoing research into the practical impact of technological advances on fashion.