Data leakage by statistical officers represents a significant statistical crime under the Indonesian Statistics Law. However, despite the legal provisions, there has been a notable absence of enforcement actions against such breaches, raising questions about the effectiveness and applicability of the current legal framework for addressing statistical crimes related to data leakage. This study identifies a research gap in the enforcement and operationalization of existing laws, emphasizing the lack of empirical data on how these legal provisions are applied in practice. Utilizing an empirical research methodology, this study analyzes data and real-world instances of statistical data leakage. The findings reveal that unethical behavior among statistical officers, inadequate data security measures, and the threat of social engineering significantly contribute to the potential for data breaches. The study's strength lies in its focus on providing a comprehensive understanding of these issues and proposing actionable solutions. This research not only fills the existing gap by offering empirical insights but also advances practical recommendations for enhancing data protection and legal enforcement mechanisms within Indonesia’s statistical framework.

Liver injury caused by various factors significantly impacts human health. Stem cell transplantation has potential for enhancing liver functionality, but safety concerns such as immune rejection, tumorigenesis, and the formation of emboli in the lungs remain. Recent studies have shown that stem cells primarily exert their effects through the secretion of extracellular vesicles (EVs). EVs have been shown to play crucial roles in reducing inflammation, preventing cell death, and promoting liver cell proliferation. Additionally, they can function as carriers to deliver targeted drugs to the liver, thereby exerting specific physiological effects. EVs possess several advantages, including structural stability, low immunogenicity, minimal tumorigenicity targeting capabilities, and convenient collection,. Consequently, these factors have garnered significant attention from researchers and are expected to become alternative therapeutic agents to stem cell therapy. This article provides a comprehensive review of the current research progress on the use of stem cell-derived EVs in the treatment of liver injury.

This article considers a different application of similar methods that have been used to reject the Sapir-Whorf Hypothesis and expounds the idea as a member of the cumulative proof against the theory. Rather than to explicitly include models of generative grammars, this model instead does not violate the ideas of Optimality Theory – and therein it preserves such qualities as freedom of analysis while retaining the idea of the pertinent constraints. However, it is suggestive of momentary disagreements with the Innateness Hypothesis.

Costa Rica, widely acknowledged for its environmental leadership, is now facing a significant challenge in managing waste, a situation that could potentially compromise both its environmental and public health achievements. This analysis delves into the waste management issues in Costa Rica, particularly in urban areas such as San José, responsible for nearly half of the country's waste. The study identifies several pressing concerns, including the heavy burden on landfill capacities, a recycling rate of just 9.6%, and substantial regional differences in waste management practices.To address these challenges, the research employs a combined methodology, utilizing Linear Regression (LR) and AutoRegressive Integrated Moving Average (ARIMA) models to project future trends in CO2e emissions and waste minimization from 2024 to 2050. This method underscores the vital need for public involvement and the introduction of innovative strategies in waste management. The research also introduces a Waste Management Innovation Index to compare Costa Rica’s practices with those of leading nations like Germany and South Korea, identifying key areas for improvement.Furthermore, the study incorporates insights from stakeholders, revealing the intricate balance of cultural, economic, and regulatory factors that shape waste management policies. Despite efforts by the government, such as the Environmental Health Route and the National Circular Economy Strategy, results show limited progress, particularly in rural regions. The study highlights the urgent need for better infrastructure, targeted public awareness campaigns, and the use of advanced technologies like Artificial Intelligence (AI) and blockchain to enhance sustainable waste management practices. The research provides strategic recommendations to help Costa Rica improve its waste management systems in line with international standards, ensuring long-term environmental sustainability and protection of public health.

Embracing Fourth Industrial Revolution(4IR) techniques is a need for a long-term sustainability and to do automation in the processes of Industrial and education sector. Rapid advancement in the information technology and other engineering technologies demand more proactive steps in the education institutes. In the post pandemic world, there is an increasing demand of changes in teaching and learning methodologies with the support of modern digital technologies. So, all of the teaching institutes need to have a significant step change in the areas of learning and teaching. The impact of 4 IR is more on the education sectors compared to previous generation’s industrial revolution. Keeping track of key technologies can be quite a challenge as they keep evolving at a very fast pace. Fourth Industrial revolution is the current technological digital revolution in which major focus is on cyber physical and biological systems such as Artificial Intelligence, Robotics, Internet on Things, Virtual Reality etc., The educators and institutions should establish the pedagogies and teaching with the aid of Industry 4.0 techniques to evolve education 4.0. So, it is very important to understand the challenges and competencies required about the implementation of 4IR by the faculty members and students who are currently studying in Higher Education Institutes (HEIs). This paper aim is to do a systematic review about industry 4.0 techniques and its application in relate to the online learning and teaching, change in pedagogies, enhancement of data management in HEIs, digital and virtual laboratories etc.

Endogenous bioelectrical patterns are an important regulator of anatomical pattern during embryogenesis, regeneration, and cancer. While there are three known classes of instructive bioelectric patterns: directly encoding, indirectly encoding, and binary trigger, it is not known how these design principles could be exploited by evolution and what their relative advantages might be. To better understand the evolutionary role of bioelectricity in anatomical homeostasis, we developed a neural cellular automaton (NCA). We used evolutionary algorithms to optimize these models to achieve reliable morphogenetic patterns driven by the different ways in which tissues can interpret their bioelectrical pattern for downstream anatomical outcomes. We found that: (1) All three types of bioelectrical codes allow the reaching of target morphologies; (2) Resetting of the bioelectrical pattern and the change in duration of the binary trigger alter morphogenesis; (3) Direct pattern organisms show an emergent robustness to changes in initial anatomical configurations; (4) Indirect pattern organisms show an emergent robustness to bioelectrical perturbation; (5) Direct and indirect pattern organisms show an emergent generalizability competency to new (rotated) bioelectrical patterns; (6) Direct pattern organisms show an emergent repatterning competency in post-developmental-phase. Because our simulation was fundamentally a homeostatic system seeking to achieve specific goals in anatomical state space, we sought to understand the role of anxiolytics that reduced concern over the distance to those goals. We simulated selective serotonin reuptake inhibitors, which diminished ability of artificial embryos to correctly interpret bioelectric patterns due to an altered organismal reward machinery, leading to higher variance of developmental outcomes, global morphological degradation, and induced in some organisms anatomical bistability. These computational findings were validated by data collected from in vivo experiments in SSRI exposure in planarian flatworm regeneration.

Crops obtained by genome editing (GE) technologies represent a notable and disruptive shift from genetically modified or transgenic plants, outpacing public perception and regulatory frame-works. Multiple aspects of GE are contributing to the advancement of modern agriculture, in-fluencing the market structure by introducing specific changes within the existing genetic blue-print of an organism. Generally, the GE toolbox includes distinct technologies able to incorporate site-specific and pre-determined genomic modifications, exploiting well-established genetic modification strategies. For these reasons, GE-derived approaches are considered new plant breeding methods, known as new breeding techniques (NBTs). Particularly, CRISPR/Cas-based technologies represent a considerable improvement over prior techniques, being more precise, accurate and straightforward for targeted gene editing, in a reliable and reproducible way, with numerous applications. CRISPR/Cas system exploitation for non-editing purposes has also been widely documented, ranging from cell imaging to expression regulation and DNA assembly. Recently, CRISPR/Cas systems have been employed for target detection and could be used to ef-ficiently identify multiple organic chemicals. High-sensitivity and high-specificity detection are two main points of the CRISPR/Cas technology, without needing complex expertise or technical pipelines. This manuscript aims to provide an overview of the main advances of CRISPR/Cas-based systems into new frontiers of non-editing. The associated implications (e.g., molecular traceability) and their relative impacts are also presented and discussed.

This paper presents a biologically inspired flocking-based aggregation behaviour of a swarm of mobile robots. Aggregation behaviour is essential to many swarm systems, such as swarm robotics systems, in order to accomplish complex tasks that are impossible for a single agent. We developed a robot controller using Reynolds’ flocking rules to coordinate the movements of multiple e-puck robots during the aggregation process. To improve aggregation behaviour among these robots and address the scalability issues in current flocking-based aggregation approaches, we used a K-means algorithm to identify clusters of agents. Using the developed controller, we simulated the aggregation behaviour among the swarm of robots. Five experiments were conducted using Webots simulation software. The performance of the developed system was evaluated under a variety of environments and conditions, such as various obstacles, agent failure, different numbers of robots and arena sizes. The results of the experiments demonstrated that the proposed algorithm is robust and scalable. Moreover, we compared our proposed algorithm with another implementation of the flocking-based self-organizing aggregation behaviour based on Reynolds’ rules in a swarm of e-puck robots. Our algorithms outperformed this method in terms of cohesion performance and aggregation completion time.

Successful work performance is an important part of everyday functioning. Different factors could contribute to efficiency at the workplace. Two studies aim to establish how different forms of knowledge transfer at the workplace are related to self-rated work performance and whether better work performance is related to mindfulness as an aspect of cognitive functioning. The participants were 742 working Bulgarians in Study 1 and 259 workers in Study 2. Ten scales were used in Study 1, measuring different forms of knowledge transfer and self-rated work performance, and two scales were used in Study 2, measuring self-assessed work performance and mindfulness. The results revealed that higher self-assessment of work performance correlated positively but weakly with the sharing of tacit knowledge at the workplace, sharing knowledge with colleagues in dyads, work climate encouraging mastership, sharing knowledge in teams, knowledge transfer by means of cooperation between different generations, institutionalized transfer of knowledge, knowledge receiving, and estimated effectiveness of participation in supervision as a supervisee. Some forms of knowledge transfer (sharing of tacit knowledge and sharing knowledge with colleagues in dyads) seemed more closely related to self-assessed work performance than others. Self-assessment of work performance did not correlate statistically significantly with performance motivational climate at work. The different forms of knowledge transfer correlated with each other. Better work performance is related to better cognitive functioning, as the second study of 259 workers additionally revealed that higher self-assessment of work performance correlated with better self-awareness measured by the Mindful Attention Awareness Scale, i.e., more attention focused on own thoughts, emotions, and needs, i.e., on what is happening right now. Interconnectedness between the different forms of knowledge transfer indicates that stimulating one kind of knowledge transfer could also stimulate many other forms of knowledge transfer, but this may happen only when organizations support a mastery-motivated climate at work. Established connections between the different forms of knowledge transfer and self-rated work performance, as well as between self-assessed work performance and mindfulness, suggest that good cognitive functioning accompanied good functioning, and both may be facilitated by knowledge transfer at the workplace.

Artificial General Intelligence (AGI) represents a pivotal advancement in AI with far- reaching implications across technological, ethical, and societal domains. This paper addresses the following: (1) an in-depth assessment of AGI’s transformative potential across different sectors and its multifaceted implications, including significant financial impacts like workforce disruption, income inequality, productivity gains, and potential systemic risks; (2) an examination of critical ethical considerations, including transparency and accountability, complex ethical dilemmas and societal impact; (3) a detailed analysis of privacy, legal and policy implications, particularly in intellectual property and liability, and (4) a proposed governance framework to ensure responsible AGI development and deployment. Additionally, the paper explores and addresses AGI's political implications, including national security and potential misuse. By analyzing and considering computer science, philosophy, economics, and policy perspectives, we offer a multidisciplinary view of AGI’s challenges and opportunities, advocating for proactive measures to align AGI development with human values and societal interests.

A complementary characterisation of the Al-alloy EN AW-5454 was carried out, intended for obtaining the laser hybrid welding parameters of sub-assemblies in the automotive industry. The investigation included a microstructural examination, and determination of the alloy's properties using several analytical methods (HV5 hardness measurement, tensile test, Charpy impact toughness, fracture mechanics analysis). Samples were prepared in the longitudinal and transverse directions of a cold-rolled sheet of EN AW-5454 with thicknesses of 3.5 mm and 4 mm. The measured hardness on the thinner sheet was 5% higher than on the thicker sheet. The tensile and yield strength were nominal, while the elongations were smaller by 2.2 - 3.2% for the longitudinal samples and by 2.7 - 13.7% for the transverse samples. The smaller deviations from the nominal values are for the thinner sheet metal. A precise topographical analysis showed brittle fractures of the samples. The Charpy impact toughness results on the thicker plate showed a 20% greater work needed to break it in the longitudinal direction than in the transverse direction. With the thinner sheet metal, 40% greater work was needed. SEM (Scanning Electron Microscope) analysis has shown that the intermetallic Al6(Mn,Fe) particles in the longitudinal samples were mostly intact, with evidence of tough areas on the upper part of the fracture, indicating a better toughness than the specimens in the transverse direction. More crushed intermetallic particles were observed at the fractures of the transverse samples, and their distribution appeared to be more oriented in the direction of rolling. Fracture mechanics SENB (single edge notch bending) tests and their analysis showed that the resistance of the material to crack propagation in the longitudinal sample was about 50% greater than that in the transverse sample. SEM analysis of the fractures showed that the state of the intermetallic particles in the fracture mechanics testing, and the fracture mechanism differed from the one in the Charpy fractures.

Conventional biochemical methods for studying cellular signaling cascades have relied on destructive cell disruption. In contrast, live cell imaging of fluorescent-tagged transfected proteins offers a non-invasive approach to understanding signal transduction events. One strategy involves monitoring the phosphorylation-dependent shuttling of a fluorescent-labeled kinase between the nucleus and cytoplasm using nuclear localization, export signals, or both. This paper introduces a simple method to visualize intracellular signal transduction in live cells by exploring the translocation properties of PKC from the cytoplasm to the membrane. We fused bait protein to PKC, allowing the bait (RFP-labeled) and target (GFP-labeled) proteins to co-translocate from the cytoplasm to the membrane. However, in non-interacting protein pairs, only the bait protein was translocated to the plasma membrane. To verify our approach, we examined the Raf–MEK–ERK signaling cascade (ERK pathway) and directly observed the Raf1/MEK2 interaction. We also identified ternary complexes containing the KSR1 scaffold protein. Additionally, the MEK/ERK interaction was dependent on the exogenous expression of KSR1.

The inhibition of kynurenine aminotransferase II (KATII) is a target for treating several diseases with an excess of kynurenic acid (KYNA). KATII inhibitors exist, but they have adverse side effects, given their irreversible inhibition mechanism. This study aimed to discover potent and safe inhibitors of KATII by using computational and in vitro methods. We identified herbacetin and (-)-Epicatechin as reversible KATII inhibitors from a small library of natural compounds, whose Glide docking scores (-8.66 kcal mol-1 and-8.16 kcal mol-1, respectively) and MM/GBSA binding energies (-50.30 kcal mol-1 and -51.35 kcal mol-1, respectively), which are higher scores than the standard inhibitor, PF-04859989 (-7.12 kcal mol-1 and -38.41 kcal mol-1, respectively). The ADMET analysis showed that herbacetin and (-)-Epicatechin have suitable pharmacokinetic parameters, are moderately bioavailable, and have a safe toxicity profile. Their KATII inhibitory activity is in the range of IC50 values of 5.98± 0.18 µM and 8.76 ± 0.76 µM, respectively. Furthermore, the MTT assay for cell toxicity showed that the two compounds do not alter HepG2 cell viability at their required concentration for KATII inhibition. These results show that herbacetin and(-)-Epicatechin are suitable for KATII inhibition and appropriate candidates for further development of KATII inhibitors.

Hepatitis B Virus (HBV) infection carries a significant health burden worldwide. It is a leading cause of acute and chronic liver disease resulting in cirrhosis and hepatocellular carcinoma (HCC). This virus affects millions globally, and despite several medical advancements, research, and efforts to develop curative treatment, the persistence of HBV in infected individuals by cccDNA and integration pose formidable challenges. This review aims to provide a broad-based overview of HBV persistence, integration and the implications for chronic infection and tumorigenesis. By considering these points, we bring forth insights into the advancement of therapies that aim to disrupt the chronicity of HBV infection and improve clinical outcomes.

Phasins are proteins, which are located on the surface of Polyhydroxybutyrate (P3HB) granules and that carry out activities that affect the polymer metabolism. This study aimed to evaluate the effect of inactivating phasins PhbP2 or PhbP3 on the growth rate, production and molecular mass of P3HB in cultures under low or high oxygen transfer rates (OTR). The results reveal that under high OTRₘₐₓ conditions (between 8.1 and 8.9 mmol L⁻¹ h⁻¹ depending on the strain evaluated), the absence of phasins PhbP2 and PhbP3 resulted in a strong negative effect on the growth rate. In contrast, the rates of specific oxygen consumption increased in the case of cultures with strains where phasins were absent. This behavior was not observed under conditions at a low oxygen transfer rate (3.9 ± 0.71 mol L⁻¹ h⁻¹); where both growth and oxygen consumption were the same for the different strains evaluated. It was observed, at high OTRₘₐₓ that the absence of phasin PhbP3 harms the P3HB synthesis, finding a decrease of 30 % in the polymer production at the end of cultivation; however, the molecular weight of the polymer remained constant during the course of cultivation. In summary, it can be concluded that both growth and polymer synthesis were affected when phasin PhbP3 was absent from the P3HB granule, especially at high OTRₘₐₓ.

Against the backdrop of the European Union's commitment to achieve climate neutrality by 2050, efforts to improve energy efficiency are being intensified. The manufacturing industry is a key focal point of these endeavors due to its high final electrical energy demand, while simultaneously facing a growing shortage of skilled workers crucial for meeting established goals. Expert systems (ESs) offer the chance to overcome this challenge by automatically identifying potential energy efficiency improvements and thereby playing a significant role in reducing electricity consumption. This paper systematically reviews state-of-the-art approaches of ESs aimed at improving energy efficiency in industry, with a focus on manufacturing. The literature search yields 1668 results, of which 62 articles published between 1987 and 2024 are analyzed in depth. These publications are classified according to the system boundary, manufacturing type, application perspective, application purpose, ES type, and industry. Furthermore, we examine the structure, implementation, utilization, and development of ESs in this context. Through this analysis, the review reveals research gaps, pointing toward promising topics for future research.

Pigeon Newcastle disease, caused by pigeon paramyxovirus type 1 (PPMV-1), is a significant infectious disease in pigeons that can result in substantial mortality and poses a severe threat to the pigeon industry. Rapid and accurate onsite diagnosis of pigeon disease is crucial for timely diagnosis and the implementation of effective prevention and control measures. In this study, we established a rapid detection method for PPMV-1 based on recombinase-aided amplification (RAA) and CRISPR/Cas12a. The RAA primers target the conserved regions of the L gene for preamplification in clinical nucleic acid samples, followed by CRISPR/Cas12a detection of the target gene. Visualization could be achieved by combination with a lateral flow dipstick (LFD). This method demonstrated high specificity, showing no cross-reactivity with non-PPMV-1 samples. The sensitivity of the method assessed by fluorescence analysis reached 100 copy/µL, and when it was combined with an LFD, the sensitivity was 103 copies/µL. The constructed RAA-CRISPR/Cas12a-LFD visual detection method was applied to clinical sample testing and was found to enable rapid and accurate detection of swab samples and tissue specimens. Its sensitivity was consistent with the current gold standard, quantitative real-time PCR results. The RAA-CRISPR/Cas12a-LFD detection method we developed provides a novel approach for the rapid, simple, precise, and specific onsite diagnosis of pigeon Newcastle disease.

The exploring comprehensive review of cutting-edge techniques for securing cloud-stored data and managing sensitive information in the context of smart cities through the application of Ciphertext-Policy Attribute-Based Encryption (CP-ABE). It highlights the innovative integration of blockchain technology with CP-ABE, which introduces a decentralized and tamper-resistant key management system, thereby enhancing the overall security framework in cloud environments where data sharing is prevalent. Introducing an online/offline multi-authority CP-ABE scheme, characterized by hidden policies, offers significant advancements in protecting user attributes and access structures, ensuring that sensitive information remains confidential even during encryption and decryption processes. This dual approach not only fortifies security but also optimizes the efficiency of data-sharing mechanisms. Furthermore, the paper delves into implementing hidden sensitive policies and keyword search techniques within smart city infrastructures, which are designed to facilitate secure and efficient data retrieval. These techniques ensure that while data remains accessible to authorized users, privacy is rigorously maintained. Collectively, these approaches represent significant strides in bolstering the security and confidentiality of data in both cloud-based and smart city applications, addressing the growing demand for robust and efficient data management solutions in increasingly interconnected environments.

The electronics industry is a significant contributor to environmental challenges, generating approximately 50 million tons of electronic waste annually and high levels of carbon dioxide emissions throughout the production and disposal of devices. This paper investigates the interplay between reliability and sustainability by analyzing how degraded components affect the environmental footprint of electronic products. Using a board with 215 components as a baseline, we calculate the mean time between failures (MTBF) and compare it to scenarios involving degraded components. Our findings demonstrate that degraded components can reduce MTBF by up to 50%, resulting in higher failure rates, increased electronic waste, and up to 30% more CO2 emissions due to shortened product lifespans. This study underscores the crucial importance of component quality control in the production process to improve both reliability and sustainability. Additionally, the broader economic and environmental impacts of using unchecked components are highlighted, showing their contribution to elevated failure rates, shortened product lifespans, and an estimated 20% increase in e-waste. By adopting a 100% inspection protocol, leveraging advanced technologies, and enforcing stringent material quality controls, manufacturers can significantly enhance the reliability of electronic products while reducing their environmental impact, thus fostering a more sustainable electronics industry.

With online wildlife trade increasing and viral videos increasing demand, the trade in primates on TikTok is becoming more prevalent. Despite wildlife trade being banned on most social media platforms, the trade in primates persists. TikTok’s policies ban the sale of live animals, and specific terms related to wildlife trade are banned; however, these search term bans are easy to get around, and sellers are still prolific on the site. This study documented primates for sale on TikTok using the search term “for sale monkey” over a four-month period (from mid-July to mid-November 2023) with the aim of determining how widespread primate trade is on TikTok, the legality of advertisements, and which countries advertise the most primates for sale. 42 individual advertisements were identified, spanning 6 different genera of primates and 5 different countries. Spider monkeys, rhesus macaques, and common marmosets were found to be the most commonly advertised species on TikTok. All spider monkeys were advertised from the USA, while the majority of the marmosets were advertised from the UK, and all of the rhesus macaques were advertised from Pakistan. The USA was the only country identified in this study that advertised a range of species, and the only species found in this study that was not advertised in the USA was the mona monkey. 44% of all species recorded were classified as either Endangered or Critically Endangered, and it was found that endangered species were more frequently sold illegally than non-endangered species. The findings of this study underline a need for stricter primate legislation and wildlife law enforcement, especially if the UN’s sustainable development goals are to be achieved by 2030. They also show a need for TikTok to broaden their restrictions on wildlife trade on their app.

This study investigates the possibility of using machine learning models created in DeepLabCut and Create ML to automate aspects of behavioral coding and aid in behavioral analysis. Two models with different capabilities and complexities were constructed and compared to a manually observed control period. The accuracy of the models was assessed before being applied to 7 nights of footage of the nocturnal behavior of two African elephants (Loxodonta africana). The resulting data was used to draw conclusions regarding behavioral differences between the two elephants and between individually observed nights, thus proving that such models can aid researchers in be-havioral analysis. The models were capable of tracking simple behaviors with high accuracy, but had certain limitations regarding detection of complex behaviors, such as the stereotyped behavior sway, and displayed confusion when deciding between visually similar behaviors. Further expansion of such models may be desired to create a more capable aid with the possibility of automating behavioral coding.

One of the efficient data mining tools is density-based clustering, including the density grid-based clustering. However, a common drawback always existing in clusters made by the density grid-based method is the existence of weakly connected grids deriving mainly from noise. Appearing such an unwanted connection with a high frequency reduces the accuracy of the obtained cluster data space (CDS) and its application efficiency. Here, we present an essential improvement to overcome this problem. First, we describe a concept of the weak-connected grid cell (WCG) and present a fuzzy-type approximation to depict the density-based distribution of data points at grid nodes. Then, we propose a strategy of searching WCG for density grid-based clustering (SWCG-DGB) to set up a CDS, filter noise, and tune the created CDS. A buffer is deployed during this phase to collect border points and filter noise, which improves the computational time significantly, especially for noisy datasets. Results from numerical surveys reflected the compared efficiency of this method in clustering validity, including the accuracy of the number of clusters.

The significant improvements in Generative Artificial Intelligence (GenAI) observed in recent years present Higher Education with both an opportunity and a significant challenge. Its successful integration will require careful planning and sound pedagogical underpinnings, both in regard to learning and teaching, as well as assessment and feedback. Drawing upon theories from the more-than-human world, as well as concepts such as originality, equality, and sustainability, it is possible to develop a dialogue around GenAI that places the students’ learning journey at the heart of the discussion.

This study was conducted to identify the actual degree of underdevelopment and spatial characteristics of rural areas in Korea, which are expected to face extinction due to population decline and aging. For this purpose, the spatial scope was set to Eup and Myeon areas classified as rural regions. To assess the degree of underdevelopment, previous studies were reviewed and analyzed to develop a comprehensive underdevelopment index, which was then applied to Eup and Myeon areas to classify them into growth regions, general regions, and depressed regions. Mapping the classified regions revealed that both growth and general regions were concentrated in the metropolitan area and southeastern regions, where Korea's population and resources are densely clustered. In contrast, depressed regions were found primarily in areas where agriculture is the main industry and in regions with unfavorable geographical conditions. This study is significant as it covers the entire nation of Korea, providing a method for identifying depressed regions and highlighting the regions that are developing. The findings can serve as fundamental data for the development of policies to address population decline in the future.

Lean practice is recognised for having a great potential in promoting safety risk management in offsite construction (OSC). This paper presents results of a study conducted to assess the impact of lean practice in safety risk management in OSC in a developing country. A quantitative approach using survey-based questionnaire was adopted. Lean management practices (LMPs) identified from literature review were empirically tested using a sample survey of 103 OSC contractors. The survey responses were subjected to descriptive and inferential statistics. The top ranked LMPs for safety risk management in OSC included two mistake proofing practices, i.e., use of personal protective equipment (PPE) and use of hazard warning equipment; two last planner system (LPS) practices, i.e., involvement of workers in safety planning and providing necessary working equipment and one first run studies (FRS) practice, i.e., critical analysis of work methods. These LMPs are useful in controlling high consequence safety risks in OSC. Based on evidence found in this study, the paper argues that lean practice can bring great value in the safety risk management in OSC in countries where OSC is transitioning.

Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality globally, posing significant challenges to public health. The rapid development of artificial intelligence, particularly Large Language Models like ChatGPT, has revolutionized various fields, including cardiovascular medicine. ChatGPT, recognized for its broad applicability, efficiency, and public acceptance, has been integrated into multiple facets of CVD management. This comprehensive review examines the current applications of ChatGPT in cardiovascular medicine, emphasizing its innovative and significant role in enhancing diagnosis and clinical decision-making, improving medical education, and facilitating research and academic communication. Furthermore, the review article addresses the significant challenges associated with ChatGPT and proposes directions for future development to overcome these obstacles and boost its effectiveness. By exploring the vast potential of ChatGPT to meet the needs of the large CVD patient population and the extensive community of cardiovascular professionals, this review aims to provide valuable insights for optimizing its use in cardiovascular medicine, ultimately improving patient outcomes and advancing the field.

In this paper, we investigate the equilibrium strategies for customers arriving at overtaking-free queueing networks and receiving partial information about the system’s state. In an overtaking-free network, customers cannot be overtaken by others arriving after them. We assume that customer arrivals follow a Poisson process and that service times at any queue are independent and exponentially distributed. Upon arrival, the received partial information consists in the total number of customers already in the network, however the distribution of these among the queues is left unknown. We prove the existence of equilibrium threshold strategies for such systems and provide methods to compute them.

As the smallest member of the fullerene family, C20 is yet to be discovered in planetary nebulae. In this work, we present a quantum chemical study via density functional theory (DFT) on the ionization tolerance of this molecule in the space environment. Considering that the ionization and excitation phenomena play key roles in alerting the lifetime of a molecule, we examined both ground and excited electronic states potential energy surfaces (PES) of C20 and its cations C20q+. Our theoretical results indicate that the C20 cage tolerates a positive charge as high as 13+ via characterizing local minimum geometries on both mentioned electronic states. We also explored theoretically and systematically, scenarios in which the electronic structure of neutral C20 is excited to very high spin multiplicity (beyond triplet state) and local minimum molecular geometries with cage structure are well characterized. We anticipate that such structural resistance to excitation and ionization deliver a prolonged lifetime necessary for the spectroscopic detection of this interesting molecule and its cations in space and potentially in planetary nebulea (PN).

The risk of a fatal outcome was 41% in unvaccinated patients (p = 0.713), which increased by 2.66% with co-infection with two pathogens (p = 0.342) and by 26% with three pathogens (p = 0.005). Additionally, 50% of ICU patients had a triple infection, compared with only 1.3% in the inpatient unit (p = 0.0029). The risk of death and/or ICU admission was 12 times higher (p = 0.042) with an additional pathogen and increased by 95% (p = 0.003) with a third concomitant pathogen. Regular multiplex testing is important for prompt treatment and targeted antibiotic use.

This research investigates the relationship between the long-term orientation of top managers and their strategic decision-making processes, emphasising the comprehensiveness, speed, and inventiveness of decisions. The study employed structural equation modelling to look into the suggested connections using a sample of CEOs. The findings demonstrated a high and positive correlation between long-term orientation and decision comprehensiveness and speed, but not for decision creativity. The study contributes to the growing body of research on how human variations, such as temporal orientation, affect business outcomes and strategic decision-making. Moreover, the fact that a number of the results differed from those of other studies carried out in other contexts highlights how important it is to consider the industry and cultural environment when assessing these connections.

AI-driven tools can identify nursing students' strengths and weaknesses, thus enabling the development of customized learning plans that target specific areas for improvement. This targeted approach can enhance knowledge retention and skill development, resulting in better prepared healthcare professionals. Nursing students are required to overcome their own psychological stress during their internship in order to understand sick children’s emotional reactions, as well as to be able to interact and communicate with such children. The application of AI picture-based teaching e-books helps nursing students to understand how to use therapeutic games to improve communication with sick children. A comparison of the differences between nursing teachers’ self-efficacy scores and nursing students’ application of AI picture-based teaching e-books (experimental group) and narrative handouts (control group) was achieved via therapeutic games to deal with sick children’s fear of medical examinations and treatments. In addition, a comparison was also conducted on the differences between the impact of the self-efficacy of the experimental group and that of the control group in dealing with sick children’s behavioral responses with respect to fears of medical examinations and treatments. This study is a quasi-experimental study, in which the self-efficacy scores of 30 nursing students were collected and analyzed in both the experimental group and the control group before and after intervention in dealing with sick children’s behavioral responses to fears of medical examinations and treatments. It was found that the self-efficacy score of the experimental group was higher than that of the control group (β coefficient is 0.356) when assessing the self-efficacy scores and behavior of the experimental and control groups in dealing with sick children’s fears of medical examinations and treatments through one-way analysis of covariance (one-way ANCOVA), moreover, a statistically significant difference (p < 0.05) was observed. The experimental group’s score of behavioral responses to the fears of sick children to examinations and treatments was lower than that of the control group (β coefficient was -1.540), wherein a statistically significant difference (p < 0.05) was shown. The self-efficacy of the experimental group in dealing with sick children’s fears of medical examinations and treatments had a statistically significant impact on the sick children’s behavioral responses to the fear of medical examinations and treatments (p < 0.05). In addition, the higher the self-efficacy scores of the experimental group in dealing with sick children’s fear of medical examinations and treatments, the lower the sick children’s behavioral responses to fears of medical examinations and treatments (the β coefficient was -0.914), the control group’s self-efficacy scores in dealing with sick children’s fears of medical examinations and treatments did not have a statistically significant impact on the sick children’s behavioral responses to fears of medical examinations and treatments (p > 0.05). This study proves that the application of AI picture-based teaching e-books by nursing teachers is more effective than narrative handouts in improving nursing students’ self-efficacy when using therapeutic games to deal with, as well as reduce, sick children’s fears of medical examinations and treatments.

Bacterial infection poses a serious threat to human life due to its rapidly growing resistance to antibacterial drugs, which is a significant public health issue. This study was focused on the design and synthesis of a new series of 25 analogues bearing 5-cyano-6-oxo-4-substituted phenyl-1,6-dihydropyrimidine scaffold hybridized with different substituted benzene-sulfonamides through thioacetamide linker M1-25 via the treatment of the key intermediates 2-mercapto-6-oxo-4-phenyl-1,6-dihydropyrimidine-5-carbonitriles 1a-c with different 2-chloro-N-substituted benzene-sulfonamide derivatives 4a-e overnight at room temperature. The spectroscopic data of all the new compounds (IR, 1H-NMR, 13C-NMR, and Mass) was studied in detail to confirm their molecular structures. The antimicrobial activity of the new molecules was studied against various Gram-positive, Gram-negative, and fungal strains. All the tested compounds showed promising broad spectrum antimicrobial efficacy, especially against K. pneumoniae and P. aeruginosa, with ZOI values ranging from 15 to 30 mm. Furthermore, the most promising compounds M6, M19,M 20, and M25 were subjected to minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) assays. In addition, the anti-virulence activity of the compounds was also examined using multiple biofilm assays. The new compounds revealed promising suppressing activity of microbial biofilm formation in the examined K. pneumoniae and P. aeruginosa microbial isolates. Additional in silico ADMET studies were conducted to represent their oral bioavailability, drug-likeness characteristics, and human toxicity risks. The obtained data suggest the newly prepared pyrimidine-benzene-sulfonamide derivatives may serve as model compounds amenable for further optimization and development of new antimicrobial and antisepsis candidates.

Zebrafish (Danio rerio) is an important animal model for a wide range of neurodegenerative diseases. However, obtaining the cellular resolution that is essential for studying the zebrafish brain remains challenging as it requires high-spatial resolution and signal-to-noise ratios (SNR). In the current study, we present the first MRI results of the zebrafish brain at the state-of-the-art magnetic field strength of 28.2 T. The performance of MRI at 28.2 T was compared to 17.6 T. A 20% improvement in SNR was observed at 28.2 T as compared to 17.6 T. Excellent contrast, resolution, and SNR allowed the identification of several brain structures. The normative T1 and T2 relaxation values were established over different zebrafish brain structures at 28.2 T. To zoom into the white matter structures, we applied diffusion tensor imaging (DTI) and obtained axial, radial, and mean diffusivity, as well as fractional anisotropy at a very high spatial resolution. Visualization of white matter structures was achieved by short-track track-density imaging by applying the constrained spherical deconvolution method (stTDI CSD). For the first time, an algorithm for stTDI with multi-shell multi-tissue (msmt) CSD was tested on zebrafish brain data. A significant reduction of false-positive tracks from grey matter signals was observed compared to stTDI with single-shell single-tissue (ssst) CSD. This allowed the non-invasive identification of white matter structures at high resolution and contrast. Our results show that ultra-high field DTI and tractography provide reproducible and quantitative maps of fibre organization from tiny zebrafish brains, which can be implemented in the future for a mechanistic understanding of disease-related microstructural changes in zebrafish models of various brain diseases.

Current paper is aiming to show the obtained results in manufacturing and characterization of graphite-based and metal composite materials suitable to be used in high voltage applications of automotive and/or aviation field. Thus, 4 types of materials have been processed and tested in order to assess their suitability to be used as current collector material for electric engines. The novelty of this work is the presentation of the new innovative technological process for obtaining the carbon/metal composite material used in the manufacture of sliding contacts for current collector heads, as well as the characteristics obtained for this material. All 4 obtained materials have been characterized from structural, mechanical and functional point of view and the most suitable of them has been chosen to be further developed by the end beneficiary.

Cleomaceae is an important group that is medicinally and economically important. However, its systematic position has been controversial. In this study, we have obtained three chloroplast genomes sequencing results of Cleomaceae species. The genome sizes ranged from 157,073 bp (Sieruela rutidosperma) to 158,152 bp (Gynandropsis gynandra). These chloroplast genomes have a typical quadripartite formation with a large single-copy (LSC) region (86,422-87,242bp), a small single-copy (SSC) region (18,485-18,548bp), and a pair of inverted repeat (IR) regions (26,083-26,184bp). The chloroplast genomes encoded 131-132 genes, comprising 8 rRNA genes, 37 tRNA genes, and 79-84 protein-coding genes. The overall GC contents of the three chloroplast genomes are almost identical (35.81%-36.02%). The phylogenetic trees concluding 8 Cleomaceae plants were constructed based on ML and BI methods. The results showed that Gynandropsis gynandra and Sieruela rutidosperma were in the same branch, with Corynandra viscosa as their sister. Six hypervariable areas were identified, containing clpP, matK, ndhF, atpH-atpI, atpF-atpH and petN-psbM which could be used in species identification and population genetics studies. This study provides valuable information for further species identification, evolutionary and genetic studies of Cleomaceae in the future.

The state of the grinding wheel in the grinding process directly affects the surface quality of the workpiece. The monitoring of the wear state of the grinding wheel can effectively identify the wear information of the grinding wheel and timely and effectively repair the grinding wheel. In this paper, a multi-sensor fusion precision grinding wheel wear intelligent monitoring system is established for typical difficult-to-machine aerospace material titanium alloy, and a multi-eigenvalue fusion algorithm based on improved support vector machine (SVM) is proposed. The extracted sound emission signal wavelet packet decomposition coefficient mean square value, force signal grinding force ratio, and vibration signal effective value are used as the input of the improved support vector machine, and the identification strategy is adjusted by the entropy weight evaluation method. The results show that the monitoring system established in this paper can effectively identify the wear state of the grinding wheel, and the monitoring accuracy can reach more than 92%.

Background/Objectives: Transforaminal endoscopic lumbar discectomy (TELD) under local anesthesia is a promising minimally invasive surgical option for intractable lumbar disc herniation (LDH). However, our understanding of access pain prediction during foraminal pathological procedures is limited. To our knowledge, no predictive rules for access pain during TELD have been established for foraminal or extraforaminal LDH. This study, with its potential to predict access pain during TELD and to discuss strategies for pain prevention and management, could significantly impact the field of endoscopic spine surgery. Methods: This observational study included 73 consecutive patients who underwent TELD for foraminal or extraforaminal LDH between January 2017 and December 2022. Preoperative clinical and radiographic factors affecting significant access pain and the impact of access pain on clinical outcomes were evaluated. Results: The rate of significant access pain was 13.70% (10 of 73 patients). Extraforaminal LDH tended to cause more severe pain than foraminal LDH during TELD under local anesthesia (p &lt; 0.05). Although the degree of access pain was not related to global outcomes, increased pain was strongly associated with a prolonged operative time and length of hospital stay (p &lt; 0.05). Conclusions: TELD can be an effective surgical option for foraminal or extraforaminal LDH under local anesthesia. More access pain may develop during TELD for extraforaminal LDH. The extraforaminal component of LDH can narrow the safety working zone. Significant access pain may prolong the duration of surgery and hospitalization. Thus, a specialized technique is required for the clinical success of TELD.

Crowd accident surveys have shown that regardless of the initial triggering factors, pedestrian fall behaviour is the most critical factor causing and aggravating crowd accidents in public traffic areas (PTAs). Application of pedestrian fall behaviour detection methods in PTAs is significant. Once deployed, it would prevent many pedestrians from losing life in crowed traffic areas accidents. However, most existing methods is still focused on medical assistance for the elderly. Therefore, this paper conducted bibliometric and content analyses, combining fall detection-related keywords to retrieve from internationally recognized literature databases and benchmark pedestrian behaviour datasets. Based on the analysis of the state-of-the-art (SOTA) achievements in fall detection methods, the fall detection methods were classified into different categories according to the research approach. Among them, it undertakes a comprehensive analysis of five predominant methods, namely computer vision, Internet of Things, smartphone, kinematic, and wearable device-based methods. Furthermore, the benchmark datasets including fall scenarios were introduced and compared. Finally, it provides a detailed discussion of existing fall detection methods, and possible future directions are identified considering application requirements in PTAs. This overview may help researchers understand the SOTA fall detection methods and devise new methodologies by improving and synthesizing the highlighted issues in PTAs.

The maritime industry contributes to over 80% of global trade and ranks as the sixth-largest emitter of greenhouse gases. The International Maritime Organization has taken strides to reduce these emissions, while the European Union spearheads efforts to establish sustainable maritime transport. However, meeting decarbonisation targets poses significant challenges, with the potential implications of adopting an EU Emission Trading System (ETS) on shipping allowances, particularly at specific borders. If not managed effectively, this could result in diversifying regular container shipping routes away from EU ports, potentially leading to increased shipping emissions and disrupting the logistics trade and security chain. These challenges are exacerbated by the International Maritime Organization's lack of a carbon fee on bunkering, which creates an imbalance. The ETS Directive has fostered unfair competition between EU and non-EU ports due to discrepancies in applying emission unit allowances. It is imperative to implement preventive measures to contain and prevent the shift of operations from EU ports to nearby ports, ensure equitable treatment of EU transhipment ports and their competitors, and thwart carbon leakage. Additionally, the recent Houthis attacks on the Red Sea have significantly impacted shipping costs on the Cape of Good Hope route to Europe, necessitating increased allowances to and from Europe.

In this paper, the P versus NP problem is studied based on a six-dimensional space-time perspective. The gap between the P-class and NP-class arises from the relationship between time and the solution, as well as the answer that is independent of time. The complexity of solving a problem depends on the time dependence of the answer. Solving problems whose answers do not depend on the passage of time is very difficult and impossible. Like the description of the gravitational factor in Newtonian physics, NP class problems require different mathematical tools. Based on the generalization of physical facts in mathematics, based on six-dimensional space-time, a method to solve problem P versus NP problem has been stated. The results of this study offer a fresh insight into information processing that occurs independent of the passage of time.

The reaction of 2-((diphenylmethylene)amino)acetonitrile with (E)-1-phenyl-3-(thiophen-2-yl)prop-2-en-1-one was performed by using 33% NaOH in CH3CN for 30 min at 0°C. The main product - rel-(2R,3S)-2-((diphenylmethylene)amino)-5-oxo-5-phenyl-3-(thiophen-2-yl)pentanenitrile was isolated and characterized by IR, 1H NMR, 13C NMR and high-resolution mass spectrometry (HRMS).

Gender and biological sex have distinct impacts on the pathogenesis of type 2 diabetes (T2D). Estrogen deficiency is known to predispose female mice to T2D. In our previous study, we found that high-fat, high-sucrose diet (HFHSD) induces T2D in male mice through the miR-10b-5p/KLF11/KIT pathway, but not in females, highlighting hormonal disparities in T2D susceptibility. However, the underlying molecular mechanisms of this hormonal protection in females remain elusive. To address this knowledge gap, we utilized ovariectomized, estrogen-deficient female mice, fed them a HFHSD to induce T2D, and investigated the molecular mechanisms pathway in estrogen-deficient diabetic female mice, cell lines, and patients with T2D in female. Initially, female mice fed a HFHSD exhibited delayed onset of T2D, but ovariectomy-induced estrogen deficiency promptly precipitated T2D without delay. Intriguingly, insulin (INS) was upregulated, while insulin receptor (INSR) and protein kinase B (AKT) were downregulated in these estrogen-deficient diabetic female mice, indicating insulin-resistant T2D. These dysregulations of INS, INSR, and AKT were mediated by a miR-10a/b-5p-NCOR2 axis. Treatment of miR-10a/b-5p effectively alleviated hyperglycemia in estrogen-deficient T2D female mice, while β-estradiol temporarily reduced hyperglycemia. Consistent with the murine findings, plasma samples from female T2D patients exhibited significant reductions in miR-10a/b-5p, estrogen, and INSR, but increased insulin levels. Our findings suggest that estrogen protect against insulin-resistant T2D in females through miR-10a/b-5p/NCOR2 pathway, indicating the potential therapeutic benefits of miR-10a/b-5p restoration in female T2D management.

Purpose: This study was designed to investigate the ability of telemonitoring involving a secure elec-tronic database called “Alsayed System for Applied Medical-Care Improvement” (ASAMI) to detect at least 95% of acute exacerbations of chronic obstructive pulmonary disease (AECOPD). Methods: This prospective observational study, which took place over 16 months in 2022-2023, was based at a medi-cal center in Jordan. The study group participants were patients with COPD (from seven countries) with mixed levels of airflow limitation severity, and every participant was followed for six months. A customized, password-protected version of ASAMI was used to collect data during the study. The implemented diary included questions on daily symptoms such as cough, dyspnea, sputum, and daily physical activities. The detection of AECOPD events was initially based on daily questionnaires com-pleted by patients, followed by the assessment of a healthcare practitioner. Results: 87 participants were monitored for AECOPD. During the study, 78/87 (89.7%) patients presented with one or more exacerbation episodes. The mean (range) age was 59.7 (45-81) years, predominantly male (66%), with moderate airflow limitation (n=42, 48%). Compliance with the provision of daily symptom data was very high, 98% over the entire study period. A total of 189 confirmed AECOPD episodes were reported. Almost all patients and practitioners showed favorable satisfaction with the role of the ASAMI-related service (96% reported a score of 10 satisfaction). The telemonitoring tool was able to detect 99% of AECOPD episodes. Conclusion: The novel ASAMI software was effective as a telemonitoring tool to detect the episodes of AECOPD. The tool was suggested for daily, continuous reporting of respiratory symptoms, with the key benefits being ease of use, patient acceptance, and collation of supplementary data when exacerbations occur. The effects of its widespread use on decision-making can be clarified by evaluating its impact on routine clinical decision-making.

Background/Objectives: Determining the outcome of epilepsy is crucial for making proactive and timely treatment decisions, as well as for counseling patients. Recent research efforts have focused on using various imaging techniques and EEG for prognostication; however, there is insufficient evidence regarding the role of blood parameters. Our study aimed to investigate the additional prognostic value of routine blood parameters in predicting epilepsy outcomes. Methods: We analyzed data from 1,782 patients who underwent routine blood tests within 90 days of their first visit and had a minimum follow-up duration of three years. The etiological types were structural (35.1%), genetic (14.2%), immune (4.7%), infectious (2.9%), and unknown (42.6%). The outcome was defined as the presence of seizures in the last year. Results: Initially, a multivariate analysis was conducted based on clinical variables, MRI, and EEG data. This analysis revealed that sex, age of onset, epileptiform discharge, structural etiology, and the number of antiseizure medications were related to the outcome, with an area under curve (AUC) of 0.708. Among the blood parameters, fibrinogen, bilirubin, uric acid, and aPTT were significant, with AUCs of 0.602, 0.597, 0.455, and 0.549, respectively. Including these blood parameters in the analysis slightly improved the AUC to 0.713. Conclusions: Some blood parameters were found to be related to the final outcome, potentially paving the way to understanding the mechanisms of epileptogenesis and drug resistance.

Background/Objectives: Disrespectful care during childbirth has been observed as a global issue and a factor influencing maternal morbidity and mortality. While the United States has experienced worsening rates of maternal mortality, perceptions of respectful maternity care have been understudied. Methods A cross-sectional study was conducted Boston from April 2023 to January 2024 among 46 labor and delivery physicians, midwives, and nurses at Massachusetts General Hospital. The survey evaluated their observation of disrespectful care, performance of respectful care, and stress and support factors influencing respectfulness of care. Results: The most reported observed disrespectful behaviors were dismissing patients’ pain (87.0%), discriminatory care based on physical characteristics (67.4%) and race (65.2%), and uncomfortable vaginal examinations (65.2%). Respondents self-reported very high levels of respectful maternity care performance. Reported barriers to respectful care included workload (76.1%) and fatigue (60.9%). Conclusions: Disrespectful care in childbirth is an issue reported by health care providers. Implicit bias and working conditions of health care providers are factors in disrespectful care. This information can be used to strategize future training and other areas of intervention to improve maternity care.

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

This paper focuses on studying the mapping properties of singular integral operators on product spaces. The boundedness of such operators is established on Triebel-Lizorkin spaces whenever their kernel functions belong to Grafakos and Stefanov class. Our results generalize and improve some previously known results.

Periprosthetic joint infection (PJI) is one of the major complications resulting from the implantation of a joint prosthesis. Staphylococci are responsible for more than 50% of prosthetic infections, about 20% can be polymicrobial, 15% are gram-negative and about 10% of cultures are negative. The complete eradication of the infection is extremely difficult. For a correct treatment is first of all useful to perform a clinical staging based on the anatomical location of the infection and on the immune characteristics of the host. However, regardless the area of infection, the role of the surgeon is crucial, firstly in terms of timing and secondly in assessing the degree of invasiveness of the infected and necrotic tissues required. The goal of the treatment must be to eradicate the infection ensuring the maximum functional result.

Based on the perspective of complex directed weighted network analysis, this paper constructs a static resilience evaluation framework for trade networks from two dimensions: structural resilience and node resilience. It studies the evolution of static resilience in the upstream, midstream, downstream, and recycling trade networks of global wood forest products from 2002 to 2021. The study finds that the total trade volume of global wood forest products has increased by 58.0%, 77.3%, 71.2%, and 77.2% in the upstream, midstream, downstream, and recycling sectors, respectively. The downstream trade network exhibits efficient information transmission and resource mobility capabilities. All four types of trade networks exhibit scale-free characteristics, with core countries such as China, the United States, and Germany playing a dominant role in the network and playing a crucial role in network robustness, but also increasing network vulnerability. In terms of unweighted assortativity, all four networks exhibit the characteristic of "small countries relying on hubs," while the weighted assortativity of the downstream network is positive, indicating enhanced network resilience. In terms of node resilience, countries exhibit significant differences and dynamic changes. China demonstrates strong resilience and adaptability, while the United States maintains its core position. China's influence in the downstream network has significantly increased, and India has rapidly emerged in the recycling network. To enhance network resilience, it is necessary to increase diversity and redundancy, establish effective emergency response mechanisms, and promote the diversification and green transformation of the supply chain.

The discharge of industrial effluents containing toxic heavy metals in water sources has serious consequences for human health and the environment, and biosorption appears to be an environmental-friendly and cost-effective alternative that can be used for wastewater treatment. The use of different types of agricultural waste as biosorbents for the removal of toxic heavy metals, although an alternative, is quite difficult to apply in practice because these wastes have many other uses. Based on these considerations, in this study, soybean biomass (SB), soybean waste biomass obtained from oil extraction (SBW) and biochar obtained from soybean waste pyrolysis (BC-SBW) were testes as biosorbents form the removal of Pb(II) and Cd(II) ions, in batch systems. Under optimal conditions (pH = 5.4, 4.0 g biosorbent/L, room temperature (25 ± 1 C), contact time = 180 min), the biosorption capacity increase in the order: SB &lt; SBW &lt; BC-SBW for both metal ions, indicating that BC-SBW has the highest efficiency in removing toxic heavy metals. In addition, experiments on wastewater samples have shown that, in addition to significant reducing the content of heavy metals, BC-SBW also significant improved other quality indicators (such as: pH, TSM, CCO-Cr, CBO5), compared to the other biosorbents (SB and SBW). Quantitative evaluation of the biosorptive performance of each biosorbent (SB, SBW and BC-SBW) shows that BC-SBW has a real chance of being used on an industrial scale for wastewater treatment. All these aspects allowed the development of a circular approach for the use of soybean biomass in the removal processes of toxic heavy metals. This approach minimizes the shortcomings of using biomass as a biosorbent and increase the chance of using these materials in industrial practice.

Aortic transection, a near-complete tear through the layers of the aorta, is a critical condition often resulting from trauma such as motor vehicle collisions. The urgency of managing aortic transection underscores the critical need for effective interventions. We report the case of a male in his early 50’s with no significant medical history who presented to the emergency department following a motor vehicle collision, sustaining multiple injuries including a descending thoracic aortic transection. Rapid diagnostic assessment confirmed the severity of the injury, necessitating immediate intervention. Endovascular aortic repair (EVAR) was successfully employed, highlighting its efficacy in managing acute aortic injuries. The patient responded well to treatment, underscoring the importance of timely intervention in improving patient outcomes. This case emphasizes the critical role of rapid diagnostic assessment and endovascular intervention in managing life-threatening thoracic aortic injuries, particularly in the acute setting.

Colorectal cancer (CRC) is the third most commonly diagnosed type of cancer worldwide and is responsible for numerous deaths. 5-fluorouracil (5-FU) is an effective chemotherapy drug commonly used in the treatment of CRC, either as monotherapy or in combination with other drugs. However, half of CRC cases are resistant to 5-FU-based therapies. To contribute to the understanding the mechanisms underlying CRC resistance or recurrence after 5-FU-based therapies, we performed a comprehensive study integrating in silico, in vitro and in vivo approaches. We identified differentially expressed genes and enrichment of pathways associated with recurrence after 5-FU-based therapies. Using these bioinformatic data as a starting point, we selected a group of drugs that restored 5-FU sensitivity to 5-FU resistant cells. Interestingly, treatment with the novel Rac1 inhibitor, 1A-116, reversed morphological changes associated with 5-FU resistance back to a control-like status. Moreover, our in vivo studies have shown that 1A-116 affected tumor growth and the development of metastasis. All our data allowed us to postulate that targeting Rac1 represents a promising avenue for the development of new therapies for patients with CRC resistant to 5-FU-based therapies.

The COVID-19 pandemic has led to the widespread use of face masks to reduce disease transmission, but one unintended consequence is that this may negatively impact sleep and arousal by altering the expression of yawning. Yawns are defined by an involuntary and powerful gaping of the jaw with inspiration, and the typical design of face masks may restrict and/or disrupt the mandibular jaw movement and counter-current heat exchange during this behavior. Face masks additionally impair the contagious nature of yawning by disrupting the recognition of yawning in others within social/community settings. Given that the neurophysiological consequences of yawning function in facilitating changes in state and arousal, and most notably transitions in waking and sleeping via thermoregulation, face mask usage could be an important variable to consider within sleep studies. We briefly outline these connections, highlight some important factors to consider, and discuss the implications for both prior and current research on sleep.

Polycystic Ovarian Syndrome (PCOS) is a common endocrine condition that affects 8–13% of women of reproductive age and is one of the leading causes of anovulation and infertility. In Kazakhstan, the prevalence of this syndrome is particularly high compared to other countries and the global average. Currently, the diagnosis of polycystic ovarian syndrome is based on internationally established Rotterdam criteria, using hyperandrogenism, i.e. high testosterone levels, as a key parameter. These criteria are applied to diagnose PCOS in all female patients, although obese patients may have excess testosterone produced by adipose tissue. To avoid possible false PCOS diagnoses, an additional criterion could be considered to diagnose PCOS among obese women. Anti-Müllerian hormone (AMH) is promising as a more effective diagnostic criterion, since its level represents the state of the ovarian reserve and adipose tissue does not produce AMH. Therefore, the use of AMH instead of hyperandrogenism as a diagnostic criterion for PCOS among obese women might improve diagnostic precision and patient treatment. In this study, data on the levels of various hormonal indicators such as estrogen, progesterone, and others were analyzed for a large cohort of female patients with and without PCOS to explore possible correlations between these indicators, AMH, and testosterone. The findings suggest that the absence of a negative correlation between AMH and follicle-stimulating hormone (FSH), and a positive correlation of AMH with testosterone could support the use of AMH as a reliable additional criterion for PCOS diagnosis among obese women.

The Greater Amanzule Peatland (GAP) in Ghana represents an important ecosystem facing dynamic land cover changes as a result of both natural and anthropogenic factors. This study integrates multispectral and radar remote sensing data from Landsat-7 and -8, L-band SAR, and SRTM to conduct a machine learning-based analysis of these changes from 2010 to 2020 and to predict future scenarios up to 2040. Findings reveal a 12% expansion in peatland cover, equivalent to approximately 6,570 ± 308.59 hectares, despite declines in specific peatland types. Concurrently, anthropogenic land uses have increased, evidenced by an 85% rise in rubber plantations (from 30,530 ± 110.96 hectares to 56,617 ± 220.90 hectares) and a 6% reduction in natural forest cover (5,965 ± 353.72 hectares). Sparse vegetation, including smallholder farms, decreased by 35% from 45,064 ± 163.79 hectares to 29,424 ± 114.81 hectares. Projections for 2030 and 2040 indicate minimal changes based on current trends; however, these do not consider potential impacts from climate change, large-scale development projects, and demographic shifts, necessitating cautious interpretation. The results highlight areas of stability and vulnerability within the GAP and offer critical insights for the development of targeted conservation strategies. Additionally, the methodological framework presented provides a robust approach for accurate and detailed landscape-scale monitoring of tropical peatlands, applicable to other regions facing similar environmental challenges.

Cryo-electron microscopy (Cryo-EM) has revolutionized structural biology by enabling the determination of high-resolution 3- Dimensional (3D) structures of large biological macromolecules. Protein particle picking, the process of identifying individual protein particles in cryo-EM micrographs for building protein structures, has progressed from manual and template-based methods to sophisticated artificial intelligence (AI)-driven approaches in recent years. This review critically examines the evolution and current state of cryo-EM particle picking methods, with an emphasis on the impact of AI. We conducted a comparative evaluation of popular AI-based particle picking methods, using both general machine learning (ML) metrics and specific cryo-EM structure determination metrics. This analysis involved constructing the 3D density map from the picked protein particles and assessing the obtained resolution and particle orientation diversity, underscoring the significant impact of AI on cryo-EM particle picking. Despite the advancements, we also identified key obstacles, such as handling complex micrographs with small proteins. The analysis provides insights into the future development of more sophisticated and fully automated AI methods in cryo-EM particle recognition.

Correct diagnosis and early treatment of respiratory diseases can significantly improve the health status of patients, reduce healthcare expences, and enhance quality of life. Therefore, there has been extensive interest in developing automatic respiratory disease detection systems. Most of these methods have recently been using machine and deep learning algorithms. The success of machine learning methods depends heavily on the selection of proper features to be used in the classifier. Although metaheuristic-based feature selection methods have been successful in addressing difficulties presented by high-dimensional medical data in various biomedical classification tasks, there is not much research on the utilization of metaheuristic methods in respiratory disease classification. This paper aims to conduct a detailed and comparative analysis of six widely used metaheuristic optimization methods using eight different transfer functions in respiratory disease classification. For this purpose, two different classification cases were examined: binary and multi-class. The findings demonstrate that metaheuristic algorithms using correct transfer functions could effectively reduce data dimensionality while enhancing classification accuracy.

With the development of the energy sector based on renewable primary sources, structural changes are emerging for the entire national energy system. Initially, it was estimated that energy generation based on fossil fuels would decrease until its disappearance. However, the evolution of CO2 capture capacity leads to a possible coexistence for a certain period with the renewable energy sector. The paper develops this concept of coexistence of the two systems, with the positioning of green hydrogen not only within the renewable energy sector but also as a transformation vector for carbon dioxide captured in the form of synthetic fuels such as CH4 and CH3OH. The paper also analyzes the economic aspects related to price differences for energy produced in the two sectors, as well as their interconnection. The technical aspect, as well as the economic one, for storage through various other solutions besides hydrogen, has been highlighted. The development of the renewable energy sector and its demarcation from the fossil fuel energy sector, even with the transcendent vector represented by green hydrogen, leads to the deepening of dispersion aspects between the electricity sector and the thermal energy sector, a less mentioned aspect in current works but of great importance.

PARP inhibitors are used in tumor therapy exploiting their inhibitory role in DNA repair. Radiation therapy leads to DNA damage in the affected region comprising both tumor tissue and adjacent normal tissue. In addition, radiotherapy has long-standing effects on DNA integrity. The mechanisms for this are not well understood. We studied normal tissue damage of the lung after application of ionizing radiation (IR) in two well established rat models comprising both external (eIR) and internal (iIR) ionizing radiation. We found a nearly complete (~350-fold) loss of PARP-1 in the irradiated tissue in both models but no change in PARylated protein. This may be explained by the strong decrease in both ARH3 and PARG, both deparylating enzymes. We demonstrate, that of all these proteins are expressed in type II pneumocytes, but also in type I pneumocytes and the bronchiolar epithelium. The data suggest a severe disturbance of PARylation in these cells which may affect their major functions such as maintenance of the integrity of the alveolar wall. It is likely that the application of PARP inhibitors could have cumulative effects in radiation-damaged normal tissue.

Aim. The aim of this study is to compare data from two cohorts separated by a 17-year interval. We assessed the prevalence and severity of symptoms with the “dialysis symptom index” in these two groups recruited in 2007 and 2024 to determine how advancements in dialysis therapy have influenced symptom burden’s prevalence and severityMethods. We conducted a cross-sectional study of patients receiving thrice weekly, in-center hemodialysis. The population, recruited between February and March 2007, was comprised of patients receiving maintenance hemodialysis at the outpatient dialysis unit of our hospital. This dialysis facility is staffed by academic nephrologists. In May 2024, in the same unit, another population sample was recruited and studied as in 2007. Study coordinators administered the Dialysis Symptom Index (DSI) during the dialysis treatment. The DSI is made up of 30 questions, each of which addresses a specific physical or emotional symptom. Patients were asked to report symptoms that had been present at any time during the previous week by responding “yes” or “no”. For symptoms that were present, the patient was then asked to describe the symptom severity on a five-point Likert scale (1= not bothersome to 5=bothers very much). We generated a total symptom burden score that represents the total number of symptoms reported as being present, as well as a total symptom severity score that represents the sum of individual severity scores assigning a severity score of zero for symptoms that were not present. Results. We studied 71 patients in 2007 and 61 patients in 2024. The demographic, clinical and laboratory characteristics of the two study populations did not differ significantly. The total symptom burden score did not differ significantly between 2007 and 2024. The prevalence of most symptoms was similar in the two groups. The prevalence of constipation, decreased interest in sex and difficulty becoming sex aroused was higher in 2024 than in 2007. The total symptom severity was similar in the two periods. The severity of most symptoms was similar in the two groups. The severity of decreased interest in sex and difficulty becoming sex aroused was higher in 2024 than in 2007. Conclusion. The present study shows that, 17 years apart, the prevalence and severity of the symptom burden in patients on maintenance hemodialysis has not changed significantly. These results suggest that effort should be made to design adequate studies on the causes and pathogenesis of physical and emotional symptoms of patients on chronic hemodialysis and, once the underlying mechanisms are identified, it is amenable that high-quality studies on possible therapeutic pharmacological and non-pharmacological interventions will be performed.

Rotator cuff (RC) repair leads to less than optimal results when dealing with massive lesions, so the use of scaffolds as biological support has been proposed to improve RC repair site biology. The present study aims to evaluate the clinical and radiographical outcomes in a series of patients suffering from massive or irreparable RC tears treated with patch (porcine or human scaffolds) repair (augmentation or bridging). Twenty-three patients with a minimum follow up of 24 months were subjectively with 3 patient-related outcome measurements (PROMs) (American Shoulder and Elbow Surgeons score, Constant score, visual analog scale), clinically and radiographically assessed before and after surgery. Regardless the technique (augmentation or bridging) or type of scaffold (porcine or human) employed, at follow up all patients experienced a statistically signif-icant improvement in all PROMs, and clinically. Patch repair represents a valid salvage procedure in massive or irreparable RC tears improving the quality of life and eventually delaying the need for prosthetic replacement.

Silver nanoparticles (AgNPs) have arisen as promising specialists in malignant growth treatment, especially in further developing radiotherapy results. In vitro examinations feature AgNPs' capacity to increment radiosensitivity in disease cell lines, prompting uplifted cell demise and decreased clonogenic endurance. Outstandingly, AgNPs display insignificant poisonousness to ordinary cells, recommending potential for specific focusing of disease cells during radiotherapy. The blend of AgNPs with cisplatin shows huge cytotoxicity and oxidative pressure, particularly in tumoral cells, underscoring the requirement for additional investigation of this therapeutic methodology. AgNPs likewise show guarantee in prompting oxidative pressure in pancreatic disease cells, recommending an expected application in pancreatic malignant growth treatment. In co-openness situations, AgNPs, when joined with cadmium, prompt metabolic variation, showing compromised cell safeguard systems. Moreover, AgNPs display potential in tweaking ABC carrier action, offering a procedure to defeat multidrug obstruction in disease cells. While featuring the significant capability of AgNPs in disease treatment, the rundown highlights the need for continuous exploration to clarify their components, well-being profiles, and clinical applications. Combining AgNPs with radiotherapy presents a promising road, tending to limits in ordinary disease therapies and underlining their multifunctionality in forming the eventual fate of malignant growth therapeutics.

Several studies have shown that non-pharmacologic treatments such as photobiomodulation (PBMT) and swimming (SW) have been increasingly used as adjuncts to clinical practice. The aim of this work is to evaluate whether SW associated or not with PBMT has its effects potentialized on different kind of pain and motor aspects in a model of chronic constriction injury and if the treatments have any improvement on morphometry of the skeletal striated muscle. Our results have demonstrated a beneficial effect on pain and motor response for animals treated with the therapies isolated, and an even better response when we associate them. Regarding muscle tissue, we observed an improvement in inflammatory cells quantification and on the collagen expression on the muscle after each isolated treatment or in combination. These findings contribute to enhancing for individuals suffering from neuropathic pain conditions, offering a promising avenue for integrated care approaches that prioritize both symptom alleviation and functional recovery. Such insights are crucial for advancing treatment strategies in pain management since the currently available treatments are not completely effective to treat all the pain and also promotes several side effects on the patients’ health.

Bioimpedance analysis (BIA) is a validated non-invasive technique already proven to be useful for the diagnosis, prognosis, and management of body fluids in subjects with heart failure (HF) and chronic kidney disease (CKD). Although BIA has been widely employed for research purposes, its clinical application is still not fully widespread. The aim of this review is to provide a comprehensive overview of the state of art of BIA utilization by analysing the clinical benefits, limitations, and the potential future developments in this clinically unexplored field.

Background: Inflammatory Bowel disease (IBD) is one of the worldwide health issues with increasing incidence. Recent data point out that those patients can have a defective immune system, rendering response to vaccinations suboptimal. Since 1990, Hepatitis B Virus (HBV) vaccination has been introduced among the compulsory childhood vaccinations in Oman. Given the questionable immunogenicity of vaccinations in IBD patients, we designed this study to quantify the immune response to scheduled HBV vaccine in IBD patients and compare it with that in a matching control cohort of healthy blood donors at Sultan Qaboos University Hospital (SQUH). Methods: This is a retrospective case-control study that was conducted between the 1st of July 2023 and the 31st of May 2024 at SQUH. Using the hospital information System (HIS), a total of 252 participants were enrolled in our study and divided into two groups. Group (1) included 126 IBD patients and group (2) included 126 healthy controls. Baseline demographics, medical comorbidities and viral serologies before and after vaccination were collected. The primary outcome of the study was to compare the long term immune response to HBV vaccine between the two groups. The secondary outcomes included identifying possible risk factors for suboptimal vaccine response. Results: One hundred and twenty-six participants were included in each group. Durable protective immune response to HBV vaccine was attained in 29 patients (23%) of the IBD group compared to 109 participants (86.5%) of the control group. There was a statistically significant association (p = 0.0156) between prednisolone treatment and low immune response to HBV vaccine. Moreover, esomeprazole usage exhibited a statistically significant association (p < 0.0001) with suboptimal response to HBV vaccine. Meanwhile, there was no statistically significant correlation between low immunogenicity to HBV vaccine and age, sex, type of IBD, comorbidities, and BMI. Conclusions: IBD patients are less immunogenic to HBV vaccination compared to healthy controls. Factors like esomeprazole and/or corticosteroid therapy might be associated with poor immune response. These findings highlight the importance of reassessing the immunogenicity of HBV vaccination and probably other protective vaccinations in all IBD patients as part of their standard of care.

Violence against healthcare workers (HCWs) is a widespread, underreported and inadequately prevented problem. Only a few companies have efficient systems for assessing the extent of the phenomenon. In 2005, the health surveillance service of a public health company introduced a system that monitored violence experienced by HCWs by means of three items from the Violent Incident Form (VIF), integrated with departmental in-depth analyses using the participatory ergonomics group technique. In 2005, the annual rate of physical assaults was 8.2%, that of threats 12.0%, while the harassment rate was 19.6%. Over the past twenty years of observation (2005-2024), the percentage of workers who reported experiencing a physical attack in the previous year at their periodic medical examination, has fluctuated between 5.8% and 11.1%, except for the years 2020 and 2021 when, during the COVID-19 pandemic, the rate was 3.9% and 3.2%, respectively. During the same pandemic period, the annual threat rate that ranged from 9.4% to 20.1%, dropped to 7.7%, while the prevalence of harassment, that was between 13.5 and 19.6, fell to 7.2%. HCWs believe that (i) limiting visitor access (ii) a better balance of the demand for services and (iii) a better attitude towards HCWs were the causes of the reduced rate of violence during the pandemic. Recording the violence experienced during health surveillance is an economical, reliable and sustainable risk assessment method.

Bacillus amyloliquefaciens GL18 has been proven to have good resistance ability and can promote the growth of Avena sativa cultivar ‘Qingyan 1’. Herein, Illumina high-throughput transcriptome sequencing technology was used to analyze the differential expression of genes between the interaction group (LB) and the control group (LCK) when ‘Qingyan 1’ roots interacted with GL18 bacterial suspension or sterile water for 12 h under 4 °C low-temperature stress. The gene function annotation information was obtained using Gene Ontology (GO) functional classification and enrichment analysis. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and annotation of differentially expressed genes was used to explore the related signal transduction pathway of ‘Qingyan 1’ response to cold stress induced by Bacillus GL18. The results showed that after ‘Qingyan 1’ seedlings interacted with GL18 bacterial suspension or sterile water for 12 h, comparing the LB with the LCK or CK (the control group at 26 °C), there were 12,061 and 25,489 differentially expressed genes, respectively. Their functions were mainly related to metabolic process, cellular process, catalytic activity, binding, and cellular parts, and they were significantly enriched in plant tissue growth and development, cell wall, organelle membrane synthesis, thiamine and its compounds, biosynthesis, polyamine decomposition, and other metabolic processes. Signal transduction was detected through KEGG pathway enrichment analysis, and corresponding genes, such as AUXI, PP2C, SNRK2, ABF, BRI1, and PIF4, were expressed. The expression of genes directly involved in plant growth was upregulated in the presence of GL18, as shown by real-time quantitative PCR analysis. It was speculated that the normal growth and development of ‘Qingyan 1’ under low-temperature stress promoted by GL18 is a result of the integration of multiple pathways in terms of regulating endogenous hormone metabolism, secondary metabolite synthesis, and photosynthesis.

The oral microbiome, studied by sampling the saliva or oral rinse, has been long thought to have diagnostic capacity for head and neck cancers (HNC). However, previous reports on the HNC oral microbiome provide inconsistent results. The aim of this study is to consolidate these datasets and determine the oral microbial composition between HNC patients to healthy and premalignant individuals. We analyzed 16 published head and neck cancer (HNC) short-read 16S rRNA sequencing datasets, specifically targeting the V3V4, V4 and V4V5 regions. These datasets included saliva and oral rinse samples from donors with HNC, as well as from healthy and premalignant donors. Differences in diversities and microbial abundance were determined. HNC saliva displayed lower alpha diversity than healthy donors. In contrast, the opposite trend was observed for oral rinse samples. Beta diversity were largely similar across different patient types. Similar oral phyla were detected for all samples, but proportions were largely dependent on sample type (i.e. saliva or oral rinse) and primer set utilised for 16S rRNA sequencing. Neisseria, Leptotrichia and Megasphaera were elevated in healthy saliva, while Mycoplasma was elevated in HNC saliva. Oral rinse and saliva displayed similar enrichment for Fusobacterium, while Veillonella, Alloprevotella, and Campylobacter had conflicting results. The sparse partial least squares discriminant analysis model performed effectively in discriminating HNC from healthy or premalignant patients using V3V4 saliva (AUC = 0.888), and V3V4 oral rinse (AUC = 0.928), while poor discriminative capacity was observed for V4 saliva (AUC = 0.688). In conclusion, our meta-analysis highlighted the limitations of 16S rRNA sequencing, particularly due to variations across study batches, primer sets (i.e. V3V4, V4), and sample types. Hence, caution should be exercised when interpreting 16S rRNA sequencing results across studies, especially when different primer sets and sample types are used.

The comfort of the elderly in hospital settings requires a special attention from the involved health care professionals, particularly nurses, since hospitalization often generates suffering and discomfort. In such contexts, it is essential to take into account the specific characteristics of the elderly, considering their life experiences and their needs, to promote the health, well-being, and comfort of this population. Hence, the present work aimed to explore the nursing interventions that promote comfort among the elderly in hospital settings. A mixed descriptive exploratory study was conducted, through the application of a questionnaire, using intentional non-probabilistic sampling. The study encompassed 55 elderly individuals hospitalized in a medical service of a public hospital located in Lisbon. The results show that the participants perceived a reasonable level of comfort (5.65 ± 6.46). The following categories emerged from the content analysis: 1) Physical interventions; 2) Psycho-spiritual interventions; 3) Socio-cultural interventions; and 4) Environmental interventions. These findings help to understand comfort-promoting nursing interventions in the studied population. It was concluded that, to improve care quality, comforting interventions should focus on the elderly patient’s individuality, through support activities, empowerment, and the preservation/correction of the surrounding environment. Additionally, the provided care should be based on the real needs, expectations, preferences, and values of the elderly individual.

Soil potentially toxic elements (PTEs) pollution in black soil is of significant concern in China. However, research is scarce regarding the current status, ecological risk, and human health risk of PTEs in the black soil area of northeast China. In our study, 304 soil samples (0-20 cm) were collected in Gonghe Town, Hailun City. The pollution degree and spatial distribution patterns of soil PTEs were analyzed by using the single-factor pollution index (PI) and the Nemerow pollution index (NPI). The source apportionment of PTEs was carried out by combining correlation analysis, principal component analysis (PCA), and positive matrix factorization (PMF) methods. Meanwhile, the potential ecological risk (RI) and the human health risk (HI) associated with soil contamination from various sources were evaluated through the RI and the HI. The results demonstrated that the average content (mean ± standard deviation) of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn were 11.16 ± 1.32 mg/kg, 0.11 ± 0.04 mg/kg, 65.29 ± 3.46 mg/kg, 22.56 ± 1.32 mg/kg, 0.03 ± 0.01 mg/kg, 27.07 ± 1.46 mg/kg, 26.09 ± 2.84 mg/kg, and 66.01 ± 4.52 mg/kg, respectively. The overall PTEs in the study area exhibited slight pollution levels. The PCA and PMF model source apportionment was validated against each other to yield four sources, which were natural source (33.2%), irrigation source (29.5%), traffic source (23.4%), and fertilizer source (13.2%). The overall RI in the study area was determined to be at the level of slight ecological risk. The non-carcinogenic risk of PTEs to children and adults was ignored, and the carcinogenic risk was at an acceptable level. The comprehensive analysis of PTEs, pollutant sources, RI, and HI concluded that the fertilizer source should be the primary control source, with Cd identified as the first control PTE. The irrigation source and the traffic source were identified as the secondary control sources, with As, Pb, and Hg identified as the secondary control PTEs. This study revealed the status, risks, and sources of PTEs in black soils and provided a scientific basis of PTEs control for policymakers.

In Mexico, 1.9% of child mortality among children aged 3 to 15 years is attributed to waterborne gastrointestinal diseases (WGD). This study employs a generalized bivariate logit econometric model to simulate the relationships between mortality risks and seven explanatory variables. Based on the model results and sensitivity analysis of the estimated parameters, a set of policies was designed to reduce the likelihood of child mortality. The proposed strategy involves im-plementing the following public policies, primarily targeting communities with extreme and high marginalization: increasing access to drinking water, improving housing conditions, expanding parental basic education coverage, and providing nutrition and healthcare to children from an early age. The findings reveal that children who speak an indigenous language face a mortality risk from WGD that is three times higher than those who do not, while children who receive medical services have a 29% lower risk of mortality compared to those who do not have access to them. It is recommended to offer free medical care in indigenous languages within high-marginalization communities. The combined impact of these policies is expected to signifi-cantly reduce child mortality due to WGD.

The present study analyses the influence of scratches and fatigue on the mechanical characteristics of aluminium alloys commonly used in the aviation industry: 2024-T4, 6061-T4, 6061-T4 uncoated, 6061-T6 uncoated, 7075-T0, and 7076-T6. 120 specimens were used for the study. Out of these, 60 samples were scratched, and the other 60 samples were also scratched under the same circumstances and then subjected to low-cycle fatigue (LCF) before undergoing the tensile test. Based on the statistical examination of the measured data, mathematical prediction models have been established. Compared to their unscratched counterparts, the results indicate a significant decrease in the mechanical properties of both scratched and scratched-fatigued sample sets. The LCF pre-treatment contributes to the negative impacts of scratches, resulting in a reduced value for the ultimate tensile strength and elongation at break. The results conclude that surface integrity plays an essential role in preserving aircraft components' structural strength.

High-frequency circuit performance is significantly impacted by impedance variations, particularly within the low-resistance regime. Traditional Ohm’s Law based modeling approaches often fail to accurately predict circuit behavior in these conditions, leading to design inaccuracies and potential system failures. The Standard Ohm’s Law based model’s prediction of infinite current as resistance approaches zero is physically unrealistic and hinders its application in practical scenarios. Despite the recognition of these limitations, existing models have not comprehensively addressed the complex impedance behavior observed in high-frequency circuits. To overcome these challenges, this paper introduces a modified version of the Ohm’s Law incorporating an exponential correction term. The Modified Ohm’s Law’s accuracy was evaluated through simulated experiments across a wide frequency range (1kHz to 1GHz) using various electronic components. The findings demonstrate the superior performance of the modified model in predicting currents under low-resistance and high-current conditions compared to the Standard Ohm’s Law model. By providing finite and accurate current values, the proposed model effectively mitigates the unrealistic infinite current predictions of the standard approach. The enhanced predictive capabilities of the Modified Ohm’s Law hold significant implications for high-frequency circuit design and analysis. Its application can lead to improved performance and reliability in power electronics, telecommunications, and other high-frequency systems. By incorporating non-linear impedance behavior, the model offers a more accurate representation of real-world circuit conditions. Future research should focus on refining the exponential term’s parameters to optimize the model's accuracy across a broader range of applications. Additionally, real-time implementation and hardware validation are essential to assess the model's practical utility in complex circuit environments.

The biological function of rapid eye movement (or REM) sleep—one of the great mysteries of neuroscience—remains unknown. Here, I demonstrate that the biological function of REM sleep is to heighten brain alertness, significantly reducing the high vulnerability of deep sleep. Every organism with a nervous system must undergo deep sleep: a necessity that comes with substantial vulnerability. Deep sleep, by lowering alertness, compromises the organism's safety, putting its survival at risk. Therefore, REM sleep is a necessary adaptation for any organism that must sleep. My primary goal here is to present a comprehensive conceptual framework—supported by extensive empirical evidence—to connect numerous disparate empirical findings under a unified theory of the biological function of REM sleep. Additionally, I also provide a historical narrative to explain its origin and subsequent evolution. The theory I developed to explain the function of REM sleep is not only widely corroborated but has also resisted numerous attempts at refutation. This allows me to claim that I solved this great enigma of neuroscience.

As well known, the torque produced by a rotating motor generates an opposite and equal reaction torque in the machine casing that must be transmitted to the base. In many applications, especially when the reaction moment has high values, it is necessary to apply some constructive solutions, which in certain cases are difficult to implement. In this context, the need to reduce the reaction moment from drive motors is a challenging topic, which has not been completely exhausted. In this paper, the authors present an original concept of a device which uses the centrifugal force generated by some equidistantly placed weights on a chain drive, for reducing the reaction torque of the motor used for driving a rotating tool. The proposed system is capable to produce a supplementary torque which can be added to driving moment. Due to this fact, by using this system, the power of the driving motor can be decreased, with the consequence of reducing the reaction moment, that must be absorbed by the base.

Drug development is a lengthy process with a high failure rate. Increasingly, machine learning is utilized to facilitate the drug development processes. These models aim to enhance our understanding of drug characteristics, including their activity in biological contexts. However, a major challenge in drug response (DR) prediction is model interpretability as it aids in the validation of findings. This is important in biomedicine, where models need to be understandable in comparison with established knowledge of drug interactions with proteins. drGAT, a graph deep learning model, leverages a heterogeneous graph composed of relationships between proteins, cell lines, and drugs. drGAT is designed with two objectives: DR prediction as a binary sensitivity prediction and elucidation of drug mechanism from attention coefficients. drGAT has demonstrated superior performance over existing models, achieving 78\% accuracy (and precision), and 76\% F1 score for 269 DNA-damaging compounds of the NCI60 drug response dataset. To assess the model's interpretability, we conducted a review of drug-gene co-occurrences in Pubmed abstracts in comparison to the top 5 genes with the highest attention coefficients for each drug. We also examined whether known relationships were retained in the model by inspecting the neighborhoods of topoisomerase-related drugs. For example, our model retained TOP1 as a highly weighted predictive feature for irinotecan and topotecan, in addition to other genes that could potentially be regulators of the drugs. Our method can be used to accurately predict sensitivity to drugs and may be useful in the identification of biomarkers relating to the treatment of cancer patients.

This study harnesses the potential of artificial intelligence (AI) focusing on machine learning (ML) to improve decision-making in advanced engineering education. The onset of the COVID-19 pandemic compelled a swift transformation in higher education methodologies, particularly in the domain of course modality. This study aims to align institutional decisions with student and faculty preferences in the face of rapid changes in instructional approaches, particularly learning styles, theoretical, practical, and theoretical-practical, prompted by the COVID-19 pandemic. To ascertain the preferences of students and instructors regarding class modalities across various courses, we utilized the Cognitive Process-Embedded Systems and E-learning conceptual framework. This framework effectively delineates the task execution process within the scope of technology-enhanced learning environments for both students and instructors. This study was conducted in seven Iranian universities and their STEM departments, examining their preferences for different learning styles. After analyzing the variables by different feature selection methods, we used three ML methods—decision trees (DT), support vector machines (SVM), and random forest (RF)—for comparative analysis. The results demonstrated the high performance of the RF model in predicting curriculum style preferences, making it a powerful decision-making tool in the evolving post-COVID educational landscape. This study not only demonstrates the effectiveness of ML in predicting educational preferences but also contributes to understanding the role of self-regulated learning in educational policy and decision-making in higher education.

It is known that Radon gas within the air could reach lung cells and cause cancer. We suggest a technique to absorb alpha particles radiated by Radon decay near DNAs within human cells. In this technique, we create some non-paired negative electrons on graphene sheets and build some negative graphene tubes. Alpha particles are attracted and negative beta particles are repelled by the graphene tubes.

Climate change has emerged as a crucial global issue that significantly threatens the survival of plants. In particular, low temperature (LT) is one of the critical environmental factors that influence plant morphological, physiological, and biochemical changes during both the vegetative and reproductive growth stages. LT, including abrupt drops in temperature, as well as winter conditions, can cause detrimental effects on the growth and development of tomato plants, ranging from sowing, transplanting, truss appearance, flowering, fertilization, flowering, fruit ripening, and yields. Therefore, it is imperative to understand the comprehensive mechanisms underlying the adaptation and acclimation of tomato plants to LT at the morphological and the molecular levels. In this review, we discuss the previous and current knowledge of morphological and physiological changes, which contains major factors and traits associated with the LT stress response during the period of the vegetative and reproductive growth and development in tomato plants. Moreover, we highlight recent advances in the understanding of physiological and biochemical responses, and molecular mechanisms, including LT perception, signaling transduction, gene regulation, and fruit ripening and epigenetic regulation. The comprehensive understanding of LT response provides a solid basis to develop the LT-resistant varieties for sustainable tomato production under the ever-changing temperature fluctuations.

To verify the efficacy of placental extracts (Pla-Ext) and human mesenchymal stem-cell-derived exosomes (hMSC-Exo) against human skin aging, we investigated the human dermal fibroblast (HDF) response following hMSC-Exo and Pla-Ext treatment and whether they elicited resistance to damage when HDFs were subjected to several types of aging stresses. The effect of hMSC-Exo alone, the combined effect of Pla-Ext and hMSC-Exo, and the effect of hMSC-Exo (Pla/MSC-Exo) recovered from cultures with Pla-Ext added to hMSC were verified using collagen, elastin, and hyaluronic acid synthase mRNA levels for each effect. Cells were subjected to photoaging (UV radiation), glycation (glycation end product stimulation), and oxidation (H2O2 stimulation) as HDF stressors. Pla-Ext did not significantly affect normal skin fibroblasts considering intracellular parameters; however, a pro-proliferative effect was observed. Pla-Ext induced resistance to several stresses in skin fibroblasts (UV irradiation, glycation stimulation, H2O2 stimulation) and inhibited reactive oxygen species accumulation following H2O2 stimulation. Although the combined efficacy of hMSC-Exo and Pla-Ext was unclear, some changes resulted in senescence stress resistance due to preconditioned hMSC-Exo stimulated by Pla-Ext. This suggests that Pla-Ext supplementation may cause some changes in the surface molecules or hMSC-Exo content (e.g., microRNA).

The article explores the integration of generative artificial intelligence with active methodologies in modern education, highlighting how this combination can transform pedagogical practices and promote more personalized and effective learning. The study investigates the application of tools such as ChatGPT , which enhance the personalization of teaching, providing immediate feedback and adjusting content to students' individual needs. The research is structured into three chapters: the first addresses practical strategies for applying these methodologies with AI, the second evaluates their effectiveness in learning and the third presents practical examples of integration. Adapting teaching through AI was a central element in developing a more efficient learning environment aligned with the specific needs of each student. The results point to a significant increase in student engagement, understanding and retention of content, although they highlight the need for careful and ethical implementation to avoid challenges such as superficiality in learning and data privacy issues. It is concluded that the integration of generative AI with active methodologies can significantly transform the educational environment, but must be carried out in a balanced and responsible way, complementing traditional pedagogical practices and ensuring the comprehensive training of students.

Introduction: Mediterranean diet has been widely suggested to exert significant beneficial effects on endothelial oxidative status and cardiometabolic health. Greek Orthodox monasteries, due to their specific nutritionaland sartorial habits, comprise a population which strictly adheres to nutritional patterns with restricted eating and a plant-based subset of the Mediterranean diet, often accompanied by profound hypovitaminosis D. Time –restricted eating is also adopted from a large part of general lay Greek population for health promoting reasons, without restrictions on animal product consumption, as imposed by Orthodox religious fasting. However, comparative effects of these nutritional patterns on oxidative stress markers remain scarce. Aim: The present study attempted to evaluate the effects of Christian Orthodox fasting (COF) in a group of vitamin D –deficient and overweight Orthodox nuns from Central and Northern Greece, compared to the implementation of TRE, 16:8 dietary regimen in a cohort of adult women from the general population from the same region, with regard to markers of endothelial oxidative status. Methods:A group of 50 women from two Orthodox monasteries in Northern Greece and one group of 50 healthy lay women were included. During enrollment a detailed recording of dietary habits was performed, along with a scientific registry of demographic and anthropometric characteristics (via bioimpedance).Orthodox nuns followed a typical Orthodox fasting regimen[daily feeding window (8 am–4 pm)] whereas lay women followed a TRE 16:8 regimen with the same feeding time-window with recommendation to follow a low-fat diet, without characteristics of the Mediterranean diet. We included a complete biochemical analysis, as well as calciotropic profiles [Calcium-Ca, Albumin, Parathyroid hormone-PTH, 25-hydroxyvitamin D- 25(OH)D] as well as and markers of TAC (trichloroacetic acid), (glutathione) GSH and thiobarbituric acid reactive substances (TBARS) concentrations, as markers of oxidative status. Results: All groups were comparable at baseline for calcium, PTH and 25(OH)D concentrations, with no significant differences between groups. Orthodox nuns manifested a lower median GSH compared to controls (6.0 vs. 7.2, p 0.04) and a higher median TAC (0.92 vs. 0.77, p

Objective This study aims to evaluate the efficacy and safety of Deep Brain Sound Stimulation (DBSS) as a novel treatment for trauma-related disorders, specifically Post-Traumatic Stress Disorder (PTSD) and Developmental Trauma Disorder (DTD). The research focuses on the user-reported outcomes of DBSS therapy delivered via a mobile application. Method DBSS employs precise auditory stimulation targeted at specific brain regions involved in trauma response, such as the amygdala, hippocampus, and prefrontal cortex. Data were collected from 267 users who purchased the DBSS therapy through the Apple App Store. User feedback regarding the effectiveness and satisfaction with the treatment was analyzed. Results The majority of users reported significant improvements in emotional regulation, reduction of hyperarousal, and stabilization of interpersonal relationships. The treatment was well-tolerated, with minimal side effects reported. Despite the absence of traditional clinical trials, the findings suggest that DBSS could be a promising non-invasive therapy for trauma-related disorders. Conclusions DBSS shows potential as an innovative therapy for PTSD and DTD by targeting key neurobiological mechanisms underlying trauma. Future studies should aim to confirm these results through clinical trials and further refine the therapy for broader clinical application.

The new digital infrastructure has become an important basis for the green development of agriculture. This paper selects provincial sample data from 2006 to 2022, constructs an indicator system to measure new digital infrastructure and agricultural green total factor productivity, and explores how new digital infrastructure can promote agricultural green development. The results show that: (1) the new digital infrastructure has a significant role in promoting the green development of agriculture, and this conclusion is still valid after processing endogeneity and heteroscedasticity; (2) Technology and land factors play an intermediary role in the promotion of green agricultural development by the new digital infrastructure, that is, the new digital infrastructure helps to improve the quality and technical level of cultivated land and thus promote green agricultural development; (3) Labor force factors optimize the mediating role of technology factors, that is, the higher the quality of labor force, the stronger the role of the new digital infrastructure in promoting the green development of agriculture through the technological path; (4) The effect of new digital infrastructure on the green development of agriculture is obviously heterogeneous, and the effect is more obvious in northwest China and the regions with lower marketization level, higher environmental regulation level. This study is helpful to clarify the relationship between new digital infrastructure and agricultural green development in China, and provide corresponding policy suggestions for promoting regional agricultural green development.

In the present work consideration is given to the primordial black holes ({\bf pbhs}) in the Schwarzschild-de Sitter Metric with small mass (ultralight) in the preinflationary epoch. Within the scope of natural assumptions, it has been shown that the quantum-gravitational corrections ({\bf qgcs}) to the characteristics of such black holes can contribute to all the cosmological parameters, shifting them compared with the semiclassical consideration. These contributions are determined by a series expansion in terms of a small parameter dependent on the hole mass (radius). For this pattern different cases have been considered (stationary, black hole evaporation...). It has been demonstrated that involvement of ({\bf qgcs}) leads to a higher probability for the occurrence of such {\bf pbhs}. Besides, high-energy deformations of Friedmann Equations created on the basis of these corrections have been derived for different patterns. In the last section of this work it is introduced a study into the contributions generated by the above-mentioned {\bf qgcs} in inflationary cosmological perturbations. Besides, it has been shown that non-Gaussianity of these perturbations is higher as compared to the semi-classical pattern.

Recently, polyacetylene (PA) is receiving a renewed scientific attention due to its electrical properties potentially useful for energy applications (e.g., fabrication of electrodes for batteries and supercapacitors) and unique functional characteristics (e.g., flame retardant, oxygen scavenger, EMI-shielding, etc.). This chemical compound can be obtained in form of a polyacetylene-PVOH copolymers simply by chemical dehydration of poly(vinyl alcohol) (PVOH), which is a very common type of polymer, widely used in packaging and other technological areas. This very inexpensive chemical reaction for the large-scale synthesis of PA/polyvinylenes has been investigated by reacting PVOH with fuming sulfuric acid at room temperature. In this process, PVOH, shaped in form of film, is dipped in fuming sulfuric acid (i.e., H₂SO₄ at 95-97%) and, after complete chemical-dehydration, it has been mechanically removed from the liquid phase by using a nylon sieve. The reduction process leads to a substantial PVOH film conversion to PA as it has been proved by infrared spectroscopy (ATR-mode). Indeed, the ATR spectrum of the reaction product included all characteristic absorption bands of PA. The reaction product has been also characterized by UV-Vis spectroscopy in order to evidence the presence in the structure of conjugated carbon-carbon double bonds of various lengths. Differential scanning calorimetry (DSC) and thermogravimetric analysis have been used respectively to investigate the PA solid-state cis-trans isomerization and thermal stability in air and nitrogen, respectively.

Estimation of tree volume typically focuses on excurrent forms, with less attention given to decurrent forms. Species with decurrent form, particularly hardwoods, lack a dominant stem and have large diameter branches that can be included in the merchantable wood volume. We developed a preliminary two-equation system comprising a taper equation and a merchantable whole-tree volume (stem and branches) equation for Quercus rubra L. growing in Navarra (Northern Spain). The equation system includes the diameter at breast height and total tree height as independent variables, along with merchantable height—the height up to which the stem maintains a well-defined excurrent form—as an additional variable. After estimating the stem volume, the branch volume is estimated by subtracting the stem volume from the merchantable whole-tree volume. A second order continuous autoregressive error structure was used to correct for autocorrelation between residuals from the fitted taper equation. The equations explained 90% of the observed variability in diameter and 86% of the observed variability in merchantable whole-tree volume. Both equations have been implemented in the Cubica Navarra software for use as a system of equations. These equations are considered preliminary and will be refitted or validated as additional data becomes available from various locations.

Due to the convenience of implementing without additional transmitter, radar communication integrated systems using conventional communication signals have become an effective means of monitoring unmanned aerial vehicles and other aircraft. However, the non-ideal radar signal form causes the strong signal to cover the weak signal, and the clutter suppression is required to expose the target. As the target signal is extremely low, and the performance of conventional clutter suppression methods is limited, the residual clutter remains after the time-varying clutter is suppressed, which leads to many false alarm points on the processed RD spectrum. The two-dimensional distribution of these false alarms in range-Doppler is very similar to the target and difficult to be distinguished, which seriously affects the target detection and tracking. To reduce false alarm points and improve the performance of target detection, the difference of spatial-temporal stationarity between target signal and clutter in a short time is discussed in this paper, a radar false alarm suppression method based on target spatial-temporal stationarity is proposed by using the difference of stationarity between target signal and false alarm points in range, Doppler, energy and azimuth. In this algorithm, to ensure the short-time stationarity of the target, the RD spectrum of sub-frames with short time interval is obtained by using the sliding matching filtering method and extract the peak points. Then, the Mahalanobis distance between the peak points of each sub-frame is used to eliminate false alarm points. Finally, false alarm points are further eliminated through the target tracking and retain the real target information, so as to improve the radar target detection performance. The simulation experiments show that this method can eliminate more than 90% false alarms points while maintaining the detection performance of the target. The processing of actual data in the field experiment shows that the after suppression false alarm by this algorithm can obtain more ideal target detection results, and realize the tracking of the UAV and other aircraft track.

Abstract: Soil salinization is an essential risk factor for agricultural development as well as for food security, and how to obtain regional soil salinity information more reliably remains a priority problem to be solved. To improve the accuracy of the inversions for soil salinity, a new inversion model for soil salinity based on stacking integrated algorithm for learning was submitted for this work which took the prediction results of several basic models as new features and then trained a secondary model to fuse the prediction results of basic models. We compared and analyzed it against four machine learning regression models, namely, random forest (RF), back propagation neural network, support vector regression, and convolutional neural network. Findings indicated the stacking integrated learning regression model fitted better and had good stability, on the test set, the stacking integrated learning regression model showed a relative increase of 8.16% in R2, a relative decrease of 13.95% in RMSE, and a relative increase of 6.47% in RPD when compared to the RF model, which was the single most effective machine learning regression model, and the stacking model was able to achieve soil salinity inversion more accurately. The soil salinity in the oasis areas of the Manas River Basin tended to decrease from north to south in 2016 to 2020 from a spatial point of view, and it was reduced in April from a temporal point of view. The percentage of pixels with a high soil salinity content of 2.75–2.8 g/kg in the study area had decreased by 19.64% in April 2020 compared to April 2016. The innovatively constructed stacking integrated learning regression model improved the accuracy of soil salinity estimation on the basis of the superior results obtained in the training of the single optimal machine learning regression model. As a consequence, this model can provide technological backup for a fast monitoring and inversion of soil salinity as well as prevention and containment of salinization.

Lung macrophage cells play a critical role in various lung disease, and their state can change depending on the progression of the disease by inducing either an inflammatory or anti-inflammatory state. In this review, we investigate the potential therapeutic effects of treatment with antioxidant nanoparticles in airway disease focusing on porcine reproductive and respiratory virus (PRRSV), considering Reactive Oxygen Species (ROS) as one of the factors that regulate M1 and M2 macrophages in the inflammatory and anti-inflammatory states, respectively. In addition, we examine the status of protein structure research on CD163 (one of the markers of anti-inflammatory M2 macrophages) in human and veterinary lung diseases and suggest directions for future applicable research areas.

Human papillomavirus (HPV) is the most common sexually transmitted infection in the US and the world. Infection with high-risk oncogenic HPV strains have been shown to induce cellular transformation leading to anogenital and oropharyngeal cancers. The HPV vaccine first developed in 2006 for females aged 9-26 years has been demonstrated to be safe and effective in preventing 90% of all HPV associated cancers. However, vaccine hesitancy, misinformation, and barriers to vaccine access has resulted in suboptimal vaccination rates among adolescent populations, especially in rural communities in the South. HPV vaccine coverage in Tennessee is currently below the national average and below the Healthy People 2030 goal of an 80% vaccination rate for individuals 13-17 years old based on recommendation guidelines for up-to-date HPV vaccination status as of 2022. HPV vaccination rates for Tennesseans with private insurance in 2022 was 68% and 38% for those that were insured. Up-to-date HPV vaccination rates for Tennesseans living in urban communities was 58% in 2022 and 46% for those living in rural communities. Overall, HPV associated cancers rates are higher in Tennessee at 12.9/100,000 compared to the overall rate in the US of 11.8/100,000 persons in 2022. Interventions to improve HPV vaccine awareness, education, and access could improve vaccine confidence and uptake especially among rural and uninsured populations in Tennessee. Most recently, the Advisory Committee on Immunization Practices (ACIP) has expanded recommendations for the HPV vaccinations for some individuals 27-45 years who were not vaccinated at a younger age with shared clinical decision-making. The impact of this recommendation on Tennesseans should be examined.

Field of Research This research study investigates the role of distributed leadership and culture within small enterprises in Malta, an area in which there has been very limited research. It focuses on the interaction between these two elements and their impact on organizational behaviour and effectiveness. Several studies (Jardon et al 2019; Cope et al., 2011; de Oliveira et al., 2015; Mohanty et al., 2012;) suggest that effective leadership and culture are vital elements in the successful endeavours of small organizations. Schein (2006) further asserts that they are two different faces on the same coin and the dyadic interaction characterizes “how leaders create culture and how culture defines and creates leaders”. Distributed Leadership is a systematic practice of interactions and synergies between leaders, followers, and the situation (Spillane 2013). It is different from traditional leadership, which involves top-down management because it tends to be more democratic and participatory, whereby influence and decision-making are shared among multiple individuals rather than being controlled by a single leader. (Xhemajli et al. 2022) Methodology Adopting a naturalistic approach and guided by an interpretivist, constructivist epistemological stance, this research delves into the intricate interplay between leadership and culture within small businesses. The chosen research methodology employs Grounded Theory, recognized for its robust and systematic process facilitating the observation of patterns through intensive iterative and highly analytical procedures (Charmaz, 2015).In the initial stages of field research, characterized by 'responsive interviewing' (Rubin and Rubin, 2012), data sampling commenced with convenience sampling. This initial phase gradually evolved into a refined strategy incorporating selective sampling and theoretical sampling (Corbin and Strauss, 2015). The shift in sampling approaches reflects a deliberate focus on collecting data based on its specific contribution to the development and comprehension of the dimensions, properties, and characteristics of key concepts.The induction of theory is a product of successive comparative analyses, involving the systematic comparison of data points to identify both similarities and variations. Abductive reasoning is integral throughout the analytical process, with particular emphasis during constant comparative analysis of categories, leading to theoretical integration (Charmaz, 2006). This meticulous approach aligns with the assertion of Strauss and Corbin (2015) that grounding theory in data enhances the likelihood of deriving theories that authentically reflect reality.The key advantage of using this approach is that the analysis carried out goes beyond deep exploration and rich description and actually develops a framework that explains the phenomena observed (Parker and Roffey, 1997, p.218). This is very important if the findings are to contribute to the knowledge enhancement of any field of practiceThe culmination of this research effort is the presentation of a set of interconnected concepts, intricately woven together into an emergent parsimonious model (Corbin and Strauss, 2015). This model serves to explain the dynamic interplay between leadership and culture, elucidating its tangible impact on the effectiveness of small enterprises. Research Contribution:Implications for Practice This research contributes to the literature on leadership and organizational culture in small enterprises, particularly in the context of Malta where such studies are scarce. By providing an initial exploration of these phenomena and their impact on organizational behavior, this paper lays the groundwork for further inquiry into this important area. Furthermore, the study offers practical insights for leaders of small enterprises by elucidating leadership behaviors observed in established organizations. Through applied cases, it demonstrates how the abstract concept of culture can be translated into a practical instrument that supports leadership and organizational development. This not only enriches the understanding of leadership dynamics but also provides tangible strategies for implementation. Moreover, the research contributes empirical evidence on the development of leadership functions within small enterprises, emphasizing the potential of shared and distributed leadership models. By grounding its findings in data, the study equips leaders with a deeper understanding of how these leadership approaches can shape organizational development, fostering more effective management practices in small businesses. Methodologically, the adoption of Grounded Theory methodology showcases its efficacy in exploring complex management phenomena. By employing this approach, the researchers were able to gather rich descriptions and develop an explanatory framework, highlighting its applicability in studying nuanced organizational dynamics. Implications for Policy and Theory This research also holds significant implications for policymakers, academic researchers and institutions involved in supporting small businesses. By providing insights into distributed leadership and cultural influences on organizational effectiveness, it offers a basis for informed policy formulation and support mechanisms. The conceptual model emerging from this study serves as a foundation for designing more targeted policies that cater to the unique needs of small enterprises. By understanding the intricacies of leadership dynamics and cultural contexts, policymakers can develop interventions that foster a conducive environment for small business growth and sustainability. Additionally, academic researchers can use these findings to further refine theoretical frameworks and empirical studies in the field, contributing to ongoing scholarly dialogue and knowledge advancement.

High production and consumption of bananas produce large amounts of banana waste. However, the perception of banana as waste depends on the degree of ripeness, and is therefore subjective, depending on the sensorial perception, emotional reactions, and cultural differences of consumers. Understanding consumer perception of different levels of banana ripeness could contribute to better management of the supply chain, leading thus to a decrease of waste or better utilization of its waste as a byproduct. The aim of this study was to compare European and Chinese consumers’ sensorial and emotional responses to different stages of banana ripeness, using facial expression analysis, liking, and emotion measurement by self-report. One hundred and thirty-five adults of European and Chinese origin watched a video of banana consumption at five different degrees of ripeness. Participants’ facial expressions were analyzed with regard to six emotions (neutral, happy, sad, surprised, angry, disgusted, scared). Emotional responses and consumer acceptance of different degrees of banana ripeness were assessed using pictures of bananas as reference. Liking on a 9-point Likert scale and emotion measurement using PrEmo® followed, to evaluate emotional responses and acceptability of different degrees of banana ripeness. Results showed significant differences in facial reactions between cultures and among ripeness degrees. The European participants were more positively inclined towards all ripeness degrees, compared to the Chinese. Cultural expectations and level of familiarity seem to affect consumer acceptability greatly. Finally, explicit methods can give a more accurate picture of consumer preference and acceptance of different banana ripeness stages. A combination of liking and emotion measurement can be very effective in understanding consumer preference.

We investigate the cosmological evolution of the universe for a spatially flat FLRW background space within the context of $f(T,B)$ gravity, which is a recently formulated teleparallel theory that connects both $f(T)$ and $f(R)$ gravity under suitable limits. The analysis has been done by focusing on four different $f(T,B)$ cosmological models corresponding to various choices of scale factor, namely emergent, logamediate, and intermediate. In addition to this, we also assume a power-law-like function of $f(T,B)$ gravity. The reconstruction of $f(T,B)$ gravity has been done by considering the Holographic Ricci Dark Energy(HRDE) (a particular case of a highly generalized holographic dark energy given in Nojiri et al. in General Relativity and Gravitation, 38, pp.1285-1304,2006) as the background fluid. We analyze the equation of state parameters and the squared speed of sound for the reconstructed models. Finally, we conducted the thermodynamical analysis for each reconstructed model. The generalised second law of thermodynamics(GSLT) is valid for the four different $f(T,B)$ cosmological models.

The goal of this study is to create and carry out a self-healing cloud system by combining an event-driven automation framework depending on the if-this-then-that principle for managing incidents and recovery. A recovery engine with Artificial Intelligence (AI)-based decision-making approaches is presented—which chooses the best remedial actions from a pre-established catalogue in order to maximise system reliability and minimise downtime. The system is tested on an OpenStack-based video on demand service—where multiple issues are replicated in order to assess the efficaciousness of various recovery actions and workflows. The decision-making module of the recovery engine examines data from these experiments to determine the most effective remedial actions, taking into account their impact on the quality of service and other factors. The recovery engine is only meant to need human input when it comes to parameterizing and optimising decision models at particular points in time. In order to show how these AI-driven decision-making techniques can enhance mean time to repair and overall service quality in cloud environments—the study presents and assesses their results. This novel strategy represents a change towards cloud systems that are more sturdy autonomous, and able to effectively manage anomalies and recover from failure.

This study systematically addresses the challenge of accurately modeling memristors, focusing on four distinct types doped with tungsten, tin, chromium, and carbon, fabricated by Known Inc. A comprehensive characterization was performed by subjecting the devices to sinusoidal excitations with varying frequencies and amplitudes, followed by data averaging and high-frequency filtering. The resulting measurements were fitted using three prominent memristor models: VTEAM, MMS, and Yakopcic, with additional bespoke modifications assessed. These models, typically formulated as coupled algebraic-differential equations integrating electrical quantities (voltage and current) with internal state variables governing device dynamics, were optimized using two robust approaches: (1) interior-point optimization with gradient-based search, and (2) Nelder-Mead gradient-free optimization, both with box constraints applied. A thorough comparison and discussion of the optimized model parameters ensued, accompanied by an examination of sensitivity to diverse frequency and amplitude ranges. The findings inform conclusions and provide a foundation for future refinements, underscoring the importance of multi-model evaluation and advanced optimization strategies in precise memristor modeling. The presented methodology offers a valuable framework for elucidating optimal modeling paradigms tailored to specific memristor architectures and operating regimes, ultimately enhancing their integration in emerging neuromorphic and computational applications.

The reactions of the angular ligand 4,4’-oxybis(N-(pyridin-3-yl)benzamide (L1) and 1,4-naphthalenedicarboxylic acid (1,4-H2NDC) with divalent metal salts yielded three distinct coordination polymers (CPs): {[Zn2(L1)(1,4-NDC)2]MeOH}n, 1, {[Cu(L1)(1,4-NDC)(H2O)]3H2O}n, 2, and {[Cd(L1)(1,4-NDC)]2H2O}n, 3. Complex 1 features a 2-fold interpenetrated 3D framework with the (412.63)-pcu topology, while complex 2 reveals a 1D triple-strained helical chain, and complex 3 displays a 3-fold interpenetrated 3D framework with (66)-dia topology. Additionally, the reactions of the flexible ligand N‚N’-bis(3-methylpyridyl)adipoamide (L2) afforded {[Co4(L2)0.5(1,4-NDC)3(H2O)3(µ3-OH)2]·EtOH·2H2O}n, 4, {[Zn2(L2)(1,4-NDC)2]·2CH3OH}n, 5, and [Cd(L2)(adipic)(H2O)]n (H2adipic = adipic acid), 6, exhibiting a self-catenated 3D framework with the (420.68)-8T32 topology, a 2D layer with the (413·62)-(4,4)IIb topology, and a 2D layer with the (44·62)-sql topology, respectively. The structural diversity observed in complexes 1 - 6 highlights the pivotal influence of the metal center on the degree of entanglement in CPs within mixed-ligand systems. The thermal stability and luminescent properties of complexes 1 - 3, 4, and 6 are also discussed.

To achieve real-time identification of soybean seedlings and weeds in soybean seedling fields on agricultural intelligent terminal devices, this article constructs a real-time evolutionary identification system ESWIM driven by computational complexity under few-shot training. Firstly, based on the identification task, high-quality image samples are collected and annotated. By analyzing the terrain, the weather during the growth period, image acquisition methods, and the status of equipment fieldwork, we obtained a suitable enhancement algorithm. This algorithm not only effectively simulates the diversity of actual images, but also solves the problem of significant imbalance in the number of targets. Then, using the computational complexity driven approach and YOLOv5n as the foundation model, a soybean seedling and weed recognition model SWIM is constructed. The model's computational space and time complexity are reduced by 45.19% and 42.86%, respectively, while mAP (0.5) increased by 0.1%. Finally, by utilizing human-computer interaction to enable real-time and effective evolution of the dataset, an evolutionary strategy is designed to rapidly iterate and update model parameters, resulting in the development of the evolutionary system ESWIM. Laboratory experiments have shown that the ESWIM system can quickly adjust the recognition system during device operation to achieve target identification tasks in different environments.

The utilisation of non-Saccharomyces yeasts in co-inoculation and non-thermal technologies for must sterilisation is becoming increasingly prevalent due to its notable utility and potential. This approach optimises the fermentation process, facilitates the production of wines with distinctive characteristics and enhances their stability. Two trials were conducted concurrently, designated as A and B. The same red must was utilized in both trials, with one undergoing UHPH treatment and the other serving as the control. The non-Saccharomyces yeasts were identical in both trials. In Trial A, different percentages of initial inoculum were employed with respect to the total volume of must to be fermented, with the objective of evaluating the influence and competitiveness between yeasts. Trial B was designed to investigate the impact of two nutrients that provide vitamins, energy and protection from oxidative stress on the development of these yeasts and their metabolic expression. A microbiological analysis was conducted, along with oenological parameter measurements, acidification assessments, volatile compound analyses, and spectrophotometric evaluations. It was observed in both trials that the prevalence of Lachancea thermotolerans (Lt) was longer than that of Metschnikowia pulcherrima (Mp), and that the use of quercetin + thiamine had a positive effect on yeast growth. Furthermore, the combination of Lt and Mp yeasts demonstrated remarkable synergy, resulting in the production of a substantial quantity of lactic acid (&gt;5 g/L). With regard to aroma compounds, the UHPH must exhibited a nearly twofold increase in ethyl lactate. Additionally, the TPI was observed to be 8-10% higher in wines derived from UHPH musts, indicating that this technology may potentially safeguard against oxidation.

Polymer Electrolyte Membrane Fuel Cells (PEMFCs) have emerged as a pivotal technology in the automotive industry, significantly contributing to the reduction of greenhouse gas emissions. The high material costs of the gas diffusion layer(GDL) and bipolar plate(BP) creates a barrier for large scale commercial application. This study aims towards addressing this challenge by optimizing the material and design of the cathode, GDL and BP. While Deterministic design optimization(DDO) methods have been extensively studied, they often fall short when manufacturing uncertainties are introduced. This issue is addressed by introducing Reliability based design optimization(RBDO) to optimize four key PEMFC design variables i.e., δ_gdl, d_ch, w_ch, and w_land. The objective is to maximize cell voltage(V_cell) considering material cost of cathode gas diffusion layer(Cost_cGDL), and cathode bipolar plate(Cost_cBP) as reliability constraints. Results of the DDO show an increment in V_cell of 31mV, with reliability of around 50% for Cost_cGDL and Cost_cBP. In contrast RBDO method provides a reliability of 95% for both Cost_cGDL and Cost_cBP.