This study presents the development of a Recurrent Neural Network (RNN) model for classifying smart home device data. Utilizing a dataset sourced from Kaggle, the study explored the processes of data gathering, loading, normalization, and model construction. The RNN, equipped with Long Short-Term Memory (LSTM), was trained and evaluated, demonstrating significant improvements in training and validation accuracy over 10 epochs, culminating in a test accuracy of 83.25%, and only 35.4% loss. The evaluation of the model on the test set provides a test accuracy result, accompanied by an in-depth analysis of ROC curves and Area Under the Curve (AUC) scores for multi-class classification, along with a confusion matrix. The AUC score of 0.9896 indicates outstanding performance in accurately classifying IoT device categories. These findings indicate that the RNN with LSTM exhibits superior learning efficiency and generalization capabilities, making it more suitable for IoT device classification tasks. This article emphasizes the concept of IoT and reviews recent studies on the application of deep learning models across various IoT domains, including smart homes, industrial systems, and healthcare. Future research could focus on improving real-time processing capabilities, and scalability, and incorporating diverse IoT data types to enhance and broaden the model's practical applications.

A longstanding debate is raised about how far does the credit sector promote local investment and whether banking sector is efficacious in the meaning it helps respond to investors financing requirements and whether it serves the conduct of monetary policy in its conventional formulation. There are much concerns also about the alternatives of monetary policy conduct when the interest rate channel transmission mechanism does no longer work in terms of effectiveness of monetary policy and how to step aside credit supply insufficiencies and disruptions in order to respond to excess credit demand. This article provides an assessment of the relevance of unconventional monetary policy to deal with the issue of the vicious role played by the credit sector in its failure to fulfill banking sector efficacy due to an excessive search for yield motivation and extreme awareness from systemic risk. We run an ARDL and Bound test to the data collected on the Tunisian banking sector and show that Banking sector efficacy is affected in the short run by Fisherian sensitivity another expression of the impact of inflation on real activity and banking sector specificities and in the long run it is affected by the impact of inflation on real activity and business cycle effects expressed in terms of sensitivities of Credit supply and demand to output gap. Banking specificities do no longer matter in the long run and banking sector efficacy is not at all affected by Monetary policy in the short run because of nominal rigidities and in the long run because of monopolistic competition and backward oriented expectations that diverts the pass through effect of monetary policy as the pricing of loans is exclusively tributary on the search for yield motivation and the agency costs and result in a non competitive price of risk premiums that do not translate the transmission of monetary policy. We conclude that the behavior of the banking sector is vicious because it conveys importance exclusively to the search for yield motivation and profitability of the banking sector and to the mitigation of systemic risk and does not play any role in the promotion of local investment which is a pillar of economic performance and growth. We propose the Credit easing as an unconventional monetary policy that can step aside the hindrance of an inelastic credit supply through modifying asset prices and thereby improving attractiveness of assets to the banking sector and forcing financing through credit allocation but warn from the difficulties that might result from unconventional monetary policy in terms of inability to withdraw from the perturbation and gauge discretionary policy accurately.

The article addresses the issues of scalability and security in blockchain networks, with a focus on sharding algorithms and decentralized data storage. Key challenges include the low throughput and high transaction latency in public networks such as Bitcoin and Ethereum. Sharding is examined as a method to enhance performance through data distribution, but it raises concerns regarding node management and reliability. Sharding schemes, such as Elastico, OmniLedger, Pyramid, RepChain, and SSchain, are analyzed, each presenting its own advantages and drawbacks. Alternative architectures like Directed Acyclic Graphs (DAG) demonstrate potential for improved scalability but require further refinement to ensure decentralization and security. Protocols such as Brokerchain, Meepo, AHL, Benzene, and CycLedger offer unique approaches to addressing performance and transaction consistency issues. The article emphasizes the need for a comprehensive approach, including dynamic sharding, multi-level consensus, and inter-shard coordination. Additionally, a conceptual model is proposed that incorporates sharding of transactions, states, and networks, which enables greater scalability and efficiency.

Background/Objectives: Obesity and nonalcoholic fatty liver disease (NAFLD) reduce life expectancy; nonoperative interventions show poor results. Individually, chitosan, acetic acid (AA), and sepiolite clay attenuate high-fat diet-induced obesity (DIO) via reduced energy digestibility and increased energy expenditure. We hypothesized that a chitosan–sepiolite biocomposite suspended in AA would attenuate DIO and NAFLD to a greater extent than AA alone via its more substantial adsorption of nonpolar molecules; Methods: We tested this dietary supplement in C57BL/6J mice fed a high-fat diet, compared to supplementation of a bile-acid sequestrant (cholestyramine) and standalone AA; Results: Biocomposite supplementation attenuated DIO rate and NAFLD progression, whereas standalone AA showed mild attenuation of DIO gain and did not prevent HFD-induced hepatic fat accumulation. Biocomposite intake was accompanied by a decreasing digestibility trend counterbalanced by increased intake; hence, it did not affect energy absorption; Conclusions: Therefore, DIO attenuation was suggested to be related to higher energy expenditure, a phenomenon not found with AA alone, as supported by calculated energy expenditure using the energy-balance method. These results support further investigation of the biocomposite’s efficacy in attenuating obesity and NAFLD, specifically when applied with a restricted diet. Future studies are needed to determine this biocomposite's safety, mechanism of action, and efficacy compared to its components given separately or combined with other ingredients.

The skin, as the body's largest organ, is vital for protecting against environmental stressors, regulating temperature, and preventing water loss. Maintaining a healthy skin barrier is essential for overall skin health and function. We examined the potential of a mixture of five traditional Korean herbal extracts—Cimicifuga racemosa, Paeonia lactiflora, Phellodendron amurense, Rheum rhaponticum, and Scutellaria baicalensis— referred to as herbal medicine composite 5 (HRMC5) for enhancing skin health. High-performance liquid chromatography identified 14 bioactive compounds, including flavonoids, phenolic acids, anthraquinones, and alkaloids, known for their antioxidant, anti-inflammatory, and antimicrobial effects. The extract showed concentration-dependent effects on cell viability and provided UV protection at 6.25 g/L. It also significantly promoted wound healing, similar to the efficacy of epidermal growth factor. Furthermore, the mixture exhibited anti-inflammatory activity by downregulating Cox2 expression and upregulated key skin barrier proteins, including Involucrin, Claudin 1, and Collagen Type 1. These findings highlight the potential of this herbal mixture for addressing skin barrier dysfunction, inflammation, and facial redness, supporting its use in developing natural skincare products. Future studies should investigate the mechanisms behind these effects and assess its clinical applications.

2-phenylchromen-4-one, commonly known as flavone, plays multifaceted roles in biological response, largely due to its abundance in natural sources. The methoxy group in naturally occurring flavones promotes cytotoxic activity in various cancer cell lines by targeting protein markers, in facilitating ligand-protein binding mechanisms and activating cascading downstream signaling pathways leading to cell death. However, the lipophilic nature of these analogs is a key concern as it impacts drug membrane transfer. While lipophilicity is crucial for drug efficacy, excessive effects in flavonoids can reduce water solubility and hinder drug transport to target sites. Recent in vitro studies suggest that the incorporation of polar hydroxyl groups which can form hydrogen bonds and stabilize free radicals may help overcome the challenges associated with methoxy groups while maintaining their essential lipophilic properties. Naturally coexisting with the methoxyflavones, this review explores the synergistic role of hydroxy and methoxy moieties through hydrogen bonding capacity in maximizing cytotoxicity against cancer cell lines. The physicochemical analysis revealed the potential intramolecular interaction and favorable electron delocalization region between both moieties to improve cytotoxicity levels. Together, the analysis provides a useful strategy for structural-activity relationship (SAR) of flavonoid analogs in distinct protein markers, suggesting optimal functional group positioning to achieve balanced lipophilicity, effective hydrogen bonding, and simultaneously minimized steric hindrance in targeting specific cancer cell types.

2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane (F4TCNQ) is a molecule widely employed as a very effective p-dopant of semi-conducting polymers, such as poly(3-hexylthiophene -2,5-diyl) (P3HT). The CN stretching transitions of F4TCNQ are exceptionally sensitive to the charge state of the molecule, thus allowing the doping diagnosis via IR spectroscopy. Less pronounced frequency shifts can reveal characteristics of the intermolecular environment. We present a systematic study based on Density Functional Theory (DFT) calculations and on experiments aimed at exploring how different factors, such as the charge state and the environment, modify the vibrational spectra of F4TCNQ. While several effects on the vibrational frequencies are well known and have been thoroughly investigated in the past, this study focuses on the infrared intensities of the CN stretching modes and reveals that they are strongly affected both by the charge state of the molecule and by the surrounding medium: it is then mandatory to consider such remarkable intensity modulation for any quantitative diagnosis based on spectroscopic measurements, e.g., concerning the amount of F4TCNQ molecules involved in the formation of charge transfer complexes.

Seafloor geodetic station positioning generally uses internal precision to judge the quality of the positioning result, which has many limitations. This paper proposes to use a seafloor geodetic station structure, which can obtain the external coincident accuracy of the acoustic beacon positioning, to evaluate the quality of the observation configuration. The results of the sea trial show that a circular line with a radius of 2 times the depth of the water has an internal precision and an external coincident accuracy of approximately 0.02m; this circular line path also happens to be the best trajectory.

Drowning is among the most common reasons of children’s death aged one to fourteen around the globe. With rising populations and the growing popularity of swimming pools in hotels and villas, the incidence of drowning has accelerated. Accordingly, the development of systems for detecting and preventing drowning has become increasingly critical to provide safe swimming settings. In this paper, we propose a comprehensive review of recent existing advancements in automated drowning detection and prevention systems. The existing approaches can be broadly categorized according to their objectives into two main groups: detection-based systems and detection and rescue-based systems. Automatic drowning detection approaches could be further categorized into computer vision-based approaches where camera-captured images are analyzed by machine learning algorithms to detect instances of drowning, and sensing-based approaches where sensing instruments are attached to swimmers to track their behavior. We explore the advantages and limitations of each approach. Additionally, we discuss the technical challenges and unresolved issues related to this domain. Based on this study, we provide researchers and practitioners with a comprehensive analysis of the state-of-the-art techniques for drowning detection and prevention.

This paper presents a strategic planning model aimed at optimizing the economic and environmental impacts of Palm Oil Mill Effluent (POME) treatment systems. The model determines the optimal selection of POME treatment systems to minimize environmental impact, specifically focusing on three systems: Anaerobic Digester Tank System (ADT), Covered Lagoon System (CL) with biogas capture, and Open Pond System (OP). The model incorporates constraints related to Fresh Fruit Bunch (FFB) production, POME generation, Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and Carbon Dioxide (CO2) emissions. The optimization framework, formulated as a mixed-integer linear programming (MILP) model, is solved using the GAMS 40.1.0 software. Integer decision variables are used to represent the choice of POME treatment systems that minimize environmental impact. The study specifically considers the ADT, CL, and OP systems, with results indicating that the ADT system is the most effective in reducing BOD, COD, and CO2 equivalent emissions, thereby highlighting its environmental benefits. The model has selected the ADT treatment system, which exhibits the lowest COD, BOD and CO2e emissions. Specifically, the COD registered an 85% reduction rate from 84,830 mg/L to 12,725 mg/L. The BOD level was reduced by 88% resulting in a BOD level from 41,208 mg/L to 4,945 mg/L. The minimum CO2e emission that can be archived was about 3,034 t CO2e per annum. This model provides a valuable tool for governmental agencies and policymakers to guide the private sector in developing environmentally sustainable POME treatment strategies.

Purpose: The objective of this study was to evaluate the facial profile changes of patients treated for class II skeletal malocclusions with the Elastodontic appliance compared to those treated with the Herbst appliance and a control group. Methods: Forty class II patients were treated using the Elastodontic appliance (Group EA) and were compared with 40 patients treated with a Herbst appliance (Group H) and with 40 untreated class II children (Group C). Aesthetic profile variables were analysed using Arnett's analysis. Cephalograms were compared pre-treatment (T0) and post-treatment (T1). The Wilcoxon signed-rank test or paired-samples t-test were used for pairwise comparison of cephalometric measurements taken at T0 and T1. One-way ANOVA and Tuckey's post-hoc test were performed to assess differences between the groups. Results: In Group C, from T0 to T1, the LL-TVL distance and POG'-TVL distance decreased. In the EA group, the 1+SN angle increased from T0 to T1, while the POG'-TVL and B'-TVL distances decreased. In Group H from T0 to T1, the POG'-TVL, B'-TVL, LL-TVL, and 1+SN measurements decreased while 1-GOME (IMPA) increased. There were significant differences on ANOVA in 1+SN, IMPA, 1+TVL, LL-TVL, and UL-TVL. Compared to Group C, 1+SN and UL-TVL were lower in Group H, while IMPA and 1+TVL were higher in group H. Compared to the EA group, 1+SN and UL-TVL were lower in the H Group, while IMPA and LL-TVL were higher in the H Group. Conclusion: The profile changes achieved by both devices are favourable for correcting class II skeletal malocclusion.

Background: Physical-sports habits in adulthood constitute one of the predictors of physical, psychological and social health within healthy lifestyles. Methods: The Acquired Healthy Lifestyle Assessment Scale was applied to a sample of 788 subjects between the ages of 22 and 72 and the dimension that makes up physical-sports practice habits was analyzed. Results: 74.4% of adults have habits of physical sports practice that are not healthy or unhealthy, 18.8% tend towards health and only 6.9% are healthy. Pearson's 2 tests show a significant association between men and healthy habits, without observing changes associated with the age variable. The t-student and one-factor ANOVA tests confirm the relationship between the level of health and physical-sports practice habits depending on sex and age. Conclusions: It is necessary to promote preventive programs to increase participation in the practice of physical and sports exercise in the adult population that has unhealthy or unhealthy levels of lifestyle.

Oxidative stress (OS), defined as a disruption in redox balance favoring oxidants, has emerged as a major contributor to numerous diseases in human and veterinary medicine. While several reviews have explored the implication of OS in human pathology, an exhaustive review for the canine species is lacking. This comprehensive review aims to summarize the existing literature on the role of OS in canine diseases, highlighting its potentially detrimental effect on various organs and systems. Some inconsistencies among studies exist, likely due to varying biomarkers and sample types. However, there is substantial evidence supporting the involvement of OS in the development or progression of numerous canine disorders, such as cardiovascular, oncologic, endocrine, gastrointestinal, hematologic, renal, neurologic, infectious and parasitic diseases, among others. Additionally, this review discusses the efficacy of antioxidant and pro-oxidant therapeutic agents for these conditions. Dietary interventions to counteract OS in dogs have gained significant attention in recent years, although further research on the topic is needed. This review aims to serve a foundational resource for future investigations in this promising field.

Background: In the aftermath of the ORAL Surveillance trial, real-world studies are needed to elucidate the effectiveness and safety of Janus kinase inhibitors (JAKis) in routine clinical practice. Methods: We performed a multicenter, retrospective cohort study investigating the real-life effectiveness and safety of filgotinib, a novel JAK1 inhibitor, in adult patients with rheumatoid arthritis (RA). Demographic information, disease characteristics, prior treatment history, and comorbid conditions, were retrieved from clinical records at baseline (T0) and after 3 (T3) and 6 months (T6) of treatment. Results: A total of 82 patients (63 women) agreed to participate in the study, of which 39 (47.6%) were older than 65 years. Average RA duration was 13 ± 9 years; 19 patients (23.1%) were current or former smokers and 4 patients (4.9%) had a past history of cardiovascular events. Most patients had previously received at least one biologic disease-modifying antirheumatic drug (range: 1–6+); in addition, 11 patients (13.4%) had been already exposed to another JAKi. During the follow-up, 7 patients discontinued treatment due to primary failure (n = 3) or adverse events (n = 4). Significant reductions in pain and number of tender and swollen joints were observed at T3 and T6. A relevant proportion of patients achieved DAS28-CRP remission at T3 and T6 (46.3% and 66.2%, respectively). Conclusion: Our data provide additional insight into the effectiveness and safety of filgotinib in a real-world setting, even among patients with difficult-to-treat RA and a high prevalence of cardiovascular risk factors.

Since its introduction, weight-bearing computed tomography (WBCT) has gained prominence due to its ability to produce accurate three-dimensional images under natural loading conditions, making it particularly useful for assessing complex foot deformities. This review aimed to focus on the diseases of the foot and categorized the pathological conditions into forefoot disease (hallux valgus), midfoot disease (Lisfranc injuries and midfoot osteoarthritis), and progressive collapsing foot deformity. Owing to its superior diagnostic therapeutic value, WBCT is poised to become the gold standard in foot and ankle imaging.

The primary cilium, the cell’s sensory and signaling antenna, is a dynamic cellular organelle during the cell cycle. It is resorbed before cells enter mitosis and reformed after cells exit mitosis, and as such acts as a structural barrier to mitosis. Cancer cells that undergo rapid cell cycle and replication have a low ciliation rate. Therefore, promoting cilia formation and elongation and thereby reducing their rates of retraction hold the key to blocking cell entry into mitosis and slowing down cancer cell replication. To perform a comprehensive and efficient literature search on drugs that can promote ciliogenesis, we developed an intelligent process that integrates the GPT-3.5 application programming interface (API) into a PubMed scraper that we coded, enabling the large language model (LLM) to directly query articles for predefined user questions. Among the top candidates identified are two FDA-approved drugs, Alvocidib and Alisertib, that showed strong potential to induce cilia formation and elongation. We first confirmed that these drugs can effectively increase the ciliation rate and the cilia length of DAOY medulloblastoma cells. We then applied Alvocidib and Alisertib individually or in combination to DAOY medulloblastoma and A549 lung cancer cells and observed a statistically significant decrease in the number of viable cells. These results demonstrated the potential of using cilia-promoting drugs to suppress cancer cell replication. Additionally, it shows the massive benefits of integrating accessible large language models to conduct sweeping, rapid, and accurate literature searches.

Surgical approaches for ovarian cancer have advanced significantly in recent years. Transvaginal natural orifice transluminal endoscopic surgery (vNOTES) is an emerging technique in gyneco-logical oncology, with limited reports of its use in ovarian cancer management. This study aimed to evaluate the feasibility and safety of vNOTES for the surgical staging of early-stage adnexal malignancies. Methods: We retrospectively reviewed all cases of borderline ovarian tumors (BOTs) and early-stage ovarian cancer surgically staged using vNOTES at our institution between October 2021 and August 2024. Results: Eleven patients were included, 7 with ear-ly-stage ovarian or tubal cancer and 4 with BOTs. The median age was 47 (27 – 81) years, and the median body mass index was 28.1 (22.4 – 39.2) Kg/m². Complete vNOTES staging was achieved in all cases, including peritoneal washing, unilateral/bilateral salpingo-oophorectomy, ab-dominal cavity inspection, peritoneal biopsies, infracolic omentectomy, and total hysterectomy when required. The median operating time was 70 (35 – 138) minutes, with a median blood loss of 50 (10 – 100) mL. No intraoperative complications occurred except for one case of minor ovar-ian spillage. No conversions to laparoscopy or laparotomy were needed. Postoperative compli-cations included one surgical site infection (9.1%) and two cases of postoperative cystitis (18.2%). No severe complications graded ≥3 on the Clavien-Dindo classification were observed. Conclu-sion: vNOTES appears to be a feasible and safe approach for the surgical staging of selected pa-tients with early stage adnexal malignancies. Further studies are needed to validate its long-term safety and oncological outcomes.

In this paper, we review and advance the application of deep learning algorithms for anomaly detection in Software Defined Networks (SDN). As SDN environments become more prevalent in modern networking infrastructures, their centralized control and dynamic nature make them susceptible to various security threats, including Distributed Denial of Service (DDoS) attacks, data breaches, and unauthorized access. Traditional anomaly detection techniques often fall short in adapting to these evolving threats, necessitating more robust, adaptive solutions. This study evaluates three prominent deep learning architectures—Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Autoencoders—for their effectiveness in detecting anomalous behavior in SDN environments. Through extensive experimentation, we compare these models in terms of accuracy, precision, recall, F1-score, and ROC-AUC, highlighting their strengths and limitations. Our results show that CNNs excel in detecting spatial anomalies, RNNs are wellsuited for temporal anomaly detection, and Autoencoders provide robust detection for previously unseen anomalies. Additionally, we examine the sensitivity of threshold settings in Autoencoders and assess the real-time feasibility of these models by measuring their inference times. The findings suggest that deep learning-based anomaly detection significantly enhances the security of SDNs, providing both accuracy and speed suitable for real-time applications. Finally, we propose future directions for optimizing deep learning models to handle large-scale, dynamic SDN deployments more effectively. This work contributes to the growing body of research focused on utilizing deep learning for improving the security and resilience of SDN-based infrastructures.

Anti-oxidative and anti-inflammatory properties of high-density lipoprotein (HDL) are thought to be mediated by paraoxonase 1 (PON1), a calcium-dependent hydrolytic enzyme carried on a subfraction of HDL that also carries other anti-oxidative and anti-inflammatory proteins. In humans and mice, low PON1 activity is associated with elevated oxidized lipids, homocysteine (Hcy)-thiolactone, and proteins modified by these metabolites, which can cause oxidative stress and inflammation. PON1-dependent metabolic changes can lead to atherothrombotic cardiovascular disease, Alzheimer’s disease, and cancer. The molecular bases underlying these associations are not fully understood. Biochemical, proteomic, and metabolic studies have significantly expanded our understanding of mechanisms by which low PON1 leads to disease and high PON1 is protective. Studies discussed in this review highlight the changes in gene expression affecting proteostasis as a cause of the pro-oxidative and pro-inflammatory phenotypes associated with attenuated PON1 activity. Accumulating evidence supports the conclusion that PON1 regulates the expression of anti-oxidative and anti-inflammatory proteins, and that disruption of these processes leads to disease.

Mosquito-borne diseases are a major public health issue. The United States has experienced mosquito-borne disease outbreaks, including West Nile virus, Zika, dengue, and malaria, highlighting widespread risk across the country. In the United States, exposure to mosquito vectors primarily occurs outdoors. Individuals experiencing unsheltered homelessness are particularly vulnerable to mosquito-borne diseases due to their increased exposure to mosquito vectors, limited access to healthcare, and high prevalence of underlying conditions. This review aims to examine the literature on mosquito-borne disease infections among individuals experiencing homelessness in the United States between 1999 and 2024. Our search strategy used Boolean methods on PubMed, Scopus, and Web of Science to select relevant publications. Two queries were used: the first query focuses on individuals experiencing homelessness and mosquito-borne diseases, and the second one focuses on epidemiological analyses of mosquito-borne disease outbreaks in the US. Only studies reporting at least 1 infection among individuals experiencing homelessness were included. Our search resulted in the identification of 347 studies. After screening them, 8 were ultimately included in this review. Half of the studies identified West Nile virus among individuals experiencing homelessness and the other half reported cases of malaria. These studies show that individuals experiencing homelessness are more exposed to mosquitoes, face a higher risk of infection, and have an increased likelihood of hospitalization due to mosquito-borne diseases compared to the general population. The full extent of mosquito-borne disease exposure among individuals experiencing homelessness remains unclear. With climate change and globalization, there is a consensus in the literature showing an increasing risk of mosquito-borne disease transmission in the next decades, and we have already started to witness this increasing trend. Therefore, we argue that developing policies aimed at providing homeless populations with housing during mosquito-borne disease outbreaks could be instrumental in protecting this segment of the population and promoting health equity.

The safety and reliability of structures operating in cold regions are seriously threatened by icing. This has attracted researchers to explore de-icing/anti-icing methods. In this work, a bubble de-icing system was established and investigated for the removal of ice from frozen structures using bubble pulsation energy. Traditional de-icing method face high energy consumption, environmental pollution, and secondary icing issues. To avoid this and promote the development of de-icing techniques, the massive amount of energy generated by bubble pulsation can be used for de-icing. In the present work, experimental methods have been adopted to demonstrate the possibilities of de-icing using cavitation energy. Many test cases, in which the bubble distance from the target and thickness of ice layer were varied 5 - 35 mm, were examined to determine the mechanism of ice removal by the bubble energy. This research demonstrated that bubble energy can be used effectively for de-icing. Bubble energy should be further explored for de-icing techniques and engineering application prospects.

Social Media use among teens specially YouTube, Instagram or Snapchat is on peak today, creators on these platforms are of diverse age groups but dominated by Teenagers and Young adults whom the audience of the same age find easy to relate with. This research seeks to figure out the viable fruits teenagers are able to consume from the help of Social Media and Influencers (SM&I) for the five aspects of their development i.e. 'holistic development”, while briefly touching the type and category of content. In this descriptive research we used a quantitative survey to analyze the perception of teens about influencers and their effects on their development. The research is solely focusing on holistic development with social media uses trends and prevalence. It does not dwell on quality quantity and negative sides of this phenomenon. This could be oveserved after this survey research that adolescents in rural settings too are heavily using social media, impact of social media on teens is huge teens are not only using social media for Identity formation but for formative development as well.

The basis of orchard production are rootstocks that reduce the vigor of sweet cherry trees. However, not all rootstocks for this species can be easily propagated using traditional methods by stooling or cutting. Some of these must be propagated using the in vitro culture method. This results in their high price and, consequently, the price of maiden sweet cherry trees. The experiment assessed the growth of maiden trees of selected sweet cherry varieties ('Bellise', 'Earlise', 'Lapins', 'Vanda') depending on the method of the semi-dwarf GiSelA 5 rootstock propagation. Additionally the intensity of life processes taking place in the obtained maiden trees was also examined. One type of this rootstock, obtained by cheaper method from softwood cuttings, was compared with the another propagated using the in vitro method. There were no significant differences in the percentage of maiden trees obtained in both types of rootstocks propagation method and in the two years of study. The efficiency of maiden trees varied little and ranged from 77.43% to 87.74% depending on the factors considered. The vigor of maiden trees growth in the first year of the study was stronger than in the second one. Only the diameter of maiden trees stem varied (from 7% to 39%), depending on the variety considered. With the exception of one variety, maiden trees produced on rootstock propagated from stem cuttings were characterized by a much larger diameter for the three varieties, ranging from 23% to 29% compare to in vitro. Also with this method of propagation, the rootstocks had a better developed root system and the maiden trees had a greater fresh mass. However, most often, no significant differences were found between the methods of propagation, only when comparing the years of research. The activity of life processes of maiden sweet cherry trees varied and no constant regularities were found. The only regularity observed was that maiden trees in the second year of the experiment were more often characterized by lower levels of the tested life parameters (Pn, E, C and Int.CO2), which was associated with worse growth results.

Gaining insight into tribological systems is crucial for optimizing efficiency and prolonging operational lifespans in technical systems. Experimental investigations are time-consuming and costly, especially for reciprocating seals in fluid power systems. Elastohydrodynamic lubrication (EHL) simulations offer an alternative but demand significant computational resources. Physics-informed neural networks (PINNs) provide a promising solution using physics-based approaches to solve partial differential equations. While PINNs have successfully modeled hydrodynamics with stationary cavitation, they have yet to address transient cavitation with dynamic geometry changes. This contribution applies a PINN framework to predict pressure build-up and transient cavitation in sealing contacts with dynamic geometry changes. The results demonstrate the potential of PINNs for modeling tribological systems and highlight their significance in enhancing computational efficiency.

Newts, a type of urodele amphibian, offer remarkable insights into regenerative medicine due to their extraordinary tissue regeneration capabilities—a challenging feat in humans. During limb regeneration of adult newts, fascinating cellular and molecular processes are revealed, including scarless healing, de-differentiation of mature cells, and regeneration of limbs and digits. Sonic hedgehog (Shh), crucial for vertebrate limb development, is regulated by the zone of polarizing activity regulatory sequence (ZRS) in the limb bud zone of polarizing activity (ZPA). The metamorphosed (terrestrial) newt can reactivate Shh during regeneration, facilitating proper limb patterning. Cell types capable of regulating the ZRS in metamorphosed newts remain unknown. The identification of such cell types provides invaluable insight to novel regenerative mechanisms. Therefore, in this study, we developed the first newt ZRS reporter. First, we isolated and characterized the newt ZRS enhancer (nZRS), identifying conserved DNA binding sites. Several binding sites with medical relevance were conserved in the newt ZRS. In a functional analysis, we developed a system composed of a transgenic nZRS reporter newt and a new newt anti-Shh antibody, together allowing Shh monitoring during limb regeneration. This system provides a valuable in vivo approach for future genetic studies of patterning during limb regeneration.

In the delicate context of climate change and global warming new technologies are being investigated in order to reduce pollution. The solid oxide fuel cell (SOFC) stands out as one of the most promising fuel cell technologies for directly converting chemical energy into electrical energy, with the added benefit of potential integration into co-generation systems due to its high-temperature waste heat. They also offer multi-fuel flexibility, being able to operate on hydrogen, carbon monoxide, methane, and more. Additionally, they could contribute to carbon sequestration efforts and, when paired with a gas turbine, achieve the highest efficiency in electricity generation for power plants. However, their development is still challenged by issues related to high-temperature materials, the design of cost-effective materials and manufacturing processes, and the optimization of efficient plant designs.To better understand SOFC operation, numerous mathematical models have been developed to solve transport equations coupled with electrochemical processes for three primary configurations: tubular, planar, and monolithic. These models capture reaction kinetics, including internal reforming chemistry. Recent advancements in modeling have significantly improved the design and performance of SOFCs, leading to a sharp rise in research contributions. This paper aims to provide a comprehensive review of the current state of SOFC modeling, highlighting key challenges that remain unresolved for further investigation by researchers.

The article is devoted to the study of economic cycles within the framework of the theory of Kondratieff's long waves or K-waves. The object of the study is Dubovsky's fractional mathematical models, which consist of two nonlinear ordinary differential equations of fractional order and describe the dynamics of the efficiency of new technologies and capital productivity, taking into account constant and variable heredity. Fractional mathematical models also take into account the dependence of the accumulation rate on capital productivity, the influx of external investment and new technological solutions. The effects of heredity lead to a delayed effect of the reaction of the system in question to the impact. The property of heredity in mathematical models is taken into account using fractional derivatives of constant and variable orders, which are understood in the sense of Gerasimov-Caputo. Dubovsky's fractional mathematical models are studied numerically using the Adams-Bashforth-Moulton algorithm. Using a numerical algorithm, oscillograms and phase trajectories were constructed for various values of the model parameters. It is shown that Dubovsky's fractional mathematical models can have limit cycles, and there are no self-oscillatory modes.

In Alzheimer’s disease (AD), tau dissociates from microtubules (MTs) due to hyperphosphorylation and misfolded. It is degraded by various mechanisms, including the 20S proteasome, chaperone-mediated autophagy (CMA), 26S proteasome, macroautophagy, and aggrephagy. Neurofibrillary tangles (NFTs) form upon impairment of aggrephagy, and eventually, the ubiquitin chaperone valosin-containing protein (VCP) and heat shock 70 kDa protein (HSP70) are recruited to the sites of NFTs for extraction of tau for the ubiquitin-proteasome system (UPS)-mediated degradation. However, impairment of tau degradation in neurons allows tau to be secreted into the extracellular space. Secreted tau can be monomers, oligomers, and paired helical filaments (PHFs), which are seeding competent pathological tau that can be endocytosed/phagocytosed by healthy neurons, microglia, astrocytes, oligodendrocyte progenitor cells (OPCs) and oligodendrocytes, often causing proteotoxic stress, and eventually triggers senescence. Senescent cells secrete various senescence-associated secretory phenotype (SASP) factors, which trigger cellular atrophy, causing decreased brain volume in human AD. However, the molecular mechanisms of proteotoxic stress and cellular senescence are not entirely understood and are an emerging area of research. Therefore, this comprehensive review summarizes pertinent studies that provided evidence for the sequential tau degradation, failure, and the mechanistic link between tau-driven proteotoxic stress and cellular senescence in AD.

The Ultra-Cold Neutron (UCN) source being commissioned at North Carolina State Universityâs PULSTAR reactor is uniquely optimized for UCN production in the former graphite-filled thermal column outside of the reactor pool. The source utilizes a remote moderation design which is particularly well suited to the PULSTAR reactor on account of its large thermal and epithermal neutron leakage from the core face. This large non-equilibrium flux from the core is efficiently transported to the UCN source through the specially designed beam port in order to optimize UCN production at any given reactor power. The increased distance to the source from the core also greatly limits the heat load on the cryogenic system. A MCNP model of this system was developed and is in good agreement with gold foil activation measurements using a mock-up setup as well as with the real UCN source heavy water moderator. These results established a firm baseline for estimates of the cold neutron flux available for UCN production and prove that remote moderation in a thermal column port is a valuable option for future designs of cryogenic UCN sources.

This paper studies on aeroelastic control for the two-dimensional airfoil-flap system with unknown gust disturbances and model uncertainties. Open loop limit cycle oscillation (LCO) happens at the post-flutter speed. Structural stiffness of the aeroelastic system is represented by nonlinear polynomials. To robustly suppress aeroelastic vibration within finite time, a backstepping terminal sliding mode control (BTSMC) is proposed. In addition a learning rate is incorporating into the backstepping TSMC to adjusthow fast the aeroelastic response converges to zero. In order to overcome the limitation of the BTSMC design that it is dependent on prior knowledge, a nonlinear disturbance-observer (DO) is designed to estimate the variable observable disturbances. The closed-loop aeroelastic control system is proved to be globally asymptotically stable and converge within finite time by using Lyapunov theory. Simulation results of an aeroelastic two-dimensional airfoil with both trailing-edge (TE) and leading-edge (LE) control surfaces show that the proposed DO-BTSMC is effective for flutter suppression, even when subjected to gusts and parameter uncertainties.

The extent of single- and multi-cropping systems in any region, and potential changes to it, have consequences on food and resource use, raising important policy questions. However, addressing these questions is limited by a lack of reliable data on multi-cropping practices at a high spatial resolution, especially in areas with high crop diversity. In this paper, we describe a relatively low-cost and scalable method to identify double-cropping at the field-scale using satellite (Landsat) imagery. The process combines machine learning methods with expert labeling. We demonstrate the process by measuring double-cropping extent in a portion of Washington State in the Pacific Northwest United States--- a region with significant production of more than 60 distinct types of crops including hay, fruits, vegetables, and grains in irrigated settings. Our results indicate that the current state-of-the-art methods for identifying cropping intensity---that apply simpler rule-based thresholds on vegetation indices---do not work well in regions with a high crop diversity, and likely significantly overestimate double-cropped extent. Multiple machine learning models were able to perform better by capturing nuances that the simple rule-based approaches are unable to. In particular, our (image-based) deep learning model was able to capture nuances in this crop-diverse environment and achieve a high accuracy (96-99% overall accuracy and 83– 93% producer accuracy for the double-cropped class with standard error of less than 2.5%) while also identifying double-cropping in the right crop types and locations based on expert knowledge. Our expert labeling process worked well and has potential as a relatively low-cost, scalable approach for remote sensing applications. The product developed here is valuable to inform several policy questions related to food production and resource use.

A game problem of structural design is defined as a problem of playing against external circumstances. The scalar criterion serves the function of the payoff function. There are two classes of players, the “ordinal” and “cardinal” players. The ordinal players, designated as the "operator" and "nature," endeavor to respectively minimize or maximize the payoff function, operating within the constraints of limited resources. The fundamental premise of this study is that the probability distribution governing nature's "choice" of states remains unknown. Statistical decision theory addresses decision-making scenarios where these probabilities, whether or not they are known, must be considered. The solution to the substratum game is expressed as a value of the game. The value of the game is contingent upon the design parameters. The cardinal players, "designers", oversee the design parameters. For the se single cardinal player, the pursuit of the maximum and minimum values of the game reduces to the problem of optimal design. In the event that there are multiple cardinal players with conflicting objectives, a superstratum game emerges, which addresses the interests of the superstratum players. The optimal design problems for games with closed forms are presented. The game formulations could be applied for optimal design with uncertain loading.

Background/Objectives: To understand the lived experiences of the informal caregivers of people with chronic noncommunicable diseases and their interpretations of the significance of that care. The perspectives of men and women interacting with people in a situation of chronicity are examined. Methods: This qualitative phenomenological-hermeneutic study was based on Van Manen’s comprehensive and interpretative vision. Criterion-oriented non-probability random sampling was used. In-depth interviews were used to collect the information. Twenty caregivers participated in the study. Results: The following five themes emerged: transformation of the caregiver’s world; uncertainty of care; tireless guardianship and unwavering devotion; isolation and emotional exhaustion; and imbalance between caring for others and the self. Conclusions: The experience of informal caregivers reveals that both men and women face significant challenges, albeit from gender-differentiated perspectives and experiences. Men and women elaborate different meanings of caregiving, reflecting their gender roles and expectations. Therefore, an inclusive and equitable approach that recognizes and addresses these gender differences is essential in order to improve the support and quality of life for all informal caregivers, respecting and valuing their unique voices and experiences. An inclusive and equitable approach that recognizes gender intersections is required to improve support and the quality of life for caregivers.

Due to the extreme complexity of Alzheimer's disease (AD), the aetiology of which is not yet known, nor are there any known effective treatments, mathematical modelling can be very useful. Indeed, mathematical models, if deemed reliable, can be used to test medical hypotheses that could be difficult to verify directly. In this context, it is important to understand how $A\beta$ and $\tau$ proteins, which in abnormal aggregate conformations are hallmarks of the disease, interact and spread. We are particularly interested in this paper in studying the spreading of misfolded $\tau$. To this end, we present four different mathematical models, all on networks on which the protein evolves. The models differ in both the choice of network and diffusion operator. Through comparison with clinical data on $\tau$ concentration, that we carefully obtained with multimodal analysis techniques, we show that some models are more adequate than others to simulate the dynamics of the protein. This type of study may suggest that, when it comes to modelling certain pathologies, the choice of the mathematical setting must be made with great care if comparison with clinical data is considered decisive.

Titanium-based Metal Matrix Composites (MMCs) manufactured by additive manufacturing offer tremendous lightweighting opportunities. However, processing the high reinforcement contents needed to substantially improve elastic modulus while conserving significant ductility remains a challenge. Ti-TiC MMCs fabricated in this study report fracture strains in tension up to 1.7% for a Young’s modulus of 149 GPa. This fracture strain is 30% higher than previously reported values for Ti-based MMCs produced by Laser Powder Bed Fusion (LPBF) displaying similar Young’s moduli. The heat treatment used after the LPBF process leads to the doubling of the fracture strain due to the conversion of TiCx dendrites into equiaxed TiCx grains. As-built microstructure shows both un-dissolved TiC particles and sub-stoichiometric TiC dendrites resulting from a partial dissolution of TiC particles. Reduction of the C/Ti ratio in TiC during the process results in an increase in the reinforcement content, from a nominal 12 vol% to an effective 21.5 vol%. The variation of the TiC lattice constant with its stoichiometry is measured, and an empirical expression is proposed for its effect on TiC’s Young’s modulus. The lower TiC initial powder size distribution displayed the best mechanical performance.

Urban indicators characterize a specific urban space through calculated values, such as building coverage, building height, floor area ratio, and many others. Urban indicators established in spatial development plans should ensure the preservation of spatial order when introducing new construction investments. They should also harmonize with the existing urban structure, and even modernize it towards sustainable development. When determining these indicators, surrounding space is analyzed. Conventionally, building indicators in the existing space are determined based on available documents, usually 2D spatial data such as large-scale maps or cadastral maps. The aim of this research is to investigate the method of calculating building indicators using 3D urban building models that will be created from airborne Light Detection and Ranging (LiDAR) measurements. In the discussion of the results, indicators calculated based on LiDAR data are compared with calculated from 2D cadastral data. The calculated 3D indicators correlate with the classically calculated indicators. The accuracy of computed building area, volume, and other indicators depends on the LiDAR point cloud density and accuracy. The indicators calculated from 3D data align with new trends in defining Building Morphology Indicators (BMI).

This paper reviews the social dimensions of the rolling out and utilization of the automobile in the United States over the past one hundred years, starting with the early days steeped largely (but not exclusively) in technological idealism; moving through an age of car-ascendancy with its peak in the 1950s and 1960 (with the birth of a literal “car culture”); and into the present day of automobile dependency in the midst of a profound changes in auto technology that have unclear societal implications. This review generally indicates that the machine, rather than the human operator, was from early on the center of the creation of a car-based system in America which effectively made the human being and society a reactor to technical progress rather than the other way around. The technological and mechanical aspects of automobiles remain impressive. But the history reviewed here shows that while technical advances are good, these should be guided and channeled into the pursuit of and service to more enlightened and humanist applications, and that such guiding did not happen naturally with the automobile.

Highly pathogenic avian influenza viruses (HPAIVs) of the H5N1 subtype (clade 2.3.4.4b) have been detected in raw milk from infected cows. Several studies have examined the time and temperature parameters to ascertain whether influenza viruses in milk can be completely inac-tivated under commercial pasteurization conditions, yielding conflicting results. This study aimed to investigate whether milk could help protect influenza viruses from heat treatment. After heat treatment at 49°C for one hour, the titer reduction of the influenza A/WSN/1933 (A/H1) virus in milk was approximately 1.6 log10TCID50/mL greater than that observed in the Dulbecco's Modified Eagle Medium (DMEM) control media. The influenza D/bovine/CHN/JY3002/2022 (D/Yama2019) virus in milk retained a high residual infectivity (4.68x103 log10TCID50/mL) after treatment at 53°C; however, the virus in DMEM completely lost its infectivity under the same conditions. Moreover, the influenza A/chicken/CHN/Cangzhou03/2023 (A/H5) virus in DMEM could be completely inactivated using any of the three pasteurization methods: 63°C for 30 minutes, 72°C for 15 sec-onds, or 80°C for 15 seconds. For the virus present in milk, only pasteurization at 80°C for 15 seconds completely inactivated it. These results suggest that milk prevents influenza viruses from heat inactivation. Pasteurization at 80°C for 15 seconds is more effective than pasteurization at 72°C for 15 seconds or at 63°C for 30 minutes in completely eliminating the virus in milk.

Since the advent of artificial intelligence, it has not only transformed the way we live, but is also accelerating the transformation of production methods. Machine learning is a pivotal technology that endows computers with intelligence. The primary driving force behind its advancement is the pursuit of rapid and precise knowledge acquisition. Nevertheless, the existing enterprise risk decision support system is inadequate in terms of both timeliness and effectiveness when confronted with the task of analysing vast quantities of data. Furthermore, it lacks the capacity to assimilate the expertise of managers and to facilitate interaction in an intuitive manner.By combining a variety of machine learning models, the ensemble learning method effectively leverages the advantages of different algorithms, thereby improving the accuracy and stability of risk prediction. Each model provides a unique perspective and decision-making boundaries when dealing with complex enterprise risk data, but a single model may not be adequate when faced with a particular data set or a particular type of risk. The ensemble method overcomes the problem of bias or overfitting that may be caused by a single model by combining the prediction results of multiple models, so as to obtain a more robust prediction effect. These methods optimize the final prediction results based on how well they perform during training by giving different weights to each model. This convergence strategy significantly improves the accuracy of enterprise risk assessment and helps to provide decision-makers with more reliable data support to make more informed risk management decisions in complex business environments.

Anxiety is a prevalent mental health disorder that significantly impairs daily functioning and social interactions. While pharmacological treatments can be effective, they are often limited by challenges such as treatment resistance, side effects, and relapse. Electroacupuncture (EA) has emerged as a promising non-pharmacological intervention for anxiety, though its mechanisms of action remain largely unexplored. This study investigates the role of microglial cells in anxiety and examines how EA influences microglial morphology and function in the basolateral amygdala (BLA) in a model of social isolation-induced anxiety. Using a socially isolated mouse model, we assessed anxiety-like behaviors through the Elevated Plus Maze (EPM) and Open Field Test (OFT), and conducted biochemical analyses and immunofluorescence imaging to evaluate NOX2 expression, microglial activation, and oxidative stress markers. Our results show that EA significantly alleviates anxiety-like behaviors, reduces NOX2 expression in BLA microglia, lowers reactive oxygen species (ROS) levels, and enhances microglial morphology. These findings suggest that EA exerts its anxiolytic effects by modulating oxidative stress and microglial activity in the BLA, offering new insights into the cellular mechanisms by which EA may aid in anxiety management.

In hot environments, thermoregulation in poultry is regulated by heat shock protein 70 (HSP70), a member of HSPs, and its expression is controlled by the heat shock factor 3 (HSF3). Although the association between genetic polymorphism of these genes and thermotolerance as well as reproductive traits has been extensively studied in mammals but scarce in chickens. This study aimed to explore the relationship between single nucleotide polymorphisms (SNPs) in these genes and egg production traits of Bangladeshi Hilly chickens. To detect new SNPs and perform genotyping, sequencing and allele-specific PCR (AS-PCR) were used. We identified two novel SNPs(G-399A and A-68G) in the 5’-flanking regions of the HSP70 that were significantly associated with higher egg numbers(EN) at 161–190 days and increased egg weight(EW) at 40 weeks of age respectively. Furthermore, SNPs A258G, C276G, and C1431A in HSP70  and A-1388G in HSF3 also existed and were associated with EN at different ages. Haplotype and combined genotypic effects of these two genes were found to be associated with age at sexual maturity(ASM), body weight at ASM, EN, and EW. Identified SNPs and corresponding haplotypes might be useful in selective breeding to enhance the productivity of chickens in hot environments..

This study addresses the challenge of predicting the dynamic behavior of the structures under seismic excitation. Accurate prediction of such systems' responses is critical for the design and evaluation of buildings and infrastructure. Traditional methods, including numerical models and differential equation solvers, often face significant computational burdens, especially with nonlinear hysteretic behaviors and large-scale problems. To overcome these limitations, a novel Fuzzy-based Convolutional Neural Network (FuzzyCNN) model is developed. This model integrates fuzzy logic principles with convolutional neural networks to effectively manage the uncertainties and complexities inherent in soil-structure interaction under seismic loads. The model's performance is validated through both numerical simulations and experimental data from a mid-rise concrete building subjected to seismic events. Comparative analysis with a traditional Physics-informed CNN (PhyCNN) model demonstrates the superior accuracy and robustness of the FuzzyCNN in predicting seismic responses. Key results show that the FuzzyCNN model not only enhances prediction accuracy but also handles uncertainties more effectively than the PhyCNN model. The findings suggest that the FuzzyCNN model can significantly improve the efficiency and accuracy of dynamic response predictions. This advancement offers valuable implications for engineering design, seismic risk assessment, and the development of more resilient infrastructure.

The field of communication and media education is constantly evolving, with recent trends emphasizing the development of abstract thinking skills in students. Abstract thinking, crucial for success in the rapidly changing media landscape, involves critical and conceptual analysis of complex ideas. By prioritizing these skills, educators can better prepare students to interpret information creatively and innovatively. Research shows that incorporating abstract thinking methodologies into communication and media curricula enhances problem-solving abilities and creativity. Encouraging students to look beyond surface-level information and consider deeper underlying principles fosters a more adaptable future workforce. This shift towards abstract thinking reflects a broader recognition of critical thinking's importance in our data-driven society. As communication and media increasingly shape public discourse and perception, the need for skilled professionals who can navigate this complex environment is paramount. By integrating abstract thinking strategies into their curricula, educators can equip students with the tools to become more effective communicators, problem solvers, and leaders in the field. This approach is essential for developing professionals capable of navigating the intricate media landscape with sophistication and discernment.

This study investigates the link between Albania's carbon emissions and various economic factors from 1995 to 2022 to establish the effect of the country’s real estate sector on the environment. Using EViews 12, we assess potential linkages between carbon emission levels, total number of con-struction permits, Gross Domestic Product (GDP), and energy resources. The results indicate the direct relationship between construction permits, economic activities, and carbon emissions, meaning that the current development path does not support environmental sustainability. However, the assessment reveals certain trends to increase the usage of renewable energy accords that may dramatically decrease carbon levels. These results indicate Albanian policymakers' need to adopt regulations supporting sustainability in the real estate market. Promoting investment in renewable energy sources is imperative so far as the Albanian country context is concerned as it is an essential step towards enhancing environmental quality.

Background: Ovarian clear cell carcinoma (OCCC) is a type of ovarian cancer with a poor prognosis if detected in the progressive stage since there is less effective chemotherapy. Recent advancements in molecular-targeted drugs have not substantially affected OCCC treatment. Therefore, we explored the potential of targeting MDM2 in OCCC cells. Methods: We used TOV-21G and KOC7c cells as the ARID1A mutant-type, and RMG-I and ES2 cells as the ARID1A wild-type. Then, we performed small interfering library screening, Western blotting, real-time polymerase chain reaction analysis, cell proliferation assay, cell cycle analysis, time-lapse cell proliferation assessment, and DNA damage assessment. Next, to generate murine intraperitoneal tumors, 7.5 × 106 TOV-21G cells in 200 μL of phosphate-buffered saline were injected subcutaneously into the intraperitoneum in 5–6-week-old athymic nude mice. Results: Using various cell lines with ARID1A mutations or without mutation, the results showed that the interference of MDM2 effectively reduced cell proliferation in ARID1A-mutant cells but not in ARID1A wild-type cells. Additionally, interference with ARID1A against ARID1A wild strains reproduced susceptibility to MDM2 interference. In vivo experiments demonstrated that nutlin-3, an MDM2 inhibitor, significantly suppressed tumor growth in the ovarian cancer mouse model. Conclusion: These findings suggest that targeting MDM2 may be a viable strategy for the treat-ment of ARID1A-mutated OCCC, offering a new therapeutic approach for this challenging type of cancer.

Problem-Based Learning (PBL) has emerged as an innovative and effective pedagogical methodology in medical education. This student-centered approach promotes the development of critical thinking, problem-solving, and self-directed learning skills, essential for the training of competent physicians. This article explores the theoretical foundations of PBL, its implementation methodology, and the benefits it offers to medical students as well as to the quality of medical care. Through a comprehensive literature review and analysis of experiences in different contexts, the importance of PBL as a tool to improve medical training is highlighted and recommendations are offered for its effective implementation.

One of the great and popular fruit produces in tropical and subtropical areas of the world is the mango (Mangifera indica Linn.), stated as the "King of Fruits,". Varietal description is a vital constituent of mango enhancement and breeding. Objective of this study was choosing important morphological and biochemical indicators for mangos to serve as future reference points for varietal identification and breeding efforts was another objective. These varieties include Anwar Ratol, Azeem Chaunsa, Alishan, Dusehri, Haiden. The results of this investigation support the hypothesis that there is substantial variance among genotypes depending on physical and biochemical attributes.  South Punjab has a varied germplasm that can be castoff for crossbreeding, and effective exploration will be carried out to fully realize the probable. Description is therefore a crucial prerequisite for initiating a breeding effort.

Global warming has recently intensified research interest in renewable polymer chemistry, with significant attention directed towards lignin nanoparticle (LNPs) synthesis. Despite progress, LNPs industrial application faces challenges: (1) reliance on kraft lignin from declining raw biomass processes, (2) sulfur-rich, and condensed lignin use, (3) complex lignin macroparticles to LNPs conversion, using harmful and toxic solvents, and above all (4) lack of control over the LNPs production process (i.e., anti-solvent precipitation parameters), resulting in excessive variability in properties. In this work, eco-friendly LNPs with tailored properties were produced from a semi-industrial organosolv process by studying anti-solvent precipitation variables. Using first a parametric and then a Fractional Factorial Design, predictions of LNPs size and size distribution as well as zeta-potential were derived from a model over beech by-products organosolv lignin, depending on initial lignin concentration (x1, g/L), solvent flow rate (x2, ml/min), antisolvent composition (x3, H2O/EtOH v/v), antisolvent ratio (x4, solvent/antisolvent v/v) and antisolvent stirring speed (x5, rpm). This novel chemical engineering approach holds promise for overcoming the challenges inherent in industrial lignin nanoparticle production, thereby accelerating the valorization of lignin biopolymers for high value-added applications such as cosmetics (sunscreen or emulsion) and medicine (encapsulation, nanocarriers), a process currently constrained by significant limitations.

This research paper investigates the impact of psychological interventions on student performance, specifically within the MIT Integration Bee, a prestigious mathematics competition that challenges students with advanced calculus problems. Conducted across multiple high schools in Texas, the study explores how different motivational techniques, including intrinsic and extrinsic rewards, influence test scores and overall student engagement. The research design includes a comprehensive analysis of motivational strategies, encompassing both baseline and reward-based conditions to assess their effectiveness in enhancing student performance. By examining a diverse sample of high schools from urban and suburban areas, this study aims to provide a holistic understanding of the factors that drive academic achievement in competitive settings. The results have significant implications for educational strategies, particularly in the realm of mathematics education, and highlight the potential for tailored motivational interventions to optimize student engagement and achievement. These findings contribute to the broader discourse on educational psychology, offering insights that could inform the development of more effective teaching practices and policies aimed at fostering a conducive learning environment for students across different educational contexts.

Wildfires increasingly threaten ecosystems and infrastructure, making accurate burn severity mapping (BSM) essential for effective disaster response and environmental management. Machine learning (ML) models utilizing satellite-derived vegetation indices are crucial for assessing wildfire damage; however, incorporating many indices can lead to multicollinearity, reducing classification accuracy. While Principal component analysis (PCA) is commonly used to address this issue, its effectiveness relative to other feature extraction (FE) methods in BSM remains un-derexplored. This study aims to enhance ML classifier accuracy in BSM by evaluating various FE techniques that mitigate multicollinearity among vegetation indices. Using composite burn index (CBI) data from the 2014 Carlton Complex fire in the United States as a case study, we extracted 118 vegetation indices from seven Landsat-8 spectral bands. We applied and compared 13 different FE techniques—including linear and nonlinear methods such as PCA, t-distributed stochastic neighbor embedding (t-SNE), linear discriminant analysis (LDA), Isomap, uniform manifold approximation and projection (UMAP), factor analysis (FA), independent component analysis (ICA), multidimensional scaling (MDS), truncated singular value decomposition (TSVD), non-negative matrix factorization (NMF), locally linear embedding (LLE), spectral embedding (SE), and neighborhood components analysis (NCA). The performance of these techniques was benchmarked against six ML classifiers to determine their effectiveness in improving BSM accu-racy. Our results show that alternative FE techniques can outperform PCA, improving classifica-tion accuracy and computational efficiency. Techniques like LDA effectively captured nonlinear relationships critical for accurate BSM. The study contributes to the existing literature by providing a comprehensive comparison of FE methods, highlighting the potential benefits of underutilized techniques in BSM

Russula, a prominent genus of ectomycorrhizal fungi, is notably abundant and diverse in China. We present here the findings from studies on various Chinese Russula collections. Two notable species within subg. Heterophyllinae, namely Russula leucoviridis and R. subswatica, were described and illustrated based on comprehensive morphological characteristics and molecular evidence. Morphologically, Russula leucoviridis is characterized by its small to medium-sized basidiomata with a cuticle that cracks and breaks into small green patches near the center after mature, a pastel green to green pileus center with light yellow spots, and a white to greenish white pileus margin; whereas R. subswatica is distinguished by an infundibuliform pileus with a deeply depressed center after mature, a light orange to grayish orange pileus center, and a yellowish white to light orange margin with a purplish to purplish-black hue, and a white to yellowish white stipe that exhibits a purplish hue at the junction with the pileus. The phylogenetic analyses were constructed using a combined dataset of ITS, nrLSU, RPB2 and mtSSU. Relying on both morphological characteristics and multigene phylogeny, the former species is classified within subsect. Virescentinae, while the latter is affiliated with subsect. Griseinae. In this study, we contribute new data on infrageneric phylogenetic relations and enrich the species diversity of Subg. Heterophyllinae in China.

Speech emotion recognition (SER) is important in facilitating natural human-computer interactions. In speech sequence modelling, a vital challenge is to learn context-aware sentence expression and temporal dynamics of para-linguistic features to achieve unambiguous emotional semantic understand-ing. In previous studies, the SER method based on the single-scale cascade feature extraction module could not effectively preserve the temporal structure of speech signals in the deep layer, downgrading the sequence modeling performance. In this paper, we propose a novel multi-scale feature pyramid network to mitigate the above limitations. With the aid of the bi-directional feature fusion of the pyramid network, the emotional representation with adequate temporal semantics is obtained. Experiments on the IEMOCAP corpus demonstrate the effectiveness of the proposed methods and achieve competitive results under speaker-independent validation.

Most existing the freeway hard shoulder running (HSR) strategy literature only focuses on the traffic flow with human-driven vehicles (HDVs) but neglects the possible impacts of connected automated vehicles (CAVs) with various penetration rates on the macroscopic traffic flow. The purpose of this paper is to detect whether the effects of mixed traffic flow for the freeway HSR strategy differ the traditional traffic flow or not. An approach based on microscopic simulation with factorial experimental design is adopted in this study, and novel results are obtained from the discussion and statistical analyses of simulation results. The results showed that the penetration rates of CAVs have a positive influence both on the traffic efficiency and safety on freeway HSR regardless of the prevailing traffic flow conditions. Furthermore, the implementation of the HSR strategy has the potential to markedly enhance traffic efficiency across a range of varying traffic demands. However, different speed limits for the HSR can lead to varying degrees of efficiency improvement under different traffic demands. Finally, we find a complex interplay between traffic flow conditions and the impact of HSR on TTC values and overall traffic safety.

Minimally invasive implantology has brought about a paradigm shift in dental and surgical practices by prioritizing techniques that minimize patient discomfort, reduce tissue trauma, and shorten recovery periods. Central to this transformation is the integration of advanced technologies that enhance procedural precision and clinical efficiency. Among these, the surgical microscope stands out as a critical innovation. By providing enhanced magnification and illumination, surgical microscopes allow clinicians to perform intricate implant procedures with greater accuracy, even in anatomically complex or challenging areas. This paper explores the diverse applications of surgical microscopes within the field of minimally invasive implantology. It examines how the use of these devices contributes to precise implant placement, improved soft tissue management, and enhanced bone augmentation outcomes, thereby reducing complications and promoting faster healing. Additionally, the paper reviews clinical case studies and recent advancements in the literature to demonstrate the role of surgical microscopes in elevating patient safety standards. In doing so, this research underscores the importance of incorporating such technologies to meet the growing demand for minimally invasive techniques in modern implantology. The findings emphasize that magnification and illumination provided by surgical microscopes are integral to achieving optimal clinical results and advancing the future of implant surgeries.

Critical thinking skills are important assets for scientific development. Therefore, there are many ways to enhance the critical thinking skills of students, researchers, etc., both in the field of education and scientific enterprise. In this article, we propose the practice of solving probability paradoxes and puzzles for the purpose since these problems are often complex, subtle and tricky; therefore, their solutions require many aspects of critical thinking. In the following, the Introduction outlines some of the current research on the topic without going into more details. We provide background descriptions of the paradoxes and probability paradoxes in Section 2. Section 3 provides a short description of critical thinking. We present several definitions since there is no clear consensus on one single definition, both in the literature and in the general literature. And in Section 4 current research on using puzzles for enhancing critical thinking is given. We then discuss several probability paradoxes, namely, the Monte Hall paradox, the two-envelope (exchange) paradox and the St. Petersberg paradox, in Sections 5, 6 and 7, respectively. In Section 8, a brief discussion of the statistical hypothesis testing is given emphasizing its controversial nature. We analyse these problems and concepts in such a way that critical thinking is evoked while keeping the technical details to a minimum.

The way we connect with the physical world has completely changed because of the Internet of Things (IoT) advancement. However, there are several difficulties associated with this change. A significant advancement has been the emergence of intelligent machines that are able to gath-er data for analysis and decision-making. In terms of IoT security, we are seeing a sharp increase in hacker activities worldwide. Botnets are more common now in many countries, and such at-tacks are very difficult to counter. In this context, Distributed Denial of Service (DDoS) attacks pose a significant threat to the availability and integrity of online services. In this paper, we de-veloped a predictive model called Markov Detection and Prediction (MDP) using Continuous Time Markov Chain (CTMC) to identify and preemptively mitigate DDoS attacks. The MDP model helps in studying, analyzing and predicting DDoS attacks in Long Term Evolution for Machine (LTE-M) networks and IoT environments. The results show that using our MDP model, the system is able to differentiate between Authentic, Suspicious and Malicious traffic. Addition-ally, we are able to predict the system behavior when facing different DDoS attacks.

When predicting the health impacts of PM2.5 from future air quality changes, it’s crucial to consider both air quality improvements and population aging. This study divided future emission scenarios into a Base and Control scenario to project air quality from 2015 to 2030 and assess health outcomes. The GUIDE model, an Integrated Assessment Model (IAM), was used to estimate future emissions, while the CMAQ (Chemical Transport Model) and BenMAP (Health Impact Model) evaluated health impacts resulting from changes in air quality in Korea. The study focused on the impact of population aging on future health outcomes. Both scenarios showed improved PM2.5 concentrations, with the Control scenario seeing more substantial improvements due to stronger policy measures. When applying current age patterns, health impacts decreased as PM2.5 concentrations. However, when considering future population aging, health impacts increased despite improved air quality. This finding suggests that, although PM2.5 concentrations are expected to decline, the increasing proportion of elderly individuals will exacerbate health risks. Therefore, accounting for aging population trends is essential when studying the health impacts of future air quality changes.

This research, titled "Sustainable Tourism Logistics Management Strategies: A Case Study of Pathum Thani Province, Thailand," is a quantitative study aimed at developing effective strategies for sustainable logistics management in the tourism sector of Pathum Thani. The study focuses on three main objectives: (1) to propose strategies to enhance the tourism value of Pathum Thani, (2) to assess the satisfaction of tourists regarding the current logistics systems, and (3) to offer recommendations for the development of sustainable tourism logistics. Logistics management is a key element in tourism, involving the coordination of physical flow, such as the movement of tourists and services; information flow, which includes real-time travel details and guidance; and financial flow, ensuring seamless transactions for bookings and services. Efficient management of these flows is essential to improve tourist experiences, enhance the destination's appeal, and support long-term sustainability in the tourism industry. Pathum Thani, located near Bangkok, holds potential as a significant tourist destination, with its rich cultural heritage, historical sites, natural reserves, and commercial attractions. However, despite its favorable location, the province faces major logistical challenges that affect tourist satisfaction. Public transportation systems are limited, and connectivity between key tourist sites is often inadequate. These inefficiencies can hinder tourists’ ability to fully enjoy what the province has to offer. In response to these issues, the research aimed to study and recommend strategies that would improve logistics management, thereby increasing both the convenience for tourists and the sustainability of tourism activities in the province. In this study, data were collected through structured questionnaires distributed to 400 tourists currently visiting Pathum Thani. The focus of the survey was to evaluate their satisfaction levels with the province’s transportation, infrastructure, and overall logistics systems. The survey was designed to measure various factors such as the ease of transportation, the availability of clear signage and tourist information, the quality of infrastructure at tourist sites, and the environmental friendliness of the logistics systems in place. The data collected were analyzed using descriptive statistics such as means, percentages, and standard deviations, as well as inferential statistics including t-tests and one-way ANOVA to examine the differences in satisfaction across various demographics. The findings from this research revealed significant insights into the current state of tourism logistics in Pathum Thani. First, the analysis showed that 55% of tourists used private vehicles for transportation due to a lack of efficient public transportation options. This reliance on private transport reflects a clear gap in the availability of affordable and convenient public transport systems. Tourists also reported dissatisfaction with the connectivity between tourist attractions, which led to logistical difficulties during their visits. Specifically, on a 5-point satisfaction scale, transportation logistics scored an average of 3.5, indicating that tourists felt inconvenienced by the lack of comprehensive transport networks linking popular sites. Attractions like Bung Kum and Patumwanarom Park, which are more difficult to access, received lower satisfaction scores compared to easily accessible commercial venues such as Dream World and the National Science Museum. Furthermore, tourists expressed concerns about the lack of digital resources to aid in navigation and trip planning. Most respondents felt that more could be done to implement user-friendly apps or platforms that provide real-time information on public transport schedules, directions, and nearby amenities. This digital gap also contributed to the tourists’ reliance on private vehicles and decreased their overall satisfaction with their trip. The research findings underscored the need for better integration of digital tools, especially those that support multiple languages to cater to international tourists. By introducing such platforms, Pathum Thani could significantly improve the convenience of travel within the province. Another key aspect of the research involved exploring how sustainable logistics practices could enhance both tourist experiences and environmental conservation. Over 60% of tourists surveyed expressed interest in eco-friendly travel options, with many indicating that they would prefer public transportation systems that reduce carbon emissions, such as electric buses. However, the lack of such options in Pathum Thani detracts from its image as a sustainable tourist destination. This highlights the importance of incorporating green transportation strategies, which not only align with global sustainability trends but also cater to the growing demand for environmentally responsible tourism. The research concludes with several important recommendations based on the analysis of tourist feedback and logistical challenges. Firstly, the study advocates for the establishment of a comprehensive shuttle bus system that connects key tourist attractions. Such a system would reduce the reliance on private vehicles and provide tourists with a more convenient and cost-effective mode of transportation. Additionally, the introduction of electric buses would help Pathum Thani reduce its carbon footprint and attract environmentally conscious travelers. The creation of a multilingual digital platform offering real-time information about transportation, accommodation, and tourist services would further enhance the convenience for tourists, especially international visitors who may struggle with language barriers. In conclusion, this research highlights the critical role that logistics management plays in the tourism sector, particularly in ensuring both tourist satisfaction and sustainability. The findings demonstrate that Pathum Thani has significant potential to enhance its appeal as a tourist destination through the implementation of effective logistics strategies. By improving transportation networks, introducing green logistics solutions, and leveraging digital technologies, the province can offer a more seamless and satisfying experience for tourists while promoting environmentally responsible tourism. The study’s recommendations provide a comprehensive framework for policymakers and tourism stakeholders to improve logistics management in Pathum Thani, making it a more attractive and sustainable destination for both domestic and international visitors.

Background Our aim was to assess the expression profiles of the messenger RNA (mRNA) expression profiles of stem cell genes (OCT4, NANOG) and pancreatic progenitor genes (CK19, HES1, INS, PDX1) in peripheral blood mononuclear cells (PBMNCs) in selected neoplastic pancreatic diseases, such as cancer and neuroendocrine tumors, to identify neoplastic disease markers in the pancreas. Methods In this study, 49 patients diagnosed with pancreatic neoplastic diseases (37 with cancer and 12 with neuroendocrine tumors) and 34 control patients, all of whom were hospitalized at a tertiary center, were enrolled. Venous blood samples were collected from the participants, and RNA was extracted from peripheral blood mononuclear cells (PBMNCs). The mRNA expression levels of six stem cell and pancreatic progenitor markers—OCT4 (POU class 5 homeobox 1), NANOG, CK19 (keratin 19), HES1 (hes family bHLH transcription factor 1), INS (insulin), and PDX1 (pancreatic and duodenal homeobox 1)—were quantified via real-time quantitative PCR. The data were statistically analysed to explore associations between gene expression levels and various clinical, biochemical, and morphological parameters (including full blood count, Ca 19.9, weight, height, and BMI) via the Kruskal‒Wallis test, Mann‒Whitney U test, and Spearman rank correlation coefficient. Results The results revealed that the expression of the gene associated with early stem cells, NANOG (median= 0.002, p=0.03), as well as the genes encoding insulin INS (median = 0,004, p=0.02) and cytokeratin 19 CK19(median 0.0003, p=0.005), was significantly elevated in patients with pancreatic cancer. However, these gene expression levels were not significantly different from those of controls in patients with neuroendocrine tumors. Additionally, no significant differences in gene expression were observed among patients at different stages of pancreatic cancer. Furthermore, CK19 overexpression was found to be positively correlated with inflammatory markers, specifically CRP and WBC, in patients with pancreatic cancer. Conclusions Elevated PBMNC mRNA expression of selected stem and progenitor genes (NANOG, INS, CK19) may serve as a marker of pancreatic cancer and is correlated with coexisting inflammation.

Introduction: Early childhood caries is a significant public health problem among preschool children. The COVID-19 pandemic, which restricted dental services, altered family dynamics, affected child development, and created new demands on caregivers, may have exacerbated this issue. Objective: Analyze the occurrence and severity of caries and identify associated factors in preschool children attending public municipal daycare centers in Salvador, Bahia, Brazil, Methodology: A cross-sectional epidemiological study was conducted to investigate caries in children aged 2 to 5 years in 2022 and 2023. Caries was assessed using the decayed, extracted, filled, and missing teeth (DEFT) index, and prevalence was calculated. Sociodemographic, family dynamic, dietary, and healthcare utilization data were obtained through a questionnaire. After descriptive and exploratory analyses, robust Poisson multivariable regression was conducted at a 5% significance level, considering socioeconomic, psychosocial, and behavioral aspects. Results: A total of 440 preschoolers participated, with a mean age of 55 months. The majority were Black or mixed race (88.86%) and female (52.73%). The prevalence of caries was 30.91% and the mean DEFT score was 1.1. In the multivariable analysis, low family income (up to one minimum wage) and not playing outdoors during the pandemic were associated with a higher prevalence of caries. Conclusion: The prevalence and severity of caries remain high, and the pandemic has contributed to the maintenance of this epidemiological scenario. Public health measures and policies promoting social justice should be planned to improve oral health conditions in childhood.

Accumulated data indicate that epigenetic regulations, including histone modifications and DNA methylation, are important means for adjusting genes’ expression in response to various stimuli. In contrast to the success in studying the role of DNA methylation in laboratory rodents, the role of DNA methylation in terrestrial snail Helix lucorum has been studied only in behavioral experiments. This prompted us to further investigate the role of DNA methylation and the interaction between DNA methylation and histone acetylation in the mechanisms of neuroplasticity in terrestrial snails using in vitro experiments. Dysregulation of DNA methylation by DNMT inhibitor RG108 suppressed significantly the long-term potentiation (LTP) of synaptic inputs in identified neurons. We then tested whether the RG108-induced weakening of potentiation will be rescued under co-application of histone deacetylase inhibitors sodium butyrate or trichostatin A. RG108-induced LTP deficiency was significantly compensated by increased histone acetylation. These data bring important insight to the functional role of DNA methylation as an important regulatory mechanism and a necessary condition for the development and maintenance of long-term synaptic changes in withdrawal interneurons of terrestrial snails. Moreover, these results support the idea of the interaction of DNA methylation and histone acetylation in the epigenetic regulation of synaptic plasticity.

Background: Over the past two decades, the Copenhagen Psychosocial Questionnaire (COPSOQ) has been established as a valid instrument to measure psychosocial stress at work. Currently, the COPSOQ international network is responsible for monitoring and improving COPSOQ. In 2019, a new questionnaire was published, and the Greek version is now being validated. Methods: The measurement qualities of the Greek COPSOQ III have been explored in accordance with the usual requirements of a validation study as defined by DIN EN ISO 10075-3. A sample of observations from 2,189 participants surveyed with the COPSOQ in Greece was used to validate the current version with appropriate statistical analyses. Exploratory factor analysis was used to assess the statistical relationships for many scales. Results: With its 108 items and 40 scales, the Greek COPSOQ III includes all psychosocial work factors that have been internationally validated and remain compatible with approximately 72% of the COPSOQ III German version content. In addition to the primary results, congruence with widely used theoretical approaches such as the demand-control (−support) model (DCM) or the job demands-resources model (JDR) is generally satisfactory. In summary, our validation study for the Greek COPSOQ III version showed adequate reliability and validity, which is in line with the findings of the COPSOQ III questionnaire from other European countries, and it is also compatible with the validation of the German COPSOQ III (Lincke, 2021). Our regression analysis revealed that 34 psychosocial work factors (34 “context” scales) could adequately predict the scores of the satisfactory and health scales (6 “outcome” scales). The analysis also revealed the top five (5) predictors (context variables) for each of the six (6) “effect” scales (outcome variables). Conclusions: With the launch of COPSOQ III in Greece, current and new workplace psychosocial aspects could be explored. COPSOQ III (GR) is a beneficial instrument for enterprise research and risk assessment.

The evaluation of wrinkles in deep-drawn fiber-based materials is crucial for the assessment of product quality and the optimization of manufacturing processes. Wrinkles, a common phenomenon in deep-drawing paper materials with high drawing depths, significantly impair the appearance and mechanical properties of the final product. The objective of this study is to identify the key process parameters affecting wrinkling and to deepen the understanding of their roles and interactions using wrinkle data for deep-drawn paper products. Image analysis techniques are employed, supported by a specially constructed darkroom platform to ensure uniform light intensity for capturing photographs. An automated program is developed for the detection and evaluation of wrinkle characteristics and distribution, which allows the free choice of the region to be detected and the representation of the wrinkle geometry not limited by the number. To enhance the precision of this program, the ellipticity is initially rectified for products without flanges, specifically cup-shaped deep-drawn products. The ellipticity is caused by the pronounced springback effect of the paperboard. The approach is employed to investigate the impact of material properties, blank holder force, drawing depth, drawing clearance, and punch speed on wrinkling formation after the deep drawing process. The findings reveal that the blank holder force and drawing clearance are critical factors in wrinkle formation, with higher forces generally leading to increased wrinkle numbers.

Runs of homozygosity (ROH) are contiguous homozygous segments occurring throughout the genome. ROH's number and size heavily depend upon shared parental ancestry, particularly relevant in the context of parental consanguinity. Homozygosity mapping, a valuable technique for the identification of genes associated with human genetic diseases, is based on the presence of ROH. Next-generation sequencing (NGS) improved this process by allowing simultaneous homozygosity mapping and detection of disease-causing variants. In this work, we present the methodology to automate the creation of personalized multigene panels based on whole-exome sequencing (WES) data using ROH (identified by combining two different algorithms: ROHMMCLI and HomozygosityMapper) and/or Human Phenotype Ontology (HPO) terms, and its integration in a Django Web application. The new resources’ applicability and impact on diagnostics are demonstrated through the genetic characterization of two siblings affected by a recessive disease. Resorting to a sample of 3,941 patients generated in this work, we provide an extensive analysis of ROH at a genomic scale for the first time in the Portuguese population. This work also describes the development of a new classification approach based on ROH features. In summary, this research advances ROH analysis from WES data, emphasizing its diagnostic potential and significance in population genetics’ characterization.

Binaural beats are an acoustic-based form of brain stimulation that relies on engineering and neurophysiological principles. One important engineering factor in binaural beat stimulation is the carrier frequency Given the limited information available on the impact of carrier frequencies, our research aims to explore how carrier frequencies of 250 Hz and 400 Hz in binaural beats affect the theta, alpha, and gamma frequency bands in healthy individuals. The research involved 30 people between the ages of 20 and 30. It was conducted as a single-group comparative study. Prior to the test, brain maps were created for the participants. Based on these brain maps and the participants' resilience, various binaural beats with different carrier frequencies were played while the participants sat quietly in a chair and looked ahead. Their brain waves were then recorded. The results showed: 1- The relative power of the gamma frequency band changed significantly between the carrier frequencies of 250 and 400. However, there were no significant changes in the relative power of the alpha band, and no changes were observed in the theta frequency band. 2- Based on the principle of theta-gamma coupling in the theta frequency band, there have been positive changes in the gamma frequency band at the carrier frequency of 255 compared to 405. 3- The brain's topography map has shown changes at different carrier frequencies. The results indicate that as the carrier frequency increases, the energy also increases, but it must be proportional to the frequency band based on the energy equation between the carrier frequency and the frequency band. Based on the brain topography map, the carrier frequency mainly affects the intensity and extent of the involved areas.

Climate change is a global issue that has driven the adoption of renewable energies due to their sustainability and environmental benefits. However, these energies face limitations, such as the lack of regional studies on wind or solar dynamics and the efficiency of energy systems. Tools that simulate and calculate energy potential while considering uncontrollable climatic variables are crucial for optimizing the design of these systems. Artificial intelligence, particularly multilayer neural networks, has proven effective in data prediction across industries. This paper focuses on training a 3-layer neural network using the ReLU activation function and quadratic error to predict wind potential density in Mexico, aiming to identify key areas for renewable energy development.

The deterioration of living conditions is highlighting the benefits of urban greenery for creating sustainable cities. Using Washington D.C. as a case, we demonstrate the performance of GVI and examine disparities in greening surrounding affordable housing. With the pre-trained segmentation model, we generated GVI based on 54,691 Street View images and applied the Spearman’s correlation analysis to examine LST cooling effect. We found that LST shows significant negative correlations (p<0.001) with GVI and newly developed GVI-3D with coefficients of -0.60 and -0.73. The distribution of GVI in residential zoning was polarised, affordable housing projects have less greenery. In conclusion, we argue that it is of practical significance to monitor GVI instead of large scale NDVI for urban environments due to its controllability and human-centric attributes.

The retina, a crucial part of the eye, is made up of various cells and structures that are essential for the conversion of a light stimulus into an electric signal for the brain to develop an image. The different cells of the retina including the retinal pigment epithelium, rods and cones, and the retinal ganglion cells together are involved in the transduction of light and the formation of connections with the optic nerve. This is a complex and efficient system with distinct layers of cells being involved in the absorbing of unnecessary light stimulus and providing physiological support. Retinal disorders are most prevalent in people above the age of 50 and cases of blindness are in the millions, making the need for better treatment options a pressing matter. Shortage of donors and patient incompatibility are reasons for which the focus has moved to the use of stem cells and tissue engineering as an alternative treatment option. Induced pluripotent stem cells can be engineered to differentiate into retinal cells using supplements like SMAD antagonists and retinoic acid. Inactivating motor proteins and using Wnt/BMP4 antagonists facilitate the development of retinal neural characteristics to give three-dimensional retinal organoids. Microfluidics provides vascularization for better nutrient supply to make improved functional tissue models. Retinal organoids are hence promising for drug discovery and development, disease modeling, transplant alternatives, and developmental biology.

The traditional method for determining the Marshall stability (MS) and Marshall flow (MF) of asphalt pavements is laborious, time consuming, and costly. This study aims to predict these parameters using explainable machine-learning techniques. A comprehensive database comprising 721 hot mix asphalt (HMA) data points was established, including variables such as aggregate percentage, asphalt content, and specific gravity. Models were constructed using the PyCaret Python library, and their performance was assessed using metrics such as the mean absolute error (MAE) and coefficient of determination (R²). The CatBoost regression model outperformed the other models, achieving R² values of 0.835 and 0.845 for MS and MF, respectively. Additionally, Shapley values were used to quantify the variable effects on the predictions. This approach enables the efficient preselection of design variables, reducing the need for extensive laboratory testing and promoting sustainable construction practices.

Scattering resonances play a crucial role in understanding wave behavior in various physical systems. While significant progress has been made in analyzing resonances in high-contrast and nonlinear media, a general characterization of resonances in small, high-contrast nonlinear media with the Kerr effect, particularly in layered configurations, has remained an open problem. In this paper, we present a novel asymptotic approach that addresses this gap by providing an approximation for resonances in terms of material properties, geometry, and nonlinearity. Our results offer new insights into the dependence of resonances on these factors, particularly for complex multilayered structures, making this the first general characterization of its kind.

Coconut water, the clear liquid extracted from coconuts (Cocos nucifera L.), is a refreshing tropical drink with a booming market. However, despite its success, its moisturising properties remain controversial. The aim of this research is to inform consumers about the hydrating power of coconut water sold on the streets of Abidjan. To achieve this objective, a survey of coconut water consumption was carried out followed by in-vitro experiments using biochemical, physicochemical and functional analyses. The survey revealed that coconut water was well known to consumers (100%) for its fortifying (33.33%), digestive (29.33%) and refreshing (28.34%) properties. Coconut water contained carbohydrates (4.5±0.0%), dry matter (5.2±0.0070%), energy (19.92±0.0%) and ash (0.44±0.0%). Coconut water also contained minerals, notably sodium (220.45±0.4949 mg), potassium (1591.02±6.10 mg) and phosphorus (276.1±10.18 mg). In other hand, the volume of urine after consumption of coconut water was lower than that after consumption of mineral water, with values falling from 138.5 mL (T1) to 66.67 mL (T4) and then from 41.67 mL (T1) to 36.33 mL (T4) for coconut water. Coconut water is diuretic and therefore cleanses the bladder. Coconut water seems to have greater hydrating power than mineral water.

In recent years, the assessment of damage scenarios of urban communities has become one of the central themes in local government policies, aimed at promoting effective seismic risk mitigation and improving the efficiency of rescue systems to manage emergencies. In Italy, the seismic hazard has become a topic issue since the 1982 Irpinia earthquake, and several ventures have been promoted to face the seismic mitigation of complex residential districts. The objective of this research is to define the damage scenarios of the city of Florence, where 97% of the building stock is designed without anti-seismic prescriptions. The urban vulnerability of Florence has been assessed on the basis of the current approaches available in technical literature, and it has been combined with the knowledge provided by the recent investigation on the subsoil. Once the possible damage scenarios have been defined, the resilience of the area has been determined and the population involved in the evacuation has been estimated.

Coronavirus disease 2019 (COVID-19) has been a global pandemic affecting millions of people’s lives, which has led to ‘post-COVID-19 fatigue’. Alarmingly, severe acute respiratory syn-drome-coronavirus 2 (SARS-CoV-2) not only infects the lungs but also influences the heart and brain. Endothelial cell dysfunction and hypercoagulation, which we know occur with this infection, lead to thrombo-inflammation that can manifest as many myriad cardio-cerebrovascular disorders, such as brain fog, fatigue cognitive dysfunction, etc. Additionally, SARS-CoV-2 has been associated with oxidative stress, protein aggregation, cytokine storm, and mitochondrial dysfunction in neurodegenerative diseases. Accordingly, the identification of molecular targets involved in these actions could provide strategies for preventing and treating this disease. In particular, the very common enzyme dipeptidyl peptidase IV (DPPIV) has recently been identified as a candidate co-receptor for the cell entry of the SARS-CoV-2 virus with its involvement in infection. In addition, DPPIV has been reported as a co-receptor for some viruses such as Middle East respiratory syn-drome-coronavirus (MERS-CoV). It mediates immunologic reactions and diseases such as type 2 diabetes mellitus, obesity, and hypertension, which have been considered the prime risk factors for stroke among other types of cardio-cerebrovascular diseases. Unlike angiotensin-converting enzyme 2 (ACE2), DPPIV has been implicated in aggravating the course of infection due to its disruptive effect on inflammatory signalling networks and the neuro–glia–vascular unit. Regarding the neurological, physiological, and molecular grounds governing post-COVID-19 fatigue, this review focuses on DPPIV as one of such reasons that progressively establishes cerebrovascular grievances following SARS-CoV infection.

We evaluated the inhibitory effects of our developed Chol-PEG2000 micelles and Chol-PEG500 vesicles on amyloid-β (Aβ) aggregation, a key factor in Alzheimer's disease. ThT assay proved that Chol-PEG2000 delayed Aβ fibril elongation by 17 hours and Chol-PEG500 by 40 hours at 10-times molar ratio against Aβ40 peptide. At 50-times molar ratio, both Chol-PEG2000 and Chol-PEG500 significantly inhibited Aβ aggregation, as shown by minimal fluorescence intensity increase over 48 hours.

Technologies for rapid and high-throughput separation of rare cells from large populations of other types of cells have recently attracted much attention in the field of bioengineering. Among the various cell separation technologies proposed in the past, dielectrophoresis has shown particular promise because of its preciseness of manipulation and noninvasiveness to cells. However, one drawback of dielectrophoresis devices is that their application of high-voltage generates Joule heat that exposes the cells within the device to high temperature. To further explore this problem, this study investigated the temperature field in the previously developed cell separation device in detail. The temperature rise at the bottom of the microfluidic channel in the device was measured using a micro-LIF method. Moreover, the thermofluidic behavior of the cell separation device was numerically investigated by adopting a heat generation model that takes the electric-field-dependent heat generation term into account in the energy equation. Under the operating conditions of the previously developed cell separation device, the experimentally obtained temperature rise in the device was approximately 20 ℃, and the numerical simulation results generally agreed well. Next, parametric calculations were performed with changes in the flow rate of the cell sample solution and the solution conductivity, and a temperature increase of more than 40 ℃ was predicted. The results demonstrated that an increase in temperature within the cell separation device may have a significant impact on the physiological functions of the cells, depending on the operating conditions of the device.

Urinary tract infections (UTI) are a significant health concern, particularly among the elderly, often leading to hospitalization. A comprehensive study conducted at the "Dr. Carol Davila" Central Military Emergency University Hospital in Bucharest analyzed 948 adult patients hospitalized for symptomatic UTI. The findings revealed that the majority of admitted patients were elderly, presenting with high comorbidity rates and severe dependence. The study also identified common risk factors for multidrug-resistant pathogens, with one-third of patients hospitalized within 30 days preceding their current admission. The research uncovered several independent predictors of prolonged hospital stay, including the degree of dependence and comorbidity, female sex, obesity, and the presence of bacteremia. These findings underscore the changing landscape of UTI epidemiology and presentation in hospitalized patients. This study emphasizes the need for improved management strategies, particularly in areas such as urinary incontinence care, judicious use of catheterization, and preventive measures. As the elderly population continues to grow, healthcare providers must adapt their approaches to effectively address the evolving challenges associated with UTIs in this vulnerable population.

Gallium-68 prostate-specific membrane antigen positron emission tomography/computed tomography (Ga-68 PSMA-PET/CT) is a diagnostic tool used in urology to detect oligometastatic disease in patients with prostate cancer (PC). Although increasingly popular and extensively studied, it is not considered a standard diagnostic tool for primary PC staging, because it lacks validation when compared to mpMRI.Numerous studies have suggested its promising role in PC diagnostics. Our objective was to comprehensively review and summarize the existing literature on the potential and feasibility of utilizing Ga-68 PSMA-PET/CT as the sole method for primary PC staging and to compare it with currently employed diagnostic tools (mainly mpMRI). The main goal of the study was to consolidate the data on the sensitivity and specificity of PSMA-PET for PCa staging when compared with other diagnostic tools. The use of PSMA-PET/CT as the only diagnostic tool of primary PC in the future might potentially reduce diagnostic time and costs. In addition, we examined the limitations of its use. Ga-68 PSMA-PET/CT demonstrates encouraging outcomes during the initial assessment of PC, potentially leading to a transformative leap in PC diagnosis and treatment, due to the utilization of Ga-68 PSMA, with high affinity to prostate cells.

The global smelting business of nickel using rotary kilns and electric furnaces is expanding due to the growth of the secondary battery market. Efficient operation of electric furnaces requires consistent calcine temperature in rotary kilns, which involves shearing processes. Direct measurement of calcine temperature in rotary kilns presents challenges due to inaccuracies and operational limitations, and while AI predictions are feasible, reliance on them without understanding influencing factors is risky. To address this challenge, various algorithms including XGBoost, LightGBM, CatBoost, and GRU were employed for calcine temperature prediction, with CatBoost achieving the best performance, followed by XGBoost, LightGBM, and GRU in terms of MAPE. The influential factors on calcine temperature were identified using SHAP from XAI in the context of the CatBoost model. By incorporating seven out of twenty operational factors, the calcine temperature increased from 840℃ in 2023 to 907℃ by April 2024, concurrently reducing the power ratio of the electric furnace by 7.8%.

The gut, often referred to as the "second brain," has significant contributions in the maintaining of the good bacteria contribute enormous role such as digestion, produce essential vitamins, support the immune system, and protect against harmful bacteria. The beneficial flora including Akkermansia muciniphila, Adlercreutzia equolifasciens, Barnesiella, Christensenella minuta, and Oxalobacter formigenes, along with their derived bioactive metabolites emerged as a key player in maintaining host metabolic and immune health. Dietary choices such as blending of prebiotic, fermented, symbiotic, anti-inflammatory foods, and secondary metabolites from a wide variety of plants and fruits promotes the diversity, composition, and stability of beneficial intestinal microbes. The colourful plant foods rich in phytochemicals bioactive compounds such as carotenoids, flavonoids, polyphenols, alkaloids, anthocyanins, and capsaicin offer a wide array of unique properties such as analgesics, antioxidants, anti-inflammatory, antimicrobials effect and promoting the abundance of beneficial gut bacteria and their bioactive metabolites confer numerous health-promoting effects. Here, we present knowledge about most beneficial gut bacteria and their derived metabolites in terms of their sources and health benefits. Finally, we discuss best foods that skew towards promoting healthy intestinal microbes.

Two-Dimensional Nuclear Magnetic Resonance (2D NMR) spectroscopy has become a cornerstone in pharmaceutical research, offering enhanced structural elucidation capabilities for complex molecules. This technique overcomes the limitations of one-dimensional (1D) NMR by providing detailed insights into molecular interactions through the use of advanced spectroscopic methods like COSY, HSQC, and HMBC. In pharmaceutical applications, 2D NMR plays a crucial role in drug characterization, quality control, formulation development, and the structural analysis of natural products. Recent technological advancements such as high-field spectrometers and cryogenic probes have further improved the sensitivity and resolution of 2D NMR, making it indispensable for ensuring drug quality and intellectual property protection. This article delves into the principles and applications of 2D NMR in the pharmaceutical industry, highlighting its critical contributions to drug discovery, development, and quality assurance. Additionally, it addresses the future prospects of 2D NMR in areas like metabolomics, structural biology, and the development of new analytical tools. Despite its numerous advantages, 2D NMR presents challenges such as high operational costs, complexity in data analysis, and sensitivity issues when dealing with low-concentration samples. The article concludes by discussing the ongoing advancements in 2D NMR technology and its evolving role in pharmaceutical innovation.

The measurement of shoulder range of motion (ROM) has gained value within the healthcare field. Thanks to modern technology, goniometers have been developed as mobile applications; however, the reliability and validity of many of these apps have not been established. Such is the case of reliability of the OnForm app and validity of the My ROM app, object of this study. Methods: With data obtained from both apps from the measurement of bilateral external and internal rotation (at 90º of shoulder abduction) of 49 subjects, four different tests were run: inter-observer, test-retest, and intra-observer and one for validity. The statistical method applied was the intraclass correlation coefficient (ICC). Results: Both, OnForm and My ROM obtained in all the tests, significant results that prove that they are valid and reliable tools for athletes’ assessment. Conclusions: Both OnForm and My ROM are valid and reliable tools for daily use in clinical practice. They may be used by a single evaluator, they are easy to find with a convenient price, and the availability of the results is immediate.

Exergames (EG) are interactive video games that require physical activity and use gamification to make exercise more engaging. EG interventions have shown various benefits for older adults, such as improved mental health, quality of life, and reduced fall risk. Enhanced cognition is thought to drive these benefits. This review aimed to identify neural correlates underlying cognitive improvements in healthy older adults and those with neurocognitive disorders. We systematically searched major databases and included 13 studies from 2,095 records. Findings revealed that EG interventions led to structural brain changes and improved functional connectivity in healthy adults, Parkinson patients, and those with mild cognitive impairment (MCI). Increased activity in the frontal, temporal, and precuneus regions during rest was specific to adults with neurocognitive disorders. Neuroplastic changes, such as elevated BDNF levels and increased neurovascular coupling, were observed in both groups. However, methodological limitations in some studies highlight the need for further high-quality research.

Background: Saphenous vein degeneration is associated with significant morphological changes. This study compares the clinical and morpho-anatomical characteristics between patients with aneurysmal and non-aneurysmal insufficient great saphenous vein (GSV). Methods: A retrospective observational analysis was conducted on 75 patients with chronic venous disease. Patients were divided into Group A (n=17, GSV diameter ≥1.2 cm) and Group B (n=58, GSV diameter 0.6-1.2 cm). Clinical data and morpho-anatomical features were assessed using ultrasonography and histopathological examination. Results: Group A had significantly larger GSV diameter (1.4 ± 0.2 cm vs. 0.7 ± 0.1 cm, p &lt; 0.001) and wall thickness (0.4 ± 0.1 cm vs. 0.2 ± 0.1 cm, p &lt; 0.001). Elastic fiber fragmentation was more severe in Group A (55% vs. 29%, p &lt; 0.01). Group A also had a higher mean BMI (31.4 ± 4.5 kg/m² vs. 27.4 ± 4.0 kg/m², p = 0.007) and longer hospitalization (2.1 ± 0.6 days vs. 1.5 ± 0.6 days, p = 0.0023). Conclusions: Aneurysmal degeneration is linked to more severe clinical and morphoanatomical changes, indicating a need for closer monitoring and more aggressive treatment strategies.

Objective: To explore the acceptability and feasibility of conducting comprehensive, community-based, hy-brid (in-person and virtual) mental health screening and feedback with diverse youth in the community. Methods: The Screening and Support for Youth intervention provides comprehensive assessment and thera-peutic feedback in community settings including symptoms, diagnoses, functional impairment, suicide, and clinical risk. Symptom severity, seven diagnoses and suicidality were assessed with the Kiddie Computer-ized Adaptive Test. Functional impairment was evaluated by youth self-report on the Weiss Functional Im-pairment Rating Scale. Measures were scored according to nationally developed norms and an algorithm was developed to aggregate symptoms and functioning to determine clinical risk. The results were discussed with participants in a motivational interview to promote insight and engagement in an online intervention. Results: Methodological innovations included using a QR code for recruitment, integrated in-person and virtual participation, and expansion from in-person recruitment within the schools to virtual engagement with youth throughout the community. The final sample included disproportionately more Black or African American and Hispanic youth as compared to school and community statistics, suggesting that hybrid re-search may facilitate recruitment of diverse populations. Qualitative interviews indicated that the screening and feedback raised youth awareness of their wellbeing and/or distress, its impact on their functioning, and engagement with options for improving wellbeing. Conclusion: Hybrid research, developed as a necessity during the pandemic, proved advantageous to the feasibility and acceptability of community-based recruit-ment of at risk, minoritized youth.

Grass pea (Lathyrus sativus L.) is a nutritious legume crop well-adapted to fragile agro-ecosystems that can survive under challenging climatic conditions. The cultivation of grass pea faces stigma primarily due to the presence of β-N-Oxalyl-l-α, β-diaminopropionic Acid (β-ODAP), which is associated with a risk of inducing neurolathyrism upon prolonged consumption of its grains as a staple diet. The grass pea improvement program of International Center for Agricultural Research in the Dry Areas (ICARDA) aims to reduce β-ODAP content to a safe level along with improving yield potential and nutritional quality of grass pea. In this study, 183 germplasm accessions representing 13 different Lathyrus species and 11 L. sativus breeding lines were evaluated for β-ODAP content based on Rao protocol and mineral concentration using ICP-OES. Significant variability was observed among the accessions for the studied traits. The results showed low β-ODAP content and high mineral concentration in 25 accessions of crop wild relatives (CWR) which included L. cicera, L. ochrus and L. cassius with one accession IG65277 of L. cassius, in addition to two lines IG117034 and ACC1335 of L. sativus having very low ODAP content. Furthermore, some accessions of L. pseudocicera, L. aphaca, L. cicera, L. marmoratus, L. gorgoni and L. tingitanus also showed low β-ODAP content. The results showed significant positive correlations between different trait combinations viz., K and P (r =0.193***), K and Fe (r =0.177***), Mn and Fe (r = 0.210***), Mn and Se (r = 0.137***), ODAP and Mg (r = 0.158**), and ODAP and Ca (r = 0.140**). L. cicera, L. ochrus, and L. cassius were identified as a great source for improving the mineral concentration and reducing ODAP content in the cultivated grass pea.

Metered Dose Inhalers (MDIs) play a crucial role in managing respiratory diseases, but their effectiveness depends on whether the intended dose is delivered to the target, which can be influenced by various factors. Accurate assessment of MDI performance is crucial for optimizing MDI delivery and ensuring drug efficacy. This study numerically examined the role of evaporation dynamics and dosimetry methods in assessing MDI delivery efficiency to different regions in a mouth-lung model extending to G11. Experimentally determined spray exit speed, applied dose, and droplet size distribution were implemented as the initial/boundary conditions. Large eddy simulations (LES) were used to resolve the transient inhalation flows, and a chemical species model was applied to simulate vapor and temperature variations in the airflow. A multi-component model was used to consider the heat and mass transfer between the droplets and airflow. The model was validated against literature data and applied to evaluate the impact of evaporation on pulmonary drug delivery using MDI, in comparison to inert particles. Three methods were used to quantify deposition: based on droplet count, droplet mass, and the drug carried by the droplets. Results demonstrate that evaporation notably alters the spray droplet size distribution and subsequent deposition patterns. Compared to inert particles, evaporation led to significantly more droplets ranging from 1–5 µm entering the pulmonary region. For a given region, large discrepancies were observed in deposition fraction (DF) using different dosimetry methods. In the lower lung, the count-based DF (33.9%) and mass-based DF (2.4%) differed by more than one order of magnitude, while the drug-based DF fell between them (20.5%). This large difference highlights the need to include evaporation in predictive dosimetry, as well as to use the appropriate method to quantify the delivery efficiency of evaporating droplets.

The present study investigated the photoprotective effect of the ultrasonic-assisted ethanol extract (USHE) from Sargassum horneri, a brown seaweed containing fucosterol (6.22 ± 0.06 mg/g) and sulfoquinovosyl glycerolipids (C23H43O11S, C25H45O11S, C25H47O11S, C27H49O11S), against oxidative damage in ultraviolet B (UVB)-exposed HaCaT keratinocytes. USHE indicated the antioxidant activity with increasing ferric-reducing antioxidant power (FRAP) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging. Moreover, USHE increased cell viability by markedly reducing the production of intracellular reactive oxygen species (ROS) in UVB-exposed HaCaT keratinocytes. Additionally, USHE reduced the apoptosis, and sub-G1 cell population, and increased the mitochondrial membrane potential. Moreover, USHE modulated the protein expression levels of anti-apoptotic molecules (Bcl-xL, Bcl-2, and PARP), and pro-apoptotic molecules (Bax, cleaved caspase-3, p53, cleaved PARP, and cytochrome C). This modulation accorded with the upregulation of cytosolic heme oxygenase (HO)-1, NAD(P)H quinone oxidoreductase 1 (NQO 1), and nuclear factor erythroid-2-related factor 2 (Nrf2), collectively known as components of the antioxidant system. These findings suggest that USHE has a photoprotective effect in UVB-exposed HaCaT keratinocytes and can be utilized to develop cosmeceuticals for UVB protection.

The dynamics of a quiescent finite-size air filament in a static liquid is investigated through axisymmetric numerical simulations. Due to surface tension, the two ends of the air filament retract and form bulges. We categorize the observed breakup behavior into four distinct regimes on a phase diagram, labeled I, II, III, and IV, each with its appropriate scaling. For Ohnesorge number, Oh ≤ Oh lim = 0.1, the mass of the detached bubble is approximately more than 2.1 times the size of the initial radius of the filament. Using this assumption, a first-order approximate mathematical model is reported for Oh ≤ Oh lim that shows if the aspect ratio (Γ) of the filament is greater than 11, the filament will always breakup. In the neck-reopening regime, the jump distance from the neck to the center of the filament exhibits an increasing trend with an increase in Γ and decreases when Oh is increased. For the same regime, a recirculating flow generated around the stagnation point at the center of the filament induces the reopening of the neck, ultimately resulting in a breakup at the center of the filament.

Energy management strategy (EMS) is a decision-making algorithm for effective power allocation between storage devices in a hybrid energy storage system (HESS). Source voltages, state-of-charge (SOC), terminal voltage of load, and rate of change of battery current must be considered while implementing EMS and hence they are termed as performance indicators. This paper presents the development of an EMS designed to manage the performance indicators of the sources and ensure efficient power distribution through a shared bus topology. A shared bus topology employs individual converters for each source, offering efficient control over these sources. Rule-based fuzzy logic control ensures efficient power distribution between batteries and ultracapacitors. Additionally, hardware has been developed to validate the power-allocation strategy and regulate the DC-link voltage in the Energy Management System (EMS). The dSPACE MicroLabBox is utilized for the implementation of real-time control strategies. A battery and an ultracapacitor bank are utilized in a Hybrid Energy Storage System. The simulation outcomes have been corroborated by experimental data, affirming the efficacy of the proposed energy management strategy.

On average, one in ten patients die because of a diagnostic error and medical errors are the third largest cause of death in the US. While LLMs have been proposed to help doctors with diagnoses, no research results have been published on comparing the diagnostic ability of many popular LLMs on an openly accessible real-patient cohort. In thus study, we compare LLMs from Google, OpenAI, Meta, Mistral, Cohere and Anthropic using our previously published evaluation methodology and explore improving their accuracy with RAG.

Numerous Epidemiologic studies have used remote sensing to quantify the contribution of greenness, air pollution, and wildfire smoke to asthma and other respiration outcomes. This is the first review paper to evaluate the influence of remote sensing exposures to specific outcome severity and risk factors in different ecologic settings. Literature searches utilizing PubMed and Google Scholar identified 61 unique studies published between 2009-2023, with 198 specific outcomes. Respiration specific outcomes were lower in greenness, and higher in air pollution, and in wildfire ecologic settings. Aerosol optical depth (AOD)-PM2.5 readings and specific outcomes were higher in economically developing than in economically developed countries. Prospective studies found prenatal and infant exposure to higher ambient AOD-PM2.5 concentration level readings contributed to higher childhood asthma incidence. Lung function was higher in greenness, and lower in the other two ecologic settings. Age, environment, gender, other, and total risk factors showed significant differences between health outcomes and ecologic settings. Published studies utilized physiologic mechanisms of immune, inflammation, and oxidative stress to describe obtained results. Individual and total physiologic mechanisms differed between ecologic settings. Study results were used to develop a descriptive physiologic asthma model and then use it to propose updated population-based asthma intervention program guidelines.

Recent studies have indicated that hindbrain [fourth ventricle (4V)] administration of the neurohypophyseal hormone, oxytocin (OT), reduces body weight, energy intake and stimulates interscapular brown adipose tissue temperature (T_IBAT) in male diet-induced obese (DIO) rats. What remains unclear is whether chronic hindbrain (4V) OT can impact body weight in female high fat diet-fed (HFD) rodents and whether this involves activation of brown adipose tissue (BAT). We hypothesized that OT-elicited stimulation of sympathetic nervous system (SNS) activation of interscapular brown adipose tissue (IBAT) contributes to its ability to activate BAT and reduce body weight in female high HFD-fed rats. To test this hypothesis, we determined the effect of disrupting SNS activation of IBAT on OT-elicited stimulation of T_IBAT and reduction of body weight in DIO rats. We first measured the impact of bilateral surgical SNS denervation to IBAT on the ability of acute 4V OT (0.5, 1, and 5 µg ≈ 0.5, 0.99, and 4.96 nmol) to stimulate T_IBAT in female HFD-fed rats. We found that the high dose of 4V OT (5 µg ≈ 4.96 nmol) stimulated T_IBAT similarly between sham rats and denervated rats (P=NS). We subsequently measured the effect of bilateral surgical denervation of IBAT on the effect of chronic 4V OT (16 nmol/day ≈ 16.1 mg/day) or vehicle infusion to reduce body weight, adiposity and energy intake in female HFD-fed rats (N=7-8/group). Chronic 4V OT reduced body weight gain (sham: -18.0±4.9 g; denervation: -15.9±3.7 g) and adiposity (sham: -13.9±3.7 g; denervation: -13.6±2.4 g) relative to vehicle treatment (P<0.05) and these effects were similar between groups (P=NS). These effects were attributed, in part, to reduced energy intake evident during weeks 2 (P<0.05) and 3 (P<0.05). To test whether these results translate to other female rodent species, we also examined the effect of chronic 4V infusion of OT on body weight and adiposity in two strains of female HFD-fed mice. Similar to what we found in the HFD-fed rat model, we also found that chronic 4V OT (16 nmol/day) infusion resulted in reduced body weight gain, adiposity and energy intake in female DIO C57BL/6J and DBA/2J mice (P<0.05 vs vehicle). Together, these findings suggest that 1) sympathetic innervation of IBAT is not necessary for OT-elicited increases in BAT thermogenesis and weight loss in female HFD-fed rats and 2) the effects of OT to elicit weight loss translate to other mouse models of diet-induced obesity (DIO).

B-cell lymphoblastic lymphoma (B-LBL) is an aggressive type of non-Hodgkin lymphoma that usually involves lymph nodes, skin and soft tissue. Bone marrow and peripheral blood are normally spared from involvement by the disease. B-LBL typically forms sold masses that have similar pathologic and immunophenotypic features of the liquid counterpart, B-cell acute lymphoblastic leukemia (B-ALL). Presentation of B-LBL with a solitary epidural mass at the cervical spine is very rare and the optimal treatment of such cases is unknown. Most of the literature on management of B-LBL comes from small case series, pediatric patients, or as part of retrospective data that combine B-LBL with B-ALL cases. The case presented herein, showed a unique presentation that was then treated with three modalities, namely, surgical resection, radiotherapy, and consolidation with systemic chemotherapy adopted from United Kingdom acute lymphoblastic leukemia (UKALL) protocol. The patient attained a complete response following the planned treatment and is still in remission five years from the time of his initial diagnosis.

SPH5030 is developed to improve the selectivity of irreversible HER2 inhibitors, which shows excellent activity and better HER2 selectivity against four common HER2 mutants than neratinib and pyrotinib, and consumed to attenuate the off-target adverse events in patients. To comprehensively evaluate SPH5030, the preclinical pharmacokinetic studies of SPH5030 were systematically completed, including in vivo pharmacokinetic, tissue distribution and excretion in Sprague Dawley rats and Cynomolgus monkeys, as well as in vitro drug-drug interaction assessment and metabolite identification in different species. The results showed low clearance, reasonable bioavailability and widely tissues distributed properties, and mainly excreted in the feces in rats. In vitro studies SPH5030 showed stable metabolism and metabolized mainly by CYP3A4/5 and CYP2C8, with no or weak inhibitory potential on CYP450 enzymes and efflux transporters. In Phase I clinical trials, SPH5030 also showed well tolerated at doses ranging from 50 to 600mg and is expected to have an ideal clinical application prospect in the future.

The serious impact of Alternaria alternata on jujube black spot disease has seriously affected the quality and yield of jujube, constraining the sustainable development of the jujube industry. The purpose of this study was to isolate and screen highly effective biocontrol strains of jujube black spot disease from jujube rhizosphere soil. Thirty-three soil samples were collected from 4 regions in southern Xinjiang. The strains with antagonistic effects were isolated and screened by the dilution spread method and plate confrontation method and identified by morphological, physiological, and biochemical characteristics, as well as 16S rDNA, gyrB, and rpoB gene sequences. Indoor and field efficacy experiments were conducted to determine their biocontrol effect. A total of 110 strains with antibacterial activity were selected, and one strain, Bacillus velezensis 26-8, with a stable antagonistic effect was further tested. Biological characteristic experiments showed that strain 26-8 could grow at NaCl concentrations of 0.5%-10% and pH 4.0-9.0. The biocontrol experiment results showed that Bacillus velezensis 26-8 could achieve an 89.83% control effect against black spot disease. In conclusion, strain 26-8 has good salt and alkali tolerance, exerts a good control effect on jujube black spot disease, and is worthy of further study.

Myocardial cells and extracellular matrix fulfil their goal thanks to the energetic availability. Indeed, mechanical and electrical properties of the heart are strongly depended on the energetic production-consumption equilibrium. The produced energy is used under several forms including kinetic, dynamic, thermal energy etc. Notably, as the time goes by; aging as well as in case of heart failure, although total energy remains almost constant the contribution of each energetic form is altered. Thermal energy is increased, whereas the dynamic and kinetic energy are decreased and hence unable to satisfy adequately the cardiac work. Consequently, toxic products, unfolded /misfolded proteins, free radicals etc. are accumulated within the myocardium. Myocardial cell contraction – relaxation coupling, ion exchange, cell growth etc. function is failed, control of cell apoptosis and necrosis is lacking and cardiac micro and macro-architecture change is the final result. Energy production and consumption depends on cardiac metabolic resources and on the functional status of the cardiac silhouette including cardiomyocytes and non-cardiomyocytes cells and their metabolic - energetic behavior. Mitochondria, are intra-cellular organelles producing more than 95% of ATP and fulfill all the above prerequisites being thus very important and as such we have to better understand their anatomy, function and homeostatic properties.

We describe a system for identifying dog emotions based on the dogs' facial expressions and body posture. Towards that goal, we built a dataset with 2184 images of ten popular dog breeds, grouped into seven similarly sized primal mammalian emotion categories defined by neuroscientist and psychobiologist Jaak Panksepp that are ‘Exploring’, ‘Sadness’, ‘Playing’, ‘Rage’, ‘Fear’, ‘Affectionate’ and ‘Lust’. We modify the Contrastive Learning framework MoCo (Momentum Contrast for Unsupervised Visual Representation Learning) to train it on our original dataset and achieve an accuracy of 43.2% on a baseline of 14%. We also trained this model on a second publicly available dataset that resulted in an accuracy of 48.46% but had a baseline of 25%. We compared our unsupervised approach with a supervised model based on a ResNet50 architecture. This model when tested on our dataset having seven Panksepp labels resulted in an accuracy of 74.32%.

The degradation of solar photovoltaic (PV) modules over time, aggravated by defects, significantly affects the performance of utility-scale PV parks. This study presents a quantitative assessment of the power loss from module defects, and evaluates the cost-effectiveness of replacing defective modules at various stages of degradation. A module test site was established in Norway with six different defects, and continuous thermographic monitoring, combined with light IV measurements and electroluminescence (EL) imaging, provides partial support for further calculations on the long-term effects of the defects. The cumulative module energy loss is calculated over a 25-year park lifespan under both Norwegian and Chilean environmental conditions, the latter representing higher solar irradiation levels. The energy gain from replacing the defective modules at various stages of degradation is compared to the costs of replacement, both for infant-life failures and mid-life failures. Minor infant-life defects of 1\% power loss are likely not beneficial to replace in low-irradiation regions like Norway. For Chilean conditions it can be cost-effective, but primarily if the module is replaced around mid park life, which gives a larger yield when replaced with a new module. For more severe defects of 10\% loss the replacement gain is above the replacement cost for high-irradiation locations, and replacing the 33\% power loss defect is cost-effective for both locations, even when discovered late in the park lifetime. Mid-life defects are primarily beneficial to replace in high-irradiation locations.

This paper presents a comparative bibliometric analysis of digital transformation in small and medium enterprises, focusing on the before and after the Covid-19 pandemic. Using data from the Scopus database, the study analyzes 1,566 articles published between 2001 and 2024, focusing on pre-COVID (2001–2018) and post-COVID (2019–2024). Bibliometric tools, including VOSviewer and Biblioshiny, were employed to conduct performance analysis, scientific mapping, and co-occurrence analyses of keywords and co-authorship. The results demonstrate a substantial increase in research output post-COVID, particularly in areas such as the Internet of Things (IoT), cloud computing, and e-commerce. These technologies have become vital for the survival and competitiveness of SMEs in the post-pandemic world. However, the study also highlights significant challenges, including limited financial resources and a lack of skilled personnel, which have hindered SMEs' ability to implement digital transformation fully. Government intervention, through financial assistance and strategic support, is identified as crucial in enabling SMEs to overcome these obstacles and successfully navigate the digital era. This analysis contributes to the growing body of literature on SMEs' digitalization, offering key insights for policymakers, researchers, and businesses aiming to adapt to the evolving digital landscape in a post-pandemic economy.

Microalgae and cyanobacteria are photosynthetic microorganisms with significant biotechnological potential for the production of bioactive compounds, making them a promising resource for diverse industrial applications. This study presents the development and validation of a modular, droplet-based microfluidic photobioreactor (µ-PBR) designed for high-throughput screening and cultivation under controlled light conditions. The µPBR, based on PTFE tubing and a 4-channel LED illumination system, enables precise modulation of light intensity, wavelength, and photoperiod, facilitating dose-response experiments. Synechococcus elongatus UTEX 2973 and Chlorella vulgaris were used to demonstrate the system's capacity to support photosynthetic growth under various conditions. The results indicate that continuous illumination, particularly under blue and mixed blue-red light, promotes higher autofluorescence and chlorophyll-a content in cyanobacteria Synechococcus elongatus UTEX2973, while Chlorella vulgaris achieved optimal growth under a 16:8 light-dark cycle with moderate light intensity. This µ-PBR offers not only a flexible, scalable platform for optimizing growth parameters but also investigating highly resolved dose response screenings of environmental stressors such as salinity. The findings highlight its potential for advancing microalgal biotechnology research and applications.